The cross-sectional study used demographic variables and genotype features from the APOE region of 542 participants in the Alzheimer’s Disease Neuroimaging Initiative, or ADNI. The researchers created a stratified development cohort of 433 participants, or 80% of the sample, and reserved the remaining 109 participants, or 20%, as a held-out cohort. The task was to classify existing cognitive-impairment status rather than predict future decline or evaluate a treatment, and the reported findings concern discrimination in this ADNI sample rather than diagnostic performance in a broader clinical population.

To test whether synthetic augmentation could support clinical-genomic modeling in a relatively small cohort, the team applied TabDDPM, a diffusion-based approach to generating synthetic tabular records. The generated data had a similar demographic structure and common-variant genotype distribution to the real cohort. This distributional similarity was an observed property of the generated samples, not evidence that the synthetic participants were equivalent to real people or could substitute for them in clinical research.

Eight predictive models were evaluated through repeated stratified fivefold cross-validation with 10 repeats. Within each outer training partition, the 346 or 347 participants were divided again: 80% was used to train the generator, 10% for hyperparameter selection, and 10% for internal generator evaluation. Run-specific synthetic samples were then added to the outer training partition at augmentation ratios of three, five, seven and 10 times the real training-set size.

This nested design allowed model selection and synthetic-data generation to remain within the available training data before the selected configuration was assessed in the held-out cohort. Among the nonlinear models, the categorical late-fusion neural network produced the highest average cross-validated AUC under sevenfold augmentation. For the direct comparison between sevenfold and no augmentation, internal cross-validation yielded a raw paired DeLong p-value of 0.0012 and a Holm-adjusted p-value of 0.03.

The statistical comparison therefore supported an improvement in cross-validated discrimination for the selected model and augmentation setting, although the study evaluated a finite collection of models and augmentation ratios rather than every possible configuration. The same sevenfold strategy also improved performance in the 109-participant held-out cohort. AUC rose from 0.721 without synthetic augmentation to 0.769 with augmentation.

Two components of classification performance increased as well: sensitivity moved from 0.68 to 0.77, while specificity moved from 0.58 to 0.66. These figures describe how well the models separated the cognitively impaired and cognitively normal groups in the held-out sample; they do not demonstrate that the system can diagnose Alzheimer’s disease, predict individual disease progression or improve clinical care. The results show that adding generated examples to a small, tabular clinical-genomic dataset can improve the performance of some neural-network classifiers in this study.

Synthetic augmentation did not replace the 542 real ADNI participants or their records. Instead, the observed gains suggest that the generated samples helped the selected late-fusion network learn more stable patterns from demographic and APOE-region genotype features under the study’s validation design. The improvement should not be interpreted as evidence that artificial records are generally superior to real patient data or that the same gain will occur in every cohort.

Several boundaries define the scope of the finding. The analysis was limited to the available ADNI participants, the selected demographic and APOE-region genotype features, and the eight models evaluated. It used cross-sectional cognitive-impairment labels and did not test prospective prediction, treatment response, causality or clinical deployment.

The authors further note that the framework uses standard tabular genomic and demographic variables, which may make it applicable to other genomic classification tasks with limited individual-level data, but such wider use was not demonstrated in this study. The supplied article also states that the final formatted version was still forthcoming, so this report is based on the published abstract and accompanying study information rather than a complete examination of the formatted article.

Subject of Research: Evaluation of synthetic tabular data augmentation for classifying cognitive impairment using APOE-region genotype and demographic data.

Article Title: Improving cognitive impairment classification in Alzheimer’s disease using tabular diffusion augmentation and late fusion deep learning

Article References: Wang, A. Z., & Shen, X. (2026). Improving Cognitive Impairment Classification in Alzheimer's Disease Using Tabular Diffusion Augmentation and Late Fusion Deep Learning. Frontiers in Bioinformatics, 6, 1949507. https://doi.org/10.3389/fbinf.2026.1949507

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1949507

Keywords: Alzheimer’s disease, cognitive impairment, synthetic data, TabDDPM, deep learning, APOE, clinical genomics, neural networks, ADNI, cross-validation, Improving, cognitive

The researchers defined AI diffusion as farmers’ awareness of AI-enabled agricultural tools, their current use of those tools and their integration into routine activities. Examples included precision-farming applications, predictive analytics for yields or pests, and sensor-based irrigation or monitoring systems. The composite also incorporated expectations about AI, including its possible role in crop-management decisions and plans for future expansion. Farm productivity represented perceived improvements in production efficiency, crop yield and resource use, while profitability represented perceived improvements in cost efficiency, income stability and financial returns. Every construct was measured through five-item, five-point Likert scales, from 1 for strong disagreement to 5 for strong agreement.

Because audited yield and profit data were rarely available for the farms represented in the study, the investigators relied on farmers’ perceptions rather than objective performance records. The questionnaire was administered in person and through local agricultural networks, and participants consented to the study under ethics approval granted by Ulster University on 16 February 2026. Reliability was acceptable for all three measures: Cronbach’s alpha was 0.779 for AI diffusion, 0.830 for productivity and 0.784 for profitability. A Kaiser–Meyer–Olkin value of 0.887 and a significant Bartlett test of sphericity indicated that the questionnaire items were suitable for exploratory factor analysis, although no formal pilot study was conducted before full data collection.

The respondents’ attitudes were generally favorable, although reported use trailed belief in AI’s potential. A total of 53.5% agreed or strongly agreed that their farm used tools such as sensors, drones or smart irrigation, while 71.3% agreed or strongly agreed that AI improves crop-management decisions. Even stronger optimism appeared around monitoring and financial applications: 75.3% agreed or strongly agreed that AI could improve monitoring of farm conditions, and 81.2% agreed or strongly agreed that it could identify profitable crop-management strategies. Across the composite measures, perceived productivity averaged 19.08 with a standard deviation of 3.66, while perceived profitability averaged 19.64 with a standard deviation of 3.20.

Linear regression supported all three relationships specified by the study’s conceptual model. In the model predicting productivity from AI diffusion, the unstandardized coefficient was 0.608, with t = 7.800 and p < 0.001; the model accounted for 38.1% of the variation in perceived productivity. When AI diffusion predicted profitability, the coefficient was 0.525, with t = 7.601 and p < 0.001, and the model explained 36.8% of the variation in perceived profitability. The strongest relationship was between productivity and profitability: the productivity coefficient was 0.656, t was 11.247, p < 0.001, and R² was 0.561. These are substantial statistical associations in this dataset, but they are not estimates of causal gains in yield, income or profit.

The study combined three theoretical perspectives to interpret the results. Diffusion of Innovations theory emphasizes farmers’ perceptions of an innovation’s relative advantage, compatibility with existing practices, complexity, trialability and observability. The Resource-Based View focuses on complementary internal capabilities, including technical knowledge, finance and organizational skills, that may enable farmers to turn access to technology into measurable value. The Technology–Organization–Environment framework adds external conditions such as infrastructure, institutional support, regulation and markets. Together, these perspectives suggest that awareness and perceived usefulness alone cannot explain adoption or performance; technology operates within a wider system of human skills, farm resources and local conditions.

