Search Results (385)

The potential deformation of underlying shield tunnels caused by extensive excavations in soft soil presents a significant practical concern. In this paper, the deformation of operating twin metro shield tunnels of Shenzhen Metro Line 2 caused by large upper excavation in soft soils is investigated. The field monitoring data vividly portrays the noteworthy tunnel deformations witnessed during the construction of excavation. A three-dimensional numerical model was established to analyze the deformation response of the underlying twin tunnels and surrounding soils. Various protective measures were explored to mitigate the potential impacts of the excavation on the tunnel deformation and structural stress, including sequential excavation, staggered excavation and soil improvement. The results indicate that the deformation of the underlying operating tunnel and surrounding soil’s deformation can be effectively alleviated by properly adjusting the excavation procedure. Compared to the sequential excavation procedure, the adoption of staggered excavation procedure can reduce the vertical deformation of the operating tunnel by at least 11.2% and maximum of 24.89% with the optimal procedure. Soil improvement is not recommended to alleviate tunnel deformation when the depth of the improvement zone is shallow. The outcomes of this study hold valuable insights for safeguarding metro tunnels beneath soft soil excavation. Full article

Animal welfare has become a cornerstone of modern zoo and aquarium animal care practices. This paper introduces the constructional approach to animal training as an evidence-based framework that can enhance the welfare of zoo animals. Developed through decades of behavioral science research and practical applications, the constructional approach emphasizes building desirable behaviors rather than eliminating problematic ones, avoiding reduction-based techniques, utilizing comprehensive contingency analysis, incorporating genuine choice, and addressing emotional welfare through contingency management. This review systematically examines the foundational principles of the constructional approach, distinguishes it from traditional animal training methodologies, presents case examples of successful implementation in zoo settings, and provides practical recommendations for zoo professionals. Methods included a narrative review of peer-reviewed literature, unpublished academic works, and documented applications in zoological settings. The results demonstrate that constructional programs offer notable advantages over commonly promoted hierarchical models of behavior change procedures, which often prescribe sequential application of techniques without adequate consideration of behavioral function. By adopting constructional programs, zoos can more effectively meet their overriding goals of providing optimal welfare, supporting conservation efforts, facilitating research, and enhancing educational experiences—all while prioritizing compassionate care that respects the agency and well-being of animals. Full article

Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (±0.62%), precision of 92.3% (±0.78%), and recall of 90.3% (±0.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (−2.8%). The hybrid architecture overcomes the accuracy–interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications. Full article

New AI technologies have empowered e-commerce personalized recommendation systems, many of which now leverage time-series forecasting to capture dynamic user preferences. However, buyers’ algorithm aversion hinders these systems from realizing their full potential in enabling data-driven decisions. Current research focuses heavily on artifact design and algorithm optimization to reduce aversion, with insufficient attention to the temporal dimensions of human–AI interaction (HAII). To address this gap, this study explores how recommendation accuracy, novelty, and diversity—key attributes in time-series recommendation contexts—influence buyers’ algorithm aversion from a temporal HAII perspective. Data from 205 online survey responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results reveal that accuracy (encompassing sequential prediction consistency), novelty (balanced with temporal relevance), and diversity (covering long-term preferences) negatively impact algorithm aversion, with perceived usefulness as a mediator. Reduced aversion further facilitates data-driven purchasing decisions. This study enriches the algorithm aversion literature by emphasizing temporal HAII in time-series recommendation scenarios, bridging human factors research with data-driven decision-making in e-commerce. Full article

