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Background/Objectives: The purpose of this study was to examine the sex-specific distribution of traumatic spinal fractures and potential predictive clinical factors for a more well-founded treatment evaluation. Methods: This study is a retrospective cohort study. Data from electronic medical records were analyzed and compiled in a database. Demographic information, trauma-specific characteristics, and radiological measurements, as well as laboratory values and surgical treatments, were collected. Only surgical cases were included in this study. Statistical analyses were performed using the IBM SPSS Statistics program. Chi-square tests, effect sizes, and 95 confidence intervals were used for comparison of categorical variables, and means and standard deviations were calculated, as well as Levene’s test for equality and t-tests for analyzing continuous variables. The statistical significance was set at a two-tailed p < 0.05. Results: A total of 164 patients were included, with a mean age of 58.03 years. Statistically significant differences between sexes were found in age (p = 0.04), GCS (p = 0.03), hemoglobin (p = 0.03), hematocrit (p = 0.007), and the one-year post-surgical intervertebral angle (p = 0.004). AIS score showed statistically significant differences in the cervical and lumbar sections (p < 0.015; p = 0.022) and the overall spine (p = 0.049). No statistically significant difference in the HU values in the vertebra above the fracture was found between men and women. Women showed significantly larger one-year postoperative intervertebral angles than men. Conclusion: Vertebrae with lower HU values tend to collapse despite stable surgical treatment; therefore, additional bone quality assessment should be contemplated. These findings highlight sex-specific considerations for future clinical decision-making. Full article

22 pages, 1403 KB

Open AccessArticle

An Overview of the Socioeconomic and Biodemographic Aspects of the Vietnamese Fishing Crews

by Phuong Viet Le, Minh-Hoang Tran, Khanh Quoc Nguyen, Lan Thi Nguyen, Hung Viet Nguyen, Thuy Phuong Hoang Le and Nghiep Ke Vu

Societies 2026, 16(4), 133; https://doi.org/10.3390/soc16040133 (registering DOI) - 21 Apr 2026

Abstract

The current study provides a comprehensive overview of the socioeconomic and sociodemographic conditions of Vietnamese fishing crews, who form the backbone of the nation’s marine capture fisheries but remain among the most vulnerable labor groups. Based on interviews with 2037 captains and crew members across six coastal provinces, the study examines demographic characteristics, education, working conditions, legal arrangements, and income determinants. Results show that the fishing labor force is entirely male, predominantly middle-aged, and characterized by limited formal education and long occupational experience. Employment relationships are largely informal and verbal, leaving crews without labor protection, social or health insurance, or contractual stability. Statistical analysis revealed significant income disparities between captains and crew members, between inshore and offshore fleets, and among fisheries and provinces. Fishing experience and professional certification were positively correlated with income, highlighting the importance of skill development. The findings underscore the urgent need for socioeconomic policies that formalize labor contracts, expand insurance coverage, promote vocational training, and modernize fishing technologies. These measures, combined with income diversification and community welfare programs, are critical to improving the well-being, safety, and resilience of Vietnam’s fishing workforce and advancing sustainable marine economic development. This study provides valuable baseline information on an underrepresented segment of the commercial fishing industry, informing fisheries managers and policymakers in designing future development programs that account for the socioeconomic and demographic conditions of fishing crews. Full article

(This article belongs to the Section The Social Nature of Health and Well-Being)

14 pages, 1116 KB

Open AccessArticle

Genetic Diversity and Population Structure Analysis of Seven Duck Populations of Bangladesh Using Microsatellite Markers

by Pranto Saha, Krishna Chandra Barman, Minjun Kim, Dongwon Seo, Md. Munir Hossain, Seung Hwan Lee, Md Azizul Haque and Mohammad Shamsul Alam Bhuiyan

Vet. Sci. 2026, 13(4), 407; https://doi.org/10.3390/vetsci13040407 (registering DOI) - 21 Apr 2026