The descriptive results point to a perception–adoption gap. Farmers generally believed that AI could improve decisions, monitor conditions, manage water and fertilizer, reduce losses and strengthen financial returns, but fewer reported that their farms currently used AI tools. The study interprets this as a transitional stage in which awareness and favorable attitudes are more developed than implementation. The authors caution, however, that they did not directly measure investment costs, connectivity, electricity reliability, technical expertise, credit access or extension support in the surveyed sample. These barriers are therefore plausible, literature-informed explanations for the gap rather than causal determinants demonstrated by the analysis.

Productivity and profitability were treated as related but distinct outcomes. Greater technical efficiency can, in principle, support financial performance by reducing input waste or improving operational decisions, but higher output does not automatically ensure higher profit because prices, production costs, market conditions and the expense of acquiring and maintaining technology can alter the final result. The study did not measure these external economic factors directly. Its significant productivity–profitability association may therefore be compatible with several mechanisms, including genuine efficiency gains, farmers’ confidence in technologies they have adopted, or optimism about tools they expect to use more extensively in future.

The sample included small farms of less than 12 acres, medium farms of 12–25 acres and large farms above 25 acres, with medium-sized operations comprising 55.4% of respondents. Wheat was the most commonly reported main crop, followed by rice, cotton and sugarcane. Nevertheless, convenience sampling, the absence of a comprehensive sampling frame and the failure to record district and village identifiers systematically limit geographic specificity. The modest sample also reduces power to detect smaller associations and increases the risk of a type II error. No residual diagnostics for normality or homoscedasticity were undertaken, and the instrument was not validated through exploratory and confirmatory factor analysis in independent samples.

The results offer context-specific evidence that AI diffusion in Punjab’s agricultural sector is neither absent nor mature, but uneven and incomplete. Strong perceptions coexist with moderate reported use, while respondents who report greater AI integration also tend to describe their farms as more productive and profitable. The evidence does not justify claims that AI has already raised incomes or yields across Punjab, nor does it identify the mechanisms responsible for the relationships. Longer-term research using probability-based sampling, objective yield and financial records, repeated observations and methods capable of addressing endogeneity would be needed to determine whether AI causes measurable gains, for which farm types and under what infrastructure, cost and capability conditions.

Subject of Research: Associations between AI adoption and perceived productivity and profitability among 101 farmers in Punjab, Pakistan

Article Title: Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan

Article References: Khalid, A., Akhurst, C., Gilani, S. A. M., Armosh, F., Zanib, S. A., Boath, J., Peel, R. S., & Jinadasa, Y. (2026). Improving farm productivity and profitability through AI diffusion: evidence from Punjab, Pakistan. Frontiers in Artificial Intelligence, 9, Article 1890862. https://doi.org/10.3389/frai.2026.1890862

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1890862

Keywords: artificial intelligence, digital agriculture, farm productivity, farm profitability, technology adoption, Punjab, Pakistan, smallholder farming, survey research, Improving, farm, productivity

The review aimed to synthesise evidence on organisational interventions that promote the quality of the nursing practice environment in long-term care settings. The authors stated that long-term care is characterised by organisational complexity and prolonged care trajectories, making nursing clinical governance highly dependent on integrated organisational information systems that support evidence-informed decision-making. Although the nursing practice environment is widely recognised as a determinant of care quality and safety, evidence about organisational interventions in this setting had been fragmented.

Researchers followed Whittemore and Knafl’s integrative review framework, searching MEDLINE, CINAHL Complete and grey literature for studies published from January 2020 through January 2025. Ten studies met the eligibility criteria. The researchers synthesised the included studies using inductive thematic analysis. The four resulting domains covered workforce structure; care processes, including person-centred care, safety culture, digital integration and missed nursing care; nursing-sensitive outcomes; and organisational value.

Based on the review abstract, the authors propose that improving the nursing practice environment in long-term care requires integrated organisational interventions addressing workforce conditions, care processes, nursing-sensitive outcomes and organisational value simultaneously. They derived a conceptual framework intended to inform clinical governance, nursing leadership and evidence-informed management. The review does not establish that a specific integrated governance model has already been shown to improve outcomes, and the reported findings describe associations between favourable organisational conditions and improvements rather than the results of a new intervention trial.

The review’s conclusions are bounded by limited longitudinal evidence and the small number of studies examining integrated governance approaches. The authors called for future research to validate integrated governance models and evaluate their effects on organisational performance and quality of care. The available evidence therefore supports the proposed framework as a basis for further study and governance development, but its direct effects remain to be evaluated.

Subject of Research: Integrative review of organisational interventions improving the nursing practice environment in long-term care

Article Title: Improving the nursing practice environment in long-term care: an integrative review to inform clinical governance and nursing management

Article References: Braga, V., Ramos, A., Braga, R., & José, H. (2026). Improving the nursing practice environment in long-term care: an integrative review to inform clinical governance and nursing management. BMC Nursing. https://doi.org/10.1186/s12912-026-05298-0

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05298-0

Keywords: long-term care, nursing practice environment, clinical governance, nursing management, integrative review, organisational interventions, care processes, nursing-sensitive outcomes, organisational value, Improving, nursing, practice

Sugarcane molasses is described in the abstract as the main feedstock for ethanol production because of its cost, availability, sugar content and ease of conversion. The authors identify variability in raw materials and suboptimal process conditions as continuing challenges for maximizing conversion efficiency. Inhibitory compounds in molasses can also hinder the production of high-titer ethanol, while fermentation performance is sensitive to the six operating variables listed above.

The review synthesizes recent work on the use of sugarcane molasses and the process factors affecting its fermentation. Its stated scope extends beyond describing individual variables to identifying optimization approaches that target shortcomings in current industrial yields. The authors present these approaches as a roadmap for improving the efficiency of converting molasses into ethanol and, more broadly, supporting renewable-energy production. The available abstract does not specify the literature-search strategy, databases, study-selection criteria, number of included studies or quality-assessment methods, so the breadth and rigor of the evidence base cannot be independently characterized from the supplied material.

The abstract also reports no pooled effect estimate, direct comparison between optimized production systems, or quantitative measure of improvement in industrial output. Accordingly, the paper should be understood as a review of process factors and proposed optimization priorities, not as proof that any particular set of operating conditions will raise commercial ethanol production. The publisher states that the accessible article is peer-reviewed, accepted research shared ahead of the final Version of Record and remains subject to further edits; the supplied evidence is limited to the abstract and publisher information rather than the full article text.

Subject of Research: Review of fermentation factors and optimization strategies for ethanol production from sugarcane molasses

Article Title: A comprehensive review of the critical process factors and optimization strategies influencing ethanol production from sugarcane molasses

Article References: Reda, T. N., Gebreyohannes, G., & Abreha, G. W. (2026). A comprehensive review of the critical process factors and optimization strategies influencing ethanol production from sugarcane molasses. Discover Sustainability. https://doi.org/10.1007/s43621-026-04994-w

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04994-w

Keywords: sugarcane molasses, ethanol production, fermentation, process optimization, industrial yields, renewable fuels, substrate concentration, fermentation time, comprehensive, review, critical, process

The experimental class’s overall score rose from 3.475 to 3.751 on the five-point scale, while the control class’s score changed little, from 3.511 to 3.481. At posttest, the difference between classes was statistically significant at p < 0.001, with a reported Cohen’s d of 1.042. These findings support the study’s narrower conclusion that inquiry-centered teaching may help students understand why and how motor skills work; they do not establish that the intervention directly improved athletic execution or physical fitness.

