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Provable data possession (PDP) is a technique that enables the verification of data integrity in cloud storage without the need to download the data. PDP schemes are generally categorized into public and private verification. Public verification allows third parties to assess the integrity of outsourced data, offering good openness and flexibility, but it may lead to privacy leakage and security risks. In contrast, private verification restricts the auditing capability to the data owner, providing better privacy protection but often resulting in higher verification costs and operational complexity due to limited local resources. Moreover, most existing PDP schemes are based on classical number-theoretic assumptions, making them vulnerable to quantum attacks. To address these challenges, this paper proposes an identity-based PDP with a designated verifier over lattices, utilizing a specially leveled identity-based fully homomorphic signature (IB-FHS) scheme. We provide a formal security proof of the proposed scheme under the small-integer solution (SIS) and learning with errors (LWE) within the random oracle model. Theoretical analysis confirms that the scheme achieves security guarantees while maintaining practical feasibility. Furthermore, simulation-based experiments show that for a 1 MB file and lattice dimension of n = 128, the computation times for core algorithms such as TagGen, GenProof, and CheckProof are approximately 20.76 s, 13.75 s, and 3.33 s, respectively. Compared to existing lattice-based PDP schemes, the proposed scheme introduces additional overhead due to the designated verifier mechanism; however, it achieves a well-balanced optimization among functionality, security, and efficiency. Full article

Broader use of multioperational machines in forestry requires efficient methods for determining various timber parameters. Here, we present a novel approach to model the bark thickness (BT) as a function of stem diameter. Stem diameter (D) is any diameter measured along the bole, not a specific one. The following four regression models were tested: marginal model (MM; reference), classical nonlinear regression with independent residuals (M1), nonlinear regression with residuals correlated within a single tree (M2), and nonlinear regression with the correlation of residuals and random components, taking into account random changes between the trees (M3). Empirical data consisted of larch (Larix sp. Mill.) BT measurements carried out at two sites in northern Poland. Relative root square mean error (RMSE%) and adjusted R-squared (R2adj) served to compare the fitted models. Model fit was tested for each tree separately, and all trees were combined. Of the analysed models, M3 turned out to be the best fit for both the individual tree and all tree levels. The fit of the regression function M3 for SITE1 (50-year-old, pure stand located in northern Poland) was 87.44% (R2adj), and for SITE2 (63-year-old, pure stand situated in the north of Poland) it was 80.6%. Taking into account the values of RMSE%, at the individual tree level the M3 model fit at location SITE1 was closest to the MM, while at SITE2 it was better than the MM. For the most comprehensive regression model, M3, it was checked how the error of the bark thickness estimate varied with stem diameter at different heights (from the base of the trees to the top). In general, the model’s accuracy increased with greater tree height. Full article

Per- and polyfluoroalkyl substances (PFAS) are of concern because of their potential thyroid hormone system disruption by binding to human transthyretin (hTTR). However, the amount of experimental data is scarce. In this work, new classification and regression QSARs were developed to predict the hTTR disruption based on experimental data measured for 134 PFAS. Bootstrapping, randomization procedures, and external validation were used to check for overfitting, to avoid random correlations, and to evaluate the predictivity of the QSARs, respectively. The best QSARs were characterized by good performances (e.g., training and test accuracies in classification of 0.89 and 0.85, respectively; R², Q²_loo, and Q²_F3 in regression of 0.81, 0.77, and 0.82, respectively) and significantly broader domains compared to the few existing similar models. The application of QSARs application to the OECD List of PFAS allowed for the identification of structural categories of major concern, such as per- and polyfluoroalkyl ether-based, perfluoroalkyl carbonyl, and perfluoroalkane sulfonyl compounds. Forty-nine PFAS showed a stronger binding affinity to hTTR than the natural ligand T4. Uncertainty quantification for each model and prediction further enhanced the reliability assessment of predictions. The implementation of the new QSARs in non-commercial software facilitates their application to support future research efforts and regulatory actions. Full article

