Search Results (6,405)

Distant Retrograde Orbits (DROs) and Near-Rectilinear Halo Orbits (NRHOs), as categories of Lagrange orbits, have been selected for the construction of future deep-space navigation constellations in the Earth-Moon space due to their unique orbital trajectories and dynamical characteristics. To obtain high-precision orbit and clock solutions, the orbit determination (OD) and time synchronization (TS) performance of DRO and NRHO based on Beidou Navigation Satellite System (BDS) L-band and Ka-band signals were analyzed. Considering the constraints of onboard resources and cost, it may be infeasible to establish Ka-band links with all BDS satellites. Therefore, multiple experiments with different link configuration schemes were designed. The results show that an orbit determination accuracy of about 500 m and the time synchronization accuracy of 50 ns can be achieved using only L-band observations. In contrast, much higher accuracy can be obtained with full Ka-band links, with orbit and clock accuracy reaching 80 m and 7 ns, respectively. Moreover, higher orbit and clock accuracies can be obtained with more Ka-band links based on L-band observations. Furthermore, with the addition of the DRO-NRHO links, the orbit determination and time synchronization performance of each scheme was further improved by 15%. And the orbit determination accuracy can be better than 65 m, while the time synchronization accuracy can be better than 5 ns. Although the analysis is based on BDS signals, the proposed framework is general in nature and can be extended to other GNSS-based or future space navigation systems, providing a reference for the design of high-precision cislunar navigation and timing architectures. Full article

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

Interior built environments influence user behavior through more than deliberate rational evaluation. They shape attention, movement, affective comfort, perceived safety, wayfinding, and well-being through bounded cognition, affective appraisal, heuristics, embodied perception, and automatic approach–avoidance processes. The research gap addressed in this review concerns the fact that prior work on interior environments, wayfinding, indoor environmental quality, neuroarchitecture, atmospherics, and behavioral decision-making remains fragmented across separate studies, and existing reviews rarely explain how these mechanisms can be organized into a design-usable framework for interior built environments. This narrative review synthesizes foundational and recent literature across building design, environmental psychology, neuroarchitecture, virtual reality, indoor environmental quality, wayfinding, and behavioral decision-making to clarify how decision mechanisms translate into interior design variables such as lighting, color, spatial organization, materiality, form, sensory atmosphere, environmental legibility, thermal comfort, and controllability. The review distinguishes bounded rationality, heuristics and biases, dual-process accounts, affective and atmospheric processing, prospect–refuge dynamics, mere exposure, and room-effect research rather than treating them as a single “non-rational” category. It proposes an integrative framework in which interior cues are processed through perceptual and affective appraisal; moderated by individual, cultural, contextual, temporal, and ethical factors; and expressed through behavioral outcomes such as navigation, approach or withdrawal, dwell time, perceived quality, usability, stress regulation, and well-being. The paper contributes to human-centered building design by formalizing a mechanism-based account of how interior environments can support behavior without reducing users to passive recipients of environmental manipulation. It concludes with practical implications for design briefing, post-occupancy evaluation, VR-based testing, healthcare and workplace audits, safety-critical settings, and future longitudinal validation. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Introduction: Hematologic malignancies account for a significant proportion of the global cancer burden. Immunotherapy is currently one of the key treatment modalities used in the management of these diseases. Objective: This study aimed to assess the physical fitness of patients with chronic lymphoid malignancies receiving monoclonal antibody-based therapy compared with healthy individuals. Materials and Methods: The study included 99 ambulatory patients being treated for hematologic malignancies—33 with chronic lymphocytic leukemia, 32 with multiple myeloma, and 34 with follicular lymphoma—as well as 43 healthy individuals. All participants underwent the Two-Minute Step Test, the 30-Second Sit-to-Stand Test, and the Timed Up and Go Test. Results: Patients with hematologic malignancies, regardless of diagnosis, were characterized by significantly lower lower-limb strength (H(3, N = 142) = 24.779, p < 0.0001), as well as poorer agility and dynamic balance (H(3, N = 142) = 24.993, p < 0.0001). Patients diagnosed with multiple myeloma and follicular lymphoma also exhibited lower cardiorespiratory endurance (H(3, N = 142) = 13.223, p = 0.0042). Age was a significant predictor of physical fitness. However, in an analysis treating diagnosis as a categorical variable with the control group as the reference category, patients with hematologic malignancies also had significantly lower physical fitness scores than controls. Conclusions: Patients with chronic lymphoid malignancies receiving monoclonal antibody-based therapy exhibit reduced physical fitness regardless of hematologic diagnosis. Patient age is an additional factor associated with physical fitness. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

