The research, conducted by Yachuan Zhang and Yi Guo of East China University of Science and Technology in Shanghai, identifies two problems that previous work has largely overlooked. The first concerns the text itself. Social media posts are short, informal, and riddled with abbreviations, slang, emojis, and grammatical irregularities. When a language model tries to extract semantic representations from such input, the intrinsic noise of the writing style makes the resulting meaning representations unreliable. Most earlier studies concentrated on filtering noise from the visual side of the pairing, treating the text as a comparatively stable anchor. Zhang and Guo argue that this assumption is flawed: if the textual foundation is shaky, everything built on top of it inherits the instability. Their first contribution is therefore a Textual Token Refinement network, designed to distill concise, dependable semantic representations from noisy inputs before any cross-modal reasoning takes place.

The second problem concerns how images are used. In many existing systems, visual information enters the model as coarse-grained, global features, essentially a single vector summarizing the whole picture. The authors point out that such coarse features often introduce irrelevant interference rather than useful relational evidence. A photograph accompanying a post may contain dozens of objects, backgrounds, and incidental details that have nothing to do with the two entities whose relationship the system is trying to determine. Feeding the entire image into the fusion process risks drowning the signal in visual clutter. DNMMA addresses this with a multi-granularity visual augmentation strategy that works at several levels of detail simultaneously, combining hierarchical fusion of global features with carefully selected local regions, so that the model can draw on both the overall scene and the small patches that actually carry relational meaning.

One of the more inventive components of the framework is the Adaptive Semantic Mixup module. Data augmentation, the practice of creating synthetic training examples to improve model robustness, is well established in computer vision, but blindly mixing images can produce combinations that contradict the accompanying text. The Adaptive Semantic Mixup balances visual diversity against text-image consistency by adaptively fusing synthetic and original images. In practice, the system decides how much of a generated or mixed image to blend with the original photograph, ensuring that the augmented visuals remain semantically compatible with the post they accompany. This matters because the framework builds on modern generative tools: the underlying literature the authors draw on includes latent diffusion models for high-resolution image synthesis and bootstrapped language-image pre-training approaches, techniques that can produce plausible synthetic imagery but require careful control when the goal is faithful relation extraction rather than creative image generation.

Another key element is the Role-Aware Attention mechanism, which brings textual knowledge to bear on the visual stream. In a social media post, the entities of interest play specific roles: one may be the subject of an action and the other its object. DNMMA explicitly incorporates these textual entity roles when processing the image, allowing the model to suppress visual features that are noisy or irrelevant to the entities in question. If a post mentions two politicians and the attached photo shows them at a podium surrounded by reporters and flags, the attention mechanism can learn to focus on the regions depicting the two individuals and their interaction, downplaying the background elements. Complementing this, a Salient Visual Augmentation component strengthens the discriminative power of local key regions, sharpening the model’s ability to distinguish the visual evidence that actually supports one relationship type over another. Object detection and visual grounding research, including open-set detection systems referenced in the paper’s bibliography, provides the technical backdrop for locating these salient regions.

Once both modalities have been refined, DNMMA performs joint entity relation optimization, integrating the cleaned textual and visual representations to make the final relation prediction. The architecture thus follows a clear pipeline: first stabilize the text, then enrich and filter the image at multiple granularities, then fuse the two streams under the guidance of entity roles, and finally optimize the relation classification jointly across the modalities. This staged design reflects a broader lesson from the multimodal machine learning literature, where researchers have repeatedly found that simply concatenating text and image features performs poorly compared with approaches that respect the distinct noise profiles and informational roles of each modality. The authors’ framework can be read as a systematic application of that lesson to the specific challenges of social media content.

The experimental evaluation was carried out on two benchmark datasets, MNRE and MRE-MI, both of which are publicly available. MNRE, introduced in 2021, is a challenging multimodal dataset for neural relation extraction with visual evidence in social media posts, and it has become the standard testing ground for this line of research. MRE-MI, published more recently, extends the task to posts containing multiple images, a setting that better reflects real-world social media but also amplifies the noise and alignment problems the new framework is designed to solve. According to the paper, extensive experiments on both datasets demonstrate the effectiveness of DNMMA in social media multimodal relation extraction, with the dual-modality noise mitigation and multi-granularity augmentation components each contributing to the overall performance gains.

