搜索结果：Criticizing

Proton Transfer Reaction Mass Spectrometry (PTR-MS) has emerged as a transformative tool in breath analysis, enabling the real-time, high-sensitivity profiling of volatile organic compounds (VOCs) down to the pptv level without sample preparation. This review critically examines the technological evolution of PTR-MS, from fundamental ion-molecule kinetics to advanced configurations, including Time-of-Flight (TOF) analyzers and Switchable Reagent Ion (SRI) technologies. We systematically evaluate the clinical potential of PTR-MS in identifying volatile signatures associated with pulmonary malignancies, infectious diseases (e.g., COVID-19), and systemic metabolic disorders. Even with such technological leaps, the transition from discovery to routine clinical practice remains stalled. We identify the lack of metrological traceability as a primary bottleneck, specifically criticizing the widespread reliance on theoretical quantification and the challenges in isomer discrimination. To bridge this "trust gap," this review proposes a rigorous validation framework. We advocate for the implementation of matrix-matched reference materials for absolute quantification and the development of standardized spectral atlases anchored by GC retention times. This metrological approach is essential to solidify the role of PTR-MS in precision medicine.

A lack of positive and the presence of negative parenting are frequently mentioned as risk factors for the development and maintenance of childhood and adolescent depression. However, meta-analytic research is mostly based on parenting as perceived by the child or parent, with the potential risk of a reporter bias. Also, specific patterns of observed child behavior among children with childhood depression are often overlooked and there currently is no meta-analysis on this topic. The current meta-analyses integrate the cross-sectional and longitudinal relation between observed parenting and childhood depression, and the cross-sectional relation between observed child behavior and childhood depression. This preregistered study (k = 90) includes 350 effect sizes on five observed parenting behaviors and 131 effect sizes on six observed child behaviors in parent-child interactions that were found via PsychINFO, Web of Science, and Proquest Dissertations and Theses (until the end of September 2024). Multilevel meta-analyses show that parental warmth/support and harsh control/criticism co-occur with and precede childhood depression, with small effect sizes, while childhood depression generally did not precede negative parenting. This aligns with previous research based on subjective reports, indicating that low parental warmth and higher criticism may form risk factors for childhood depression. Observed parental autonomy granting, guidance/structure, and parental depressed affect did not relate to childhood depression. Further, children with depression showed more negative affect, reduced autonomy and engagement, illustrating the interactional challenges these families face. Family interventions could validate and address the challenges posed to children as well as parents in families with a child with depression.

[This corrects the article DOI: 10.3389/fpsyg.2026.1811648.].