Abstract

The objectives of this paper were to assess the genetic diversity, population structure, genetic differentiation, and phylogenetic relationships among seven duck populations using 14 microsatellite (MS) markers. This paper included 176 individuals representing seven duck populations of Bangladesh: indigenous duck (BLD), Nageswari (NAG), Rupali (RUP), Jinding (JIN), Pekin (PEK), BAU Black and White (BWC), and BAU White (WHC). A total of 133 alleles were observed with a mean of 9.50 alleles per locus. Genetic diversity was evaluated using measures such as allele frequency, observed and expected heterozygosity, and Shannon’s information index with average values of 5.44 ± 0.31, 0.59 ± 0.02, 0.64 ± 0.02, and 1.28 ± 0.05, respectively. Population differentiation and inbreeding analysis (F-statistics) indicated moderate genetic diversity and a slight degree of inbreeding across populations. Analysis of molecular variance indicated that 75% of the total genetic diversity was attributable to the within-population variation, whereas 9% and 16% were attributed to the variation among individuals and population differentiation, respectively. Indigenous duck populations (BLD, NAG, and RUP) had a close genetic relationship with JIN ducks and an intermediate relationship with two crossbreds (BWC and WHC), and the highest genetic distance was observed with PEK ducks. Neighbor-joining phylogenetic analysis revealed that the Bangladeshi indigenous duck populations formed a single cluster, while the two crossbreds (BWC and WHC) and PEK exhibited their distinct genetic identities in separate clusters. Furthermore, structure analysis at K = 2 to 5 confirmed the distinct genetic architecture (ΔK = 4.00) of the studied duck populations. This paper provides important insights into genetic diversity measures and population differentiation that will be helpful in future genetic improvement, conservation initiatives, and the design of appropriate breeding programs. Full article

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32 pages, 487 KB

Open AccessArticle

Top Management Teams’ Environmental Attention and ESG Rating Divergence: Evidence from China

by Yishi Qiu and Susheng Wang

Sustainability 2026, 18(8), 4131; https://doi.org/10.3390/su18084131 (registering DOI) - 21 Apr 2026

Abstract

While Environmental, Social, and Governance (ESG) rating divergence poses a barrier to accurate sustainability measurement and sustainable investment, how internal managerial cognition addresses this external market misalignment remains underexplored. To address the research question of how executive focus shapes market consensus on corporate sustainability, this study integrates the Attention-Based View and Signaling Theory to examine the potential mitigating role of Top Management Team (TMT) environmental attention on ESG rating divergence. Utilizing high-dimensional fixed-effects regressions and textual analysis, we analyze a sample of Chinese A-share non-financial listed firms from 2015 to 2023. Empirical results indicate that a transparent and forthcoming managerial environmental focus helps reduce rating divergence, thereby partially aligning informational baselines. This cognitive alignment can act as an information calibrator, particularly when environmental issues match the firm’s core industry materiality, and this association appears more pronounced in regions with stringent environmental regulations. Robustness checks support the notion that substantive, quantitative sustainability disclosures driven by executive attention assist in alleviating informational misalignment among external rating agencies. These findings offer socio-economic and policy insights for advancing sustainable development, suggesting that regulators could consider encouraging structured sustainability reporting to support the role of executive cognition in standardizing ESG measurements. Full article

(This article belongs to the Special Issue Circular Economy and Sustainable Transition Under the Dual Carbon Target)

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31 pages, 994 KB

Open AccessArticle

Integrated Governance Model for Monitoring Potable Water Quality and Laboratory Effluents in Universities

by Maria Gabriela Mendonça Peixoto, Gustavo Alves de Melo, Denisie Ellen de Iovanna, Matheus de Sousa Pereira, Davi de Freitas Evangelista, Francisco Gabriel Gomes Dias and Rafaela Fogaça Resende

Environments 2026, 13(4), 230; https://doi.org/10.3390/environments13040230 (registering DOI) - 21 Apr 2026

Abstract

This study proposes and analyzes an integrated framework for monitoring potable water quality and laboratory effluent management in universities, with emphasis on its practical application in a Brazilian public institution. Adopting a qualitative and documentary approach, the research was based on high-impact scientific publications, institutional reports, and environmental databases. The results demonstrate that effective water and effluent governance depends on the interaction of three core dimensions: regulatory compliance, technological innovation, and institutional governance. These elements operate synergistically to ensure transparency, risk prevention, and environmental accountability. The proposed University Laboratory Water Monitoring Framework (UL-WMF) illustrates how universities can transform water control into a managerial and educational tool aligned with sustainability goals. The illustrative institutional application revealed potential for integrating Internet of Things (IoT) and Laboratory Information Management System (LIMS) technologies into environmental management routines, reinforcing universities’ strategic role in achieving global sustainability objectives. Despite relying on secondary data, this study provides a scalable foundation for decision support systems and future empirical validation. The novelty of the University Laboratory Water Management Framework (UL-WMF) lies in its integration of potable water monitoring and laboratory effluent governance into a single operational framework, addressing a gap in the existing literature and offering a model specifically tailored to the context of universities in developing countries. The applied component of the study consists of an illustrative institutional case constructed exclusively from publicly available environmental and governance reports. This illustration serves to demonstrate the operational relevance of the proposed framework, without implying field measurements or primary data collection. Full article