The significance of this work extends beyond a single benchmark. Relation extraction is a foundational technology for knowledge graph construction, the process of building machine-readable networks of entities and their relationships that power search engines, question-answering systems, and recommendation platforms. As the field’s own survey literature notes, relation extraction has entered a new era in which large language models and multimodal inputs are reshaping what is possible. Yet the web’s most dynamic relational data, the constant chatter of social media, remains difficult to harvest because of its low quality. A framework that explicitly models and mitigates noise in both text and images, while augmenting the visual modality at multiple granularities, offers a template for making that harvest practical. The authors’ emphasis on fine-grained alignment, in particular, addresses a recognized gap: earlier systems that relied on coarse visual features were often injecting as much confusion as evidence.

The publication also situates itself within a rapidly growing research conversation. The bibliography spans work on text-image relation propagation models, hierarchical visual prefixes for extraction, contrastive alignment methods, variational information bottlenecks for denoising, and mixup-based image augmentation for multimodal named entity recognition. DNMMA synthesizes ideas from these threads into a coherent whole, and its dual focus, mitigating noise in both modalities rather than treating the image as the sole culprit, marks a conceptual shift in how the community frames the problem. The authors declare no competing financial interests and report that no funding was received for the preparation of the manuscript, and the datasets underpinning the evaluation are openly available, which should make the results straightforward for other groups to scrutinize and extend.

For the broader public, the work hints at a future in which the flood of mixed text-and-image content online can be automatically organized into structured knowledge rather than simply archived. Better relation extraction means better answers to questions like who attended an event, which companies are linked to which products, or how communities relate to public figures, all derived from the informal, image-laden posts that dominate contemporary communication. It also raises the familiar stakes of automated social media analysis: the same machinery that builds knowledge graphs could be applied to monitoring, profiling, or misinformation detection, making the technical quality of these systems a matter of broad societal interest. With DNMMA, Zhang and Guo have shown that confronting the noise problem on both fronts, the written word and the accompanying picture, is not merely a cleanup exercise but a route to substantially more reliable machine understanding of the social web. The paper was received in February 2026, accepted in September 2026, and published on 1 October 2026 in volume 68 of Knowledge and Information Systems.

Subject of Research: A dual-modality noise mitigation and multi-granularity augmentation framework for multimodal relation extraction in social media posts

Article Title: Dnmma: dual-modality noise mitigation and multi-granularity augmentation for multimodal relation extraction in social media

Article References: Zhang, Y., & Guo, Y. (2026). Dnmma: dual-modality noise mitigation and multi-granularity augmentation for multimodal relation extraction in social media. Knowledge and Information Systems, 68(1), Article 271. https://doi.org/10.1007/s10115-026-02905-z

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02905-z

Keywords: multimodal relation extraction, social media, knowledge graphs, noise mitigation, data augmentation, attention mechanism, text-image alignment, information extraction, machine learning, MNRE dataset, visual augmentation, natural language processing

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, the rigorous protocol that governs how evidence syntheses are searched, screened, and reported in modern health science. The team queried three major bibliographic databases—PubMed, Scopus, and Web of Science—for studies published since 2000, written in English or Persian, that quantitatively examined associations between environmental noise exposure and socioeconomic outcomes. Study selection and data extraction were carried out independently by multiple reviewers, a safeguard against individual bias, and methodological quality was appraised using design-specific instruments, including the Critical Appraisal Skills Programme checklists. The result is one of the most structured pictures to date of how sound, money, and social position interact in the urban environment.

