Search Results (145,104)

As the boundary between indoor and outdoor spaces, the heat flux of a building envelope is a crucial factor influencing the indoor thermal environment and human thermal comfort, and also an important indicator reflecting the impact of outdoor meteorological factors on the indoor environment. In scenarios involving rapid assessment of existing buildings and engineering projects, the dynamic thermal performance of the building envelope are often affected by factors such as outdoor weather fluctuations, window–wall coupling, wall heat storage, and thermal bridging. To address this issue, this study proposes a dynamic heat flux calculation method that accounts for hysteresis. Simultaneously, the heat conduction equation of the implicit finite difference method (IFDM) and boundary conditions based on wall energy balance are used to optimize the wall surface temperature. An adaptive step size control strategy (Runge–Kutta–Fehlberg) is introduced in the time step setting. Results show that the heat flux R² of the proposed dynamic heat flux calculation method is 0.9207, and the optimized R² is 0.9435, both within an acceptable range for engineering applications. Studies have shown that the simplified framework derived from the heat flux analysis of building envelopes retains the characteristics of wall heat storage and delayed heat release, while effectively solving the window–wall coupling problem and significantly reducing the reliance on computationally expensive numerical methods. This method therefore provides an efficient and scalable technical pathway for thermal performance assessment and energy-retrofit decision support for existing building envelopes. Full article

Modern road infrastructure asset management faces increasing pressure to improve the quality of decision-making processes, also due to limited public resources. The field of road diagnostics is no exception. The aim of the research is to analyze, through a literature review, the possibilities of applying the theoretical concept of information value. The selected point of interest is the tasks associated with the selection of specific sections intended for inspection, monitoring the level of information gain that this inspection can bring. Methodologically, the research is based on a systematic bibliometric analysis of the literature from the Web of Science and SCOPUS databases for the period January 2010 to June 2025. This is supplemented by a non-systematic content review, while the identified publications were processed by the Bibliometrix and VOSviewer tools and subsequently qualitatively interpreted. The result of the research is a synthesis of knowledge from the finally analyzed set of relevant scientific papers. The findings point to a growing interest in linking the process of planning and performing road infrastructure diagnostics with asset management decision-making processes. At the same time, they point to the development of data-oriented and digital approaches, as well as the limited application of the concept of information value in planning inspections before their implementation. The findings indicate that the assessment of expected information benefit represents a promising tool for reducing uncertainty, determining priorities, and allocating resources more efficiently, while its implementation in road infrastructure management requires further methodological research and practical verification. Full article

Histone deacetylases (HDACs) 1–3 are key regulators of gene expression and represent important therapeutic targets in cancer, neurodegenerative, and immune disorders. Many potent class I HDAC inhibitors display slow- and tight-binding kinetics, which profoundly influence their efficacy and pharmacodynamics. In particular, their dissociation rate (off-kinetic) is critical, since prolonged target engagement greatly influences drug efficacy in vivo. However, the off-kinetics of HDAC inhibitors are often overlooked in the early stages of drug development. Here, we investigated the dissociation kinetics of tucidinostat, trapoxin A, and TNG260 in comparison to the pan-HDAC inhibitor vorinostat. Using biochemical 100-fold jump dilution assays, NanoBRET assays, and cellular washout experiments, we characterized the dissociation of these compounds from purified proteins and in a cellular context. Tucidinostat showed moderately slow off-kinetics, while the clinical candidate TNG260 demonstrated pronounced tight-binding properties. Trapoxin A displayed remarkable discrepancies between assays, as it showed fast dissociation kinetics in the biochemical assay, but tight-binding properties in a cellular setting. These findings not only address the previously unexplored dissociation kinetics of two clinically relevant inhibitors, but also underscore the importance of comprehensive kinetic profiling of novel HDAC inhibitors in cellular models. Full article

A high-speed magnetic pump rated at 7800 r/min was studied. A numerical model was established, and a hydraulic, vibration, and noise testing system was set up to conduct flow simulations, noise, and vibration experiments at different speeds. The results show that increasing speed leads to a higher pressure difference between the pump chamber and the cooling circuit. Meanwhile, the turbulent kinetic energy at the impeller outlet increases. Despite an increase in energy loss, the loss ratio decreases, and overall efficiency improves. The internal flow noise collected by the outlet hydrophone mainly comes from Rotor–Stator Interference (RSI), and it can sensitively capture changes in rotational speed. The dominant frequency of the outlet noise agrees well with the blade frequency calculated from the set speed, with a maximum deviation of 0.26%. As the speed increases, the overall sound pressure level (OASPL) at the inlet and outlet and the Root Mean Square (RMS) acceleration values at the outlet and pump body generally increase, while the acceleration at the motor base shows a decreasing trend. The conclusions are helpful for the design and optimization of rotary machinery such as high-speed magnetic pumps. Full article

