Abstract: Faced with the urgent challenges of climate change and environmental degradation, governments and frameworks like the EAT–Lancet Commission advocate for sustainable diets. Yet a persistent gap remains between individuals’ intentions to eat sustainably and their actual behaviour. Current approaches often attribute this gap to deficits in individual attitudes or motivation. In this paper, we challenge that view. We introduce the construct of dietary affordances to describe how opportunities for action emerge from the interplay between individuals and their environments. Crucially, we define this affordance as the synergistic product of value (goal alignment) and precision (reliability) thus implying that without sufficient reliability, high motivation (value) is mathematically insufficient to drive behaviour. Drawing on ecological psychology and contemporary active inference accounts of perception and action, we treat dietary choices not as an individual failure, but as the selection of “policies” (action sequences) that minimise risk and “expected surprise” given constraints on time, money and access. Within this framework, unsustainable eating is reframed as a rational and predictable response to a misaligned affordance field: for many, the safest, most predictable, and lowest-effort course of action is to choose foods that undermine climate goals. We argue that closing the intention–behaviour gap requires shifting the focus of interventions away from individual consumers and towards the institutions that design and govern food environments. We identify specific leverage points within the food system, including pricing, urban design and social protection, that researchers and policymakers can use to reshape the feasible behavioural policy set, ensuring that sustainable habits are not just intended but practically achievable. By reducing the expected risk and computational cost of sustainable diets, policymakers can align these options with the brain’s drive to minimize uncertainty, ensuring that sustainable habits are selected not through self-regulation, but as the natural outcome of a successfully optimized system.

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Background/Objectives: Stenotrophomonas maltophilia is an emerging opportunistic pathogen associated with severe infections, particularly in patients with cystic fibrosis (CF). Its intrinsic multidrug resistance and ability to form biofilms significantly complicate treatment. While biofilm growth is widely linked to antimicrobial tolerance, the relationship between biofilm-forming capacity and planktonic antibiotic resistance in S. maltophilia remains unclear. This study aimed to investigate the association between antibiotic resistance profiles and biofilm formation in clinical isolates from CF and non-CF patients. Methods: A total of 86 clinical S. maltophilia isolates (40 from CF airways and 46 from non-CF patients) were analyzed. Antibiotic susceptibility to seven agents was assessed by disk diffusion, with results interpreted according to EUCAST and CLSI criteria. Multidrug resistance phenotypes were defined using standard criteria. Biofilm formation was quantified after 24 h using a crystal violet microtiter plate assay and categorized into five levels of production. Statistical analyses were performed to compare biofilm formation across resistance profiles and clinical origins and to assess correlations between biofilm biomass and multidrug resistance. Results: Overall, high resistance rates were observed, particularly to meropenem (87.2%), ciprofloxacin (80.2%), and rifampicin (72.1%). CF isolates showed significantly higher resistance to piperacillin/tazobactam and a higher prevalence of multidrug resistance. Biofilm production was detected in 94.2% of isolates, with strong and powerful biofilm producers predominating. However, isolates from CF patients formed significantly less biofilm than those from non-CF patients. Notably, resistance to piperacillin/tazobactam and meropenem was associated with significantly reduced biofilm formation, as reflected in both median biomass and the proportion of high biofilm producers. Across the entire collection, the number of antibiotic resistances displayed by an isolate was negatively correlated with biofilm biomass. These trends were maintained after stratification by clinical origin, although some comparisons did not reach statistical significance. Conclusions: These findings demonstrate an unexpected inverse relationship between planktonic antibiotic resistance and biofilm-forming efficiency in S. maltophilia. Enhanced biofilm production may represent an alternative persistence strategy in more antibiotic-susceptible strains, with important implications for infection management and therapeutic failure.

