Mental illness is a leading cause of global disability, underscoring the urgent need for scalable, data-driven approaches to early identification and intervention. Passive sensing technologies in mobile and wearable devices enable continuous and unobtrusive measurement of behavioural and physiological signals that may be relevant to mental health. When translated into interpretable digital markers through digital phenotyping, these data hold significant promise for advancing our understanding of the onset, course, and treatment of mental disorders. However, achieving real-world clinical utility requires large-scale, harmonised, and ethically governed databanks that enable replication and generalisability for diverse populations. Informed by recent international initiatives, the academic literature, and our own expertise in digital phenotyping, this Perspective outlines four key priorities for advancing digital phenotyping databanks in depression and anxiety. First, ensuring data quality through standardisation and harmonisation is essential to comparability across studies and to prevent fragmentation. Second, ethical data stewardship demands hybrid consent models that combine the scalability of broad consent with the flexibility of dynamic consent, ensuring meaningful participant control as analytics evolve. Third, robust, privacy-preserving information governance co-created with people with Lived Experience is vital to maintain trust and prevent misuse, with federated learning and open-source pipelines offering promising technical pathways. Finally, the field must promote data reuse by reforming incentive structures, recognising databank-based scholarship, and investing in sustainable infrastructures that reward secondary analyses. Collectively, these priorities offer a pragmatic framework for building equitable, transparent, and scientifically robust digital mental health databanks. Implementing these recommendations will require sustained international collaboration among researchers, funders, institutions, and people with Lived Experience. By aligning scientific rigour with ethical responsibility, digital phenotyping databanks can become transformative tools for advancing the global understanding and treatment of mental illness.

For 6G wireless networks, efficient resource allocation is a significant problem, especially with the growing need for ultra-low latency, high-speed communication, and efficient energy consumption. The traditional approach is found to be inadequate to meet the dynamic changes and service-allocation requirements. The application of AI and DL is seen as an efficient approach to making intelligent, timely decisions in complex scenarios. This paper proposes an integrated AI- and DL-based approach for efficient, intelligent resource allocation in 6G wireless communication. The difficulties encountered in dynamic spectrum allocation, energy depletion, and attenuation are addressed through an integrated approach that combines optimal path selection with efficient allocation mechanisms. The input parameters considered are residual battery indicator (RBI), channel matrix (H), normalized spectrum availability (v), SINR values, node pairs (s, d), service levels, and historical statistics. To ensure data quality, a Recursive Hampel Filter-Based Estimation Model (ReHF-EM) has been employed. Furthermore, for fundamental decision-making, a Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation. Additionally, the parameters of the proposed model have been fine-tuned using the Pied Kingfisher Optimizer (PKfO) for better efficiency, thus reducing complexities associated with the model. The proposed model has been implemented using Python, and various performance parameters such as Spectrum Efficiency (SE), Energy Efficiency (EE), SINR margin, Bit Error Rate (BER), Computational Time (CT), and Accuracy have been considered to evaluate the proposed model. The results show a 23.6% increase in Energy Efficiency and a 19.2% reduction in Bit Error Rate.