The study was designed as a comparative teaching intervention in which the same teacher taught the same basketball course over one semester to both classes, with the principal difference being the instructional model. The experimental class used Argument-Driven Inquiry, or ADI, while the control class continued with traditional teaching. Before instruction, the researchers found no significant difference between the groups in overall motor-skill comprehension, with a pretest p value of 0.712 and Cohen’s d of 0.083.

Nor were there significant baseline differences in any of the questionnaire’s individual dimensions: explanation, interpretation, application, perspective, empathy, and self-knowledge. All corresponding confidence intervals included zero, and every baseline effect size was below 0.2. The results therefore support the researchers’ decision to regard the two classes as having comparable starting levels for the purpose of the comparison, although the use of intact classes rather than random assignment limits stronger causal attribution.

Motor-skill comprehension was operationalized through a 30-item questionnaire administered before and after the course. Its six dimensions were intended to capture more than the ability to reproduce a movement. Explanation covered concepts and methods of performing skills; interpretation concerned the functions and effects of mastering them; application addressed their use in practical situations; perspective assessed whether students could question motor skills and formulate high-quality questions; empathy concerned reflection from other learners’ perspectives; and self-knowledge evaluated students’ capacity to assess their own performance objectively.

Items used a five-point Likert scale. The investigators first surveyed 150 students to examine the instrument’s reliability and validity. Internal consistency was high: dimension-level Cronbach’s alpha coefficients ranged from 0.768 to 0.841, and the overall coefficient was 0.864.

Composite reliability values ranged from 0.845 to 0.888, while average variance extracted values ranged from 0.526 to 0.613. Sampling adequacy, discriminant-validity and multicollinearity checks also met thresholds used in the analysis. Within the experimental class, the questionnaire identified statistically significant pretest-to-posttest improvement in five of the six dimensions.

Explanation increased from 3.660 to 3.975, with p = 0.016 and Cohen’s d = 0.398. Interpretation rose from 3.645 to 3.960, with p = 0.019 and d = 0.386. Application increased from 3.530 to 3.815, with p = 0.005 and d = 0.474.

Perspective changed from 3.485 to 3.705, with p = 0.043 and d = 0.331, while empathy increased from 3.300 to 3.640, with p = 0.008 and d = 0.439. Self-knowledge was the exception, moving from 3.230 to 3.410 without statistical significance at p = 0.176. By contrast, the control class showed no statistically significant change in any dimension or in the overall score; its overall p value was 0.692.

The authors noted that self-knowledge may require longer development because it depends on substantial skill knowledge, self-regulated learning, and mastery of evaluation methods, but the study did not directly test that explanation. When the two classes were compared after the semester, the experimental group scored higher in explanation, interpretation, application and perspective. The posttest group differences were significant for explanation at p = 0.005, interpretation at p = 0.001, application at p = 0.001, and perspective at p = 0.044.

The reported effect sizes for those comparisons ranged from d = 0.462 to 0.828, with the largest corresponding to application. Empathy and self-knowledge did not differ significantly between the classes at posttest, with p values of 0.577 and 0.524, respectively. The researchers attributed those null comparisons to some improvement in the control group in the same areas, although the experimental group’s pretest-to-posttest gains were larger.

Because these are questionnaire reports, the statistical contrasts describe changes in students’ assessed understanding; they should not be read as direct measurements of technique, speed, accuracy, retention or transfer to competitive play. ADI places argumentation within structured inquiry rather than treating debate as an end in itself. The article describes an eight-stage implementation pathway adapted for physical education.

First, an instructor creates an authentic sporting problem from which students derive a question—for example, investigating why a stop-and-jump shot becomes distorted at high speed rather than simply copying a demonstration. Second, students propose hypotheses, including predictions that can initially be incorrect. Third, groups design a practice protocol, identify the variables to control, choose equipment and define how evidence will be recorded.

Fourth, they perform the inquiry and gather both quantitative observations, such as successful attempts or flight distance, and qualitative bodily feedback, such as perceived force production, balance or difficulty. Fifth, students analyze the results and build explanatory models. Sixth, they present arguments, cross-examine one another and receive biomechanical or movement-principle feedback.

Seventh, they document the inquiry and reflect on methodological limitations. Eighth, they return to practice, correct faulty actions, explain the principles behind the skill and attempt to transfer the technique to a new situation. The proposed pedagogy changes the teacher’s role during practical work.

Rather than immediately correcting a student’s movement, the instructor is expected to maintain safety, ask questions about controlled variables and prompt students to examine their own evidence. For example, when students compare a high badminton clear struck above the head with one struck at shoulder height, the task can be defined by the flight trajectory and landing position. A forearm-pass investigation might record the success rate of straight and bent arms and ask students to explain the difference mechanically.

The model also makes assumptions explicit: students write down their reasoning, classify proposed explanations, test them under planned conditions and confront competing interpretations. These examples illustrate the proposed framework across sports, but the paper explicitly states that they were not the specific activities tested in the reported intervention. Delivering all eight stages in a time-limited physical education lesson could be impractical, the author argues, because extended debate and report writing may reduce time available for movement.

The suggested compromise is to retain the full inquiry sequence while simplifying its presentation. Stages involving task definition, hypotheses, inquiry design, review, revision and skill internalization can remain central, while data collection, argumentation, reporting and reflection can be condensed into prepared forms, motor-learning journals and brief structured exchanges. The aim is to preserve the cognitive sequence without sacrificing sufficient practice.

Even so, the classroom comparison tested a basketball course as delivered by the study’s instructional design; it did not isolate which individual ADI stage, dose or classroom feature produced the observed questionnaire changes. The intervention also was not directly compared with other established inquiry-based physical education approaches. The trial has important limitations.

The design lacked random assignment, and the researchers did not report blinded assessment. The self-report instrument measured perceived understanding rather than observed movement execution, so the study cannot show that ADI improved basketball technique directly. The authors also emphasize that inquiry does not replace essential practice: students still need repeated physical activity, corrective feedback and opportunities to perform.

Future work would require larger and more diverse samples, direct performance measures and quantitative tests across other physical education courses. Subject to those constraints, the study offers a detailed classroom mechanism for moving beyond mechanical repetition: a cyclical sequence in which cognitive challenge, physical testing, argumentative reflection and revised practice are linked. Its clearest demonstrated result is an association between ADI-based basketball instruction and higher self-reported motor-skill comprehension in two small college classes.

Subject of Research: Perceived motor-skill comprehension in two college basketball classes taught with Argument-Driven Inquiry or traditional instruction

Article Title: Implementation approaches of improving college students’ comprehension level of motor skills based on argument-driven inquiry teaching model

Article References: Li, X. (2026). Implementation approaches of improving college students’ comprehension level of motor skills based on argument-driven inquiry teaching model. Frontiers in Sports and Active Living, 8, Article 1887661. https://doi.org/10.3389/fspor.2026.1887661

Image Credits: AI Generated

DOI: 10.3389/fspor.2026.1887661

Keywords: Argument-Driven Inquiry, college physical education, basketball teaching, motor-skill comprehension, classroom study, student questionnaire, inquiry-based learning, motor learning, physical education pedagogy, Implementation, approaches, improving

The order Orthoptera comprises roughly 30,000 valid extant species, divided into the suborder Caelifera, which includes grasshoppers, and the suborder Ensifera, which includes crickets and katydids. In most studies of flower-visiting arthropods, orthopterans are excluded from analyses because they are rarely encountered or are not considered effective pollinators. Yet sporadic reports from around the world have documented these insects on flowers for decades, and some researchers have postulated that regular flower visitation may be an important precursor for orthopteran-plant mutualism through pollination services. The new study, building on a pivotal 2017 report that documented 41 orthopteran species visiting flowers of 35 plant species in Southeast Asia, argues that these insects are not rare flower visitors at all, but simply overlooked.