Antisemitism and hostility toward Israel reliably co-occur, causing some to regard opposition to Israel as the "new antisemitism," a socially acceptable way to express an ancient prejudice. Others dismiss new antisemitism as a specious rhetorical tactic used to shame and silence earnest critics of Israel. Three preregistered studies addressed this issue. Study 1 (N = 373) found that Time 1 antisemitism predicted Time 2 anti-Israel attitudes via conspiracy beliefs about Israel and Zionists. We named this pattern the "Conspiracies Mediated Model of New Antisemitism." Study 2 (N = 243) cross-sectionally assessed the distinct mediational roles of anti-Israel conspiracies (Israel conspiring for itself), Zionist conspiracies (Jews conspiring for Israel), and Jewish-related conspiracies (Jews conspiring in ways unrelated to Israel). Conspiracies implicating Israel and Zionists again mediated the association between antisemitism and anti-Israel attitudes, but those related to Jews qua Jews did not. Study 3 (N = 493), using a three-time-point longitudinal design, showed that conspiracies related to Israel and Zionists, but not to Jews qua Jews, positively mediate the predictive relationship between antisemitism and anti-Israel beliefs. Across studies, the Conspiracies Mediated Model of New Antisemitism accounted for over 55% of the variance in anti-Israel attitudes-a substantial effect. Results were not due to a general conspiratorial mindset. Democrats, compared to Republicans, expressed less antisemitism but stronger anti-Israel attitudes and greater endorsement of anti-Israel and anti-Zionist conspiracies. These studies confirm that the relationship between antisemitism and anti-Israel hostility is reliable, predictive, and substantial, and that it is mediated by anti-Israel and anti-Zionist conspiracy beliefs. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This article examines Buffon's general theory of reproduction, concentrating on his discussion of the similarities and differences between animals and plants and his explanation of nourishment, development and reproduction as effects of the same general cause. In the 'First Discourse' and various of the chapters of Volume II of Buffon's Natural History he seems to affirm two contradictory claims concerning the distinction between animals and plants: both that this distinction is absolute and indubitable and that it is false and insupportable. In this article I argue that this apparent contradiction is explained by distinguishing between what are for Buffon two distinct tasks in natural history: first, the classification of the natural world and the natural history of particular animals and plants; and second, enquiry into the general phenomena of life (nutrition, development and reproduction). It is in the first task, I argue, that the distinction between animals and plants is maintained, while it is largely inapplicable in the second. Distinguishing between these two tasks then also allows us to discern in Buffon's work, I argue, an implicit conceptual distinction between the empirical classificatory group of plants (les végétaux), distinguished from animals, and the vegetal (le végétal), concerning processes common to both animals and plants. The emphasis on the comparison of animals and plants, together with the conceptual distinction between plants (vegetables) and the vegetal, further allows us to see, I argue, that Buffon's general theory of reproduction is based on a vegetal model. That is, analysis of the details of Buffon's theory shows not just-as almost all of his predecessors agreed-that animals and plants share the vegetative functions of nutrition, development and reproduction, but that the process of vegetative multiplication provides the model for reproduction in general. Theories of generation and embryological development based on analogies with the plant seed were still not uncommon in the eighteenth century. But I argue that Buffon's general theory of reproduction presents something different in being based on the model of what we now call 'vegetative reproduction' (a new whole growing from a part) and in explaining the seed in these terms too. I conclude with some brief reflections on the implications of this vegetal understanding of Buffon's general theory of reproduction for other aspects of Buffon's natural history, particularly the introduction of the novel terminology of 'reproduction', the criticism of pre-existence theories and the controversy concerning the theory of the sexes of plants.

With the rapid development of Internet of Vehicles (IoV) applications, the demand for serving computation-intensive and delay-sensitive tasks, which are executed in a dynamic mobility environment, continues to grow, while the embedded computing power carried by vehicles remains limited, and they are facing strict requirements in terms of latency. In vehicular edge computing, to offload computation to the nearby roadside units (RSUs) and to enable centralized learning-based offloading, mobility, task, channel, and resource information at the whole system needs to be gathered at a central learner, leading to a higher communication overhead and raw data exposure. This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading with RSU assistance. The novelty of the proposed framework does not lie in the common usage of federated learning and deep reinforcement learning (DRL), but rather in the compactness of four coupled mechanisms: generation of a hybrid action representation of federated binary offloading decision and continuous resource allocation for RSUs, a personalized federated aggregation mechanism for non-IID vehicular observations collected on the RSU, a task-criticality-aware deadline reliability model with class-dependent violation penalties, and a handover-aware multi-RSU model that incorporates signaling delay, service-context transfer delay, and processing/authentication delay. In the proposed framework, the model parameters of the local SAC policies are provided to the federated coordinator rather than the locally observed information, such as raw vehicular trajectories, which can instead be used for local training of the SAC-based policies. Controlled simulation experiments are conducted to compare the proposed method with both local execution and edge-offloading methods, two centralized DRL baselines (DQN and DDPG), and three federated DRL baselines (FedAvg-DQN, centralized-SAC, and federated-MADRL). The results indicate that under the adopted simulation settings, the proposed FDRL framework achieves competitive and/or better system cost, delay, energy, deadline-violation performance, and communication overhead compared with other schemes. This privacy usefulness really means having less raw data exposed when federated training is used, and does not mean any formal privacy guarantee against inference attacks against model updates.