The current situation in the automotive, aerospace, marine, and rail industry involves addressing component wear and tear. The composite materials are very appropriate for the problem's replacement because they attain great strength, high hardness, strong wear resistance, exceptional corrosion resistance, and high impact toughness. The present investigation of C355 aluminium alloy hybrid composites (C355AHCs) shows they are utilized highly suitably for the various component preparations of the automotive, aerospace, marine, and rail industries. The C355 aluminium alloy addition with graphene (0, 2 and 4 wt.%) and boron carbide (0, 3 and 6 wt.%) hybrid nanocomposites are prepared by utilizing the selective laser melting (SLM) additive process. According to ASTM G99 rules, the pin-on-disc device was utilized to perform the wear experiment test. The optical microscope (OM) and Field Emission Scanning Electron Microscope (FESEM) to examine the characterization and worn surface analysis of the C355AHCs. The wear response was assessed via experiments carried out through pin-on-disc testing tribometer, considering applied load, sliding velocity, sliding distance, and sliding time, which were modelled by means of a Box-Behnken design approach. The novel aspect of this research work is the combination of SLM-processed C355/Gr/B4C hybrid nanocomposites with ANFIS-based tribological prediction, comparative modeling using RSM, and multi-objective optimization. From the experimental findings, it was found that the wear rate falls between 0.274 g/min and 0.475 g/min, whereby the applied load emerged as the most significant factor affecting the wear behaviour. The significance of the fitted regression equation via RSM and ANOVA analysis showed a level of significance (P-value < 0.05), albeit with moderate prediction (R2 = 0.576). As a means of overcoming this problem, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, and the results were quite accurate and smooth. ANFIS showed better accuracy and smoothness than regression modelling in terms of prediction. Wear studies of worn surfaces showed that there was a shift from adhesive and abrasive wear in the base alloy to mild abrasive wear in the hybrid nanocomposites owing to the synergy of the graphene-lubricating effect and the B4Creinforcement effect. Multi-criteria optimization of wear, frictional force, and coefficient of friction through desirability analysis resulted in optimum conditions. This paper concludes that ANFIS is highly dependable in modelling tribological properties and that the produced SLM C355/Gr/B4C hybrid nanocomposites have promising application in automobile brakes and other engineering fields.

Discovering governing equations directly from observational data remains a fundamental challenge in science and engineering, particularly when measurements are noisy, high-dimensional, or multi-scale. Existing approaches often cast equation discovery as a regression problem that selects candidate terms to fit observed trajectories, which can limit structural stability and identifiability under realistic data conditions. We propose a structured operator-learning framework that reformulates equation discovery as a constrained dynamical inference problem integrating spectral decomposition, physics-guided sparse projection, and cross-view consistency regularization within a unified architecture. By decomposing dynamics into scale-resolved components and enforcing invariance across perturbed observations, the framework promotes stable and interpretable equation recovery. Here, we show that the method consistently identifies compact governing equations while maintaining strong long-horizon predictive accuracy across canonical nonlinear systems and representative industrial processes, even under noisy and distribution-shifted data.

Diabetic foot ulcers are among the most challenging complications of diabetes. Diabetes heightens patients' risk of severe complications, including amputations and death, while also driving up healthcare system costs. Given the significance of this problem, the present study conducted a systematic review of studies that used Classical Statistical and Machine Learning Approaches to identify factors influencing the development of diabetic foot ulcers in individuals with diabetes. A thorough literature search was conducted in PubMed, Scopus and Web of Science, covering their inception through 7 September 2025. This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMAs) guidelines. Data were analysed narratively using content analysis. Eligible studies used predictive methods to identify factors associated with the development of diabetic foot ulcers. A total of 4396 articles were screened, and 66 studies were selected for full-text review after application of the inclusion and exclusion criteria. The review of these studies identified 95 factors associated with predicting diabetic foot ulcers, among which neuropathy, diabetes duration, age, body mass index and peripheral vascular disease were the most frequently reported. In addition, among the predictive models used in the studies, logistic and Cox regression models were the most useful for predicting factors associated with diabetic foot ulcers. This study identifies key predictive factors for diabetic foot ulcers, enabling healthcare systems to target high-risk patients through early screening. Proactive identification of vulnerable diabetic patients can prevent severe complications, such as amputation.

Pharmacy education is currently facing numerous challenges, such as concerning first-time pass rates on licensure examinations, decreasing applicant pools, new accreditation standards, progression concerns, and changing financial and regulatory landscapes. These complex and shared issues require collaborative and innovative approaches across the academy. One potential strategy is the development of Communities of Inquiry (CoI), a collaborative model grounded in Garrison et al.'s theoretical framework of social, cognitive, and teaching presence. CoI emphasize active engagement, shared problem-solving, reflective discourse, and collective responsibility for learning and innovation. This manuscript describes the theoretical foundation of the CoI model and its application within pharmacy academia. Existing applications of CoI principles in pharmacy education literature, including faculty development initiatives and webinar-based learning experiences, are also reviewed. Additionally, a real-world example of a successful CoI is presented through the development of a NAPLEX Preparation CoI. Communities of Inquiry represent an effective framework for addressing new challenges in pharmacy education. Faculty, administrators, and professional organizations are encouraged to establish and participate in CoIs. As pharmacy education continues to evolve, the CoI approach presents a promising strategy for fostering innovation, meeting accreditation standards, and enhancing student outcomes through community-driven solutions.

