Quantum localization phenomena have fascinated physicists since the discovery of Anderson localization, which illuminated how disorder quenches the diffusion of quantum waves. Traditional random systems give rise to well-characterized phases classified by their extended or localized nature. However, quasiperiodic systems—those exhibiting order without translational periodicity—have long resisted a unified theoretical treatment due to their intrinsic structural complexity and the rich variety of localization behaviors they present. Unlike ordinary disordered materials, quasiperiodic chains host not only pure extended or localized states but also critical states characterized by multifractal self-similarity, a property that had defied a clear theoretical description.

The research team centered their study on a class of one-dimensional spin-1/2 quasiperiodic systems that inclusively represent both spinful and spinless models as special cases. This generalized model serves as a versatile platform for exploring the coexistence and transitions among the seven fundamental localization phases identified in these systems. These phases comprise pure extended, localized, or critical states, along with their coexisting mixtures, enabling the realization of intricate mobility edge phenomena where transitions between localization regimes occur within the same energy spectrum.

Three pivotal theorems underpin this new framework. Firstly, the researchers established that when chiral symmetry is preserved, mobility edges vanish, meaning pure phases prevail without coexistence of different states. This insight directly links fundamental symmetries in the system to the nature of its quantum phases, offering a symmetry-based criterion to distinguish distinct localization behaviors. Secondly, the team discovered an unprecedented mechanism specific to spinful quasiperiodic chains that gives rise to critical states, thus broadening the avenues for engineering complex localization phenomena far beyond the scope of prior spinless models. Finally, they identified an exact solvability condition: when the hopping-coupling matrix governing particle motion becomes singular, the spinful problem reduces to a spinless one that admits closed-form analytical solutions. This criterion not only confirms known special cases but also provides a systematic recipe for constructing exactly solvable models within this rich class of quantum systems.

Leveraging these theoretical breakthroughs, the team introduced two novel exactly solvable models. The first is a spin-selective quasiperiodic chain that features all three fundamental mobility edges—those separating extended from localized, extended from critical, and localized from critical states. This model is unprecedented in its ability to realize the full spectrum of localization transitions within a unified, analytically tractable framework. The second model is a quasiperiodic optical Raman lattice that maps out the complete phase diagram encompassing every fundamental localization phase, marking the first instance where such a comprehensive landscape is explicitly characterized with closed-form solutions accessible at multiple exactly solvable points.

The implications of this work extend well beyond theoretical novelty. The proposed models and their solvable points serve as precise blueprints guiding experimental efforts to observe and manipulate these exotic localization phases in ultracold atomic setups. Optical Raman lattices have proven to be versatile and highly controllable platforms in quantum simulation, and the current framework prescribes concrete parameters and protocols for realizing and probing the predicted phase transitions and critical states. Experiments along these lines are already underway, promising to validate and refine the theoretical predictions while opening avenues for exploring nonequilibrium dynamics, quantum information applications, and disorder-induced phenomena in engineered quantum matter.

Moreover, this unified framework lays a robust foundation for future investigations extending to systems of higher complexity and dimensionality. The authors envision natural generalizations toward systems with SU(N) symmetry, where multiple internal degrees of freedom enrich quantum correlations and symmetry-protected phenomena. The methodology also suggests pathways to incorporate many-body interactions beyond single-particle localization physics, a frontier where the interplay between disorder, correlations, and topology could reveal novel phases of matter and critical behavior.

The study revitalizes the quest to understand the fundamental nature of quantum states in deterministic but aperiodic structures, showing that their enigmatic localization phenomena are not disjoint curiosities but manifestations of a universal paradigm governed by symmetry and topology. By knitting together disparate threads of prior sporadic results and translating abstract mathematical structures into experimentally relevant models, the work exemplifies how deep theoretical insights fuel advances in quantum technologies and condensed matter discovery.

As quantum materials and synthetic quantum platforms grow ever more sophisticated, the ability to classify, control, and engineer localization phenomena with exact models offers a powerful toolkit for harnessing the rich physics of quasiperiodic order. The research by Prof. Liu’s team thus represents a landmark step toward a unified theory of quantum localization in complex systems, holding promise for transformative applications in quantum simulation, materials science, and future quantum technologies.

Subject of Research: Universal Localization Phases in Spinful Quasiperiodic Quantum Chains

Article Title: Universal Results in a Spinful Quasiperiodic Chain: A Complete Framework for Localization Phases

News Publication Date: Not specified (recently published online)

Web References:

• Science Bulletin DOI: 10.1016/j.scib.2026.03.002

References:

• Prior works by Xiong-Jun Liu’s group, including studies published in Phys. Rev. Lett., Nature Physics, and others on critical states in quasiperiodic systems.

Image Credits: ©Science Bulletin

Keywords:
Quantum Localization, Quasiperiodic Systems, Anderson Localization, Mobility Edge, Critical States, Spin-1/2 Chains, Chiral Symmetry, Multifractal Wavefunctions, Optical Raman Lattice, Exactly Solvable Models, Ultracold Atoms, Quantum Simulation

In a groundbreaking study featured in The Journal of Finance and Data Science, researchers from CentraleSupélec in France, alongside mathematicians from the University of Catania and University of Palermo in Italy, introduce a novel neural-network-based method to refine the estimation of these asset co-movements. Rather than allowing artificial intelligence to supplant traditional financial theories outright, their technique integrates AI within a transparent, theoretically sound optimization framework. This approach enables the neural network to “clean” the covariance matrix, which represents how assets move together, before it is utilized in portfolio construction.

Central to their methodology is the focus on global minimum-variance portfolios, which aim to minimize risk by reducing volatility. The neural network is trained not just on historical price patterns but specifically on the realized risk outcomes once the portfolio is allocated. This end-to-end training ensures that the covariance matrix cleaning is directly optimized to produce risk-efficient portfolios rather than simply fitting statistical correlations in isolation. Crucially, the process retains transparency by making its individual steps explicit, bridging the gap between black-box AI models and conventional financial engineering.

The novelty lies in their conceptualization of the covariance matrix as more than a mere tabulation of pairwise asset correlations. It encapsulates layered, collective market structures: broad systemic trends, sector-specific behaviors, and idiosyncratic noise elements. Effective cleaning involves discerning which of these collective patterns genuinely reflect market dynamics and which are artifacts of noise, then weighting them appropriately before the allocation step. This nuanced trust assignment to patterns stands in contrast to typical shrinkage or regularization methods used for covariance estimation.