Abstract: Background: Persistent pain following orthopaedic trauma is common, often disproportionate to structural healing, and increasingly attributed to central sensitisation (CS). However, the mechanisms, clinical features, diagnostic approaches, prognostic indicators, and management strategies relevant to trauma-related CS remain poorly understood. Objective: To map and synthesise existing evidence on CS following orthopaedic trauma, addressing mechanistic pathways, clinical manifestations, epidemiology, assessment methods, management approaches, and health system implications. Methods: A scoping review was conducted in accordance with PRISMA-ScR. Twenty-one studies met the eligibility criteria, comprising nine primary trauma cohorts and 12 contextual mechanistic or review studies relevant to trauma-associated CS. Data were charted across six prespecified domains of mechanistic processes, clinical presentation and diagnostic features, epidemiology and prognosis, assessment tools and outcome measures, interventions, and health system and care delivery considerations. Results: Mechanistic studies demonstrated trauma-induced neuroimmune activation, altered cortical and spinal excitability, and molecular pathways consistent with sensitisation. Clinical studies have identified neuropathic features, widespread pain, and heightened sensory responsiveness following fractures and other injuries. Neurophysiological evidence has indicated early cortical disinhibition following upper limb trauma, whereas epidemiological cohorts have reported persistent pain and disability years after major trauma. Measurement studies have highlighted the limited reliability and specificity of current tools in trauma populations, including quantitative sensory testing and self-report instruments. Early predictors of adverse trajectories include severe acute pain, neuropathic descriptors, psychological distress, and opioid-dominant analgesia. Evidence regarding early intervention, rehabilitation strategies, and system-level screening pathways remains limited. Conclusions: CS after orthopaedic trauma is supported by convergent mechanistic, neurophysiological, and clinical findings. However, trauma-specific diagnostic criteria, prognostic models, and management frameworks remain underdeveloped. High-quality longitudinal research is needed to clarify early trajectories, refine assessment methods, and establish targeted interventions to reduce long-term pain and disability.

Abstract: Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (0–25%), moderate (25–50%), and high (>50%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management.

Abstract: The role of microneedles (MNs) in enhancing tissue permeability has long been established. Their capacity to serve as drug-delivery vehicles or biosensing platforms makes them ideal candidates for applications in which tissue serves as the primary pathway. Such potential can only be thoroughly validated through tissue-dependent tests. Although MNs are not limited to human tissues, humans remain the most relevant target group. This highlights the need to develop platforms that closely replicate the structure of human tissue for the intended applications. To date, many studies have addressed the limited availability of human samples, constrained by ethical concerns and other challenges, by using artificial, human-like tissue mimics. These models have been widely used to evaluate various aspects of MN performance, including penetrability, drug delivery, and biosensing. Despite limitations, artificial tissues provide a practical assessment tool in MN development. This review offers new insights into the role of synthetic tissue models in evaluating MN functionality. It discusses the underlying rationale for their use, highlights their flexibility and potential in MN application studies, addresses their limitations, and presents their future perspective. Finally, it highlights the need for standardized, scalable artificial tissue platforms to support the translational and commercial advancement of MN technologies

Abstract: With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning and deep reinforcement learning have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems.

Abstract: Accurate forecasting of product sales and inventory is a critical task for cross-border e-commerce platforms, where demand volatility, long logistics cycles, and dynamic pricing present significant challenges for efficient supply chain management. Traditional statistical and short-term machine learning models often fail to capture long-term dependencies and complex seasonal variations in sales data, leading to inaccurate demand planning and inefficient inventory allocation. To address this limitation, we propose a forecasting framework based on the Autoformer model, a deep learning architecture designed for long-sequence time series prediction. The model leverages series decomposition blocks to separate trend and seasonal components, and employs an auto-correlation mechanism to enhance the capture of periodic demand patterns in product sales and inventory turnover. We evaluate the framework on a real-world cross-border e-commerce dataset comprising transaction-level order volume, prices, inventory records, and external market indicators such as exchange rates and shipping costs. Experimental results show that the proposed Autoformer-based approach achieves superior forecasting accuracy compared with baseline models including LSTM, Transformer, and Informer. Specifically, our model reduces prediction error with a Mean Absolute Error (MAE) of 18.6 and a Root Mean Square Error (RMSE) of 25.4, representing a 17.3% improvement over the best-performing baseline. These findings highlight the potential of Autoformer for enhancing sales forecasting, reducing stockouts, and improving inventory turnover in cross-border e-commerce platforms, thereby supporting more effective logistics management and strategic decision-making.

Abstract: Magnetocardiography has reached its clinical breakthrough, at least as a contactless, highly sensitive method to diagnose or exclude an ischemic cardiomyopathy, with or without coronary obstruction, in patients with chest pain of uncertain origin and still normal troponin and ECG patterns. This diagnostic advantage has already been recognized with regulatory approvals. However, despite its intrinsic advantages, including unrivalled non-invasive inverse estimation of cardiac currents and a strong potential for 3D- and 4D multimodal integration with other imaging modalities, its clinical adoption remains limited by the absence of internationally shared standards. This perspective review aims to highlight the point of view of the clinical end-user and propose the establishment of an interdisciplinary expert commission for the standardization and interpretation, now essential to define consensus-based recommendations for MCG clinical use.