Smart fault diagnosis is a crucial component of modern industrial systems, offering early detection of machine conditions to prevent costly breakdowns and safety issues. While state-of-the-art deep learning models are capable of near-perfect classification on controlled laboratory data, they often perform poorly in practice because of noisy environments, class imbalance and distribution shifts across operating conditions. This paper addresses these challenges by re-formulating industrial acoustic monitoring as a supervised image classification problem with superlet time-frequency representations, which capture both the transient dynamics and phase-envelope. An efficient CNN-Transformer hybrid model is proposed by placing a lightweight Transformer encoder on an ImageNet-pretrained ResNet-50 backbone with spatial attention gating to learn both local and global spectro-temporal features. The proposed architecture is lightweight, with 25.6M parameters and 4.1G FLOPs, supporting efficient resource usage. An asymmetric focal contrastive learning strategy is also proposed to improve anomaly discrimination under extreme imbalance. Experimental results on the MIMII dataset demonstrate that the proposed framework achieves 94.82% accuracy, 91.08% balanced accuracy, 91.72% F1-score, and 0.9758 ROC-AUC under a strict group-aware evaluation protocol, while requiring only 25.6M parameters, 4.1G FLOPs, and 4.5 ms inference time per sample. In cross-domain evaluation on the CWRU dataset, the model further achieves a zero-shot AUC of 0.8886 and a fine-tuned AUC of 1.0000 within three epochs. These results validate that the proposed framework provides an effective, robust, and generalizable solution for industrial fault diagnosis.

Efficiency calibration of HPGe detectors for environmental gamma-ray spectrometry requires knowing the full energy peak efficiency (FEPE) for each combination of photon energy, sample geometry and material composition. We present a hybrid model, combining an artificial neural network with a fixed analytical correction layer, that decouples this problem into a multilayer perceptron-trained on a single reference material to learn FEPE as a function of energy and sample height-and the analytical layer, which corrects for self-attenuation using XCOM mass attenuation coefficients. Two levels of geometric correction are evaluated: the classical parallel beam (PB) formula and a geometric ray-tracing (RT) approach with solid-angle weighting. The methodology is validated on two HPGe detectors of fundamentally different geometry-a well-type and a planar extended-range (XtRa) detector-using characterised PENELOPE Monte Carlo models as the data source. For each detector, the model is trained with IAEA-RGU-1 and tested against eight materials spanning densities from [Formula: see text] (water) to [Formula: see text] (ilmenite), at unseen energies and unseen sample heights. The solid-angle weighted ray-tracing correction is universally superior to the parallel beam formula for both detector geometries, achieving a mean pipeline MAPE across all eight test materials of 2.7% ([Formula: see text]) for the well-type detector and 1.8% ([Formula: see text]) for the XtRa, compared to 4.0% and 2.4% with the parallel beam formula. With the ray-tracing correction, the pipeline MAPE remains below 5% for all materials on both detectors, including ilmenite ([Formula: see text]). Once trained, the model yields FEPE predictions in under one second for any sample height and material, compared to tens to hundreds of seconds per Monte Carlo simulation, depending on sample size and the statistical precision required.