The composition of the evidence base is itself revealing. Of the fifty-eight studies that met the inclusion criteria, forty-eight—82.8 percent—were cross-sectional, capturing a snapshot of noise and socioeconomic conditions at a single point in time. Three studies were cohort designs, three were quasi-experimental, three were case studies, and one was observational. Transportation noise dominated the exposure landscape: fifty studies, or 86.2 percent, examined noise from road, rail, or air traffic. Community and residential noise accounted for six studies, or 10.3 percent, while only two studies, 3.5 percent, considered noise from multiple sources simultaneously. This distribution reflects both where research attention has flowed and where the loudest, most measurable exposures occur—along the corridors and flight paths that stitch cities together.

When the researchers sorted outcomes, economic effects emerged as the most frequently investigated category, appearing in twenty-six studies, or 44.8 percent of the total. Within that group, housing-market effects were the single largest subcategory, examined in twenty-one studies, or 36.2 percent. The logic of these studies is straightforward: property markets act as a kind of acoustic accounting system, translating decibels into currency. Homes exposed to persistent traffic rumble or aircraft overhead noise tend to command lower prices, and buyers effectively discount the disamenity of sound. Because housing wealth is the dominant asset for most households, noise-induced depreciation quietly drains capital precisely where families store their financial security.

But the review’s most striking conclusion concerns distribution rather than magnitude. Across the synthesized evidence, higher noise exposure was disproportionately concentrated in socioeconomically disadvantaged populations. The pattern is a familiar one in environmental health: cheaper housing sits closer to arterial roads, rail lines, and airport approach zones, so lower-income households absorb more of the acoustic burden that wealthier residents can afford to avoid. The review linked this unequal exposure to lower income, greater material deprivation, heightened annoyance, psychological distress, sleep disturbance, and reduced quality of life. In other words, noise does not merely reflect existing inequality—it compounds it, degrading the health, rest, and wellbeing of people who already face the steepest socioeconomic gradients in nearly every other health outcome.

The psychosocial pathways deserve particular attention. Annoyance, the standard psychological metric in noise research, is not trivial irritation; it is a validated indicator of the stress response that chronic unwanted sound provokes. Sleep disturbance compounds the problem, because nighttime noise fragments the restorative processes on which cognitive function, metabolic health, and emotional regulation depend. A body of evidence synthesized in this review connects these mechanisms to psychological distress and diminished quality of life, suggesting that noise operates on wellbeing through both direct physiological arousal and the cumulative erosion of the ability to rest, concentrate, and recover. For households already stretched by economic precarity, that erosion arrives on top of every other stressor they face.

Technically, the review highlights a methodological asymmetry that readers should keep in mind. Cross-sectional designs, which make up the overwhelming majority of the included studies, can establish association but not causation: they show that noisy areas and disadvantaged populations coincide, but disentangling whether noise drives socioeconomic decline, whether disadvantaged people move into noisy areas, or whether both are shaped by deeper structural forces requires longitudinal and quasi-experimental evidence—which remain scarce, at three studies each in this synthesis. The dominance of transportation noise in the literature also leaves community noise, industrial sources, and low-frequency noise comparatively underexplored, and the authors’ inclusion of Persian-language studies hints at geographic and linguistic gaps that persist in a field still concentrated in high-income countries.

Those caveats notwithstanding, the convergence of findings across fifty-eight studies and multiple outcome domains gives the review considerable weight. The consistency of the housing-market signal, the repeated association of noise with annoyance and sleep disturbance, and the recurrent finding of unequal exposure burdens together sketch a coherent causal architecture: noise is generated by infrastructure, infrastructure is sited along lines of least economic resistance, and the resulting soundscapes depress property values while taxing the health of residents who cannot buy quiet. The authors frame the totality of this evidence as substantial socioeconomic outcomes—economic losses, psychosocial burden, and environmental inequality—rooted in an exposure that is almost entirely a byproduct of human activity and therefore almost entirely modifiable.