Abstract: Laboratory-based gait analysis using motion capture and force plates remains the gold standard for quantifying ground reaction forces (GRFs) and temporal gait parameters. However, its high cost and limited accessibility restrict routine clinical use. Wearable smart insoles offer a portable alternative, yet require rigorous validation before clinical adoption. This study evaluates the clinical and technical validity of the SuraSole® smart insole, a low-cost pressure sensor–embedded insole, by comparing its GRF and temporal gait measurements with those obtained from a laboratory force plate and 3D motion capture system. Twenty healthy adults completed five walking trials while wearing standardized footwear equipped with SuraSole insoles, with simultaneous force plate and motion capture data collection. Agreement between systems was assessed using Bland–Altman plots and intraclass correlation coefficients (ICCs). SuraSole demonstrated excellent agreement with force plates for GRF across weight acceptance, mid-stance, and push-off (ICCs 0.97–0.99), with mean differences of 15.93 ± 45.90 N, 2.38 ± 23.98 N, and 8.64 ± 40.45 N, respectively. Temporal parameters showed moderate to good reliability (ICCs 0.62–0.81), with limitations likely related to the insole's 20 Hz sampling rate. These findings indicate that SuraSole provides reliable GRF measurement and acceptable portable gait assessment, supporting its potential for use in clinical practice, rehabilitation, and community health monitoring. Future hardware improvements, particularly higher sampling frequency, may enhance temporal accuracy.

Abstract: This study proposes an indicator system for evaluating AI-assisted learning in higher education, combining evidence-based indicator development with expert-validated weighting. First, we review recent studies to extract candidate indicators and organize them into coherent dimensions. Next, a Delphi session with domain experts refines the second-order indicators and produces a measurable, non-redundant, implementation-ready index system. To capture interdependencies among indicators, we apply a hybrid Decision-Making Trial and Evaluation Laboratory–Analytic Network Process (DEMATEL–ANP, DANP) approach to derive global indicator weights. The framework is validated through an empirical application and qualitative feedback from academic staff. The results indicate that pedagogical content quality, adaptivity (especially difficulty adjustment), formative feedback quality, and learner engagement act as key drivers in the evaluation network, while ethics-related indicators operate primarily as enabling constraints. The proposed framework provides a transparent and scalable tool for quality assurance in AI-assisted higher education, supporting instructional design, accreditation reporting, and continuous improvement.

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A reliable and reproducible method for the isolation, culture, and identification of an osteoblast cell line from crucian carp (Carassius auratus) was established in this study using vertebral bone tissue from Chongming crucian carp, a locally important aquaculture strain from the lower Yangtze River region. Osteoblast cells were isolated using a tissue explant culture method, and optimal in vitro culture conditions were systematically evaluated. The established osteoblast cell line, designated Chongming Carassius auratus osteoblast cells (COBC), was characterized through chromosomal karyotype analysis, osteocalcin enzyme-linked immunosorbent assay (ELISA), and osteogenesis-related gene expression analysis. Additionally, cellular responses to environmental stress were assessed. The results showed that COBC exhibited optimal proliferation in L-15 medium supplemented with 20% fetal bovine serum at 28 ℃ under 5% CO₂. Alkaline phosphatase staining, Alizarin Red staining, and von Kossa staining all yielded positive results, thereby confirming that the isolated cells possessed typical and stable osteoblastic properties, with the osteocalcin content of 36,884 ng/L. Quantitative PCR analysis revealed that osteogenic marker genes, including runx2a and runx2b, were expressed at significantly higher levels in COBCs than in muscle tissue. Under hypoxia-reoxygenation stress, COBC exhibited enhanced apoptotic responses, marked alterations in related gene expression, and modulation of antioxidant enzyme activities, suggesting a certain degree of adaptive capacity to oxygen fluctuations. This study provides the first systematic description of the establishment and biological characterization of COBC, as well as its responses to hypoxic stress. These findings offer a valuable in vitro cell model and technical support for studies on fish bone tissue biology and the assessment of environmental stress effects.

Abstract: In 2015 a polymorphic variant of Bactericidal/permeability-increasing protein (BPI) Fold containing Family B Member 4 (BPIFB4) was genetically associated with longevity. Following on from this intriguing observation, a literature has developed that suggests that this poorly characterised secreted protein, plays a pleiotropic role in maintaining human health and extending lifespan. In this article we briefly review what is known about BPIFB4 and discuss how its sites of expression may impact on these proposed functions.