The behavior of floor-raised chickens is closely linked to their health status and environmental comfort. As a type of broiler chicken with special behaviors, understanding the daily actions of yellow-feathered chickens is crucial for accurately checking their health and improving breeding practices. Addressing the challenges of high computational complexity and insufficient detection accuracy in existing floor-raised chicken behavior recognition models, a lightweight behavior recognition model was proposed for floor-raised yellow-feathered chickens, based on a Dual-Backbone Heterogeneous YOLO Network. Firstly, DualHet-YOLO enhances the feature extraction capability of floor-raised chicken images through a dual-path feature map extraction architecture and optimizes the localization and classification of multi-scale targets using a TriAxis Unified Detection Head. Secondly, a Proportional Scale IoU loss function is introduced that improves regression accuracy. Finally, a lightweight structure Eff-HetKConv was designed, significantly reducing model parameters and computational complexity. Experiments on a private floor-raised chicken behavior dataset show that, compared with the baseline YOLOv11 model, the DualHet-YOLO model increases the mAP for recognizing five behaviors—pecking, resting, walking, dead, and inactive—from 77.5% to 84.1%. Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. This approach provides an effective solution for lightweight object detection in poultry behavior recognition. Full article

The paper introduces EVOCA—Explainable Verification Of Claims by Graph Alignment—a hybrid approach that combines NLP (Natural Language Processing) techniques with the structural advantages of knowledge graphs to manage and reduce the amount of evidence required to evaluate statements. The approach leverages the explicit and interpretable structure of semantic graphs, which naturally represent the semantic structure of a sentence—or a set of sentences—and explicitly encodes the relationships among different concepts, thereby facilitating the extraction and manipulation of relevant information. The primary objective of the proposed tool is to condense the evidence into a short sentence that preserves only the salient and relevant information of the target claim. This process eliminates superfluous and redundant information, which could impact the performance of the subsequent verification task and provide useful information to explain the outcome. To achieve this, the proposed tool called EVOCA—Explainable Verification Of Claims by Graph Alignment—generates a sub-graph in AMR (Abstract Meaning Representation), representing the tokens of the claim–evidence pair that exhibit high semantic similarity. The structured representation offered by the AMR graph not only aids in identifying the most relevant information but also improves the interpretability of the results. The resulting sub-graph is converted back into natural language with the SPRING AMR tool, producing a concise but meaning-rich “sub-evidence” sentence. The output can be processed by lightweight language models to determine whether the evidence supports, contradicts, or is neutral about the claim. The approach is tested on the 4297 sentence pairs of the Climate-BERT-fact-checking dataset, and the promising results are discussed. Full article

The UI GreenMetric World University Ranking has become a widely adopted instrument for benchmarking institutional sustainability performance; nevertheless, empirically grounded evidence from universities in diverse regional contexts remains scarce. This study undertakes a rigorous appraisal of the extent to which Mahasarakham University (MSU) has institutionalized the United Nations Sustainable Development Goals (SDGs) within its pedagogical offerings, research portfolio, community outreach, and governance arrangements during the 2021–2024 strategic cycle. Employing a mixed-methods design and guided by the 2024 UI GreenMetric Education and Research indicators, this investigation analyzed institutional datasets pertaining to curriculum provision, ring-fenced research funding, 574 peer-reviewed sustainability publications, student-led community initiatives, and supporting governance mechanisms; the analysis was interpreted through a Plan–Do–Check–Act management lens. The number of sustainability-oriented academic programs expanded from 49 to 58. Student participation in community service activities strongly recovered following the COVID-19 pandemic, and MSU’s GreenMetric score increased from 7575 to 8475, thereby elevating the institution to the 100th position globally. These gains were facilitated by strategic SDG-aligned investment, cross-sector collaboration, and the consolidation of international partnerships anchored in Thailand’s Isaan region. The MSU case provides a transferable model for universities—particularly those operating in resource-constrained contexts—endeavoring to align institutional development with the SDGs and internationally recognized quality benchmarks. The findings substantiate the capacity of transformative education and applied research to engender enduring societal and environmental benefits. Full article

Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (κ = 0.955 right, κ = 0.900 left; p < 0.001), with perfect agreement reported across all groups—including between the orthodontists themselves—for Angle classification (κ = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (p = 0.226) or overall Bolton values (p = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (p < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (≥0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use. Full article