Their framework views covariance transformation as a decision problem extending beyond finance. Any context where a noisy covariance matrix informs subsequent decisions can benefit from such learned corrections. By embedding the problem’s mathematical structure into the neural network architecture, the researchers ensure that the model respects essential symmetries inherent to covariance matrices. For instance, the network’s output remains invariant to the permutation of stock orderings or different mathematical representations of the same risk structure. This design choice prevents overfitting to a specific asset universe and promotes the discovery of generalized covariance cleaning principles.

Empirical evaluation on U.S. equity markets from 2000 to 2024 highlights the effectiveness of the model. Remarkably, a neural network trained on a relatively small set of around a few hundred stocks demonstrated exceptional generalizability when applied to an expanded universe of close to one thousand stocks without any additional retraining. The optimized portfolios consistently outperformed those constructed using conventional covariance estimators, including cutting-edge nonlinear shrinkage techniques. They exhibited materially lower realized volatility, reduced drawdowns during periods of market stress, and enhanced risk-adjusted returns as quantified by Sharpe ratios.

Significantly, the superiority of the neural-network-based portfolios did not diminish when subjected to realistic trading simulations. Accounting for practical market frictions such as transaction costs, bid-ask spreads, exchange fees, and financing expenses, the strategy maintained its performance edge. This robustness underscores the practical applicability of embedding AI-driven covariance cleaning directly within portfolio optimization workflows, rather than treating them as separate, heuristic preprocessing steps.

The study signals a paradigm shift for artificial intelligence in finance. Rather than relegating AI techniques to inscrutable black-box models divorced from domain knowledge, their approach champions intertwining neural networks with financial principles and mathematical constraints. By respecting the structure and symmetries of covariance matrices, the models become interpretable and generalizable tools that enhance decision-making in complex, noisy environments.

Furthermore, the integration of deep learning with covariance matrix estimation offers new avenues for managing uncertainty and noise in myriad complex systems. The neural network’s ability to learn optimal corrections tailored to portfolio risk objectives enables more reliable risk management in large-scale, high-dimensional financial settings. This is critical as portfolios expand in size and complexity, where traditional estimation methods often falter.

The implications extend beyond equities to any domain requiring covariance estimation under noise, such as macroeconomic risk analysis, insurance modeling, or energy system optimization. This research foreshadows a future in quantitative finance where machine learning methods are not standalone black boxes but core components of rigorously designed, mathematically grounded frameworks.

As AI continues to permeate financial decision-making, this study exemplifies how deeper collaboration between domain experts and machine learning researchers can yield transformative advances. The neural-network approach to covariance cleaning sets a new standard for integrating data-driven insights with theoretical finance, enabling more robust, efficient portfolios that withstand the complexities and uncertainties of real-world markets.

Contact:
Christian Bongiorno
Université Paris-Saclay, CentraleSupélec, MICS, Gif-sur-Yvette, France
christian.bongiorno@centralesupelec.fr

Subject of Research: Neural-network-based covariance matrix cleaning for portfolio optimization

Article Title: End-to-end large portfolio optimization for variance minimization with neural networks through covariance cleaning

Web References:

• The Journal of Finance and Data Science
• Article DOI: 10.1016/j.jfds.2026.100179
• Study on ScienceDirect

Image Credits: Christian Bongiorno, Efstratios Manolakis, and Rosario N. Mantegna

Keywords: Machine learning, Artificial intelligence, Finance, Risk management, Neural networks, Covariance, Portfolio optimization, Variance minimization

The 2025 grant distribution granted 130 awards worth almost 25 million euros, designed explicitly to consolidate the professional trajectories of both national and international researchers across Spain. This program emphasizes the establishment of permanent research positions, while fostering the initiation or expansion of research lines through dedicated funding. These grants not only fuel R&D&I projects but also contribute significantly to the enhancement of laboratories, scientific equipment, and the broader infrastructure essentials to cutting-edge research pursuits.

Jesús Lancis, the Rector-elect and current Vice-Rector for Research at Universitat Jaume I, hailed these results as a testament to the success of the institution’s strategic approach for attracting and nurturing research talent. This success highlights the effective execution of the university’s strategic plans aimed at creating a fertile environment for scientific inquiry and innovation. Underpinning this strategy is UJI’s Research and Knowledge Transfer Promotion Plan, a comprehensive framework that includes a robust support system for recruitment, predoctoral programs, internal research initiation funding for undergraduate and master’s students, as well as co-funding opportunities for researchers securing external contracts.

Among the array of ambitious projects funded, “Guided reduction of overparameterised MLPs for efficient inference in Transformers” (GROVER), spearheaded by Manuel F. Dolz, stands out in the domain of computer science and information technology. This research seeks to enhance the efficiency of Transformer-based neural networks, a transformative technology powering much of today’s artificial intelligence. The project targets a substantial reduction in computational and energy costs, aiming to decrease inference times and energy consumption by 20% while also cutting memory requirements by 25%. Achieving these reductions could revolutionize AI applications, making deep learning models more accessible and sustainable.

The intersection of psychology and neuroscience is explored with Raphael Kaplan’s project, “The processing of time during the mental exploration of memories” (TIEMPO). This investigation delves into the brain’s remarkable ability to flexibly scale the perception and manipulation of event durations within episodic memory. By understanding the mechanisms through which temporal information is stored and retrieved, TIEMPO aims to shed light on how temporal processing may deteriorate during neurological illnesses—potentially opening new pathways to diagnose or intervene early in cognitive disorders where timeline distortions are prominent.

Environmental sciences find crucial representation in Lubertus Bijlsma’s project, “Urban and environmental water surveillance to reveal antimicrobial resistance dynamics and improve interventions” (AquaSurv). This project harnesses wastewater-based epidemiology (WBE) to provide near real-time, population-scale insights into health indicators, such as antimicrobial use and resistance markers. Despite its vast potential, WBE remains underutilized in this domain. By systematically analyzing wastewater, AquaSurv aims to unveil the dynamics of antimicrobial resistance—an escalating global health threat—providing data critical for deploying effective public health strategies.