Abstract: This paper introduces DK-PRACTICE, an intelligent educational platform that combines Knowledge Tracing (KT) and recommendation systems to support personalized learning in higher education. The platform utilizes a novel Paired-Bipolar Bag-of-Words (PB-BoW) model to assess students’ knowledge states, forecast performance, and offer targeted recommendations. To test its effectiveness in real-world settings, DK-PRACTICE was implemented in the “Computer Organization and Architecture” undergraduate course, involving 138 students in Pre-Test and 106 in Post-Test. Empirical analysis of benchmark datasets and a newly created course dataset showed that the PB-BoW model outperformed an RNN-based KT model in predictive accuracy. Student surveys indicated high levels of satisfaction with usability, relevance of recommendations, and overall learning support, with most participants expressing willingness to reuse the platform in other courses. These results demonstrate the potential of DK-PRACTICE as a scalable and adaptable tool for improving personalized learning and bridging the gap between AI-driven KT research and classroom implementation.

Abstract: The resummation of Stieltjes series remains a key challenge in mathematical physics, especially when Pad\'e approximants fail, as in the case of superfactorially divergent series. Weniger’s $\delta$-transformation, which incorporates a priori structural information on Stieltjes series, namely the inverse factorial series representation of their converging factors, offers a superior framework with respect to Pad\'e. Here, the problem of the pole distribution of the $\delta$-transformation is addressed. We show that the algebraic structure of the transformation, together with the intrinsic log-concavity of Stieltjes moments, satisfy the necessary conditions for having real poles. Moreover, by recasting the denominator of the $\delta$-transformation rational approximant as a high-order derivative of a log-concave polynomial and invoking the Gauss-Lucas theorem, a possible geometrical justification of the pole positioning along the negative real axis is proposed. While a fully rigorous proof remains an open challenge, our conjecture is substantiated by a comprehensive numerical investigation across an extensive catalog of Stieltjes series. In particular, our results provide systematic evidence that the mandatory branch cut conditions are respected even in the more delicate case of superfactorial growth, recently addressed from a converging factor perspective.

Abstract: As Akasofu noted, no two geomagnetic storms are identical, yet the storm that occurred between November 12 and 14, 2025, stands out as an exceptional phenomenon. Its impact was evident across multiple layers of the ionosphere and numerous parameters, making it essential to conduct a comprehensive multi-parameter analysis of this event. Such an analysis relied upon data from the four LAERT topside sounders mounted aboard the recently-launched Ionosfera-M satellites. Global ionospheric dynamics was thoroughly examined during the storm period, particularly focusing on the polar and auroral zones, along with the equatorial anomaly region. Notable features included sharp electron density gradients, widespread F-layer disturbances, and the formation of giant plasma bubbles. These elements collectively contributed to the dynamic picture of the ionospheric storm captured through multi-parameter measurements by the LAERT sounders.

Abstract: Engineered microbes are emerging as a new class of living immunotherapeutics capable of sensing, interpreting, and actively reshaping host immune systems. Unlike conventional biologics or cell therapies with fixed mechanisms of action, engineered microbial platforms operate as dynamic systems that integrate environmental, metabolic, and immunological cues, process these inputs through programmable biological circuits, and execute context-dependent immune modulation with spatial and temporal precision. This review presents an immune-first framework that conceptualizes engineered microbes as distributed immune-computational systems defined by coordinated sensing, signal processing, memory, and effector functions embedded within host immune networks. Organizing the field around immune logic rather than microbial taxonomy or disease category, we examine how engineered microbes detect tissue-specific and immune-state signals, translate these inputs through synthetic processing modules, and generate immune outputs that activate, suppress, educate, or reprogram immunity across cancer, autoimmunity, and infectious disease. We further define immune safety architecture as a core design principle governing inflammatory control, tolerance preservation, adaptive immunity, and therapeutic termination, and discuss the translational and regulatory implications of immune-state–resolved clinical evaluation. Together, this framework positions engineered microbes as programmable immune systems and establishes a unifying conceptual foundation for their development as next-generation living immunotherapies.

Abstract: Proteins with additives, especially in small quantities, are of great interest as a subject of a study. Machine learning approaches implemented to Raman spectroscopy data could provide an insight into chemical structure of such mixtures or conjugates. Although, de-cision tree model could be powerful in solving either classification or regression task and could provide accessible predictions, it is prone to overfitting. Ensemble models that implement several decision trees could overcome the determined problem. Five different model types are discussed: RandomForest, GradientBoosting, AdaBoost, Voting, and Stacking. Raman spectroscopy data of whey protein isolate (5 wt. %) with different amounts of hyaluronic acid (0, 0.1, 0.25, and 0.5 wt. %) were used as datasets. Optimiza-tion established that ensembles of 200 decision trees with a maximum depth of four were optimal. AdaBoostClassifier found to be the most efficient in finding differences between whey protein isolate and its conjugates with hyaluronic acid: 99.5% accuracy, 100% sen-sitivity, and 98.0% specificity. Stacking of RandomForest, GradientBoosting, and Ada-Boost regressors with final estimator of RidgeCV was the most effective approach in the regression task (R2 = 0.963). According to the feature importance plots, the Raman bands that were most influential in predicting the results were 1003 cm-1 (phenylalanine, ring breath), 1206 cm-1 (C–C stretching), 1240 cm-1 (amide III (β-sheet), N−H in-plane bend, C−N stretch), and 1399 cm-1 (aspartic and glutamic acids, C=O stretch of COO−).