The policy implications follow directly. Because transportation noise dominates the exposure landscape, the levers of change sit largely with urban planners and transport authorities: traffic calming, road surface engineering, rail and aircraft noise abatement procedures, building insulation programs, and land-use zoning that separates residential life from major noise corridors. The review’s authors emphasize that noise mitigation should be incorporated into urban-planning and public-health strategies aimed at promoting healthier and more equitable living environments. Framed in equity terms, this means that interventions should be targeted where exposure is highest—which, the evidence shows, is where socioeconomic disadvantage is concentrated. Quieting a disadvantaged neighborhood is, on this reading, not an amenity project but a redistribution of environmental health.

What makes the study resonate beyond academic circles is how it reframes an experience everyone shares. Nearly every reader has paid for noise—through a lower offer on a home near a busy road, a sleepless night before an early flight, a headache after an hour in traffic—and nearly everyone has assumed those costs were personal and trivial. This systematic review aggregates the dispersed evidence showing that they are neither: they are measurable, they accumulate across millions of households, and they fall hardest on those with the fewest resources to buffer them. As cities grow denser and transport networks expand, the authors’ central message becomes harder to ignore. Silence, or something close to it, is not a luxury good. It is a component of public health, and the evidence now says so in decibels, dollars, and quality-of-life scores alike.

Subject of Research: Socioeconomic and health equity impacts of environmental noise exposure

Article Title: Environmental noise as a socioeconomic stressor: evidence from a systematic review

Article References: Environmental noise as a socioeconomic stressor: evidence from a systematic review. (n.d.). https://doi.org/10.1186/s12889-026-29282-0

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29282-0

Keywords: environmental noise, systematic review, socioeconomic factors, transportation noise, housing market, health equity, sleep disturbance, psychological distress, urban planning, public health, quality of life, noise mitigation

The study, published in the journal Quantum Information Processing, was carried out by Mohit Dhanik, Shraddha Sharma and Pitamber Mahanandia, with affiliations at the National Institute of Technology Rourkela and the Center for Quantum Science and Technologies at the Indian Institute of Technology Mandi. Their work builds on an established idea known as the weak-measurement, flip and reversal framework, but modifies it in a way that the authors say yields significantly higher teleportation fidelity, particularly in the presence of bit flip noise. The result matters because noise is not a hypothetical inconvenience in quantum technologies; it is the central obstacle standing between laboratory demonstrations and practical, large-scale quantum communication.

To understand what the researchers did, it helps to recall how teleportation works. In the canonical scheme, Alice holds an unknown single-qubit state she wishes to transmit, and she shares an entangled pair of qubits with Bob. Alice performs a joint Bell-state measurement on her unknown qubit and her half of the entangled pair, sends the two-bit classical outcome to Bob over an ordinary channel, and Bob applies a corrective operation to his qubit, which then assumes the original unknown state. In the study’s setup, Alice prepares a four-qubit entangled state and shares one of the entangled qubits with Bob, establishing the quantum channel over which an arbitrary single-qubit state is teleported. In an ideal world, the fidelity of this process, a measure of how closely the received state matches the intended one, would be perfect. In the real world, the channel is noisy, and the teleported state arrives corrupted.

The noise models considered in the work are three of the most common and physically relevant in quantum information science. The amplitude damping channel, or ADC, describes processes in which a qubit loses energy to its environment, for example when an excited atom decays or a photon is absorbed in a lossy fiber. The phase flip channel, or PFC, randomly flips the relative phase of a quantum superposition without changing the populations, a dephasing process that destroys the interference properties quantum computing relies on. The bit flip channel, or BFC, randomly flips the logical state of the qubit from zero to one or vice versa. Each of these channels degrades entanglement in a different way, and a protection scheme that works well against one may fail against another. That asymmetry is precisely what the new protocol is designed to exploit.

Weak measurement is the surprising ingredient at the heart of the scheme. Unlike a standard projective measurement, which collapses a quantum state onto a definite outcome, a weak measurement extracts only a small amount of information about the state while disturbing it only slightly. The concept traces back to the 1988 work of Yakir Aharonov, David Albert and Lev Vaidman, who showed that measurements of a spin component could, under the right conditions, yield anomalous values far outside the ordinary range. In the context of quantum error mitigation, weak measurements have a second life: because they disturb the state gently and reversibly, they can be used as a kind of soft filter that biases the quantum state away from the states most vulnerable to a given noise process. If the weak measurement indicates that the state has drifted toward a fragile configuration, a subsequent reversal operation, essentially the inverse of the measurement, can partially restore it.