Nishant Singh’s “Supramolecular ligation through hierarchical three-dimensional self-assembly” (SUPRALITHS) explores uncharted territory in material science by designing novel three-dimensional colloidal crystals formed from amorphous organic nanoparticles. This innovative approach to constructing hierarchical superstructures could herald the advent of materials with advanced functionalities suitable for a diverse spectrum of applications, ranging from catalysis and gas capture to energy harvesting, drug delivery, and water purification. The ability to precisely engineer these materials at a nanoscale level stands to significantly elevate the performance and adaptability of future smart materials.

In the biomedical sciences, Juana Mari Delgado leads the project “Assessment of the impact of air pollution exposure on neurocognitive health during the climacteric period” (NAMASTE). Groundbreaking in its focus, this study investigates the correlation between prolonged exposure to air pollutants and cognitive function impairments alongside neurobiological biomarkers in adults undergoing the climacteric period—a phase marked by dropping estrogen levels that can exacerbate vulnerability, especially in women. By identifying early neurocognitive alterations and biological markers influenced by environmental factors, NAMASTE aspires to inform preventive public health measures targeting neurodegenerative risks linked to pollution.

The overarching objective of the 2025 grant initiative extends beyond project funding; it is a strategic effort to solidify Spain’s standing in the global scientific community by empowering researchers with resources, permanence, and infrastructure needed for innovation breakthroughs. Through these grants, the Spanish System for Science, Technology and Innovation (SECTI) fosters environments where investigative rigor and creativity can thrive, ultimately translating into technological advances with societal impact.

At Universitat Jaume I, this funding success underscores a robust ecosystem where emerging and established researchers are encouraged and supported to pursue fearless inquiry. From computer science innovations enhancing AI efficiency to environmental methodologies addressing global health issues, and from material science breakthroughs to neurobiological studies intimately tied with public health, the wide-ranging projects reflect the university’s multidisciplinary strengths and forward-thinking vision.

The tangible outcomes anticipated from these projects are poised to intersect various sectors of society and industry. Innovations like efficient neural network architectures could reshape AI-driven technologies and their sustainability footprint. Simultaneously, elucidating time perception dysfunctions may revolutionize cognitive health diagnostics, while wastewater epidemiology offers essential tools for antimicrobial resistance management—a cornerstone issue in modern medicine.

Furthermore, advancements in supramolecular materials can transform industrial applications, enabling greener and more efficient processes, while deepened understanding of air pollution’s cognitive impacts paves the way for enhanced urban health policies. Collectively, these endeavors exemplify how strategic investment in diverse scientific fields fuels comprehensive progress impacting technological, health, and environmental realms globally.

In conclusion, Universitat Jaume I’s success in the 2025 State Plan grants marks a pivotal milestone, reflecting effective institutional strategy, scientific excellence, and a commitment to multidisciplinary innovation. As the research unfolds, the knowledge generated promises to advance foundational science, influence policy, optimize technologies, and improve human health—solidifying UJI’s position among Spain’s leading research universities.

Subject of Research: Multidisciplinary scientific research in AI efficiency, cognitive neuroscience, environmental epidemiology, material science, and neurocognitive health.

Article Title: Universitat Jaume I Secures Nearly One Million Euros to Propel Multidisciplinary Scientific Innovation Across Five Cutting-Edge Research Projects.

News Publication Date: Information not explicitly provided.

Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/45fb8da6-ebdb-4d1f-a92f-cd7577f22b4c/Rendition/low-res/Content/Public

Image Credits: Universitat Jaume I of Castellón

Keywords: Universitat Jaume I, scientific research funding, Human Resources programme, State Plan for Scientific Research, AI efficiency, neural networks, cognitive neuroscience, time perception, antimicrobial resistance, wastewater epidemiology, supramolecular materials, neurocognitive health, air pollution, research grants, Spanish scientific innovation

Colorectal cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, with surgical resection being a cornerstone of curative treatment. However, the physiological stress associated with major surgical interventions often precipitates a spectrum of postoperative complications, adversely affecting recovery trajectories. With an aging global population and increasing surgical volumes, optimizing preoperative patient conditioning has become paramount. This study systematically evaluates a uniform prehabilitation strategy designed to bolster patient resilience prior to surgery.

Multimodal prehabilitation integrates physical exercise, nutritional optimization, psychological support, and patient education to holistically prepare candidates for the impending surgical challenge. Its goal is to enhance functional capacity, mitigate catabolic effects, and reduce the systemic inflammatory response postoperatively. Prior studies have explored components of prehabilitation in isolated settings; however, this research rigorously assesses a comprehensive uniform protocol applied across a broad, unselected patient population undergoing colorectal procedures.

The researchers utilized a robust cohort design, incorporating diverse patient demographics and varying ASA classifications to ensure representativeness and generalizability. By doing so, the study transcends previous limitations where prehabilitation was often confined to select subgroups, thereby broadening the clinical applicability of its conclusions. Statistical analyses underscore a consistent association between the multimodal program and favorable surgical outcomes, evident through decreased complication rates and abbreviated lengths of inpatient care.

Importantly, the study highlights that the benefits of such prehabilitation protocols are not confined to the traditionally low-risk groups. Even patients categorized under higher ASA scores, indicative of greater perioperative risk due to comorbidities or overall frailty, demonstrated marked improvements. This finding challenges existing paradigms that often deprioritize intensive preoperative interventions for sicker patients, suggesting a reevaluation of perioperative planning is warranted.

Underlying the observed clinical benefits is the physiological principle that enhancing muscle strength, cardiovascular fitness, and nutritional status prior to surgery supports improved wound healing, immune competence, and organ function. The multimodal approach addresses these interconnected domains, thereby creating a comprehensive buffer against surgical stress. Furthermore, the psychological component potentially mitigates anxiety and depression, which are known to negatively influence postoperative recovery and pain perception.

Another crucial insight pertains to healthcare economics and resource utilization. By reducing complications and shortening hospital stays, routine incorporation of multimodal prehabilitation holds promise for alleviating pressures on healthcare systems, particularly in the context of constrained hospital bed availability and escalating costs. This aligns with the growing emphasis on value-based care, where optimizing patient outcomes while reducing expenditures remains a pivotal goal.

While the study demonstrates compelling associations, it also underscores the necessity for further randomized controlled trials to elucidate causality and refine intervention protocols. Additionally, implementation science perspectives will be vital to address barriers in integrating such programs into everyday clinical workflows across diverse healthcare settings, ranging from tertiary hospitals to community clinics.