23 pages, 465 KB

Open AccessArticle

Entropy-Based Fuzzy Data Analytics for Time-Sequential Decision Making: A Case Study in Supply Chain Optimisation

by Bahram Farhadinia, Raza Nowrozy, Atefe Taghavi, Mansoureh Maadi and Savitri Bevinakoppa

Electronics 2026, 15(8), 1760; https://doi.org/10.3390/electronics15081760 (registering DOI) - 21 Apr 2026

Abstract

Decision-making problems in complex environments are often characterised by uncertainty, vagueness, and dynamically evolving information. In such contexts, decision makers may express hesitant and fluctuating evaluations over time, which cannot be adequately captured by classical hesitant fuzzy frameworks. To address this limitation, time-sequential hesitant fuzzy sets (TSHFSs) have been introduced as an effective tool for modelling temporal hesitancy. However, the development of information measures for TSHFSs, particularly entropy measures for quantifying uncertainty and deriving criteria weights, remains limited. In this paper, we propose a novel class of entropy measures for TSHFSs by constructing transformation mechanisms based on proximity-driven formulations derived from similarity structures. The proposed measures are developed using arithmetic and algebraic operators to capture the dispersion of information across time sequences, enabling a more refined representation of temporal uncertainty. These entropy measures are further integrated into a multi-criteria decision-making (MCDM) framework, where they are employed to determine criteria weights under incomplete information and combined with the TOPSIS method for ranking alternatives. The effectiveness of the proposed framework is validated through comparative analysis with existing TSHFS entropy measures and sensitivity analysis under varying decision conditions. The results demonstrate that the proposed measures maintain ranking consistency while providing improved discrimination and interpretability of alternatives. In particular, the framework effectively captures fluctuating hesitancy and enhances the robustness of decision outcomes in dynamic environments. The proposed approach contributes to the advancement of TSHFS-based decision analysis by offering a mathematically grounded and practically applicable entropy-driven framework for handling time-dependent uncertainty in complex decision-making problems. Full article

(This article belongs to the Special Issue Fuzzy Data Analytics: Current Trends and Future Perspectives)

23 pages, 5106 KB

Open AccessArticle

A Multidimensional Framework for Analyzing Image–Text Consistency in Social Media

by Hongqi Xia, Zhijie Zhao, Binbin Zhao, Hong Lan, Han Wu, Xujing Jing and Yanrong Zhang

Appl. Sci. 2026, 16(8), 4044; https://doi.org/10.3390/app16084044 (registering DOI) - 21 Apr 2026

Abstract

As image–text posts have become a dominant form of social media communication, understanding how the two modalities jointly convey meaning remains a key challenge in multimodal analysis. This study aims to examine whether image–text consistency is inherently multidimensional rather than reducible to a single similarity metric. Existing studies often reduce consistency to a single relevance score, which cannot capture semantic, emotional, and functional interactions. We construct a dataset of 28,650 multimodal posts and model image–text relationships along three dimensions: semantic consistency (CSC), emotional consistency (CEC), and informational matching consistency (IMC). Semantic and emotional alignment are measured using cross-modal representation and similarity computation, while IMC is defined through rule-based classification of informational roles. Results show that emotional consistency (CEC = 0.621) is higher than semantic consistency (CSC = 0.549,

p < 0.001

), while 61.0% of posts maintain consistent informational orientation. These findings demonstrate that image–text consistency exhibits distinct cross-dimensional patterns that cannot be captured by single-metric approaches. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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35 pages, 1484 KB

Open AccessSystematic Review

Soil Property Monitoring in Africa via Spectroscopy: A Review

by Mohammed Hmimou, Ahmed Laamrani, Soufiane Hajaj, Faissal Sehbaoui and Abdelghani Chehbouni