Seawater desalination has emerged as a crucial solution for addressing global freshwater scarcity. However, it generates significant volumes of concentrated brine waste. This brine is rich in dissolved salts and minerals, primarily, chloride (55%), sodium (30%), sulfate (8%), magnesium (4%), calcium (1%), potassium (1%), bicarbonate (0.4%), and bromide (0.2%), which are often discharged into marine environments, posing ecological challenges. This study presents a comprehensive global review of innovative technologies for recovering these constituents as valuable products, thereby enhancing the sustainability and economic viability of desalination. The paper evaluates a range of proven and emerging recovery methods, including membrane separation, nanofiltration, electrodialysis, thermal crystallization, solar evaporation, chemical precipitation, and electrochemical extraction. Each technique is analyzed for its effectiveness in isolating salts (NaCl, KCl, and CaSO₄) and minerals (Mg(OH)₂ and Br₂), with a discussion of process-specific constraints, recovery efficiencies, and product purities. Furthermore, the study incorporates a detailed techno-economic assessment, highlighting revenue potential, capital and operational expenditures, and breakeven timelines. Simulated case studies of a 100,000 m³/day seawater reverse osmosis (SWRO) facility demonstrates that a sequential brine recovery process and associated energy balances, supported by pilot-scale data from ongoing global initiatives, can achieve over 90% total salt recovery while producing marketable products such as NaCl, Mg(OH)₂, and Br₂. The estimated revenue from recovered materials ranges between USD 4.5 and 6.8 million per year, offsetting 65–90% of annual desalination operating costs. The analysis indicates a payback period of 3–5 years, depending on recovery efficiency and product pricing, underscoring the economic viability of large-scale brine valorization alongside its environmental benefits. By transforming waste brine into a source of commercial commodities, desalination facilities can move toward circular economy models and achieve greater sustainability. A practical integration framework is proposed for both new and existing SWRO plants, with a focus on aligning with the principles of a circular economy. By transforming waste brine into a resource stream for commercial products, desalination facilities can reduce environmental discharge and generate additional revenue. The study concludes with actionable recommendations and insights to guide policymakers, engineers, and investors in advancing brine mining toward full-scale implementation. Full article

Acute severe ulcerative colitis (ASUC) is a medical emergency affecting up to 25% of patients with ulcerative colitis (UC), with colectomy required in approximately 25–30% of cases during the initial admission. Intravenous corticosteroids remain the first-line therapy, though one-third of patients do not respond, necessitating rescue with infliximab or calcineurin inhibitors, which are both supported by randomized trials and guideline recommendations. Comparative studies and meta-analyses have shown similar efficacy between these agents, while sequential use is associated with higher adverse event rates and should be restricted to specialized centers. Recent data have refined infliximab use, with the PREDICT-UC trial showing no superiority of intensified dosing over standard regimens. Emerging therapies are under investigation: vedolizumab has been used as maintenance following calcineurin induction; ustekinumab has shown benefits in retrospective UC cohorts, particularly after cyclosporine; and Janus kinase (JAK) inhibitors represent the most recent addition. The randomized TACOS trial and the prospective TRIUMPH study demonstrated an improved short-term response with tofacitinib in steroid-refractory ASUC, and real-world reports suggest promising outcomes with upadacitinib. While infliximab and cyclosporine remain as standard rescue therapies, ongoing trials with novel agents are likely to broaden treatment options. This review summarizes the clinical features, initial management, and the role of advanced therapies in ASUC. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

The transformation of national education policy during Indonesia’s governmental era has led to regulatory disruptions through the rapid revocation of previous policies and swift introduction of new ones. This landscape requires teachers to possess technological proficiency as well as 21st-century competencies and pedagogical readiness to adopt innovative learning. This study examines the influence of technological knowledge and 21st-century competencies on teachers’ readiness to adopt deep learning pedagogy, while also exploring perceptions of opportunities and challenges. A sequential explanatory mixed-methods design was employed, involving a survey of $802$ teachers from regions of Indonesia. The instrument comprised $25$ items across three variables, validated by experts, and tested with confirmatory factor analysis, which showed acceptable fit and reliability. Quantitative data were analyzed statistically, while qualitative insights came from interviews with 30 teachers and analyzed thematically. Results indicate that 21st-century competencies $(\beta =0.639,R^2=0.432)$ exert stronger influence than technological knowledge $(\beta =0.575,R^2=0.310)$ in shaping readiness. The integration of connecting and embedding strategies revealed personal, structural, and cultural complexities in implementing deep learning. The study recommends localized training and partnerships with professional organizations, higher education institutions, and NGOs to generate systemic support for school reform toward learning organizations. Full article

Despite converting large, laterally unbounded, highly connected low-permeability lithologic gas reservoirs—without faults or fixed lithological boundaries—into underground gas storage, the evolution of transition zone pressures and the mechanisms of gas escape under multiple injection–production cycles remain poorly understood. This knowledge gap critically hinders the safe and efficient operation of such facilities. A core–transition zone injection–withdrawal model for the S4 underground gas storage was developed using the numerical well test module of Saphir software v4.20. The model quantifies transition zone pressure dynamics over ten injection–withdrawal cycles and elucidates how the interplay of formation permeability and operating conditions governs gas leakage. During multi-cycle injection–withdrawal operations, formation pressure in the transition zone steadily accumulates under the combined influence of core zone gas crossflow and local gas advection equilibrium within the non-utilizable region. Assessed by the transition zone boundary formation pressure, suppressing gas leakage depends primarily on total injection and withdrawal volume, followed by the injection schedule and, lastly, the location of the boundary injection well. To achieve cost-effective containment, we therefore recommend prioritizing a shorter injection duration, moderately reducing total injection and withdrawal volume, and increasing the distance between the boundary injection wells and the transition zone. Under the geological conditions of the S4 UGS, by sequentially adjusting the injection duration, reducing the total injected–withdrawal gas volume to 6000 × 10⁴ m³, and increasing the distance between boundary injection wells and the transition zone to 900 m, the transition zone boundary pressure rise over ten cycles was controlled to below 1 MPa, thereby effectively preventing gas leakage. Full article