Magnetic Resonance Imaging can be a challenging experience for many, despite improvements in scanner design and acquisition speed. A key area of importance to patients is communication about what is happening throughout their scan experience, which can be overlooked in increasingly busy departments. With the new scanner technology available in this community diagnostic centre, there is an updated autovoice function that automatically provides the patient with an update on the expected scan duration. A post-scan survey was administered to patients attending for MRI scanning. Open and closed questions were used to explore patients' views on the usefulness of this autovoice functionality and to better understand how it was received and how it influenced their experience. A total of 313 questionnaires were collected during the two-month period. Most participants perceived the autovoice prompt as useful, with one in five patients preferring the auto prompt over that of a radiographer, and the majority having no preference either way (68%). Entry into the scanner and preference for the delivery of information during the scan were not significant (p = 0.054), with no strong preference one way or the other, although a greater number of headfirst opted for autovoice. Age was not significant (p = 0.063), but those aged 50 years or less showed a greater preference for autovoice than those over 50. Those reporting heightened perceived anxiety on the day suggested greater benefit from the autovoice during the examination. Content analysis of the survey responses indicates a generally positive experience and response towards the autovoice, with some criticisms and suggestions for improvement highlighted. The feedback underscores the importance of clear communication, time awareness, and supportive staff in improving the patient experience during scans. For many, automated systems seem to be perceived as beneficial, offering consistent information, helping them anticipate what to expect, and reducing the likelihood of miscommunication or omission. Findings from this evaluation indicate that patients generally report a positive MRI experience, with the integration of autovoice prompts contributing meaningfully to their sense of communication and reassurance. MRI scans can be stressful, and clear communication during the scan is important for patient comfort. This study asked people to complete a survey after their MRI scan to understand their views on an automated voice system that gives updates about scan timing. This study found that most people felt the automated messages were helpful, especially those who felt more anxious, and many had no strong preference between automated messages and staff communication. This matters because improving how information is shared during scans can help people feel more reassured and improve their overall experience.

The use of AvertD, a genetic test to assess an individual's risk of developing opioid use disorder, will expose physicians to liability. Critics of the test argue that the test itself is unreliable, often resulting in false positives, which will lead to potential undertreatment of pain, and false negatives, which will lead to inappropriate opioid prescriptions. But if the test is available, physicians will use it. And often, due to a combination of genetic determinism and genetic illiteracy, physicians will rely on the test to make opioid prescription decisions without also looking to the environmental, socioeconomic, lifestyle, and other factors that contribute to an individual's opioid use disorder risk.Genetic test results that purport to predict opioid use disorder - or really, any behavioral or psychiatric trait or diagnosis - may have enormous consequences for individual lives. When patients are harmed by physicians' negligent clinical decision making, they can often turn to tort law for a remedy. This article covers various physician liability scenarios, from inappropriate prescribing based on genetic test results to failure to test and informed consent issues. Physicians could face liability for prescribing opioids after a positive test result, or for denying pain medication due to false positive test results, or for prescribing opioids to patients despite negative results that prove false, or for failing to administer the test at all. This article concludes that existing tort frameworks offer insufficient protection for patients and fail to ensure appropriate integration of these tests into clinical practice. And relying on tort law will not mitigate the individual and societal harms raised by the introduction of a polygenic risk score test for opioid use disorder.