Background/Objectives: This preliminary, single-centre study evaluated infectious diseases consultation (IDC) patterns as indicators of hospital workflow and care complexity, aiming to characterise routinely available variables that may inform future organisational research and EHR-based clinical decision support development. Methods: In this retrospective study, 39,275 IDC requests from 16,430 patients were analysed using hospital information management system records. Paediatric patients and specialised immunosuppressed patient units were excluded. Request volumes, diagnostic categories, consultation purposes, and factors associated with in-hospital mortality were evaluated. Multivariable logistic regression models were constructed separately for two hospital blocks. Results: A total of 39,275 IDC records for 16,430 unique patients were reviewed. Mean consultation access time was 82.2 ± 64.3 min. Requests originated from surgical clinics (43.8%), followed by intensive care units (37.6%) and medical/internal clinics (18.6%). Pneumonia was the most common indication (30.5%), followed by unspecified infections (25.4%) and skin/soft tissue infections (17.2%). Consultation objectives included treatment, diagnostic assessment, and clinical guidance as non-mutually exclusive components. Significant block-level differences were observed in consultation timing, ICU-related consultation, diagnostic profiles, consultation purposes, and mortality. Age and ICU-related consultation were independently associated with mortality in both blocks, whereas consultation access time and COVID-19 diagnosis showed block-specific associations. Conclusions: IDC patterns may reflect not only diagnostic demand but also case severity, ICU-related care, consultation timing, and hospital location. As a preliminary single-centre study, these hypothesis-generating findings highlight the importance of integrating clinical, organisational, and contextual variables in future prospective, multi-centre studies aimed at developing EHR-based decision-support models. External validation, incorporation of comorbidity indices and microbiological data, and assessment of explainability are required before clinical implementation. Full article

Background/Objectives: Bile acid synthesis disorders (BASDs) represent a distinct category of progressive familiar cholestatic liver disease. A novel targeted mass spectrometry assay was developed for the accurate measurement of the major urinary atypical bile acids and bile alcohols that are biomarkers for HSD3B7, AKR1D1, CYP7B1 and CYP27A1 deficiencies, the four most common BASDs. Methods: Stable-isotope dilution UPLC tandem mass spectrometry was used for the simultaneous quantification of 12 key atypical bile acid biomarkers in urine from patients with BASD. Typical concentration ranges for these metabolites were established from urine samples from patients with biochemically and/or genetically confirmed BASD and compared with non-cholestatic and cholestatic controls. Results: The separation of major 3β-hydroxy-Δ⁵-bile acid sulfates, taurine- and glycine-conjugated 3-oxo-Δ⁴-bile acids, and bile alcohol glucuronides was achieved in a 20 min chromatographic run with intra- and inter-batch imprecisions of <15% for all metabolites. The mean ± SEM urinary concentration of total 3β-sulfated-Δ⁵-cholenoic acids in patients with HSD3B7 deficiency was 704 ± 204 µmol/L (n = 22), approximately 2000-fold higher than in cholestastic patients (n = 168) or non-cholestatic controls (n = 127). Similarly, the concentration of 5β-cholestane-3α,7α,12α,24,25-pentol-glucuronide, the major bile alcohol, in patients with CYP27A1 deficiency was 95 ± 17 µmol/L (n = 12). For CYP7B1 deficiency, two confirmed cases showed elevated levels (average, 7.5 µmol/L) of the glycine conjugate of 3β-sulfooxy-Δ⁵-bile acid. In AKR1D1 deficiency, total 3-oxo-Δ⁴-bile acids in urine were elevated (81 ± 16 µmol/L, n = 48), but concentrations showed overlap with cholestatic and non-cholestatic controls. Conclusions: A novel quantitative tandem mass spectrometry assay is described for the measurement of the major atypical metabolites and biomarkers in urine applicable to the accurate monitoring of treatment responses, and for the first time typical concentration ranges are established for each of these BASDs. Full article