In recent years, research in sequential recommendation has primarily refined user intent by constructing sequence-level contrastive learning tasks through data augmentation or by extracting preference information from the latent space of user behavior sequences. However, existing methods suffer from two critical limitations. Firstly, they fail to account for how random data augmentation may introduce unreasonable item associations in contrastive learning samples, thereby perturbing sequential semantic relationships. Secondly, the neglect of temporal dependencies may prevent models from effectively distinguishing between incidental behaviors and stable intentions, ultimately impairing the learning of user intent representations. To address these limitations, we propose TCLRec, a novel temporal-aware and intent contrastive learning framework for sequential recommendation, incorporating symmetry into its architecture. During the data augmentation phase, the model employs a symmetrical contrastive learning architecture and incorporates semantic enhancement operators to integrate user preferences. By introducing user rating information into both branches of the contrastive learning framework, this approach effectively enhances the semantic relevance between positive sample pairs. Furthermore, in the intent contrastive learning phase, TCLRec adaptively attenuates noise information in the frequency domain through learnable filters, while in the pre-training phase of sequence-level contrastive learning, it introduces a temporal-aware network that utilizes additional self-supervised signals to assist the model in capturing both long-term dependencies and short-term interests from user behavior sequences. The model employs a multi-task training strategy that alternately performs intent contrastive learning and sequential recommendation tasks to jointly optimize user intent representations. Comprehensive experiments conducted on the Beauty, Sports, and LastFM datasets demonstrate the soundness and effectiveness of TCLRec, where the incorporation of symmetry enhances the model’s capability to represent user intentions. Full article

The widening gap between life expectancy and healthy life years underscores the need for scalable, adaptive, and privacy-conscious healthcare solutions. In this study, we integrate the AMPER (Aim–Measure–Predict–Evaluate–Recommend) framework with Bidirectional Encoder Representations from Transformers (BERT), Automated Machine Learning (AutoML), and privacy-preserving Federated Learning (FL) to deliver personalized hypertension management. Building on sequential data modeling and privacy-preserving AI, we apply this framework to the MIMIC-III dataset, using key variables—gender, age, systolic blood pressure (SBP), and body mass index (BMI)—to forecast future SBP values. Experimental results show that combining BERT with Moving Average (MA) or AutoRegressive Integrated Moving Average (ARIMA) models improves predictive accuracy, and that personalized FL (Per-FedAvg) significantly outperforms local models while maintaining data confidentiality. However, FL performance remains lower than direct data sharing, revealing a trade-off between accuracy and privacy. These findings demonstrate the feasibility of integrating AutoML, advanced sequence modeling, and FL within a structured health management framework. We conclude by discussing theoretical, clinical, and ethical implications, and outline directions for enhancing personalization, multimodal integration, and cross-institutional scalability. Full article

Recommender systems are data-driven tools designed to assist or automate users’ decision-making. With the growing demand of personalized sequential recommendations in business intelligence or e-commerce, effectively capturing temporal information from massive user-sequence data has become a crucial challenge. State-of-the-art attention-based models often struggle to balance performance with computational cost, while traditional convolutional neural networks suffer from limited receptive fields and rigid architectures that inadequately model dynamic user interests. To address these limitations, this paper proposes TimeWeaver, a time-aware dual-stream network for sequential recommendation, whose core innovations comprise three key components. First, it employs a re-parameterized large-kernel convolution to expand the effective receptive field. Second, we design a Time-Aware Augmentation mechanism that integrates inter-event time-interval information into positional encodings of items. This allows it to perceive the temporal dynamics of user behavior. Finally, we propose a dual-stream architecture to jointly capture dependencies across different time scales. The context stream employs a modern Temporal Convolutional Network (TCN) structure to strengthen the memorization of users’ medium- and long-term interests. In parallel, the dynamic stream leverages an Exponential Moving Average (EMA) mechanism to weight recent behaviors for sensitively capturing users’ immediate interests. This dual-stream design allows TimeWeaver to comprehensively extract both long- and short-term sequential features. Extensive experiments on three public e-commerce datasets demonstrate TimeWeaver’s superiority. Compared to the strongest baseline model, TimeWeaver achieves average relative improvements of 4.62%, 9.59%, and 4.59% across all metrics on the Beauty, Sports, and Toys datasets, respectively. Full article