In the new work, Alice performs a weak measurement on the shared qubit before transmitting it through the noisy channel, reducing the impact of decoherence before it can occur. After the qubit has passed through the channel, corresponding reversal operations are applied to recover the original quantum state as faithfully as possible. The authors describe their contribution as a modified weak measurement and reversal protocol, abbreviated WMR, tailored for different noise types acting on a four-qubit entangled system. The approach applies the weak measurement and flip operations prior to transmission to enhance resilience, followed by reversal operations after transmission. The team then systematically compared the performance of this WMR protocol against the previously proposed weak-measurement, flip and reversal method under all three noise models.

The comparison revealed a clear pattern. The modified WMR scheme achieves significantly higher teleportation fidelity and improved robustness, with the most pronounced gains appearing in bit flip noise environments. The authors explain the reason for this advantage in a technical appendix comparing two versions of the weak measurement protocol. In the earlier protocol, the measurement operators are built from trigonometric functions of an angle parameter, while the reversal operators fix one diagonal entry to unity, producing an asymmetric structure. That asymmetry helps against amplitude damping, but bit flip noise demands something different: a more symmetric measurement operator. The modified protocol rewrites the same underlying idea in a symmetric POVM form, using parameters that range linearly between minus one and one, keeping both amplitudes normalized symmetrically. This symmetric parametrization allows better balancing between the zero and one sectors of the qubit’s state space, which is exactly what is needed to counteract errors that swap those sectors.

The phase flip channel tells a more sobering story. An effective protection scheme against phase errors should employ measurement operators that directly target the relative phases of the quantum state. But the weak measurement and reversal operators used in both protocols mainly modify population amplitudes rather than relative phases. As a consequence, both protocols deliver only the same marginal improvement in teleportation fidelity under phase flip noise. This honest limitation is itself informative: it delineates where weak-measurement-based mitigation is a natural fit and where other tools, such as dynamical decoupling, decoherence-free subspaces or full quantum error correction codes, remain the appropriate choice. The study’s authors position their findings as highlighting the potential of optimized weak measurement strategies for developing more reliable and noise-tolerant quantum communication protocols, not as a universal solution to decoherence.

The broader context of this research is a decades-long effort to make quantum information processing survivable outside pristine laboratory conditions. Peter Shor’s 1995 scheme for reducing decoherence in quantum memory and Andrew Steane’s 1996 error correcting codes established the theoretical foundations of fault tolerance, showing that quantum computation is possible in principle despite noisy hardware. But full error correction is expensive, requiring many physical qubits per logical qubit, so lighter-weight mitigation techniques remain highly valuable, especially for near-term devices and for communication channels where encoding overheads are prohibitive. Prior work has explored fighting noise with noise in realistic teleportation, purifying noisy entanglement, and using weak measurements to protect two-qubit states from amplitude damping. The new study extends this lineage by tailoring the measurement structure to the specific noise model at hand, an approach that echoes a growing theme in quantum engineering: protection strategies should be matched to the physics of the dominant error source.

The practical implications extend to several emerging platforms. Teleportation experiments have now been performed across superconducting quantum processors, including a 2024 demonstration that teleported two-qubit entanglement across nineteen qubits on an IBM-class device, and photonic and trapped-ion systems continue to push fidelity records. Every one of these platforms faces some mixture of amplitude damping, dephasing and bit flip errors, and protocols like the one proposed here could be layered onto existing teleportation circuits at modest cost, since weak measurements require only additional controlled rotations and conditional operations rather than full redundancy. The authors acknowledge Sajede Harraz for her help with the work, and Shraddha Sharma acknowledges support from the Department of Science and Technology, Government of India, through the DST-INSPIRE Faculty Fellowship. As quantum networks edge closer to reality, results like this one suggest that the path to high-fidelity quantum communication will not be paved by a single technology, but by a carefully tuned arsenal of measurement, reversal and correction techniques, each deployed where it does the most good.