Environments 2026, 13(4), 228; https://doi.org/10.3390/environments13040228 (registering DOI) - 21 Apr 2026

Abstract

Efficient soil fertility monitoring is essential for sustainable agriculture, food security, and environmental management across Africa, yet conventional laboratory methods remain prohibitively costly and slow for continental-scale applications. Soil spectroscopy is considered as a rapid, non-destructive alternative with transformative potential. This review provides a systematic synthesis of spectroscopic applications across Africa, encompassing laboratory, field, airborne, and satellite-based platforms, while examining major data sources including the Africa Soil Information Service (AfSIS) and GEO-CRADLE spectral libraries. We critically evaluate the evolution of modeling approaches, revealing that Partial Least Squares Regression (PLSR) dominates, but a shift toward advanced frameworks like hybrid physically based models, ensemble learning and deep neural networks is essential. Critically, we identify a pronounced imbalance wherein laboratory spectroscopy prevails while imaging and satellite-based approaches remain comparatively underutilized, despite their unparalleled potential for scaling point measurements to continental extents. The review consolidates findings on key soil properties, demonstrating consistent successes for primary constituents with direct spectral responses (i.e., organic carbon), while revealing relative uncertainty for properties inferred indirectly via covariance (e.g., available phosphorus, potassium). Despite significant local and regional progress, the absence of a standardized pan-African spectral library and the intractable transferability problem remain formidable barriers. Future research must pivot decisively toward imaging spectroscopy and satellite platforms, mitigating PLSR dominance through systematic adoption of ensemble methods, transfer learning, and model harmonization frameworks to fully operationalize these technologies in support of Africa’s sustainable development goals. Full article

(This article belongs to the Topic Soil Quality: Monitoring Attributes and Productivity)

33 pages, 1697 KB

Open AccessArticle

Designing Effective Multi-Window Map Interfaces: The Role of Highlighting and Luminance Contrast in Visual Search

by Jing Zhang, Liyu Hu, Yunqi Zhu, Yu Zhang, Xuanyi Kuang, Jingjing Li and Wa Gao

ISPRS Int. J. Geo-Inf. 2026, 15(4), 180; https://doi.org/10.3390/ijgi15040180 (registering DOI) - 21 Apr 2026

Abstract

Multi-window map interfaces are widely used in geospatial monitoring systems and map-based analytical environments, where users must rapidly locate task-relevant information across multiple spatial displays. Designing visual cues and display conditions that effectively support visual search in such environments remains an important challenge for map interface design. This study examines how luminance contrast and highlighting influence visual search performance in multi-window map interfaces. A within-subject eye-tracking experiment was conducted using five highlighting conditions (No Highlighting as the control condition, Outer Border Highlighting, Text Highlighting, Title-Bar Highlighting, and Background Highlighting) and three luminance-contrast levels (low, medium, and high). Behavioral performance (accuracy and reaction time) and eye-movement measures (total viewing duration, fixation count, saccade count, and time to first fixation) were analyzed to evaluate how perceptual visibility and visual cue structures affect spatial information search. Results show that higher luminance contrast improved accuracy and reduced reaction time, although differences between medium and high contrast were small, suggesting that performance stabilized once a sufficient visibility threshold was reached. All highlighting conditions facilitated search relative to the control condition, with background and title-bar highlighting producing the most efficient gaze behavior and earlier target acquisition. A significant interaction between luminance contrast and highlighting was also observed, indicating that structured highlighting mitigates the performance costs associated with low contrast. Eye-movement evidence further indicates that region-based cues guide attention at the level of spatial interface regions rather than simply increasing local salience. These findings provide empirical guidance for improving spatial information retrieval efficiency in multi-window geospatial interfaces. Full article

23 pages, 4408 KB

Open AccessArticle

Measurement-Informed Latency Limits for Real-Time UAV Swarm Coordination

by Rodolfo Vera-Amaro, Alberto Luviano-Juárez, Mario E. Rivero-Ángeles, Diego Márquez-González and Danna P. Suárez-Ángeles

Drones 2026, 10(4), 310; https://doi.org/10.3390/drones10040310 (registering DOI) - 21 Apr 2026

Abstract

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)