To reconstruct the bipartite network between orthopterans and the flowers they visit, the researchers drew on photographic records collected between late 2017 and mid-2024 across Singapore, the Philippines, Sabah in Borneo, Brunei Darussalam, Bach Ma National Park in Vietnam, and Johor State in Peninsular Malaysia. Both adults and nymphs were photographed as evidence of the visitor and the visited flowers, a technique particularly important when either the insects or the plants could not be collected. The interaction network was visualized using the plotweb function in the bipartite package in R, but with a crucial twist: rather than arranging taxa to minimize crossings, the researchers positioned the orthopterans according to their relative placements in the most recent orthopteran phylogenies, allowing the network to reveal which evolutionary lineages are most floriphilic.

The results are striking. Ensiferans, the katydids and crickets, account for 45 of the 58 flower-visiting taxa, or 78 percent of all orthopteran taxa observed on flowers, far outpacing the 13 caeliferan taxa, or 22 percent. Among the ensiferans, the subfamily Phaneropterinae stands out as the most prominent flower visitor, with 21 taxa representing 36 percent of all flower-visiting orthopteran taxa and 45 percent of all records, or 110 incidences. The genera Phaneroptera, Nisitrus, and Conocephalus were the top three genera observed visiting the most diverse genera of flowers, with Phaneroptera alone documented on flowers from 20 genera across 7 families. Among the caeliferans, only taxa from the subfamilies Pyrgomorphinae, Acridinae, Oxyinae, Catantopinae, and Cyrtacanthacridinae were observed on flowers, with the grasshopper genera Xenocatantops and Valanga recorded visiting the greatest number of flower genera.

Flowers of the family Asteraceae were the most visited, with 16 genera documented, followed by Fabaceae with 11 genera. Some genera, such as Sphagneticola, Ageratum, Prexalis, and Bidens, were visited by 10 to 15 orthopteran taxa each. Notable non-Asteraceae genera include Costus, visited by seven orthopteran taxa, Dillenia by six, Claoxylon by five, and Lantana by seven. The researchers also analyzed floral traits of the visited plants, finding that flowers occurring in clusters or inflorescences, head or capitulum forms, and solitary forms were the most frequently visited, accounting for 32.4, 15.5, and 14.1 percent of visited flower taxa respectively. White and yellow flowers dominated the color palette, at 25.4 and 21.1 percent, and radially symmetrical flowers were visited far more often than bilaterally symmetrical ones, at 73.2 versus 21.1 percent.

The phylogenetic arrangement of the network reveals patterns that would be invisible in a standard affiliation web. Phaneropterinae katydids, which are generalist flower visitors found across habitats ranging from open grasslands to different forest types, were documented visiting orchids, panicles of Cinnamomum, clusters of Caryota mitis, and the petals of Costus. Some species are more specialized, such as the mangrove-associated Holochlora signata, observed visiting the flowers of the mangrove tree Bruguiera cylindrica. The researchers note that Phaneropterinae can occur in high abundance and diversity in the forest canopy, where they are often only encountered at light traps, making their flower-visiting behavior particularly difficult to observe and document. Advances in non-invasive canopy sampling methods, such as macro-camera traps, may address this in the future, particularly during rare mass flowering events in dipterocarp forests.

Not every orthopteran lineage appears to be floriphilic. The absence of any record of flower-visiting Gryllacrididae in the dataset was unexpected, given that gryllacrids were the first orthopterans shown to pollinate flowers. Even during synchronous flowering events in the forest understory where gryllacrids tend to inhabit, no flower visitation was observed. Similarly, predominantly herbivorous katydids from the subfamilies Pseudophyllinae and Mecopodinae, close relatives of Phaneropterinae, do not appear to visit flowers. Small crickets from the families Trigonidiidae and Mogoplistidae, by contrast, are opportunistic feeders that dwell among the leaves and branches of herbs, shrubs, and understory trees where flowers are most accessible, and more records of these crickets visiting flowers were documented.

The researchers caution that floral traits alone do not explain why Asteraceae and Fabaceae dominate the visited flower list. Many of these taxa are free-flowering and tend to dominate their habitats, with genera such as Bidens, Prexalis, Neptunia, and Mimosa easily dominating ground vegetation and being highly invasive in some parts of the world, including Southeast Asia. Experimental work has demonstrated that more Phaneroptera individuals visit patches of vegetation with higher abundances of these flowers. Coincidentally, many of the flower genera are also attractive to butterflies, and some taxa have been deliberately planted to attract butterflies in managed parks and gardens, leading to increased encounters with flower-visiting orthopterans. The preference for radially symmetrical flowers may suggest that flower-visiting orthopterans are typically opportunistic and generalist florivores or pollinators, though Phaneroptera exhibit a specific preference for flowers over leaves, as shown by a series of experimental studies.

The color preference for white and yellow flowers is congruent with findings that generalist flower-visiting dipterans preferentially visit white and yellow flowers and colored traps, and behavioral experiments have shown that grasshoppers approach yellow targets more frequently than other colors. The ecological significance of this color preference among orthopterans remains unclear. Only slightly more plant taxa with terminal flowers were frequented compared to those with axillary flowers, and the researchers suggest that flower location may inform the visitor’s accessibility to resources and risk of exposure to predation, though these considerations are also dependent on other floral traits such as size, color patterns, wavelengths outside the human visible spectrum, and plant height.

Despite the insights offered, the researchers are careful not to overinterpret the data. They recommend caution against drawing too much inference at present owing to the unresolved phylogenetic relationships between the taxa involved and the incompleteness of the current dataset. Questions about whether these orthopterans help with pollination or cause more florivory damage to the plants remain open, and future studies combining cost-benefit analysis of both pollination and florivory by orthopterans are crucial to better understand their ecological roles. With many species from Southeast Asia still undescribed, with unknown natural history, and with a vast part of the region remaining unexplored and understudied, the researchers conclude that we are still extremely far from understanding the actual diversity of flower-visiting orthopterans. Continued natural history research, they argue, is essential for a more comprehensive understanding of these insects and their ecological impacts in the region.

Subject of Research: research news

Article Title: Improving and reconstructing data about the network between Orthoptera and flowers in Southeast Asia

Article References: Tan, M. K., Salvador, J. A. G., Nuñeza, O. M., Muhammad, A. A., Abdullah, N. A., Wahab, R. A., Baroga-Barbecho, J. B., Yap, S. A., Vu, T. T., Truong, T. C. T., Pham, T. H., Japir, R., & Chung, A. Y. C. (2025). Improving and reconstructing data about the network between Orthoptera and flowers in Southeast Asia. Journal of Orthoptera Research, 34(1), 135-142. https://doi.org/10.3897/jor.34.137349

Image Credits: AI Generated

DOI: 10.3897/jor.34.137349

Keywords: Improving, reconstructing, data, network, between, Orthoptera, flowers, Southeast, Asia, scientific research

The valve in question is a two-stage hydraulic directional valve, or TSHDV, a device that uses a small pilot valve to steer a much larger main spool. The pilot stage is deliberately built with what engineers call positive overlap: the spool and sleeve overlap slightly at the neutral position so that oil cannot leak out when the machine idles. That design saves energy and reduces waste, but it comes at a hidden cost. Near the neutral position, the metering edges remain closed and no effective flow path exists between the pilot supply or return ports and the working ports. This region forms a geometric dead zone, meaning a pressure difference between the two main-spool control chambers cannot develop until the pilot spool crosses the corresponding overlap boundary. The result is a delayed main-spool response and reduced position-tracking accuracy, particularly during zero crossings, small-opening operations, and direction reversals.