Background: In youth football, sprint performance depends on the capacity to produce and orient force horizontally during acceleration. Resisted sprinting may preferentially target the force end of the sprint force–velocity profile, whereas free sprinting may favour velocity-oriented adaptations. Purpose: To compare the effects of resisted versus non-resisted sprint training on sprint performance and sprint force–velocity variables in youth footballers, while monitoring countermovement jump (CMJ) as a secondary outcome. Methods: This parallel-group randomised controlled trial included 44 players from two age categories (U14, n = 21; Youth, n = 23). Within each category, players were randomly allocated to resisted sprint training (RST; U14 n = 11, Youth n = 12) or non-resisted sprint training (NRST; U14 n = 10, Youth n = 11). Both groups completed two supervised sessions per week for six weeks. Outcomes were CMJ and sprint-derived variables including maximal theoretical horizontal force (F0), maximal theoretical velocity (V0), maximal power (Pmax), measured maximal sprint velocity (Vmax), peak ratio of horizontal force (RFpeak), decrease in RF with increasing velocity (DRF), and force–velocity slope (FV). Results: CMJ remained essentially unchanged in both age categories. Sprint performance improved over time, with the pattern of adaptation generally favouring RST for force-oriented sprint mechanical variables (F0, Pmax and RFpeak), whereas improvements in Vmax were observed in both groups. In the Youth category, the FV slope differed between groups post-test (p = 0.002). Overall, resisted sprint training tended to produce larger improvements in acceleration-oriented mechanical qualities, while non-resisted sprint training was associated with more velocity-oriented adaptations. Conclusions: Low-volume resisted sprint training using a sled load of ~20% body mass was associated with more favourable adaptations in force-oriented sprint mechanical variables, whereas non-resisted sprint training tended to favour velocity-oriented characteristics. CMJ performance remained unchanged in both groups. These findings should be interpreted cautiously given the small age-stratified subgroup sizes and the single-club nature of the study. Trial registration: This study was retrospectively registered at ClinicalTrials.gov (NCT07418892). Full article

Background: Cervical cancer remains a significant health issue in Indonesia, where structural barriers, fragmented information, and sociocultural norms continue to hinder timely diagnosis and treatment. Families play a central role throughout the illness journey, yet their perspectives are often overlooked in the development of digital patient navigation systems. This study explored family experiences, caregiving challenges, and expectations for a family-centered digital navigation model, DIVA.ID, by integrating Digital Health frameworks and Family Systems Theory. Methods: A qualitative descriptive approach was employed through semi-structured, in-depth interviews with 18 purposively selected family caregivers of women with cervical cancer at a major referral hospital in West Java. Participants were selected because they were directly involved in daily care, treatment decisions, logistical support, or emotional assistance. Interviews were conducted between August and October 2025 and continued until thematic saturation was reached, as indicated by repetition of categories and the absence of new major codes in the final interviews. Data were analyzed using inductive–deductive content analysis guided by Elo and Kyngäs, with five researchers conducting independent coding, iterative code comparison, consensus meetings, and theoretical mapping. Results: Four main themes emerged: (1) family involvement in decision-making, including collective discussion, shifting authority roles, and patient autonomy; (2) caregiver burden, involving physical exhaustion, psychological distress, social restriction, stigma, financial pressure, and employment disruption; (3) psycho-spiritual coping mechanisms, including emotional sharing, prayer, crying, patience, and surrender to God; and (4) digital healthcare needs, covering BPJS guidance, treatment information, scheduling, communication pathways, shelter support, and mental–spiritual support. Mapping these themes to Digital Health frameworks and Family Systems Theory clarified how DIVA.ID could translate family experiences into practical navigation functions. Conclusions: This study provides empirical foundations for a culturally sensitive, family-centered digital navigation model in Indonesia. Rather than demonstrating effectiveness, the findings identify design requirements for DIVA.ID that should be tested in subsequent feasibility, usability, and intervention studies. Full article

Break-bulk cargo operations are characterized by high variability and complex resource synchronization, yet they have received limited research attention compared to containerized logistics. This paper proposes an integrated lean-informed simulation framework for evaluating vessel service time (VST) in multipurpose terminals handling break-bulk cargo. The framework sequences three analytical stages: Value Stream Mapping paired with Ohno’s waste taxonomy to diagnose non-value-adding activities, a discrete-event simulation model built in Simio to quantify their impact on VST, and Sobol sensitivity analysis to decompose the remaining variability across operational factors. Demonstrated at DP World Lirquén, a multipurpose terminal in Chile, the lean diagnostic identified 101 min of waste per cycle across waiting, motion, and overproduction categories. Scenario evaluation showed that eliminating shift-transition delays and standardizing load composition reduced VST by 14.3% and 10.6%, respectively, without capital investment. The sensitivity decomposition revealed that warehouse machinery composition, particularly the interaction between equipment types, dominates VST variability, while truck fleet size operates as an independent factor. These findings demonstrate that coordination-related policy interventions outperform incremental resource additions. More specifically, machinery allocation must be optimized jointly rather than by equipment type in isolation. Full article