The dynamic assessment of mental health has emerged as a hotspot for study and application due to the rise in social pressure. However, onventional methods rely mostly on static scales or single-modal data, failing to fully capture multifaceted emotional and behavioral features. This study suggests a deep learning model based on multi-modal data fusion to address this problem. By combining information from multiple sources, including text and visuals, the model effectively identifies and dynamically monitors mental states. In the study, a multi-modal dataset containing text and image information is constructed. Mental health levels and emotional tendency labels are generated by combining self-assessment questionnaires and expert annotations. The model uses Bi-LSTM to process the text modality, convolutional neural network (CNN) to process the image modality, and realizes deep feature interaction and integration through an improved multi-modal fusion strategy. Experimental results show that in the emotion recognition task, the precision, recall, and F-score of the proposed model reach 91.3%, 88.9%, and 90.1% respectively. In the classification of "possible depression" and "existing depression", the accuracy rates are 75.89% and 85.32%. All these performance indicators are superior to those of single-modal models and traditional average fusion methods, indicating that multi-modal fusion can significantly improve discriminative ability, robustness, and the accuracy of dynamic assessment. This study's primary contributions are the creation of a jointly annotated dataset, the proposal of a multi-modal deep learning framework appropriate for dynamic mental health assessment, and the confirmation of the fusion strategy's contribution to enhanced assessment performance. The study's findings broaden the range of applications for multi-modal deep learning in the field of mental health and offer a feasible technological solution for public health management, psychological services, and smart healthcare. They also offer theoretical and practical support for early screening, risk early warning, and personalized intervention of mental health.

This pilot randomized controlled trial evaluates the preliminary effectiveness of the Couple Learning Program (CLP), previously tested in Europe, following its cultural adaptation for premarital couples in Turkey. Within a mixed-methods framework, the six-session CLP training was implemented with the experimental group. Communication skills, conflict resolution strategies, and relationship satisfaction were assessed using pre-test, post-test, and follow-up measurements. Prior to the main trial, a preliminary feasibility study was conducted with two couples to examine the clarity of the measures and the applicability of the adapted program; data from this phase were not included in the main analyses. The primary implementation phase, based on a rigorously designed pilot randomized controlled trial, involved eight couples. Quantitative findings indicated that the CLP led to significant improvements in conflict resolution strategies-particularly in exit, voice, and neglect dimensions-while no significant change was observed in the loyalty response. In addition, a significant increase in relationship satisfaction emerged only at the follow-up stage. Qualitative findings revealed improvements in empathetic communication, the provision of positive feedback, structured problem-solving, and the expression of expectations. Participants also perceived the program as culturally relevant and applicable to their daily relationships, supporting its acceptability. In addition, all participants completed the intervention and follow-up assessments without attrition, providing preliminary evidence of feasibility. Overall, the findings support the cultural adaptability, acceptability and feasibility of the culturally adapted CLP in the Turkish context and highlight its potential as an effective preventive intervention for enhancing relational skills during the premarital period.

Hydro- and aero-mechanical devices convert fluid flows into useful motions, force, and power. The operating principles can involve subtle and poorly understood physics, as epitomized by open questions about systems that aspirate flows through curving tubular arms. Since its introduction by Mach and popularization by Feynman, the so-called reverse sprinkler problem has evoked many competing theories and fluid mechanical effects that have not to date been distinguished by experiments. Here we conduct a series of experiments that directly report on the motions, torques, and flows for devices whose geometries are tailored to disambiguate the leading hypotheses. Our observations run counter to several ideas, such as those based on the total angular momentum of the fluid and others focusing on the flow and pressure distributions at the outer portions of the arms. The measurements instead reveal strong correlations between the sense of torque/rotation and the fluid momentum fluxing into the device. These results suggest an operating principle for the reverse sprinkler involving isotropic input of fluid from the far field and swirl-up in the arms that generates angular momentum, a residual portion of which is injected inside and drives rotation. The mass-to-momentum flux conversion is governed by the geometry of the curving arms. The physics learned here is fundamental to flow-structure interaction problems and may inform applications for harvesting and transforming flow energy.

The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established approaches into a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices. The toolbox provides a flexible, modular architecture that adjusts to different needs. It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques). Such a customisable toolbox aims to lower the barrier for beginners and to facilitate access to advanced modelling approaches in psychiatry and beyond.