Scalability and coordination remain major challenges in training Multi-Agent Reinforcement Learning (MARL) algorithms. One approach postulates the Centralized Training and Decentralized Execution, which assumes full access to observations from the environment during training but limits agents' reliance on the joint observations within the execution phase. However, this often leads to a rapid increase in input dimensions of the centralized component (critic). Previous studies have suggested using attention mechanisms to enhance scalability and coordination in domains like Treasure Collection and Rover-Tower. This paper aims to complement these findings and offer new insights into the role of attention in MARL, focusing on these two domains on which attention was shown to be beneficial. We show that the impact of attention is very specific and different in the two domains studied. We use manually designed policies to inform our analysis, and explore the challenges concealed in the domains. We argue that the role of attention in the first domain is mainly to provide convenient inductive bias because the local observations of the agents surprisingly contain the same information as the joint observations. In the second domain, the local observations make the exploration challenging due to partial observability in one type of agents and the 'lazy agent' phenomenon. In this case, the role of centralized critic with attention is to mitigate the lazy agent phenomena and partial observability, and the attention itself acts as a simple averaging mechanism.

Interpreting gene clusters derived from RNA sequencing (RNA-seq) remains difficult in functional genomics, particularly in antimicrobial resistance studies where mechanistic context is needed for downstream hypothesis generation. We present BIOGEN, an evidence-grounded multi-agent framework for post hoc interpretation of RNA-seq transcriptional modules that integrates biomedical retrieval, structured interpretation, and multi-critic verification. BIOGEN organizes knowledge from PubMed and UniProt into traceable cluster-level explanations with explicit evidence reporting and confidence tiering. On the primary Salmonella enterica dataset, BIOGEN achieved strong grounding and biological coherence, with BERTScore 0.689, Semantic Alignment Score 0.715, KEGG Functional Similarity 0.342, and a non-verifiable identifier rate of 0.000, compared with 0.100 for the LLM-only baseline. Across four additional bacterial RNA-seq datasets, BIOGEN preserved zero ungrounded outputs under the identifier-based criterion. In a controlled multi-dataset comparison against representative open-source agentic AI baselines, BIOGEN was the only framework that consistently produced zero non-verifiable identifier outputs across all five datasets. These results indicate that retrieval access alone is insufficient to ensure reliable biological interpretation. Evidence-grounded orchestration is essential for transparent, source-traceable transcriptomic reasoning under distribution shift.

Disruptive classroom behavior is prevalent in physical education classes. Teachers typically respond to such behaviors with criticism, but different criticism styles may evoke distinct emotional responses in students. The present study examines how students' perceptions of constructive and destructive criticism used by physical education teachers in response to disruptive classroom behavior influence students' intention to improve such behavior through the mediating roles of classroom anger, classroom anxiety, and classroom helplessness. Questionnaire data were collected from 583 Chinese middle school students, and the mediation effects in the model were estimated using the product-of-coefficients approach with bias-corrected Monte Carlo bootstrapping. (1) The indirect effects of constructive criticism through classroom anger and classroom helplessness were significant, whereas the indirect effect through classroom anxiety was not significant. (2) The indirect effects of destructive criticism through classroom anger, classroom anxiety, and classroom helplessness were all significant. (3) Constructive criticism is significantly more effective than destructive criticism in enhancing students' intention to improve disruptive classroom behavior. In the context of physical education classes in China, students' disruptive classroom behavior may elicit different types of teacher criticism, and students' perceptions of constructive and destructive criticism from teachers further influence their intention to improve disruptive classroom behavior through academic emotions. Constructive criticism is more likely than destructive criticism to promote students' behavioral regulation and improvement, and physical education teachers should use specific and improvement-oriented constructive criticism more frequently.