This paper consists of various kinds of wave solitons to the mathematical model known as the truncated M-fractional FitzHugh–Nagumo model. This model explains the transmission of the electromechanical pulses in nerves. Through the application of the modified extended tanh function technique and the modified $(G^{\prime }/G^2)$-expansion technique, we are able to achieve the series of exact solitons. The results differ from the current solutions because of the fractional derivative. These solutions could be helpful in the telecommunication and bioscience domains. Contour plots, in two and three dimensions, are used to describe the results. Stability analysis is used to check the stability of the obtained solutions. Moreover, the stationary solutions of the focusing equation are studied through modulation instability. Future research on the focused model in question will benefit from the findings. The techniques used are simple and effective. Full article

LC continues to be the leading cause of cancer mortality globally, among both males and females, representing a major public health challenge. The impact of mitochondria on human health and disease is a rapidly growing focus in scientific research, due to their critical roles in cellular survival and death. Mitochondria play an important role in controlling imperative cellular parameters, and alterations in mtDNAcn might be crucial for LC development. MtDNAcn has been studied as a possible marker for LC risk, but its role in prevention is still unclear. This review and meta-analysis aims to summarize the current evidence and provide an overall estimate of the relationship between the mtDNA copy number in human samples like blood and sputum. PubMed, Web of Science, and Scopus databases were used for studies published up to February 2024, following PRISMA and MOOSE guidelines. Studies were combined using a random-effects model, and we assessed the heterogeneity between studies with the chi-square-based Cochran’s Q statistic and the I² statistic. Publication bias was checked using Begg’s and Egger’s tests. Five studies, including a total of 3.748 participants, met the eligibility criteria. The MtDNA copy number was measured in blood or sputum samples and compared across different quantiles. The pooled analysis did not find a significant association between the mtDNA copy number and LC risk (OR = 0.94; 95% CI: 0.49–1.78). Moreover, when looking at different study designs, no significant results were found, due to the small number of studies available. No significant publication bias was detected. Further studies are needed to better understand the connection between the mtDNA copy number and LC risk and to better understand the role of potential confounders. Full article

Background/Objectives: Intergenerational care plays a significant role in shaping household dietary quality and human capital development in China. Influenced by the legacy of the one-child policy, the care provided in these families often prioritizes child-focused practices. This study examines how intergenerational care influences schoolchildren’s sugar-sweetened beverage (SSB) consumption. Methods: This study utilizes data from the 2014–2015 China Education Panel Survey (CEPS) to investigate the impact of intergenerational care on schoolchildren’s dietary behaviors, with a focus on sugar-sweetened beverage (SSB) consumption. We apply both ordinary least squares (OLS) regression and the ordered logit model to estimate the impacts, and we use the instrumental variables approach to address potential endogeneity. Results: Schoolchildren from only-child families report greater SSB consumption, while those from multi-child families consume less. Intergenerational care is linked to more digital media exposure, more pocket money, and less parental supervision. These findings withstand rigorous validation through multiple robustness checks, including sample restriction strategies and propensity score matching (PSM) analysis. The effect is especially pronounced among boys, schoolchildren from families with higher parental education levels, and schoolchildren attending schools without formal nutrition education programs. Conclusions: The result indicates that intergenerational care significantly increases SSB consumption among schoolchildren from only-child families. Community nutrition and school health education programs can reduce schoolchildren’s SSB consumption, thereby lowering risks of obesity and other public health concerns. Full article

This paper combines Christopher Alexander’s pattern language with generative AI into a hybrid design framework. The result is a narrative synthesis that can be useful for informed project design. Advanced large language models (LLMs) enable the real-time synthesis of design patterns, making complex architectural choices accessible and comprehensible to stakeholders without specialized architectural knowledge. A lightweight, web-based tool lets project teams rapidly assemble context-specific subsets of Alexander’s 253 patterns, reducing a traditionally unwieldy 1166-page corpus to a concise, shareable list. Demonstrated through a case study of a university department building, this method results in environments that are psychologically welcoming, fostering health, productivity, and emotional well-being. LLMs translate these curated patterns into vivid experiential narratives—complete with neuroscientifically informed ornamentation. LLMs produce representative images from the verbal narrative, revealing a surprisingly traditional design that was never input as a prompt. Two separate LLMs (for cross-checking) then predict the pattern-generated design to catalyze improved productivity as compared to a standard campus building. By bridging abstract design principles and concrete human experience, this approach democratizes architectural planning grounded on Alexander’s human-centered, participatory ethos. Full article