Text clustering is an essential activity in unsupervised natural language processing (NLP), and allows the automatic structure of large-scale textual collections (in natural language processing) in news classification, routing of technical questions, and text summarisation. With the emergence of contextual embedding models, such as SBERT, RoBERTa, and DistilBERT, there has been a significant improvement in the quality of clustering, with these models producing dynamic and context-sensitive representations that are more likely to reflect domain-specific semantics than the traditional word embeddings of Word2Vec and GloVe. Although the models of contextual embedding have their own advantages, they show varying performance in different domains and the current ensemble techniques propose fixed, flat fusion weights which do not utilize their complementary abilities. There is no current solution to dynamic, label-free optimisation of weight to multi-model contextual embedding fusion in unsupervised clustering which would bridge a major gap between fixed integrative approaches and adaptive, domain-accommodative model combination. This paper presents a new Genetic Algorithm (GA)-based ensemble model that dynamically optimizes the best fusion weights of SBERT, RoBERTa, and DistilBERT embeddings without ground-truth labels. The hypothesis is that evolutionary optimisation can discover domain-adaptive weight sets that outperform standalone models as well as fixed ensemble baselines on linguistically heterogeneous datasets. The framework uses L2-normalisation and topology in which the topology is a sum-to-one constraint so that the models can be fairly integrated, and a composite fitness measure based on Silhouette Score, Adjusted Rand Index, and Topic Coherence to direct the weight evolution through tournament selection, uniform crossover, and Gaussian mutation. The proposed framework on three heterogeneous benchmarks, AG News, 20 Newsgroups, and Stack Overflow, has Silhouette Score improvements of +14-16 percent, and Topic Coherence gains of +17 percent, all statistically significant at p < 0.05. Evolved weights can be completely understood, depicting domain-specific model roles, which guide future architecture choice. The framework is suggested as a scalable, modular system to practical unsupervised NLP problems, and its architecture can be easily extended to new transformer systems and semi-supervised systems.

Speech prosody richly encodes both linguistic and social meaning. However, the prosody-meaning mapping varies across contexts and talkers, contributing to the lack of invariance problem in speech perception. This article extends a recent line of work targeting the adaptivity of the human comprehension system, mapping variable input to linguistic meanings in a talker-sensitive manner. Specifically, we investigate the stability of adaptation: Once a listener adapts to the prosody of a particular talker, how long does the effect last? We conducted a large-scale (N = 424) adaptation experiment using a two-session paradigm in which listeners go through a pretest, exposure, posttest sequence in the first session, in which exposure and feedback disambiguate an ambiguous prosodic contour as a question or a statement in a between-participants manner. They were then invited back on the 2nd day after a delay of 3, 5, or 7 days to repeat the same sequence. The results provide novel evidence that adaptation is both rapid and stable. Listeners showed robust adaptation within the first few trials of exposure in the first session, and they maintained some adaptive shift even after a delay of up to 7 days. This finding extends the scientific knowledge about the longevity of adaptive shifts seen across levels of speech hierarchy. We discuss possible memory mechanisms that support the long-term maintenance of cross-talker adaptation. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Identification of drug-target interactions (DTI) is an important and challenging task in drug discovery and development. Traditional methods generally require biological experiments, which are costly and time-consuming. Machine learning-based methods can rapidly predict DTI using only computer algorithmic models, allowing researchers to validate only the most promising interactions through biochemical experiments. This holds promise for effectively addressing the current challenges of lengthy development cycles and high costs in new drug development. However, it is difficult for the existing DTI prediction methods to learn complete and effective feature information from the compound and protein. Therefore, this work proposes a DTI prediction method based on the global self-attentive pooled graph neural network and protein pretraining model, called T-pGNN4DTI. On the one hand, T-pGNN4DTI uses a global self-attention pooled graph neural network to learn more meaningful features of the drug molecule by paying more attention to the information features of certain important atomic nodes of the molecular structure and ignoring some weakly relevant node information features. On the other hand, T-pGNN4DTI uses a pre-trained Transformer-based model to capture the semantic relationships of contexts in long sequences of proteins, which can learn more complete feature information. The results of comparing experiments on three benchmark datasets show that the performance of the proposed T-pGNN4DTI model is better than that of the existing DTI prediction methods, effectively improving the DTI prediction. It provides a new way of thinking to help solve the DTI-related problems.