The dead zone is only the beginning of the trouble. The electromagnetic actuator in the pilot stage has a limited bandwidth, and the flow-pressure-drop relationship at the valve orifice exhibits significant nonlinearity. Meanwhile, the motion of the main spool is jointly affected by hydraulic pressure force, spring force, friction force, and flow force. Add the external mechanical vibration that mobile equipment endures on construction sites, and the control problem becomes fiercely nonlinear, parameter-sensitive, and dependent on operating conditions. In the small-flow region and during switching processes, these coupled effects are particularly evident, making precise spool position control a genuinely hard engineering challenge.

For years, engineers have relied on proportional-integral-derivative control, the workhorse of industrial automation, to regulate spool position. PID control owes its popularity to a simple structure and ease of implementation, but its performance depends strongly on gain tuning, and the dead-zone and flow-pressure nonlinearities of hydraulic valves are not explicitly compensated. More sophisticated approaches such as sliding-mode control and state-feedback control can enhance robustness, but they generally require sufficiently accurate plant models or state information. Adaptive and neural control methods can accommodate certain parameter uncertainties, yet their implementation often involves sophisticated parameterization and the tuning of multiple gains, complicating deployment in embedded valve controllers. Reinforcement learning offers a data-driven alternative, but its performance depends heavily on training data and generalization to conditions never encountered during training.

Earlier compensation methods took a more direct route. Some researchers constructed piecewise mappings of the reference current with self-tuning gains, while others accounted for the micro-flow characteristics of the pilot valve or employed polynomial excitation-current compensation to attenuate dead-zone and hysteresis effects. The study adopts one such approach as a benchmark: dead-zone current offset compensation, or DCOC, which applies fixed current offsets according to offline-calibrated dead-zone thresholds in the positive and negative directions. During calibration, a slowly increasing drive current is applied to each proportional solenoid, and once the main-spool displacement exceeds a prescribed threshold, the corresponding differential current is recorded as the equivalent dead-zone current. DCOC enables the pilot spool to traverse the overlap region more rapidly, but it corrects only the dead-zone thresholds and does not explicitly account for the coupled relationships among valve-orifice flow, pressure, and acting forces.

A more physics-based strategy, known as physics-based inverse compensation, or PBIC, builds a simplified model of the valve and inverts it to compute the current needed for any desired operating state. By assuming the effective areas at the two ends of the main spool are approximately equal and treating the hydraulic fluid as incompressible, the differential flow rate becomes proportional to the main-spool velocity. Setting the dynamic terms to zero yields steady-state force-balance equations for both spools, and inverting the orifice-flow relationship produces the desired pilot-spool displacement, from which the desired current follows. The architecture uses a cascaded design with an outer position-control loop and an inner current-control loop. The trouble is that this inverse mapping remains fixed after identification. When the valve-orifice geometry, electromagnetic characteristics, friction properties, or hydraulic-fluid parameters vary, the control accuracy of both fixed compensation methods deteriorates, and the problem is most pronounced near the dead-zone region and during valve reversals.

The new method, called adaptive residual compensation, or ARC, takes a cleverly modest approach. Rather than reconstructing the nonlinear surrogate models or replacing the nominal inverse mapping, ARC augments the fixed PBIC with a low-dimensional residual current that is updated online using the main-spool tracking error. The residual is represented by just five normalized basis functions built from two scheduling variables: the desired differential flow rate and the measured main-spool position. The linear terms describe the dominant variation of the residual with operating point, the quadratic terms account for directional asymmetry, and the cross term represents coupling between flow demand and spool position. No independent constant term is included because the nominal compensation already handles the dominant dead-zone and static-bias characteristics, so the correction naturally returns to zero at the neutral point. A projection-based adaptive law keeps the parameter estimates within a bounded set, saturation constraints prevent excessive corrections, and an adaptation-gating function suspends parameter updates when the tracking error approaches the measurement-noise level, when excitation is insufficient, or when the current command approaches saturation.

The mathematics behind the method carries real theoretical weight. Using a Lyapunov-based analysis, the researchers established that the tracking error converges asymptotically under ideal conditions, and that the error remains uniformly ultimately bounded when approximation errors and unmodeled disturbances are present. In plain terms, the correction cannot run away with itself: the five-term basis, the projection operator, and the saturation limits together guarantee that the adaptive parameters stay bounded and that the tracking error shrinks to a predictable size. These additional operations require neither extra sensors nor modifications to the valve hardware or the original cascaded control structure, and they impose only a modest increase in computational burden, making the method a practical compromise between adaptability and implementation complexity.

Co-simulations built in AMESim and MATLAB/Simulink put the method through its paces against PID, DCOC, and PBIC under sinusoidal, triangular, and square-wave reference trajectories. Under nominal conditions, all controllers maintained stable operation, but ARC achieved the closest agreement with the reference trajectory, with tracking errors generally confined to a narrower range and error accumulation near the neutral position effectively suppressed. The researchers then reduced the nominal electromagnetic force coefficient by 10 percent to simulate the thermal drift that prolonged energization imposes on proportional solenoids. Under this parameter mismatch, the error metrics and reversal delays of DCOC and PBIC increased noticeably, while ARC maintained relatively small absolute tracking errors and exhibited only limited variation in reversal delay time, demonstrating that the online residual correction reduces the influence of parameter drift on the nominal inverse mapping.

Real hardware sealed the case. The team deployed ARC on an in-house-developed embedded controller based on an S32K144 microcontroller, running with a control cycle of one millisecond, and tested it on a hydraulic test rig equipped with a custom-built linear variable differential transformer displacement sensor accurate to better than 0.1 percent of full scale. Under nominal conditions, the mean absolute tracking error measured just 0.0756 millimeters with a reversal delay of 17 milliseconds. When the solenoid temperature was allowed to climb to approximately 70 degrees Celsius, coil resistances rose roughly linearly with temperature, introducing a mismatch between the actual valve behavior and the nominal inverse mapping. Even so, ARC maintained stable tracking, with errors increasing mainly near the zero-crossing reversals but decaying rapidly without sustained oscillations. The mean absolute error rose only to 0.119 millimeters under the elevated-temperature condition. The authors acknowledge that comparative experiments against the benchmark methods on the test rig remain necessary to quantitatively establish the practical performance advantage, and future work will investigate load-pressure variations, oil temperature, and friction characteristics. Still, the demonstration that a five-parameter self-correction can run on ordinary valve hardware, without extra sensors, marks a meaningful step toward hydraulic machines that stay precise as they heat up, vibrate, and age.