In light of emerging evidence, multidisciplinary collaboration emerges as an essential facet of successful prehabilitation programs. Surgeons, anesthesiologists, nutritionists, physiotherapists, and psychologists must coordinate efforts to tailor interventions, monitor progress, and adapt protocols based on individual patient responses. Such holistic care models promote personalized medicine and optimize surgical preparedness comprehensively.

This pivotal research adds to the accumulating body of knowledge advocating for a paradigm shift from reactive postoperative care to proactive preoperative preparation. As cancer surgeries continue to evolve with technological advancements and minimally invasive techniques, the role of patient-centered enhancement strategies will likely gain even greater prominence in optimizing outcomes.

In summary, the study robustly supports routine multimodal prehabilitation for all patients undergoing colorectal cancer surgery, transcending traditional age and health status boundaries. Incorporation of these findings into clinical practice guidelines could revolutionize perioperative care, reduce surgical morbidity, and improve quality of life for countless patients globally.

For further information and access to the full-text article, interested readers and practitioners are encouraged to consult JAMA Surgery’s official publication channels upon embargo lift.

Subject of Research:
The effectiveness of a uniform multimodal prehabilitation program to reduce postoperative complications and hospital stay in patients undergoing colorectal cancer surgery.

Article Title:
Not specified in the provided content.

News Publication Date:
Not specified in the provided content.

Web References:
Not specified in the provided content.

References:
Not specified in the provided content.

Image Credits:
Not specified in the provided content.

Keywords:
Colorectal cancer, surgery, multimodal prehabilitation, cohort study, postoperative complications, hospital stay, ASA score, functional capacity, nutritional optimization, psychological support, perioperative care, value-based healthcare.

Depression, a complex and multifactorial psychiatric condition, has traditionally been treated through neurotransmitter-focused interventions. However, a subset of patients shows resistance to these approaches, prompting researchers to investigate alternative biological underpinnings. IL-6, a pro-inflammatory cytokine, emerges as a compelling candidate due to its documented elevated presence in the serum of depressed individuals and its ability to cross the blood-brain barrier, potentially influencing central nervous system function and mood regulation.

The trial harnessed a randomized, controlled design—a gold standard in clinical research—to meticulously evaluate the effects of IL-6 and IL-6 receptor antagonism in depressed patients. This proof-of-concept investigation was not only designed to assess clinical efficacy but also to refine patient selection criteria, recognizing that not all individuals with depression might benefit equally from this immunomodulatory approach. Precision medicine, thereby, becomes central to the translation of this therapeutic avenue into clinical practice.

Inflammation’s role in psychiatric disorders has gained traction with advances in psychoneuroimmunology, and IL-6 holds particular interest given its dual role in acute-phase immune responses and chronic low-grade inflammation. The cytokine’s elevated systemic levels correlate with increased depressive symptomatology in numerous epidemiological and clinical studies, and intervening therapeutically at this juncture could modulate neuroinflammatory pathways that exacerbate mood dysregulation.

Mechanistically, IL-6 signals through its membrane-bound receptor and a soluble receptor variant, initiating intracellular cascades via the JAK/STAT pathway. This cytokine receptor interaction culminates in gene expression changes that propagate inflammatory responses. By inhibiting IL-6 or its receptor, the trial aimed to blunt these molecular cascades, thereby potentially reducing inflammatory signaling in the brain that may contribute to depressive symptoms such as anhedonia, fatigue, and cognitive impairment.

The study’s outcomes highlight intriguing clinical improvements among carefully selected patients, offering hope for a tailored immunotherapeutic modality. Notably, these preliminary findings underscore the importance of biomarker-guided treatment paradigms, where IL-6 levels or related inflammatory markers could serve as predictors of therapeutic responsiveness. Such stratification could redefine depression treatment algorithms, shifting from symptom-based to biology-based frameworks.

Beyond clinical symptom reduction, the trial sheds light on the broader neurobiological interplay between immune signaling and brain function. It bolsters a conceptual shift in psychiatry, recognizing depression as not solely a disorder of neurotransmitters but also one of systemic immune dysregulation. This integrative perspective may pave the way for synergistic treatment regimens combining traditional psychotropics with cytokine inhibitors, optimizing patient outcomes.

Moreover, the research team elucidates critical challenges in this domain, including the need for rigorous clinical trial designs that accommodate the heterogeneity of depression and inflammation. Timing of intervention, dosage optimization, and long-term safety profiles of IL-6 inhibition are pivotal areas for future inquiry. The trial functions as a critical stepping stone, inspiring both clinical and translational research endeavors aimed at innovative, biologically grounded therapies.

This study also invites a reassessment of the overarching pathophysiology of depression, encouraging researchers to explore the crosstalk between the immune system and neural circuits implicated in mood regulation. Understanding how peripheral cytokines like IL-6 influence microglial activation, neurotransmitter metabolism, and synaptic plasticity may unlock new biomarkers and therapeutic targets beyond IL-6 itself.

Crucially, the implications of IL-6 inhibition extend beyond depression, with relevance to other psychiatric and neurodegenerative disorders where inflammation plays a pathogenic role. This expands the horizon for personalized medicine strategies, wherein immunomodulation could concurrently address comorbidities that commonly embed themselves within the depressive spectrum, such as anxiety or cognitive decline.

The findings presented in this clinical trial signal a transformative phase in psychiatric treatment. By combining molecular immunology with neuropsychiatry, it showcases the power of interdisciplinary research to confront treatment-resistant depression and reduce the global health burden of mood disorders. Continued exploration and validation in larger, diverse cohorts will be paramount to cement the role of IL-6-targeted therapies in clinical psychiatry.

For further engagement, the corresponding authors Dr. Éimear M. Foley and Dr. Golam M. Khandaker can be contacted to discuss clinical insights and future research directions. Their work ushers in a paradigm that melds immunological precision with psychiatric care, promising a new dawn for patients grappling with depressive illness rooted in inflammatory biology.

Subject of Research: Therapeutic targeting of interleukin 6 (IL-6) and its receptor in depression.