Subject of Research: Enhancing quantum teleportation fidelity in noisy channels using weak measurement and reversal operations on four-qubit entangled states

Article Title: Weak-measurement assisted quantum teleportation fidelity enhancement under noise

Article References: Dhanik, M., Sharma, S., & Mahanandia, P. (2026). Weak-measurement assisted quantum teleportation fidelity enhancement under noise. Quantum Information Processing, 25(10), Article 314. https://doi.org/10.1007/s11128-026-05318-7

Image Credits: AI Generated

DOI: 10.1007/s11128-026-05318-7

Keywords: quantum teleportation, weak measurement, quantum fidelity, decoherence, bit flip channel, amplitude damping, phase flip channel, entanglement, quantum communication, noise mitigation, POVM, quantum information processing

The review, conducted by a single investigator using PubMed and Google Scholar, began with 1,608 publications and, after rigorous screening against modified PRISMA 2020 guidelines, distilled the evidence down to 34 peer-reviewed studies, comprising 31 research papers and three case studies. Nearly all of the included work was published between 2014 and 2025, underscoring how recently the topic has captured scientific attention. The United Kingdom and the United States together produced almost two-thirds of the studies, with additional contributions from Australia, Brazil, New Zealand, China, Pakistan, Portugal and Singapore. Mammals dominated the species roster, with non-human primates studied most often, but birds were also well represented, including flamingos, penguins, hornbills, kiwi and free-flight aviary collections, alongside a handful of reptiles such as an American alligator, a Siamese crocodilian and semi-aquatic turtles. Notably, no study examined teleost fish, and half of all investigations evaluated two or fewer individual animals, a small sample size that limits how broadly the results can be generalized.

Behavioural observation was by far the most common assessment tool, deployed in 28 of the studies through species-specific ethograms that catalogue activities such as resting, foraging, pacing and social interaction. The single most frequently documented response was increased vigilance, reported in ten studies across macaques, gibbons, capuchins, peccaries, orangutans, giraffes, hornbills, cheetahs and mixed aviary species. Vigilance, the scanning behaviour animals use to monitor their environment for threats, is widely interpreted as a sign of heightened arousal or anxiety. Other documented responses included retreating away from visitors or out of sight, decreased rest, increased self- or social grooming, increased pacing, reduced appetite, increased swimming, altered circadian activity patterns, greater time spent underwater in a Siamese crocodile, and even increased protectiveness of infants in orangutans. Importantly, six studies found no statistically significant behavioural change linked to noise, and no publication reported overt abnormalities such as self-inflicted trauma or serious aggression.

Physiological measurement, by contrast, was rare. Only six studies assessed glucocorticoid concentrations, the hormonal fingerprints of activity in the hypothalamic-pituitary-adrenal axis, with five pairing faecal or urinary hormone analysis with behavioural observation. Three of the six reported elevated glucocorticoids: one in okapis attributed to visitor presence, one in orangutans linked to ventilation system noise, and one in a giant panda later attributed to normal physiological variation unrelated to noise. The remaining studies found no elevation or no correlation with behaviour. The review’s authors note that the broader literature increasingly cautions against interpreting glucocorticoids as stand-alone welfare indicators, since levels can fluctuate with reproductive status, circadian rhythms and sampling methods, and argue that pairing hormone data with complementary measures, such as the cognitive bias testing used in one primate study, would produce far more robust welfare conclusions.

Perhaps the most surprising finding concerns the sources of the noise itself. Visitor noise was the focus of 27 studies, but construction noise appeared in five and concerts in two. Yet in two publications, researchers suspected that ventilation systems, not visitors, were the real acoustic culprit, because decibel levels did not rise appreciably when crowds grew. A third study found that behavioural changes tracked a habitat’s waterfall feature more closely than visitor noise. One investigation concluded that reducing visitor numbers or limiting opening hours did not guarantee a quieter environment, highlighting the persistent hum of mechanical infrastructure in zoo soundscapes. At the same time, enclosure acoustics can work in animals’ favour: one study showed that a habitat’s soundscape substantially buffered short-term fluctuations in external noise, suggesting that thoughtful acoustic design can be a protective factor rather than a hazard.