Abstract: Background/Objectives: Stable graft fixation is a key determinant for predictable guided bone regeneration (GBR) performed simultaneously with implant placement. Titanium tacks are commonly used for graft and membrane stabilization but require additional hardware and may necessitate removal at re-entry. Periosteal mattress sutures have been proposed as a hardware-free alternative; however, clinical evidence comparing both ap-proaches remains limited. The objective of this randomized clinical trial was to compare postoperative buccal bone dimensions following horizontal GBR in the aesthetic zone us-ing periosteal mattress sutures versus titanium tacks for graft stabilization. Methods: This pilot randomized controlled clinical trial included patients requiring horizontal GBR with simultaneous implant placement in the anterior maxilla. Participants were randomly al-located to graft stabilization using either periosteal mattress sutures (S) or titanium tacks (T). In all cases, a composite xenogeneic collagenated bovine block graft was applied and covered with a resorbable collagen membrane, followed by tension-free flap closure. Cone-beam computed tomography (CBCT) was obtained after 6 months. Buccal bone thickness was measured at 1, 3, and 6 mm apical to the implant platform. Mean buccal bone thickness and reconstructed buccal bone area between 1 and 6 mm were calculated and compared between groups. Results: Thirty implants were included in the final analy-sis (S, n = 16; T, n = 14). Mean buccal bone thickness was 1.16 ± 1.01 mm in the periosteal suture group and 1.23 ± 1.16 mm in the titanium tack group, with no statistically signifi-cant differences between groups. No significant intergroup differences were observed at any individual measurement level or in the reconstructed buccal bone area. Conclusions: Within the limitations of this pilot trial, periosteal mattress sutures provided buccal bone regeneration outcomes comparable to titanium tacks during GBR with simultaneous im-plant placement. Periosteal suturing may represent a reliable hardware-free alternative for graft stabilization in horizontal ridge augmentation.

Abstract: This study explores the role of the Internet of Things (IoT) in enhancing supply chain resilience, risk management, and operational efficiency. Employing a qualitative research approach, data were collected through semi-structured interviews and document reviews with supply chain professionals across manufacturing, logistics, and distribution sectors. The findings reveal that IoT enables real-time monitoring of assets and processes, predictive maintenance, anomaly detection, and data-driven decision-making, which collectively improve operational performance and reduce vulnerabilities. IoT also facilitates collaboration within organizations and across supply chain partners, enhancing coordination, communication, and collective problem-solving. Additionally, IoT contributes to cost reduction, energy efficiency, and optimized resource allocation, supporting both operational and strategic objectives. The study highlights that the successful adoption of IoT requires integration with organizational processes, skilled workforce capabilities, and governance measures, including cybersecurity and data integrity practices. Overall, the research demonstrates that IoT is a transformative enabler for supply chains, promoting adaptive, resilient, and efficient operations capable of responding effectively to dynamic challenges and market demands. The insights provide practical guidance for organizations seeking to leverage IoT to achieve operational excellence, strategic advantage, and long-term sustainability.

Abstract:

For many years, the hard coal mining industry has been searching for engineering solutions ensuring greater reliability of the machines operating in difficult underground conditions. The foregoing applies in particular to the scraper conveyors used in longwall systems, started up very frequently and exposed to variable dynamic loads, leading to accelerated wear of powertrain components. The authors of this study have developed a longwall scraper conveyor equipped with a torsionally flexible metal clutch of novel design. The article provides a description of a mathematical model of a conveyor featuring two centrally arranged chains along with a main (discharge) and auxiliary (return) drive, as well as results of the computer simulations performed for two variants of the drive system setup analysed: one with a typical flexible clutch and the other with the innovative torsionally flexible clutch. Analysis of these results has revealed that the solution proposed significantly reduces the amplitude of dynamic loads, which contributes to increased durability and reliability of conveyors under mining conditions.