Subject of Research: An adaptive residual compensation method for improving spool position tracking in two-stage hydraulic directional valves

Article Title: An adaptive residual compensation method for improving spool position tracking in two-stage hydraulic directional valves

Article References: Guo, H., Shi, B., Chen, H., Pang, J., & Han, D. (2026). An adaptive residual compensation method for improving spool position tracking in two-stage hydraulic directional valves. Results in Engineering, 32, Article 113519. https://doi.org/10.1016/j.rineng.2026.113519

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113519

Keywords: adaptive, residual, compensation, method, improving, spool, position, tracking, two-stage, hydraulic, directional, valves

The scientific problem the study tackles is deceptively simple to state and notoriously hard to solve. A basketball shot is a coordinated whole-body action lasting roughly a second, and its quality cannot be inferred from whether the ball goes in. Biomechanical research has long shown that skilled shooters maintain stable postural organization and coordinated ball-release control, and that compensatory coordination among the shoulder, elbow, and wrist helps preserve stable release conditions even when individual joints vary. Scoring can be affected by occlusion, changes in viewing perspective, and defender proximity, while technique varies across attempts and conditions. A single make-or-miss outcome, the study emphasizes, is a poor proxy for technical quality. What coaching actually requires is an interpretable measure of elbow positioning, release mechanics, jump-to-release timing, and follow-through stability, mapped to a structured rubric that a human assessor can verify.

MSEEM addresses this by synchronizing five complementary data streams. Two Sony cameras recording 1080p video at 120 frames per second captured the scene from frontal and sagittal views, providing context such as defender proximity and visibility. OpenPose software extracted markerless pose keypoints, which were triangulated into three-dimensional joint coordinates after checkerboard calibration, yielding measures such as elbow flexion at release, shoulder flexion, trunk lean, jump-to-release interval, and follow-through variance. Two Shimmer3 inertial measurement units strapped to the shooting hand and mid-forearm sampled acceleration and angular velocity at 200 hertz, capturing wrist-forearm dynamics such as peak wrist angular velocity and angular velocity impulse. A fine-tuned YOLOv8s detector tracked the ball in both views, allowing reconstruction of release angle, apex height, and entry angle at the 3.05-meter rim plane. Shot outcome was logged separately as a make indicator. Cross-modal synchronization used a simultaneous LED event and mechanical tap, with residual alignment error of just 3.2 milliseconds at the median, well within a predefined 10-millisecond tolerance.

The reference standard for all of this measurement was deliberately human. Two certified basketball coaches scored every shot on a structured rubric finalized before model development, producing a total score from 0 to 100 built from four equally weighted components: alignment, timing, release mechanics, and stability, each scored from 0 to 25. The coaches reviewed synchronized video while masked to model outputs, study condition, shot outcomes, and each other’s ratings, and their two-rater mean achieved an intraclass correlation of 0.88. Shot outcome was excluded from all rubric criteria. This independence matters because the study’s central claim is not merely that a model can predict numbers, but that those numbers agree with an independently derived coaching judgment.

The model itself is a study in efficiency. MSEEM uses parallel modality-specific encoders, with temporal transformer encoders processing pose and inertial sequences, multilayer perceptrons embedding video and trajectory features, and a learned token representing shot outcome. A gated attention layer learns modality-specific weights for each shot window before a fusion layer feeds one regression head for the total score and four component heads. Remarkably, the entire architecture contains roughly 0.74 million trainable parameters, compared with 34.5 million for the SlowFast video model, 121.4 million for TimeSformer, and 28.2 million for the Video Swin Transformer used as external comparators. Trained with an AdamW optimizer, a Huber loss for the total score, and mean squared error losses for the component heads, the model was frozen before any feedback was delivered and never updated during or after the training intervention.

The performance results are striking. Across five-fold participant-grouped cross-validation, in which every baseline shot from a held-out player remained within the same outer fold, MSEEM achieved a mean absolute error of 4.82 points on the 0-to-100 scale, with a Spearman rank correlation of 0.824 and calibration slopes within 0.02 of unity. The best video-only comparator, SlowFast, reached an error of 6.24 points, a statistically significant gap of 1.42 points after Holm correction. Removing the pose stream increased error to 5.79 points, and removing the inertial stream to 5.55 points, confirming that each modality carries information the others cannot fully replace. Simple concatenation fusion performed worse than gated attention, and an outcome-only model that tried to infer technique from make-or-miss status alone fared worst of all, with an error of 7.10 points.

Perhaps the most intriguing finding concerns how little the model cares about whether the ball went in. When the researchers flipped the binary outcome input while holding all other streams fixed, the predicted total score changed by only 0.63 points on average, with a maximum displacement of 1.88 points. The mean gated-attention weight for the outcome stream was just 0.071, compared with 0.267 for pose, 0.231 for inertial signals, 0.226 for video, and 0.205 for ball trajectory. In other words, the model derives its technique assessment almost entirely from how the shot is executed rather than from where the ball lands, which is precisely the property a coaching tool needs. A shooter can miss with perfect mechanics and make with flawed ones, and the framework reflects that distinction.

The training experiment put these measurements to a practical test. A total of 156 active basketball players, aged 18 to 30 with at least two years of organized experience, were randomized in equal groups after baseline assessment. All participants completed six supervised training sessions of 120 shots each, spanning free throws, catch-and-shoot attempts, off-dribble shots, and lightly contested shots with a passive defender positioned 0.75 meters away. The MSEEM-feedback group received a tablet display within five seconds of each 10-shot block, showing the overall score and the two highest-priority correctable components drawn from a locked cue library. The control group received standard coaching at the same cadence, without numerical scores or component rankings. Adherence was complete: all 5,616 scheduled training blocks and feedback episodes were delivered in each condition, with no adverse events and no missing primary outcomes.

The feedback condition produced a steeper improvement trajectory and stronger retention. In a mixed-effects model adjusted for baseline score, playing level, and baseline-score tertile, the interaction between MSEEM feedback and time was positive and statistically significant, indicating faster rubric-score improvement per session-minute in the feedback group. At the 14-day delayed re-test, the adjusted retention contrast favored MSEEM feedback by 5.90 points, corresponding to a standardized effect size of 0.68, with descriptive retention scores of 70.4 versus 64.1 points. Component-level analysis showed that alignment and stability carried the largest training effects, and the lightly contested shot condition showed both the largest shot-type training effect and the largest retention contrast. Exploratory moderation analyses found no significant evidence that baseline skill or playing level modified these effects, though the authors caution that the study was not powered for subgroup interactions.

The authors are careful to frame the scope of their claims. Testing occurred under standardized indoor conditions with fixed cameras and controlled lighting, and the lightly contested condition used a passive defender that did not reproduce the spatial pressure and occlusion of active competition, so transfer to game performance remains unestablished. The control group lacked a visually matched sham tablet condition, meaning motivational or expectancy effects of receiving structured digital feedback cannot be fully separated from the component-specific information itself. The reference standard reflects rubric-specific coaching judgments rather than universal measurement validity. Still, as a proof of concept, the study makes a compelling case that automated shooting assessment can be both accurate and actionable: a compact multimodal model that agrees with blinded coach ratings, resists degradation when individual sensors fail, and converts its component estimates into feedback that measurably accelerates learning and helps it stick. Broader deployment in academies, schools, and competitive environments, the researchers note, will require external validation under variable camera geometries, active defenders, and independent coaching settings, but the framework’s data and code are publicly available for exactly that purpose.