The objective acoustic data gathered across the studies spanned an extraordinary range, from 51.5 decibels to a peak of 122.2 decibels recorded near a sloth habitat in a walk-through enclosure. Concert noise reached a maximum of 88 decibels and construction noise 76 decibels. One study measured construction and non-construction days at different frequencies in hertz, while another compared how A-weighted and C-weighted decibel filters affected penguin species, finding that only southern rockhoppers responded positively to pool use under elevated C-weighted levels. These technical details matter because sound meters positioned on the public-facing side of a habitat may not accurately represent what an animal actually perceives, a limitation the review flags as a significant obstacle to drawing welfare conclusions from sound pressure levels alone.

When it came to characterizing welfare outcomes, the literature revealed a striking inconsistency. Eleven studies suspected a negative welfare impact from anthropogenic noise based on observed behavioural changes, nine could not determine whether welfare was affected, seven reported no substantial impact, and seven did not address welfare as an outcome at all. Welfare assessment protocols were rarely described in a standardized way, and many studies acknowledged confounding factors, including the visual presence of visitors, the sight of construction workers, species-level and individual differences in noise sensitivity, and even the acoustic interference of precipitation. Under the Five Domains model of animal welfare, which integrates nutrition, physical environment, health, behavioural interaction and mental state, these complications underscore that noise cannot be evaluated in isolation. The ventilation system example illustrates the dilemma: mechanical noise may be stressful, yet reducing ventilation could compromise thermal comfort and air quality, producing a net welfare loss across multiple domains.

Fourteen publications offered mitigation recommendations, clustering around three practical strategies. Public education, delivered through signage or trained staff, was the most frequently cited, and one study found that ‘watching eyes’ signage requesting quietness measurably reduced noise levels. The second strategy centred on giving animals choice and control over their location, an established contributor to positive welfare, with several studies documenting animals retreating to indoor or off-exhibit spaces to escape noise. Additional suggestions included controlling visitor numbers and timing, habituation training, adding soothing background sound, increasing vegetation, redesigning enclosures to dampen sound and continuing behavioural monitoring during disruptive periods. Operational planning, such as relocating noise-sensitive species during extended construction, could preserve visitor engagement while protecting animal welfare.

The review concludes that anthropogenic noise alone may not harm welfare in every case, since some studies could not disentangle acoustic from visual disturbances, and that species and individuals differ markedly in their responses. Individual-level welfare assessments, the authors argue, allow institutions to plan more appropriately and may prevent costly habitat reconstruction by identifying minor, targeted modifications before welfare is seriously compromised. As zoo accreditation standards evolve, the authors suggest that noise deserves more formal inclusion in welfare assessment frameworks, and that future research should explore how animals respond to different human-generated frequencies, whether reactions differ between chronic and acute exposure, and which documented behaviours genuinely reflect negative welfare states. For a field barely a decade old, the message is clear: the soundscape of the modern zoo is not just background noise, but a variable that zoological institutions can measure, design around and ultimately manage to the benefit of the animals in their care.

Subject of Research: The effects of anthropogenic noise on animal welfare in zoological settings

Article Title: A Scoping Review: The Effect of Anthropogenic Noise on Animal Welfare in Zoological Settings

Article References: Kruse, T. N., & Flint, M. (2026). A Scoping Review: The Effect of Anthropogenic Noise on Animal Welfare in Zoological Settings. Veterinary Medicine and Science, 12(5), Article e71197. https://doi.org/10.1002/vms3.71197

Image Credits: AI Generated

DOI: 10.1002/vms3.71197

Keywords: anthropogenic noise, zoo animal welfare, animal behaviour, soundscape, glucocorticoids, visitor effects, Five Domains model, noise mitigation, scoping review, captive animals, vigilance, zoo acoustics