Clinical standardization is widely debated. Advocates emphasize its potential to reduce unwarranted variation and improve quality, while critics warn it may constrain professional judgment or undermine local innovation. These tensions become especially salient during enterprise-wide electronic health record (EHR) transitions, when configuration and workflow decisions can institutionalize practices across facilities. The Department of Veterans Affairs (VA), undertaking the largest EHR transition in history, provides a critical case for understanding how frontline staff perceive system-wide standardization. To assess VA employee attitudes toward care process standardization during VA's enterprise-wide EHR transition and identify implementation challenges and opportunities. Cross-sectional survey with mixed-methods analysis of a survey fielded in September 2024. We surveyed n=1748 EHR users at the first 5 VA sites implementing a new EHR. Awareness of and support for VA's Enterprise Standardization Initiative, assessed using 5-point Likert items; free-text comments were analyzed thematically to identify perspectives on standardization. Among respondents, 43% reported awareness of VA's standardization initiative, and 65% supported standardizing care processes across facilities. Qualitative analysis revealed 3 themes: (1) support for standardization as complementary to EHR transition; (2) preference for decoupling standardization from technical change; and (3) emphasis on appropriately targeting standardization to preserve innovation capacity. VA employees expressed support for standardization with important caveats about implementation approach and scope. Findings support iterative standardization that balances consistency with local adaptation, while carefully sequencing standardization relative to technical change.

Large-scale pharmacogenomic screens provide extensive measurements of drug response across diverse cancer cell lines; however, most computational approaches emphasize point-wise sensitivity prediction or static ranking, which are poorly aligned with practical decision-making, where only a limited number of candidate drugs can be tested. We propose NetPolicy-RL, a biologically informed and decision-centric framework for pharmacogenomic drug prioritization that integrates network diffusion modeling with offline reinforcement learning. Drug selection for each cell line is formulated as an offline contextual bandit problem, enabling implicit optimization of ranking quality through a decision-oriented reward formulation rather than surrogate regression objectives. Mechanistic biological context is incorporated by propagating drug targets over curated interaction networks (STRING and Reactome) using random walk with restart, and combining the resulting diffusion profiles with cell-specific molecular importance derived from multi-omics data to compute network disruption scores. These biologically grounded signals are integrated with normalized drug response measurements to construct a joint state representation, which is optimized using an offline actor-critic architecture. Across held-out test splits, NetPolicy-RL consistently outperforms global ranking heuristics and learning-to-rank baselines, achieving statistically significant improvements in per-cell Normalized Discounted Cumulative Gain (NDCG@10) and substantial reductions in per-cell regret. Relative to GlobalTopK, the policy improves NDCG@10 for 88.7% of cell lines, while improvements exceed 95% compared with LambdaMART and regression-to-ranking baselines. Ablation analyses indicate that neither empirical response signals nor network-derived features alone are sufficient within the evaluated setting and that their integration yields the most robust performance. Overall, this study demonstrates that combining mechanistic network biology with offline policy learning provides an effective and interpretable framework for drug prioritization in precision oncology.

The home environment has been a central topic in the discipline of environmental gerontology. Home becomes the place where inhabitants' emotions and senses intertwine with their physical environment. As time spent at home increases with age, so does the importance of home environments for inhabitants' wellbeing. However, contemporary understandings of wellbeing within environmental gerontology have been criticized for their individualistic and human-centered approaches, treating the home environment primarily as a passive determinant of subjective wellbeing and neglecting the relationality of wellbeing in material, spatial and temporal contexts. This paper adopts a material gerontology perspective to conceptualize the relationship between aging inhabitants and their home environment as "person-home-assemblages". The aim of this study is to understand wellbeing in relation to aging and the home environment through these assemblages. To do so, we discuss the assemblage analysis of three cases. These cases were selected based on entanglements of transitional life events and changes in architectural environments. Each case combines data from an interview concerning an inhabitant's housing biography, an interview with the architect who designed the inhabitant's current dwelling, and material representations of the home environment. Our findings pull together aging persons, architectural spaces, personal objects, evolving landscapes and socially constructed housing ideals in relation to aging and wellbeing. Two primary perspectives emerged. Firstly, wellbeing emerges in fundamentally specific and situated ways from person-home-assemblages. Secondly, wellbeing emerges from person-home-assemblages while also actively shaping the evolution of these assemblages. These perspectives highlight how the home environment becomes an active actor and how home environments and inhabitants co-evolve and age together over time.