Subject of Research: Automated evaluation of basketball skills using multimodal data fusion for improving shooting training efficiency

Article Title: Automated evaluation of basketball skills using multimodal data fusion for improving shooting training efficiency

Article References: Sun, P. (2026). Automated evaluation of basketball skills using multimodal data fusion for improving shooting training efficiency. iScience, 29(11), Article 117915. https://doi.org/10.1016/j.isci.2026.117915

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117915

Keywords: Automated, evaluation, basketball, skills, multimodal, data, fusion, improving, shooting, training, efficiency, scientific research

The trustworthiness problem has two distinct faces, and the team led by Majid Ramedani and Martin Dyrba attacked both. The first is demographic confounding. Aging is the single greatest risk factor for Alzheimer’s disease, and the structural overlap between normal aging and pathology means a disease classifier may inadvertently rely on aging patterns rather than disease-specific atrophy. Brain age prediction models offer telling evidence of this: they typically estimate older ages for patients with neurodegenerative disease, a signature that the same confounding signal has leaked into the learned representations. The second face is unreliable probability estimates. The softmax layer in a convolutional neural network transforms raw output scores into a probability distribution over classes, but those disease likelihood scores indicate only the model’s relative confidence — not true statistical probabilities — and they come without confidence intervals. A model that says a patient has a 0.8 probability of Alzheimer’s is making a claim about its own internal scores, not about observed disease frequencies.

To test their solutions rigorously, the researchers built all their models on a shared 3D convolutional neural network architecture, ensuring that any performance differences could be attributed to the debiasing strategy rather than to architectural advantages. The baseline CNN consists of four convolutional blocks with 3×3×3 kernels, batch normalization, ReLU activation, and 2×2×2 max-pooling, followed by a bottleneck module that compresses whole-brain voxel-wise gray matter probability maps into a 4,096-dimensional feature vector. The inputs were whole-brain, voxel-wise gray matter maps derived from T1-weighted MRI scans, spatially normalized to MNI space and processed through a single standardized pipeline applied identically to all three cohorts. The models operated directly on whole-brain voxel-wise information rather than on predefined regional volumes, letting the networks find their own features in the raw gray matter signal.

The first debiasing approach, called CF-Net, is an adversarial confounder-free network built on a min–max optimization game. A feature extractor generates a feature vector that a classifier uses to predict diagnosis, while a separate confounder predictor attempts to predict the patient’s age from the very same features. The adversarial loss is defined using the squared Pearson correlation between true and predicted confounders, and minimizing it drives that correlation toward zero, encouraging statistical independence between the learned features and age. Training proceeds in three phases: the encoder and classifier train jointly first, then the confounder predictor activates while the others freeze, and finally a joint loop lets the encoder and confounder predictor interact to refine the representation. Learning rates follow an 8:1 ratio to balance the capacities of the 6.3-million-parameter classifier and the 0.8-million-parameter confounder predictor.

The second approach, PMDN — penalty-based metadata normalization — takes a fundamentally different route. Instead of playing an adversarial game, it treats the confounding effect as a general linear model, modeling the feature vector as f minus Mβ equals r, where M contains metadata, β are regression coefficients, and r is the residual, confounder-free feature vector. Because deep learning optimizes batch-wise, naive least-squares estimates of β would vary across batches and introduce instability. PMDN solves this by formulating a differentiable, bilevel optimization problem that jointly updates the network weights and the deconfounding parameters, with a penalty term that suppresses feature correlation with metadata. Alternating updates of the weights and β ensure stable convergence while the network learns task-relevant, residualized features. At inference, only the confounding components are subtracted from the 4,096-dimensional encoder features.

The results, tested on external cohorts after training on ADNI data, tell a striking story about stability. PMDN achieved the highest balanced accuracy at 0.71 with a standard deviation below 0.02 — roughly ten times lower than the baseline’s variability of about 0.11 and five times lower than CF-Net’s 0.06. All models posted comparable AUC values around 0.82, meaning their ability to discriminate diseased from healthy brains was essentially identical. But PMDN delivered the lowest Brier score at 0.22, against 0.32 for the baseline, indicating far more reliable probability estimates. The age bias quantification was even more revealing. On an age-balanced test set, the baseline model showed a consistent positive correlation between chronological age and predicted Alzheimer’s logits in cognitively normal subjects (r = 0.16), meaning older healthy people were systematically assigned higher disease risk. CF-Net barely moved that needle (r = 0.15) with greater variability, but PMDN reduced it to near zero (r = −0.07).

The mild cognitive impairment subgroup — the clinically critical category where age-related atrophy and pathological changes overlap most — showed the clearest separation between strategies. The baseline model exhibited a substantial age-logit correlation of r = 0.19, and CF-Net was actually worse at r = 0.20, suggesting adversarial training was ineffective at mitigating age bias in this population. PMDN achieved a near-zero correlation of r = 0.06, reflecting successful removal of age-confounded variance exactly where it matters most for early detection. In the diagnosed Alzheimer’s group, however, age-related associations persisted across all three models, with correlations around r = 0.15 unchanged by either debiasing method. The researchers attribute this to the greater overlap between age-related brain changes and disease-related neurodegeneration in established pathology, which makes it genuinely harder to disentangle the two signals. Even so, PMDN showed lower variability in the age-logit association for the Alzheimer’s group, indicating more consistent behavior across evaluation folds.

The second half of the study tackled the calibration problem with four post hoc methods applied to the CNN’s softmax outputs: two parametric approaches (logistic regression and probit regression) and two nonparametric ones (isotonic regression and Bayesian binning into quantiles, or BBQ). Logistic regression applies Bayes’ theorem under Gaussian class-conditional assumptions, reducing to an affine transformation of the raw scores, while probit regression substitutes the inverse normal link function. Isotonic regression fits a nondecreasing, piecewise-constant function using the pool-adjacent-violators algorithm, achieving an optimal linear-time solution without assuming any parametric form. BBQ divides outputs into bins based on empirical quantiles and estimates probabilities within each bin using a Bayesian beta-distribution approach, averaging over models with varying bin counts. The uncalibrated CNN outputs showed the largest calibration errors, with an expected calibration error of 0.28 and a maximum calibration error of 0.64 — meaning the model’s stated confidence could deviate from observed accuracy by nearly two-thirds at its worst.

Calibration transformed those numbers dramatically. Parametric methods produced moderate improvements, cutting the expected calibration error to roughly 0.13 and the maximum to about 0.29 with smooth, constrained curves. The nonparametric approaches did better still: isotonic regression and BBQ achieved expected calibration errors of 0.07–0.08 and maximum errors of 0.19–0.20, with isotonic also posting the lowest log-loss (0.50) and Brier score (0.16) while preserving the same F1-score of 0.73. Classification performance remained largely stable across all methods, confirming that calibration improves the reliability of predicted probabilities rather than the model’s discriminative ability. The researchers note that monotonic approaches produced smooth and reliable calibration curves, whereas the nonmonotonic BBQ method introduced irregularities — a reminder that flexibility in calibration comes at the cost of potential instability, and that evaluation should combine metrics like ECE and MCE with visual inspection of reliability plots.

The deeper lesson of the study is that deep learning models achieving similar classification performance can differ dramatically in calibration, stability, and fairness — dimensions that headline accuracy figures never capture. The contrast between the two debiasing strategies stems from their underlying optimization: PMDN applies a deterministic GLM-based correction that produces stable, consistent feature representations, while adversarial training relies on a min–max game with competing objectives that may converge incompletely, leaving residual confounding and explaining CF-Net’s variable performance. The findings carry real clinical weight for mild cognitive impairment populations, where models that lean on age as a proxy risk overestimating disease probability in older individuals. The team acknowledges limitations: age was the only confounder explicitly addressed, while sex, scanner variability, and site effects may also shape predictions; dataset size remains limited relative to the dimensionality of neuroimaging data; and adversarial training might reach its full potential with longer or more carefully tuned optimization. Future work will extend the approach to multiconfounder settings, larger and more diverse cohorts, and anatomically informed regional inputs such as hippocampal measures, which may encourage reliance on clinically established disease markers while reducing the influence of widespread age-related structural change.