13 pages, 1190 KB

Open AccessArticle

Electromyographic Activity of the Shoulder Muscles During Arm Elevation in Asymptomatic Subjects—A Cross-Sectional Study

by Martin E. Barra-López, Carlos López-de-Celis, Erik Garcia-Ribell, Sergi Rodríguez-Rodríguez, Miguel Malo-Urriés and Jacobo Rodríguez-Sanz

J. Funct. Morphol. Kinesiol. 2026, 11(2), 161; https://doi.org/10.3390/jfmk11020161 (registering DOI) - 21 Apr 2026

Abstract

Background: Although several studies have compared muscle activity in ‘healthy’ and ‘unhealthy’ shoulders, studying ‘healthy’ shoulders alone could improve the understanding of shoulder biomechanics. Objective: This study aims to describe the electromyographic activity of several shoulder muscles during a full range of free active flexion, as well as during abduction and scaption movements, and to compare gender differences in subjects with no history of shoulder pain or pathology. Methods: A cross-sectional descriptive study was conducted with 34 subjects aged between 18 and 60 years of both genders. The activity of the anterior, middle, and posterior deltoid, serratus anterior, infraspinatus, latissimus dorsi, and teres major muscles was measured using surface electromyography. Root Mean Square (RMS) values were calculated as a percentage of Maximal Voluntary Isometric Contraction (MVIC). Results: Regardless of whether they are considered agonists or antagonists, these muscles were active, with no statistically significant differences (Mann–Whitney U test), during both the lifting and lowering phases of the studied movements. Statistically significant differences between movements were observed only in the deltoid (Kruskal–Wallis H test, p < 0.004), which was more active during abduction. Women showed statistically significant muscle activity increase compared with men in some movements, except in the infraspinatus muscle—for example, in the three parts of the deltoid during the lifting phase of scaption (ANCOVA, p = 0.002–0.024). Conclusions: In this sample, the shoulder muscles studied showed comparable activity, acting as agonists or antagonists during shoulder elevation. These findings are exploratory and may help inform future studies on muscle activation in healthy shoulders during more varied functional tasks. Full article

(This article belongs to the Section Functional Anatomy and Musculoskeletal System)

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17 pages, 2015 KB

Open AccessArticle

Efficient Battery State of Health Estimation Using Lightweight ML Models Based on Limited Voltage Measurements

by Mohammad Okour, Mohannad Alkhalil, Mutaz Al Fayad, Juhyun Bak, Kevin R. James, Sulaiman Mohaidat, Xiaoqi Liu, Fadi Alsaleem, Michael Hempel, Hamid Sharif-Kashani and Mahmoud Alahmad

J. Low Power Electron. Appl. 2026, 16(2), 16; https://doi.org/10.3390/jlpea16020016 (registering DOI) - 21 Apr 2026

Abstract

Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article

(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)

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22 pages, 2369 KB

Open AccessFeature PaperArticle

Multivariate Integration of Functional and Compositional Transitions in Gluten-Free Composite Flours Based on Amaranthus caudatus and Lupinus mutabilis

by Marco Rubén Burbano-Pulles, Pedro Gustavo Maldonado-Alvarado, Santiago Alexander Rojas-Porras, Lorena Susana Sciarini, Norma Cristina Samman and Manuel Oscar Lobo

Appl. Sci. 2026, 16(8), 4027; https://doi.org/10.3390/app16084027 (registering DOI) - 21 Apr 2026

Abstract

The transition from starch-dominated to protein-enriched gluten-free systems represents a critical step in improving the functional and nutritional quality of composite flours. This study investigated the effects of progressive substitution of Amaranthus caudatus (amaranth) with Lupinus mutabilis (Andean lupin) on the physicochemical, rheological, and antioxidant properties of gluten-free flour blends. A multimodal dataset comprising 33 variables across six measurement domains (proximal composition, hydration properties, thermomechanical behavior, pasting profiles, particle size, and antioxidant activity) was analyzed using an integrated framework combining univariate inference (FDR-adjusted p-values), PCA, Multiple Factor Analysis (MFA), and sparse Partial Least Squares Discriminant Analysis (sPLS-DA). Results revealed that increasing lupin content (10–40%) significantly increased protein and fiber levels while decreasing starch content, leading to higher water absorption capacity and reduced peak viscosity and setback. Multivariate models showed that the protein/fiber–starch trade-off was the principal axis of compositional differentiation (PC1, ~41% variance), while PC2 captured rheological and antioxidant variability, with formulations containing higher proportions of amaranth exhibiting greater antioxidant activity. The sPLS-DA model achieved 72% separation accuracy with moisture, protein, water absorption, and torque parameters as top discriminants. These findings provide an evidence-based framework for gluten-free flour optimization using Andean crops and highlight how statistical modeling can inform targeted formulation decisions. The approach is transferable to other compositional transitions in food systems, underscoring the utility of multivariate analytics in applied food research. The multivariate framework further suggests that intermediate substitution levels may offer an optimal balance between nutritional enrichment and rheological functionality. Full article