Article Title: [Information not provided]

News Publication Date: [Information not provided]

Web References: [Information not provided]

References: (Based on citation) 10.1001/jamapsychiatry.2026.1053

Image Credits: [Information not provided]

Keywords: Interleukins, Depression, Inflammation, Cytokines, Clinical trials, Psychiatry, Randomization, Medical treatments, Inhibitory effects

The fundamental challenge in deploying autonomous UAVs in disaster scenarios lies in their ability to dynamically adapt to unforeseen obstacles while maintaining an optimal, smooth flight path. Current trajectory planners typically rely on decoupling spatial routing from temporal planning, estimating travel time as a fixed parameter and then determining the path within that constraint. This conventional approach restricts adaptability, often forcing UAVs to accelerate dangerously or deviate inefficiently to meet rigid time budgets, compromising safety and mission success. Addressing this limitation, MIGHTY integrates a novel mathematical formulation based on Hermite splines that jointly optimizes both temporal and spatial parameters, enabling simultaneous determination of flight paths and travel times.

Hermite splines are parametric curves renowned for their capacity to ensure smooth transitions by controlling both position and derivative constraints. By leveraging this mathematical tool, MIGHTY constructs continuous trajectories that not only avoid collisions with obstacles, represented as multicolored “rainbow clouds” in simulation environments but also minimize travel duration. However, coupling time optimization with spatial pathfinding inflates the computational complexity substantially, risking real-time applicability. The MIT and Penn researchers ingeniously circumvented this challenge by implementing an iterative refinement mechanism. Instead of calculating trajectories ab initio, MIGHTY initiates each planning cycle with an educated guess, incrementally improving the trajectory using onboard LIDAR data updates, thereby slashing computational overhead.

This iterative optimization method embodies a form of real-time model predictive control, adapting continuously as sensor data unveils new environmental dynamics. The system’s efficiency is underscored by benchmarks showing MIGHTY requires only about 90% of the computation time of leading commercial solvers, while consistently achieving up to a 15% reduction in time to goal. Importantly, MIGHTY operates solely with the UAV’s onboard processors and sensors without reliance on external computational resources, a critical feature for missions in remote or communication-blackout zones.

MIGHTY is not just a research prototype but an open-source platform, deliberately crafted to democratize high-performance trajectory planning. Unlike commercial offerings that can command exorbitant licensing fees running into hundreds of thousands of dollars, MIGHTY removes financial barriers, making cutting-edge autonomous navigation tools accessible to researchers, developers, and companies worldwide. This accessibility paves the way for rapid innovation, adaptation, and deployment across diverse sectors beyond disaster response, including urban last-mile logistics where UAVs must deftly avoid pedestrians, power lines, and buildings, and industrial inspection missions in geometrically complex sites like wind turbine farms.

The inspiration for MIGHTY’s development is deeply personal for lead author Kota Kondo, who recounts the harrowing aftermath of the Fukushima Daiichi nuclear disaster following Japan’s Great East Japan Earthquake. Witnessing the peril faced by human operators in highly radioactive and unstable environments galvanized Kondo’s dedication to developing autonomous systems capable of undertaking these dangerous tasks, safely relaying critical data back to human decision-makers. The MIGHTY system reflects this ethos, ensuring that autonomous robots can traverse volatile settings with a blend of agility, speed, and safety previously unattainable.

Extensive experimental validation has reinforced MIGHTY’s real-world viability. When deployed on actual UAV platforms, the system consistently managed a remarkable top speed of 6.7 meters per second, dynamically maneuvering around unexpected obstacles detected mid-flight. This agility is a testament to the system’s seamless integration of trajectory planning and onboard sensor data assimilation, producing flight paths that are not only optimal but inherently safe for rapid traversal of complex terrains.

Looking beyond single-agent scenarios, the research team is already charting paths to extend MIGHTY’s capabilities toward coordinated control of multiple UAVs simultaneously. Such multi-agent coordination could exponentially enhance mission efficiency in vast disaster zones or large-scale industrial inspections, providing comprehensive spatial coverage and collaborative hazard avoidance. Future research will delve into scalable architectural enhancements and incorporate user feedback from ongoing field deployments to refine the system’s robustness and adaptability.

A critical aspect propelling MIGHTY’s success is its holistic approach: by integrating the entire trajectory planning pipeline into a unified, self-contained module, it eliminates dependencies on external software stacks, which often contribute latency and complexity. This fully integrated software design allows MIGHTY to outperform even some premium commercial solvers in speed, while maintaining open accessibility. Developers and operators can thus deploy and customize the system according to their unique mission requirements without vendor constraints.

Beyond practical gains, MIGHTY’s open-source nature fosters an inclusive engineering culture, inviting algorithmic innovation and cross-disciplinary contributions. By lifting the veil on advanced trajectory optimization methodologies and making them freely available, the system could catalyze advancements in autonomous robotics, control theory, and sensor integration technologies, creating a virtuous cycle of research and application.

This trajectory planning advancement balances precision, efficiency, and practicality—a trifecta essential for real-world UAV operations where milliseconds matter and every path must be a calculated blend of speed and caution. Funded partly by the U.S. Army Research Laboratory and Singapore’s Defense Science and Technology Agency, the research exemplifies international collaboration in pushing UAV autonomy boundaries for societal and strategic benefits.

As autonomous vehicles become increasingly important in diverse contexts, MIGHTY exemplifies the transformative power of blending sophisticated mathematical tools with pragmatic engineering insights. Its Hermite spline-based approach could serve as a blueprint for future systems seeking to reconcile computational tractability with the demand for agile, safe navigation amid uncertainty. The legacy of MIGHTY promises a new era in which autonomous robots are trusted allies in our most challenging environments.

Subject of Research: Autonomous UAV trajectory planning and real-time obstacle avoidance.

Article Title: “MIGHTY: Hermite Spline-based Efficient Trajectory Planning”

Web References:

• IEEE Robotics and Automation Letters

References:
Kota Kondo, Yuwei Wu, Vijay Kumar, Jonathan P. How, “MIGHTY: Hermite Spline-based Efficient Trajectory Planning,” IEEE Robotics and Automation Letters, 2026.

Image Credits: Courtesy of Kota Kondo, et al

Keywords: UAV, trajectory planning, autonomous robots, Hermite spline, path optimization, real-time navigation, obstacle avoidance, search and rescue, last-mile delivery, LiDAR, computational efficiency, open-source robotics

The scientific community has long been invested in understanding the prenatal factors that influence cognitive development, and vitamin D, a vital nutrient known for its role in calcium homeostasis and bone development, has garnered significant attention for its broader neurodevelopmental effects. This detailed post hoc analysis builds upon earlier findings by examining the specific domains of cognition that are most responsive to prenatal vitamin D3 supplementation, emphasizing the importance of adequate maternal nutrition in optimizing neurodevelopmental outcomes.