Subject of Research: Artificial Intelligence

Article Title: Improving the fairness and robustness of deep learning classification models through bias mitigation and post hoc calibration

Article References: Ramedani, M., Terli, J. C., Martensen, O. H., Strauß, T., Buerger, K., Dechent, P., Düzel, E., Fliessbach, K., Gemenetzi, M., Glanz, W., Hellmann-Regen, J., Hetzer, S., Incesoy, E. I., Janowitz, D., Kronmüller, M. T., Laske, C., Lüsebrink, F., Perneczky, R., Peters, O., … Dyrba, M. (2026). Improving the fairness and robustness of deep learning classification models through bias mitigation and post hoc calibration. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03805-x

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03805-x

Keywords: deep learning, Alzheimer's disease, machine learning, medical imaging, MRI, algorithmic fairness, calibration, neurodegenerative disease, Improving, fairness, robustness, deep

The answer, according to the evidence, is a qualified yes. Using PRISMA 2020 guidelines, the researchers searched PubMed, Web of Science, and Google Scholar for studies published between 2015 and 2024. The initial sweep identified 495 records. After removing duplicates, screening titles and abstracts, and assessing 66 full-text papers for eligibility, only twelve studies survived the rigorous selection process. Each was critically appraised using the Joanna Briggs Institute checklist for analytical cross-sectional studies, and all twelve scored between six and eight out of eight — a rating the authors classified as good quality.

Because the included studies measured such different outcomes — healthcare utilisation, enrolment rates, out-of-pocket spending — a quantitative meta-analysis was impossible. Instead, the team employed a thematic narrative synthesis, coding findings iteratively into descriptive and analytical themes. The results converge on three central conclusions: health insurance increases healthcare uptake, reduces dependence on out-of-pocket payments, and yet remains so low and so unequal that its benefits reach only a fraction of those who need them most.

The strongest evidence on utilisation came from nationally representative analyses of the Nigeria Demographic and Health Survey. One study of 8,006 women of reproductive age, using binary logistic regression, found a statistically significant positive association between insurance enrolment and the use of antenatal care, skilled birth attendants, and postnatal care. A second analysis of 6,761 women found that insured women were more likely to deliver in health facilities than their uninsured counterparts. The NDHS itself, covering 41,668 households, reported higher uptake of maternal healthcare services and child immunisation among women with insurance coverage. Smaller facility-based studies in Osun, Ogun, and Lagos states reinforced the pattern, showing higher immunisation coverage, greater willingness to seek care, and increased uptake of preventive services among enrollees.

On financial protection, the numbers are striking. The Nigerian Health Watch report found that insured individuals spent an average of ₦11,142 per year out of pocket, compared with ₦24,345 among the uninsured — a difference of more than half. The NDHS similarly reported substantially lower out-of-pocket expenditure for maternal and reproductive healthcare among insured women. Facility-based studies in Ondo and Ogun states confirmed that enrollees were less likely to experience financial hardship, while uninsured households devoted a larger proportion of their income to healthcare. Taken together, the evidence suggests that insurance functions as a genuine financial safety net, reducing the risk of catastrophic health expenditure that pushes millions of Nigerians into poverty each year.

Yet the review’s most sobering finding concerns who actually gets covered. Enrolment remains abysmally low, and the disparities are stark. According to the NDHS, insurance coverage among urban residents stands at 23.9 per cent — roughly twice the 11.8 per cent recorded among rural residents. Enrolment is concentrated among wealthier households, more educated individuals, and populations in Southern Nigeria, leaving Northern regions, informal sector workers, and the rural poor largely excluded. Only four African countries — Rwanda at 79 per cent, Ghana at 58 per cent, Gabon at 41 per cent, and Burundi at 22 per cent — have achieved insurance coverage of 20 per cent or more of their populations, out of 36 sub-Saharan African countries assessed in 2022.

The authors interpret these patterns through Andersen’s Healthcare Utilisation Model, which frames healthcare use as a function of predisposing, enabling, and need-based factors. Education emerged as a major determinant of enrolment: higher health literacy improves awareness of insurance benefits, strengthens the ability to navigate registration procedures, and increases trust in formal healthcare systems. Uneducated individuals, by contrast, may perceive premium payments as immediate financial burdens rather than future protection, particularly in contexts of widespread economic insecurity. Geographical disparities reflect deeper structural inequalities — Southern Nigeria records higher literacy rates, greater formal-sector employment, and better healthcare infrastructure than the North, meaning higher enrolment in the South cannot be understood simply as individual preference.

The review also acknowledges its own methodological constraints. The cross-sectional nature of most included studies limits causal inference, as reverse causality and residual confounding may still be present. Several studies relied on simple binary yes-or-no measures of insurance enrolment, oversimplifying a complex construct that ignores duration of coverage, benefit package differences, and continuity of enrolment. Recall bias may distort self-reported expenditure figures, and facility-based samples drawn from urban areas likely overrepresent frequent healthcare users. Most studies also clustered in the South-Western region, limiting national generalisability.

The policy implications are urgent. The authors call for full implementation of the Vulnerable Group Fund under the National Health Insurance Authority Act of 2022, which aims to target over 83 million vulnerable and indigent Nigerians — approximately 38 per cent of the population — who cannot afford premiums. They also recommend strengthening primary healthcare centres, boosting government health expenditure through broadened tax revenue, and scaling up community-based health insurance programmes in the informal sector, where more than 90 per cent of Nigerians work. Countries that have succeeded, such as Rwanda and Ghana, broadened community-based schemes into the informal sector and subsidised premiums for the poor.

As the 2030 terminal year for the Sustainable Development Goals draws near, Nigeria’s universal health coverage service index stands at a mere 38 per cent — far below the global average of 68 per cent. The review’s verdict is clear: health insurance in Nigeria works, reducing out-of-pocket spending and increasing healthcare uptake among those enrolled. But a scheme that covers only the educated, the wealthy, the urban, and the Southern is not universal coverage — it is a mirror of the very inequalities it was designed to erase. Genuine universality, the authors argue, requires equitable financing, targeted subsidies, premium waivers, and a fundamental commitment to reaching the 83 million Nigerians the system has left behind.

Subject of Research: Systematic literature review and narrative synthesis of the effectiveness of health insurance in improving access to healthcare and providing financial protection in Nigeria

Article Title: Systematic literature review and narrative synthesis of the effectiveness of health insurance in improving access to healthcare and providing financial protection in Nigeria

Article References: Ahuru, R. R., & Okenya, N. A. (2026). Systematic literature review and narrative synthesis of the effectiveness of health insurance in improving access to healthcare and providing financial protection in Nigeria. Frontiers in Health Services, 6, Article 1946888. https://doi.org/10.3389/frhs.2026.1946888

Image Credits: AI Generated

DOI: 10.3389/frhs.2026.1946888

Keywords: Systematic, literature, review, narrative, synthesis, effectiveness, health, insurance, improving, access, healthcare, providing