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27 pages, 3977 KB

Open AccessReview

Recovering Speech from Vibrations: Principles and Algorithms in Radar and Laser Sensing

by Emily Bederov, Baruch Berdugo and Israel Cohen

Sensors 2026, 26(8), 2553; https://doi.org/10.3390/s26082553 (registering DOI) - 21 Apr 2026

Abstract

Sensing audio using non-acoustic modalities such as millimeter-wave radar and laser-based systems has emerged as an active research area with significant implications for privacy, security, and robust speech processing. These approaches recover speech-related information from vibration measurements captured by non-acoustic sensing modalities. Prior work spans a wide range of techniques, from classical signal-processing pipelines to modern machine-learning and deep-learning models, enabling applications such as speech reconstruction, eavesdropping, automatic speech recognition, and noise-robust enhancement. Some systems rely on radar or laser sensing as a standalone audio surrogate, while others fuse radar-derived features with microphone signals to improve robustness in noisy or non-line-of-sight environments. Experimental results across the literature demonstrate that recovering intelligible speech or discriminative speech features from radar or laser-sensed vibrations is feasible under controlled conditions. However, performance remains sensitive to practical factors including sensing distance, object material and geometries, environmental interference, multipath effects, and task complexity. Not all speech-related tasks are reliably solved, particularly in unconstrained real-world scenarios. Overall, the field is rapidly evolving, with open challenges in robustness, generalization, and deployment, offering several promising directions for future research. Full article

(This article belongs to the Special Issue AI and Smart Sensors for Intelligent Transportation Systems—2nd Edition)

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42 pages, 10596 KB

Open AccessSystematic Review

Measurement and Modeling of Sustainable Food Choice and Purchasing Behavior: A Systematic Review of Methods and Models

by Tiago Negrão Andrade and Helena Maria André Bolini

Foods 2026, 15(8), 1442; https://doi.org/10.3390/foods15081442 (registering DOI) - 21 Apr 2026

Abstract

Despite decades of methodological sophistication, research on sustainable food behavior remains critically limited in predicting actual purchases. This study aims to examine how methodological fragmentation across psychometric, econometric, and behavioral approaches affects the predictive validity of sustainable food choice and purchasing behavior. This integrative systematic review of 62 empirical studies across psychometric validation, discrete choice experiments (DCEs), trust and cognitive biases, and objective behavioral measurement diagnoses the structural disarticulation between these traditions as the primary cause of limited predictive validity. Findings reveal a pronounced inversion of the evidence hierarchy: while self-report studies report moderate attitude–behavior correlations (β ≈ 0.40–0.50, self-report), the only long-term study using objective scanner data demonstrates that this relationship collapses to a virtually null effect (β = 0.022), representing a 95.6% decay in predictive capacity. Psychometric instruments demonstrate strong structural validity but lack ecological validation against actual purchases. DCEs have evolved econometrically (from MNL to GMNL models), yet remain isolated from psychological theory and real-world validation. Critically, no reviewed study integrated validated scales, a DCE, and objective behavioral data within a single design. Key moderators—skepticism, halo effects, and affective heuristics—are systematically underoperationalized. To overcome this impasse, we propose Hybrid Choice Models (HCM) as the central tool to formally articulate latent attitudes, stated preferences, and observed behavior, enabling cumulative evidence to inform policy and market strategies with greater predictive accuracy. These findings indicate that predictive advances depend on integrating measurement paradigms to achieve ecologically valid and policy-relevant models of sustainable consumer behavior. Full article

(This article belongs to the Special Issue The Food Perspective: Understanding the Drivers of Consumer Food Choices)

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