Cognitive flexibility, a critical executive function that enables individuals to adapt their thinking and behavior in response to changing environments, emerged as significantly enhanced in children exposed to higher prenatal vitamin D3 levels. This upstream impact on set shifting abilities underscores the vitamin’s potential role in bolstering neural circuits involved in cognitive control and attentional processes. The researchers employed rigorous cognitive assessments at the 10-year mark, utilizing standardized neuropsychological instruments to quantify visual and verbal memory alongside executive functions, ensuring robust and reproducible data.

Visual memory, which is central to tasks requiring the recall of pictorial or spatial information, showed marked improvement. The supplementation’s effect on this domain suggests that prenatal vitamin D3 may influence the development of hippocampal regions or related cortical areas responsible for encoding and retrieving visual information. Similarly, verbal memory, a cornerstone cognitive capacity related to language processing and learning, benefited from the enhanced prenatal vitamin D3 milieu, pointing to its possible modulation of neurogenesis or synaptic plasticity in language-associated brain regions.

This investigation leveraged a randomized clinical trial framework, ensuring that the observed cognitive enhancements are less likely to be confounded by extraneous variables such as socioeconomic status, maternal education, or postnatal environmental factors. High-dose vitamin D3 supplementation was systematically administered and compared to standard dosing, which adds methodological rigor to the inference that prenatal vitamin D status has a causal relationship with later cognitive outcomes. Such findings pave the way for public health initiatives aiming to optimize prenatal care through targeted nutrient interventions.

The researchers highlight that this post hoc analysis substantiates and extends the growing body of literature that prenatal vitamin D3 is more than just a micronutrient critical for skeletal health; it is a potent modulator of neurodevelopment with lasting consequences on childhood cognition. The intricate mechanisms through which vitamin D3 exerts these effects remain a fertile ground for further investigation, particularly concerning its genomic and epigenetic influences on developing neural tissues.

The translational implications are profound. Cognitive deficits in children can have cascading effects on educational attainment, social integration, and long-term mental health. Thus, ensuring adequate vitamin D levels during pregnancy could serve as a strategic intervention to enhance neurodevelopmental trajectories and potentially mitigate risks of neuropsychiatric conditions where cognitive flexibility and memory processes are compromised.

Moreover, this study reinforces the necessity for clinicians to assess and address vitamin D insufficiency during prenatal visits. The safety profile of high-dose vitamin D3, combined with its emerging benefits highlighted by this research, may prompt revisions in clinical guidelines and prenatal supplementation protocols to reflect these novel insights.

Beyond clinical practice, these findings raise intriguing questions about vitamin D’s neurobiological roles in brain maturation. Future research employing advanced neuroimaging and molecular methodologies could elucidate the specific pathways influenced by vitamin D3 during critical windows of brain development, informing both preventive and therapeutic strategies against cognitive impairments.

In a broader context, this study exemplifies the vital interconnection between nutrition, developmental biology, and cognitive science. It underscores the prenatal period as a critical phase where interventions can yield significant dividends in cognitive health, advocating for integrated approaches combining nutritional science with developmental neuroscience to foster optimal brain development.

In conclusion, the evidence from this post hoc analysis fortifies the concept that maternal nutrition, particularly vitamin D3 intake, is intricately linked to key cognitive outcomes in offspring. This landmark study invites continued exploration and validation across diverse populations and paves the way for evidence-based dietary recommendations that can positively influence the cognitive futures of the next generation.

Subject of Research:
The effect of high-dose prenatal vitamin D3 supplementation on cognitive outcomes in offspring at age 10.

Article Title:
Not available.

News Publication Date:
Not specified.

Web References:
DOI: 10.1001/jamanetworkopen.2026.11464

References:
Not provided in the available information.

Image Credits:
Not provided.

Keywords:
Pregnancy, Cognition, Cognitive development, Vitamin D, Children, Memory, Visual memory, Verbal memory, Cognitive flexibility, Randomization, Clinical trials, Postnatal care.

The NRMP operates on a complex algorithm designed to pair applicants and residency programs based on mutual rankings, ensuring that the optimal match reflects the true preferences and priorities of both parties. Fundamentally, the algorithm is built on stable matching theory, which holds that truthful rank-order lists theoretically maximize individual outcomes and overall fairness. Importantly, the algorithm is strategy-proof for applicants, discouraging manipulative tactics and encouraging honesty. Despite this robust design, the research demonstrates that real-world outcomes deviate from this expectation, exposing fault lines rooted in human behavior rather than the algorithm’s mechanics.

Central to this anomaly is the concept of algorithmic literacy—or the lack thereof—among the users themselves. The study draws on a rich dataset comprising over 1,700 medical students participating in a simulation of the residency match as well as extensive interviews with 66 participants navigating the actual match process. The findings reveal a consistent gendered pattern: male students were more inclined to independently seek, assimilate, and apply external information about the matching algorithm, whereas female students tended to rely more heavily on standard institutional advice. This inclination shaped their strategy, confidence levels, and ultimately the quality of their match outcomes.

The crux of the issue lies in the suboptimal ranking strategies employed by many participants, particularly among female applicants who exhibited greater uncertainty and less sophisticated understanding of the matching algorithm’s function. These candidates often deviated from the theoretically optimal strategy of ranking programs in true order of preference. Instead, some chose to artificially elevate less-preferred programs in the mistaken belief that doing so would increase their chances of matching, despite this behavior undermining the very stability and efficiency the algorithm guarantees.

The broader implication of this research extends beyond medical residency placements into diverse domains where similar matching or allocation algorithms are prevalent, including educational admissions, military tasking, public sector hiring, workforce allocation, and talent promotion systems. While these algorithms are often heralded for their capacity to reduce human bias, increase transparency, and improve efficiency, this study highlights a pivotal and frequently overlooked factor: differential user engagement with and comprehension of these systems can inadvertently reproduce and reinforce inequalities.

This disparity in outcomes despite an ostensibly unbiased algorithm forces a reckoning in how algorithmic fairness is conceptualized. It challenges technologists, organizational leaders, and policymakers to broaden their vision beyond the internal logic and coding of the algorithm itself to encompass the ecology of human interaction—the diverse behaviors, informational asymmetries, and confidence levels that users bring to the table.