Background/Objectives: Outpatient parenteral antimicrobial therapy (OPAT) has been implemented throughout the world for the treatment of most infections. Published studies have focused on OPAT delivery, with limited data on coordination and monitoring practices. Methods: A mixed methods study, using an exploratory sequential design, was conducted at a tertiary hospital in Paris, France. Ten semi-structured interviews were conducted with prescribing physicians and professionals involved in OPAT coordination and monitoring. A general inductive approach was used to analyze verbatim data and build a framework for OPAT model characterization. Cost estimates, using a standardized scenario, were applied to each model. Results: Five OPAT coordination and monitoring models were identified. All OPATs were administered by visiting nurses in the patient’s home. Referral to an infectious disease physician was not systematic, and three models, with 3 to 50 OPAT episodes/year each, outsourced hospital-to-home coordination and monitoring to external medical service and device providers. Only one OPAT model, with 450 OPATs annually, included a nurse specialist within the unit to coordinate and monitor treatment. Clinically and/or socially vulnerable patients received OPAT through hospital at home services, which reported 30 OPATs/year. Under the standardized clinical scenario applied to each OPAT model, weekly costs ranged from EUR 1445 to EUR 2308. Conclusions: The diversity of OPAT coordination and monitoring practices identified within a single hospital suggests that similar trends may be observed in other settings, in France and elsewhere. Identifying the most cost-effective OPAT service model could guide stakeholders and facilitate the implementation of best practice recommendations in line with antimicrobial stewardship principles. Full article

Background: Helicobacter pylori infection is associated with peptic ulcer disease, chronic gastritis, and gastric cancer. Classic triple therapy (CTT) has been widely used, but increasing antibiotic resistance has reduced its effectiveness. Objectives: To evaluate the effectiveness of CTT compared with alternative regimens and to summarize adverse events and adherence. Methods: We searched PubMed, Scopus, Web of Science, and Cochrane Library from January 2000 to March 2025. Randomized trials and observational studies assessing eradication rates were included. Two reviewers independently screened the studies, extracted data, and assessed bias using Cochrane RoB or the Newcastle–Ottawa Scale. Outcomes included eradication rate, adverse events, and adherence. Results: Thirteen studies (n = 3490) were included. CTT eradication rates ranged from 61.9% to 88.8%. Sequential, bismuth-based quadruple and high-dose PPI regimens achieved higher rates (>90% in several trials). Adverse events were mild–moderate and most frequent in quadruple therapy, though adherence remained >90%. Evidence certainty varied (moderate to low in most comparisons). Geographic variation in resistance limited generalizability. Conclusions: CTT is less effective in high-resistance regions. Quadruple, sequential, and high-dose PPI regimens provide superior outcomes. Region-specific treatment guided by susceptibility testing is recommended. Registration: Not registered. Full article

In recent years, the remarkable versatility of large language models (LLMs) has spurred considerable interest in leveraging their capabilities for recommendation systems. Critically, we argue that the intrinsic aptitude of LLMs for modeling sequential patterns and temporal dynamics renders them uniquely suited for sequential recommendation tasks—a foundational premise explored in depth later in this work. This potential, however, is tempered by significant hurdles: a discernible gap exists between the general competencies of conventional LLMs and the specialized needs of recommendation tasks, and their capacity to uncover complex, latent data interrelationships often proves inadequate, potentially undermining recommendation efficacy. To bridge this gap, our approach centers on adapting LLMs through fine-tuning on dedicated recommendation datasets, enhancing task-specific alignment. Further, we present the temporal Integration Enhanced Fine-Tuning of Large Language Models for Sequential Recommendation (TisLLM) framework. TisLLM specifically targets the deeper excavation of implicit associations within recommendation data streams. Its core mechanism involves partitioning sequential user interaction data using temporally defined sliding windows. These chronologically segmented slices are then aggregated to form enriched contextual representations, which subsequently drive the LLM fine-tuning process. This methodology explicitly strengthens the model’s compatibility with the inherently sequential nature of recommendation scenarios. Rigorous evaluation on benchmark datasets provides robust empirical validation, confirming the effectiveness of the TisLLM framework. Full article