The growing complexity of web attacks highlights the need for adaptive, intelligent defense systems that overcome the limitations of traditional rule-based web security. Thus, the architecture proposed in this paper integrates data-driven deep learning with deterministic rule-based logic to enhance real-time detection accuracy and adaptability in dynamic web threat environments. The practical integration of a deep learning-based Gated Recurrent Unit (GRU) model with ModSecurity, an open-source Web Application Firewall (WAF), is employed to improve the detection and classification of malicious HTTP requests. The model, pre-trained on a large labeled up-to-date dataset of web traffic and attack types collected post-2020, is designed to classify requests in real-time, identifying both whether a request is malicious and the corresponding attack category (e.g., SQL Injection, Cross-Site Scripting, Command Injection). We demonstrate how the trained model is incorporated into ModSecurity’s inspection pipeline, allowing it to analyze real-time web traffic alongside traditional rule-based inspection. This hybrid approach aims to significantly reduce false positives and improve adaptability to new attack patterns. Evaluation metrics such as accuracy, receiver operating characteristic (ROC), area under the curve (AUC), Principal Component Analysis (PCA), confusion matrix, and t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization are discussed, along with performance considerations and implementation architecture. The integration presents a robust framework for ML-improved intelligent web security defense. Full article

Pose estimation is an important task in the field of machine vision, being widely used in robot grasping, augmented reality, and other applications. Weakly textured objects pose severe challenges due to scarce texture and low-density features, becoming a bottleneck in robot grasping. This paper systematically reviews recent progress in weakly textured object pose estimation, classifying methods into traditional and deep learning categories, and further dividing deep learning methods into instance-level, category-level, and unseen object-level. This review further summarizes the core issues of generalization limitations, real-time contradictions, and data bottlenecks in existing research. Combined with the practical needs of weakly textured scenes, the review points out that multimodal fusion optimization, lightweight model design, and low-cost annotation technology development are the future core research directions. The research results can provide a reference for algorithm design, experimental verification, and engineering applications in the field of weakly textured object pose estimation. Full article

Web service classification plays an important role in software reuse, service discovery, and automatic metadata organization. Although recent deep learning approaches have improved classification performance by using service names and natural-language descriptions, most existing methods still operate as black-box models and offer limited insight into how different metadata sources influence classification decisions. This lack of transparency reduces their practical usefulness for developers who need to verify predicted categories, analyze incorrect classifications, and improve service metadata quality. A well-trained interpretable model can not only help developers choose more appropriate and reliable categories for each web service, but also help write a more reasonable service name and description. In this paper, we present xServeNet, an explainability-oriented extension of ServeNet for transparent web service classification. xServeNet preserves the BERT-based representation and CNN–BiLSTM feature extractor of ServeNet and introduces (i) an instance-wise dynamic source-fusion mechanism that adaptively combines service-name and service-description features according to their semantic contribution, and (ii) model-internal importance indicators at both the source and word levels that support inspection of classification decisions without introducing additional trainable parameters. We benchmark xServeNet against eleven machine learning baselines on two real-world ProgrammableWeb datasets of 10,943 and 14,086 services covering 50 categories. xServeNet reaches 71.08% Top-1/91.35% Top-5 accuracy on the original dataset and 74.10% Top-1/92.95% Top-5 accuracy on the updated dataset, consistently improving Top-1 accuracy over ServeNet while remaining competitive on Top-5, and achieving the lowest per-category Top-5 standard deviation among all twelve compared methods. In practice, the importance indicators support three concrete activities at the service registry: helping developers verify predicted categories at registration time, iterating on description wording when the predicted category looks wrong, and supporting registry curators in flagging likely mislabelled services for review. Full article

The aim of this study was to analyze the effects of different small-sided game (SSG) formats on internal load, perceived enjoyment, and technical-tactical performance in elite youth water polo players. Twenty male athletes (U16, n = 10; U18, n = 10) performed in three 4 vs. 4 SSG formats with different time of ball possessions and size of field areas. Technical-tactical variables were assessed using the Team Sport Assessment Procedure (TSAP), while internal load and enjoyment were measured through session-RPE (s-RPE) and a 7-point enjoyment Likert scale (ENJ). Data were analyzed using linear mixed-effects models and Spearman correlations. SSG format significantly influenced internal load, with higher RPE values (F = 6.878; p = 0.004) and s-RPE (F = 6.27; p = 0.006) observed in larger formats of the SSG. Technical-tactical indices were also affected, with significant differences found for volume of play (VP) (F = 17.041; p < 0.001) and performance score (PS) (F = 18.574; p < 0.001), showing higher values in the smallest format (SSG1). Enjoyment differed between categories (F = 13.136; p = 0.003), with higher scores in U16 players. No significant correlations were found between final RPE and TSAP indices (p > 0.05). These findings suggest that SSGs are effective tools for simultaneously developing physical and technical-tactical skills. Coaches should manipulate task constraints to balance training intensity and skill development, while also enhancing player motivation and engagement. Full article