Implementation emerges as an Achilles heel. It is not sufficient to design a system that is fair “on paper.” The effectiveness of these systems critically depends on comprehensive user education, nuanced communication, and sustained support mechanisms. The researchers advocate for a multifaceted approach to training, including repeated exposure to educational content, interactive simulations that allow users to experience the consequences of ranking decisions firsthand, and encouragement for users to seek multiple, credible sources of information beyond formulaic institutional guidelines.

Notably, the study critiques overly simplistic institutional messaging, encapsulated by well-meaning but vague admonitions such as “rank according to your true preferences” or “follow your heart.” While directionally accurate, such advice often fails to equip users with the cognitive tools necessary to internalize why this strategy works, leaving room for fear-driven or heuristic decision-making that undercuts system efficiency and fairness.

Thus, this research underscores the intimate interplay between technical system design and the sociocognitive dimensions of user interaction. It serves as a cautionary tale that even state-of-the-art matching algorithms, meticulously engineered to ensure neutrality and truthfulness, cannot insulate outcomes from the variances in users’ informational landscapes and confidence boundaries.

Looking forward, this insight compels organizations deploying matching algorithms and other consequential decision-support tools to embed robust educational frameworks and user-centered communication strategies as integral components of system implementation. Only by acknowledging and addressing the human factors at play can the promise of algorithmic fairness be truly realized in practice, thereby fostering outcomes that not only appear fair but are equitable in lived experience.

As algorithms increasingly become gatekeepers to career trajectories, educational opportunities, and resource allocations, this paradigm-shifting work by Samuel E. Skowronek and colleagues reminds us that fairness is a socio-technical construct. The equilibrium lies not solely within lines of code but equally in the knowledge empowerment of those who interact with these computerized decision systems daily.

Subject of Research: People
Article Title: Gendered Navigation of Advice and Suboptimal Behavior in Matching Algorithms: Evidence from the Residency Match
News Publication Date: May 15, 2026
Web References:

• https://pubsonline.informs.org/doi/10.1287/orsc.2024.19652
• https://www.informs.org/A%20computerized%20matching%20system%20can%20be%20designed%20to%20be%20fair%20and%20still%20produce%20unequal%20outcomes%20if%20the%20people%20using%20it%20do%20not%20understand%20how%20it%20works
  References: Samuel E. Skowronek et al., “Gendered Navigation of Advice and Suboptimal Behavior in Matching Algorithms: Evidence from the Residency Match,” Organization Science, March 13, 2026. DOI: 10.1287/orsc.2024.19652
  Keywords: Algorithms, Algorithmic Fairness, Matching Algorithms, Residency Match, Gender Differences, User Understanding, Behavioral Economics, Decision-Making, Algorithm Implementation

Major depressive disorder has long been a target for therapeutic interventions, yet conventional antidepressant medications often require weeks to months to manifest clinical efficacy, with a significant subset of patients exhibiting resistance to these treatments. The urgent need for novel therapeutics capable of delivering swift and durable symptom relief has propelled research into the neuropharmacological properties of psychedelics, with psilocybin emerging as a compelling candidate.

This recent study employed rigorous randomized clinical trial methodology, meticulously balancing patient characteristics across treatment arms to ensure robust validity. The primary outcome measured was the trajectory of depressive symptomatology following a single dose of psilocybin, with evaluation points extending to three months post-intervention. Remarkably, significant antidepressant effects were observable as early as 48 hours, highlighting the rapid onset characteristic that stands in contrast to traditional selective serotonin reuptake inhibitors and related drug classes.

The biological mechanisms underlying psilocybin’s therapeutic action are multifaceted, involving agonism of the 5-HT2A serotonin receptor subtype known to modulate neural circuits implicated in mood regulation and affective processing. This receptor engagement promotes neuroplasticity and reorganization of dysfunctional neural networks, which may underpin the rapid amelioration of depressive symptoms observed clinically. Moreover, psilocybin’s capacity to induce profound experiential and cognitive shifts during administration may facilitate psychological insights and recalibration of maladaptive thought patterns.

While most participants tolerated the psilocybin intervention well, a subset required additional psychological support in the immediate post-dosing period due to emergent anxiety symptoms. This finding underscores the necessity for careful patient selection, comprehensive preparatory counseling, and presence of trained facilitators during and following the administration to optimize safety and therapeutic outcomes.

The persistence of antidepressant effects beyond three months signals an enduring restructuring of neuropsychological functioning that contrasts sharply with the need for daily dosing typical of conventional antidepressants. However, these data simultaneously suggest that exploration into repeated dosing schedules or adjunctive therapies could potentiate and sustain clinical benefits over even longer horizons and across more heterogeneous patient populations.

This study’s findings pave the way for a new era of clinically validated psychedelic-assisted psychotherapy and pharmacotherapy, inviting a reexamination of regulatory frameworks and clinical guidelines to accommodate these emerging modalities. As the evidence base expands, integrating psilocybin into mainstream psychiatric practice will necessitate ongoing interdisciplinary collaboration among neuroscientists, psychiatrists, psychologists, and policymakers.

These results arrive amidst a resurging global interest in psychedelic compounds as legitimate therapeutic agents, supported by enhanced scientific rigor and destigmatization efforts. They provide a compelling narrative that not only challenges entrenched treatment paradigms but also enriches our conceptualization of psychiatric disorders as dynamic brain states amenable to innovative forms of intervention targeting both neurobiology and subjective experience.

Future research directions aim to elucidate the optimal dosing regimens, identify biomarkers predictive of treatment response, and ascertain long-term safety profiles. Additionally, comprehensive assessments of psilocybin’s integration with psychotherapy modalities and exploration of its efficacy across diverse psychiatric conditions remain paramount endeavors.

The convergence of neuropharmacology, cognitive neuroscience, and clinical psychiatry embodied in this study illustrates how transformative scientific inquiry can yield novel solutions to longstanding challenges in mental health treatment. As such, the antidepressant potential of psilocybin encapsulates an exciting frontier ripe for further exploration and clinical application.

For clinicians, researchers, and patients alike, these findings offer renewed hope for accelerated and enduring relief from the pervasive burden of depression. This study, while preliminary, marks an inflection point that invites both cautious optimism and robust scientific engagement to unlock the full therapeutic promise of psychedelic compounds.

In light of these achievements, the psychiatric community is called upon to advance methodological standards, ethical considerations, and training paradigms to responsibly harness psilocybin’s unique capabilities. Doing so promises not only to enhance patient outcomes but also to expand our fundamental understanding of consciousness, brain function, and the intricate substrates of mental health and illness.