This article develops a prescribed-time (PT) optimal tracking framework for nonlinear systems with concurrent state and input constraints. The main focus is a flexible PT constraint-handling mechanism that is introduced first in the design: a time-varying auxiliary function performs PT error transformation, while a state-triggered adaptation law adjusts state/input performance envelopes online. Specifically, the envelopes are relaxed only when constraint violation risk is detected and are otherwise kept tight, which reduces the conservatism of fixed-boundary designs, enlarges feasible operation regions, and preserves safety margins. Based on the resulting unconstrained error dynamics, to achieve optimality within this framework, an actor-critic adaptive dynamic programming (ADP) scheme is constructed to solve the nonautonomous Hamilton-Jacobi-Bellman (HJB) equation online, guaranteeing user-assigned convergence accuracy and time independently of initial conditions. Rigorous analysis proves uniform ultimate boundedness of all closed-loop signals and PT convergence of the tracking error. Simulations on a general nonlinear system and a fault-tolerance tracking scenario, with comparisons to representative baselines under different initial conditions, verify the proposed method's superior transient tracking and reliable convergence-time performance.

Clinical prediction models are valuable tools that can support medical decision-making. Concise and interpretable models are more likely to be adopted in clinical practice, therefore appropriate selection of predictor variables is often considered essential in model development. Typically, researchers specify a list of candidate predictors based on literature reviews and expert knowledge. Data-driven variable selection methods are then often used to further reduce the number of variables in the final model. However, many commonly used approaches, such as univariable selection, have been generally discouraged in prediction modelling. This systematic review aims to examine current practice regarding the use of data-driven variable selection when developing clinical prediction models for binary outcomes using logistic regression. We focused on published articles in PubMed between 1-21 October 2024 that developed prediction models for binary health outcomes using logistic regression. We extracted information on the study characteristics and, if applicable, the methodology used for variable selection. In total, 141 studies were included in the review. We found that nearly all studies (140/141) used data-driven variable selection. Univariable selection was by far the most used method; it was used in 78% (110/141) of studies. Other frequently used methods included backwards elimination (60/141, 43%), 'bulk removal' of variables (BR) from a single multivariable model (58/141, 41%) and LASSO (35/141, 25%). Many studies applied a sequential application of variable selection methods; the most common 2-step combinations were univariable selection followed by backwards elimination (45/139, 32%) and univariable selection followed by BR (43/139, 31%). In addition, many studies lacked sufficient detail in their reporting. Common problems included incomplete reporting of candidate predictors, and unclear specification of variable selection methods. Although data-driven variable selection is generally discouraged in clinical prediction modelling, nearly all studies in our review employed at least one such method, with many studies using two or more methods. Some of the most frequently criticized methods such as univariable selection and backwards elimination were commonly used. Modern penalised methods such as LASSO, which directly aim to optimise out-of-sample predictive performance while also removing redundant variables, were used less frequently.

The vegetation in climatically heterogeneous regions exhibits significant spatial variability and temporal succession characteristics. It is crucial to obtain consistent vegetation characteristics in this region over time. Traditional single indices such as NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), and NPP (Net Primary Productivity) each have their own advantages, but they often show inconsistent trends when applied to complex vegetation. To effectively capture spatial heterogeneity and enhance the ecological interpretability, we propose a Dynamic Spatially Variable Weighted Synthesis Vegetation Index (DWS-SVI). Based on four Global Land Surface Satellite Dataset (GLASS) vegetation parameters (FVC (Fractional Vegetation Cover), LAI, NDVI, and NPP) and land cover types, this method employs the CRITIC method to perform dynamic weighting and generate continuous weight surfaces, ultimately synthesizing a comprehensive vegetation index at the pixel level. It combines "global trend and local adaptation" by integrating spatial heterogeneity modeling and multi-variable dynamic weighting, thereby overcoming the limitations of traditional methods in terms of spatial heterogeneity and ecological interpretability. Results show that over the past two decades, more than 69.4% of the area in the YRB has witnessed a significant improvement in vegetation conditions. The improvement was most notable in the summer, and it was mainly attributed to the improvement in the temperature and humidity conditions in this region. Compared with a single indicator, the DWS-SVI index can reflect the coordinated evolution of ecosystem structure and function, and can effectively suppress the observation errors caused by the bias of a single vegetation index, especially the "false greening" signals in transition zones and arid areas. Furthermore, the dominant factor map constructed based on DWS-SVI further reveals the differentiated driving mechanisms of ecosystems such as farmland, grassland, and forest, demonstrating that it has superior interpretability. This study provides a transferable framework for constructing spatially adaptive vegetation indices, enabling more reliable monitoring of ecosystem changes in large river basins and other climatically heterogeneous regions.