The widespread rise of misinformation across digital platforms has increased the demand for accurate and efficient Fake News Detection (FND) systems. This study introduces an enhanced transformer-based architecture for FND, developed through comprehensive ablation studies and empirical evaluations on multiple benchmark datasets. The proposed model combines improved multi-head attention, dynamic positional encoding, and a lightweight classification head to effectively capture nuanced linguistic patterns, while maintaining computational efficiency. To ensure robust training, techniques such as label smoothing, learning rate warm-up, and reproducibility protocols were incorporated. The model demonstrates strong generalization across three diverse datasets, such as FakeNewsNet, ISOT, and LIAR, achieving an average accuracy of 79.85%. Specifically, it attains 80% accuracy on FakeNewsNet, 100% on ISOT, and 59.56% on LIAR. With just 3.1 to 4.3 million parameters, the model achieves an 85% reduction in size compared to full-sized BERT architectures. These results highlight the model’s effectiveness in balancing high accuracy with resource efficiency, making it suitable for real-world applications such as social media monitoring and automated fact-checking. Future work will explore multilingual extensions, cross-domain generalization, and integration with multimodal misinformation detection systems. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. Full article

Wind turbine performance in cold regions is affected by icing which can lead to power reduction due to the aerodynamic degradation of the turbine blade. The development of airfoil shapes applied as blade sections contributes to improving the aerodynamic performance under a wide range of weather conditions. The present study considers inverse design coupled with numerical modelling to simulate the effects of varying airfoil thickness and maximum camber. The inverse design process was implemented in MATLAB R2023a, whereas the numerical models were constructed using ANSYS Fluent and FENSAP ICE 2023 R1. The inverse design process applied the modified Garabedian–McFadden (MGM) iterative technique. Shear velocities were calculated from the flow over an airfoil with slip conditions, and then this velocity distribution was modified according to the prevailing icing conditions to obtain the target velocities. A parameter was proposed to consider the airfoil thickness as well when calculating the target velocities. The airfoil generated was then exposed to various atmospheric conditions to check the improvement in the aerodynamic performance. The ice mass and lift-to-drag ratio were determined considering cloud characteristics under varying liquid water content (LWC) from mild to severe (0.1 g/m³ to 1 g/m³), median volume diameter (MVD) of 50 µm, and two ambient temperatures (−4 °C and −20 °C) that characterize freezing drizzle and in-cloud icing conditions. The ice mass on the blade section was not significantly impacted by modifying the shape after applying the process developed (i.e., <5%). However, the lift-to-drag ratio that describes the aerodynamic performance may even be doubled in the icing scenarios considered. Full article

The modern workplace grapples with a human energy crisis, characterized by chronic exhaustion, disengagement, and emotional depletion among employees. Traditional well-being initiatives often fail to address this systemic challenge, particularly in industrial contexts. This study introduces the Human Energy Management System (HEMS), a strategic framework to develop, implement, and refine strategies for optimizing workforce energy. Grounded in Industry 5.0’s human-centric, resilient, and sustainable principles, HEMS integrates enterprise risk management (ERM), design thinking, and the Plan-Do-Check-Act (PDCA) cycle. Employing a qualitative Design Science Research (DSR) methodology, the study reframes human energy depletion as an organizational risk, providing a proactive, empathetic, and iterative approach to mitigate workplace stressors. The HEMS framework is developed and evaluated through theoretical modeling, literature benchmarking, and secondary case studies, rather than empirical testing, aligning with DSR’s focus on conceptual validation. Findings suggest HEMS offers a robust tool to operationalize human energy reinforcement strategies in industrial settings. Consistent with the European Union’s vision for human-centric industrial transformation, HEMS enables organizations to foster a resilient, engaged, and thriving workforce in both stable and challenging times. Full article