Contact for further information and correspondence regarding this trial is directed to lead author Hampus Yngwe, MD, MSc, reachable via email at hampus.yngwe@ki.se.

Subject of Research: Major depressive disorder treatment using psilocybin
Article Title: Not specified
News Publication Date: Not specified
Web References: Not provided
References: doi:10.1001/jamanetworkopen.2026.12589
Image Credits: Not provided
Keywords: Depression, Major depressive disorder, Psilocybin, Antidepressant effects, 5-HT2A receptor, Neuroplasticity, Psychedelic therapy, Clinical trials, Drug therapy, Anxiety, Mental health, Psychiatric treatment

Data fusion, the process of integrating information from multiple sources to produce more consistent, accurate, and useful information than that provided by any individual source alone, is central to numerous technologies, including smart manufacturing, autonomous systems, and predictive maintenance. Traditional statistical approaches, notably Canonical Correlation Analysis (CCA), have long provided foundational techniques for this endeavor by identifying linear relationships between two datasets. Extending beyond linear methods, Kernel CCA (KCCA) enables the capture of nonlinear dependencies through kernel functions, yet its computational complexity hampers scalability for today’s vast data landscapes.

Recognizing these limitations, researchers advanced to Deep Canonical Correlation Analysis (DCCA), leveraging the representational power of deep neural networks to model complex nonlinear correlations. Despite their success, CCA-based methods—including DCCA—primarily embed correlation maximization into the optimization objective. This embedded focus can sometimes distract from task-specific goals such as accurate classification or precise prediction, compromising overall performance on engineering problems. The CCDNN paradigm shifts this perspective by incorporating canonical correlation not as a primary objective but as an optimization constraint, preserving the essence of correlated representation while emphasizing task-oriented learning.

Led by Professor Zhiwen Chen at Central South University, the research team introduced this innovative architecture that elegantly harnesses deep neural networks’ capabilities to learn meaningful, correlated representations across heterogeneous data sources. The team notably includes Professors Weihua Gui, Zhaohui Jiang, and Chunhua Yang from Central South University, alongside international collaborator Professor Steven X. Ding from the University of Duisburg-Essen, Germany, with doctoral contributions from Mr. Siwen Mo and Mr. Haobin Ke, hailing from Central South University and The Hong Kong Polytechnic University respectively.

Professor Chen elaborates that, unlike conventional approaches where the goal is to maximize correlation directly, CCDNN constrains canonical correlation within the optimization framework. This strategic paradigm enhances the model’s ability to focus on primary engineering tasks such as reconstruction, classification, and prediction. Further, to address redundancies possibly introduced by correlational structures, the model integrates a unique redundancy filter that operates without adding learnable parameters, ensuring efficient representation without overfitting or unnecessary complexity.

Evaluations of CCDNN demonstrated remarkable improvements in multiple benchmark tasks and data domains. On the widely recognized MNIST dataset, CCDNN outperformed existing techniques like DCCA and deep canonically correlated autoencoders, achieving significant reductions in mean squared error (MSE) and mean absolute error (MAE)—reducing MSE by 0.43 and MAE by 0.42 relative to DCCA. Such quantitative gains underscore the model’s superior reconstruction capabilities in processing complex visual data representations.

Extending beyond image data, CCDNN showcased adaptability and enhanced effectiveness in industrial applications such as fault diagnosis and remaining useful life prediction. These are critical areas in predictive maintenance and operational reliability, where integrating heterogeneous data types—such as time-series sensor measurements and image-based inspections—is essential. CCDNN’s flexible architecture allows it to reconcile diverse data modalities into coherent, correlated representations, thus enhancing classification and forecasting accuracy.

The team emphasizes the inherent flexibility of CCDNN to accommodate varied deep neural network designs tailored to specific tasks. This adaptability is vital given the wide spectrum of engineering challenges where data properties and signal structures vary significantly. For instance, in complex fault diagnosis scenarios, CCDNN can integrate visual data from imaging studies with temporal patterns from sensor arrays, effectively combining these heterogeneous viewpoints to enhance decision-making.

Beyond the technical achievements, this innovation highlights the growing intersection of machine learning with industrial engineering disciplines. The canonical correlation guidance principle anchors a framework where data-driven models can be steered by domain knowledge, embedding statistical insights directly into neural network training processes to harmonize representation learning with engineering objectives.

Professor Chen also underscores the broader implications of CCDNN in shaping the future of intelligent control and automation systems. By achieving robust multi-source data fusion, this method paves the way for smarter, more reliable cyber-physical systems capable of adaptive, data-informed behavior in real-time environments. The improved interpretability and task-specific optimization mechanisms promise to elevate the efficiency of next-generation Industrial Internet of Things (IIoT) platforms and beyond.

Published in the April 2026 issue of the prestigious IEEE/CAA Journal of Automatica Sinica, this work represents a significant advancement in both theory and practical application of deep learning-driven data fusion. The research received generous support from several scientific funding bodies, including the National Natural Science Foundation of China, ensuring that these pioneering developments continue to progress and impact various industrial domains.

As industries grapple with increasingly complex systems and data ecosystems, approaches like CCDNN offer an innovative roadmap to not only learn from but also harness multi-dimensional information streams. This could accelerate advancements in fault-tolerant control, predictive analytics, and autonomous operations, catalyzing a new wave of intelligent industrial solutions.

Ultimately, the canonical correlation guided framework exemplified by CCDNN challenges and expands our understanding of how to meld deep learning with fundamental statistical principles. For researchers and practitioners aiming to unlock the full potential of multi-source data fusion, this development marks an inspiring milestone toward more intelligent, responsive, and effective engineering technology.

Subject of Research:
Not applicable

Article Title:
CCDNN: A Novel Deep Learning Architecture for Multi-Source Data Fusion

News Publication Date:
1-Apr-2026

Web References:
DOI: 10.1109/JAS.2025.125411

References:
Chen, Z., Gui, W., Jiang, Z., Yang, C., Ding, S. X., Mo, S., & Ke, H. (2026). CCDNN: A Novel Deep Learning Architecture for Multi-Source Data Fusion. IEEE/CAA Journal of Automatica Sinica, 13(3).

Image Credits:
Professor Zhiwen Chen from Central South University, China

Keywords:
Artificial intelligence, Machine learning, Deep learning, Data analysis, Algorithms, Industrial engineering, Pattern recognition, Computer science