Sixth-generation (6G) networks are likely to support advanced Internet of Vehicles (IoV) applications that have rigid latency, reliability, and computation demands. Nevertheless, efficient task offloading is a challenging problem because vehicle environments are characterized by mobility, changing channels, varying task requirements, constrained edge resources, and growing energy demands. To address these issues, this study presents an Optimized Multi-Tier Task Offloading Strategy (OMTOS) for sustainable IoV systems. The proposed framework comprises a four-tier computing architecture comprising vehicles, roadside units (RSUs), mobile edge computing (MEC) servers, and cloud infrastructure. The generalized latency-energy optimization problem is formulated to allocate tasks across these levels, accounting for task due dates, resource capacity, communication delay, computation delay, and energy consumption. To address dynamic offloading, OMTOS employs a centralized training and decentralized execution (CTDE) based multi-agent Soft Actor-Critic (SAC) method, where the vehicle agents can make decentralized offloading decisions with centralized critics guiding the coordinated learning process during training. It is tested against rule-based and heuristic as well as deep reinforcement learning and various multi-agent reinforcement learning baselines, including LE, EO, RO, GO, DQN, DDPG, SAC, MADDPG, and MAPPO. The aforementioned results reveal that OMTOS achieves low average delay, low energy consumption, a high task success rate, and high convergence compared to the competing methods. Sensitivity analysis also indicates that the latency and energy weightings can be changed to suit various IoV service requirements, including delay-critical safety services, and energy-conscious delay-tolerant services. These results show that OMTOS offers an adaptive and sustainable task-offloading tool in 6G-enabled IoV environments.

Climate-induced migration in deltaic environments such as the Indian Sundarbans requires vulnerability assessments that are both spatially explicit and analytically objective. This study advances existing approaches by integrating the distance correlation-based CRITIC (D-CRITIC) technique with village-level environmental and socio-economic indicators to develop an unbiased Migration Risk Index (MRI) for 86 villages in the Pathar Pratima Block. Unlike traditional equal-weighted or expert-based methods (e.g., AHP, TOPSIS), D-CRITIC quantifies interdependence among indicators to derive data-driven weights, minimizing redundancy and improving diagnostic precision. Results indicate that 10.34% and 24.14% of the total area fall into very high and high vulnerability categories, respectively, while incorporating adaptive capacity reduces overall vulnerability by about 10%. The MRI reveals that 59.8% of villages exhibit moderate-to-very high migration risk, with significant hotspots along the southern coast. By quantifying interdependence among indicators, D-CRITIC offers a data-driven alternative to subjective weighting methods, potentially minimizing redundancy and improving consistency in vulnerability assessment. A ground-truth validation using household survey data from ten villages demonstrated a moderate-to-strong agreement between the model predictions and observed migration patterns, with a Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) score of approximately 0.85. This result indicates that the model possesses good discriminatory and descriptive capability in capturing migration dynamics, thereby providing preliminary empirical evidence supporting the applicability and reliability of the proposed framework.. Beyond reaffirming coastal exposure, this study uncovers emerging mid-delta transition zones where exposure and adaptive capacity interact to shape migration pressures. The methodological innovation of D-CRITIC thus provides a replicable, data-driven framework for mapping migration risk and informing resilience planning in deltaic and other climate-sensitive regions.