This study evaluated activities of crude extracts from different parts of the tree of heaven (Ailanthus altissima (Mill.) Swingle) collected in Slovenia and Hungary, using effect-directed analyses based on hyphenation of high-performance thin-layer chromatography (HPTLC) and nine in vitro assays performed in situ on chromatographic plates after the separation. HPTLC separation combined with a set of four antibacterial assays, two antifungal assays, and three enzyme inhibitor assays to evaluate the extracts of 14 plant parts: young shoots, young leaves, mature leaves, yellow leaves, petioles of leaves, petioles of male inflorescences, petioles of fruits, female inflorescences, male inflorescences, mature male inflorescences, bark of 1–2-year branches, bark of 2-year branches, bark of tree trunk, and bark of roots. Antibacterial activities against Gram-positive bacteria (Bacillus subtilis, Rhodococcus fascians) and Gram-negative bacteria (Aliivibrio fischeri, Pseudomonas syringae pv. maculicola (Psm)), as well as inhibition of enzymes α-glucosidase, lipase, and acetylcholinesterase, were observed for all extracts. Extracts differed in their antifungal activities. Extracts of young shoots, mature leaves, petioles of leaves, and bark of roots showed antifungal activity against plant pathogens Fusarium avenaceum and Bipolaris sorokiniana. Extracts of yellow leaves, male inflorescences, bark of 1–2-year branches, and bark of tree trunks were only active against F. avenaceum, whereas extracts of young leaves were only active against B. sorokiniana. This study is the first to report that A. altissima extracts exhibit (1) antifungal activity against F. avenaceum and B. sorokiniana; (2) antibacterial activity against A. fischeri, Psm, R. fascians, and B. subtilis (except leaves, bark of branches and bark of tree trunks); and (3) inhibitory activity toward lipase, α-glucosidase (except bark of tree trunks), and acetylcholinesterase (except bark of tree trunks). Full article

Cultural routes, an important concept in heritage conservation, represent an innovative paradigm that is reshaping the contemporary trajectory of cultural heritage research. The Nanling Corridor satisfies the four core criteria for cultural routes—temporal continuity, spatial distribution, cross-cultural attributes, and specific historical functional roles—and stands as a paradigmatic indigenous cultural route in China. Focusing on the Hunan–Guangdong Ancient Road—a core segment of the Nanling Corridor—this study integrates historical document analysis, representative sample field surveys, and a historical restoration method to systematically classify and characterize the ancient road’s landscape features. The study findings indicate that the Hunan–Guangdong border region within the Nanling area is endowed with a distinctive natural geographical setting and a complex socio-cultural context. Against this background, landscape elements are categorized into two primary clusters: those directly associated with the ancient road and those indirectly linked to it. The directly associated landscapes are further subdivided into four categories: the cross-territorial route, meso-scale hubs enabling land–water transfer, widely distributed micro-scale ancillary facilities, and intangible engineering techniques. The indirectly associated landscapes encompass four dimensions—military defense, population migration, commercial trade, and religious practice—each demonstrating unique landscape attributes while sharing homologous formative mechanisms. This study aims to provide a China-focused research reference for the international theory of cultural routes through the systematic study of the landscapes along the Hunan–Guangdong Ancient Road within the Nanling Corridor. Full article

Objectives: Clinical trials are essential for therapeutic innovation in rheumatology. A recent decline in clinical trial activity in Hungary has highlighted the need to better understand patient experiences and motivations. This study assessed patient satisfaction and motivation in clinical trials, compared these with routine specialist care, and evaluated healthcare professionals’ motivations. Methods: In this single-center, cross-sectional study, 129 patients completed self-administered questionnaires (61 trial participants and 68 receiving routine care) primarily using a 6-point Likert scale; additionally, 21 healthcare professionals rated their motivations on a Visual Analog Scale (VAS 0–10). Categorical variables were analyzed using chi-square or Fisher’s exact tests, and continuous variables using paired two-tailed t-tests. Results: The main drivers of trial participation were physician recommendations (100%) and trust in the treating physician (100%). Access to novel therapies (98%), closer monitoring (83%), and additional diagnostic procedures (95%) were also significant motivators. Trial participants reported significantly higher satisfaction compared with routine care in terms of consultation time (97% vs. 36%, p < 0.001), staff availability (95% vs. 41, p < 0.001), assistance (93% vs. 36%, p < 0.001), and visit organization (98% vs. 34%; p < 0.001). Overall satisfaction with routine care remained high in both groups. In the control group, fears of disease worsening and the burden of frequent visits were key deterrents. Among healthcare professionals, access to innovative treatments was the strongest motivator, while administrative workload and documentation demands were the primary barriers. Conclusions: Clinical trial participation is associated with high patient satisfaction, driven by physician–patient trust and structured, personalized care. Reducing administrative burdens may be crucial for sustaining clinical research in academic settings. Full article

Accurate liver and tumor segmentation from abdominal computed tomography (CT) scans is essential for diagnosis and treatment planning; however, centralized deep learning approaches are often constrained by privacy regulations and inter-institution data-sharing limitations. To address these challenges, we propose a skip-free feature-forward collaborative segmentation framework called Feature-Forward Residual U-Net (FF-ResUNet), in which each institution executes the encoder locally and transmits only compact bottleneck representations to a central server. High-resolution encoder features and skip connections remain strictly within institutional boundaries, reducing privacy exposure and communication overhead. The server reconstructs segmentation masks using a multi-scale dilated residual decoder with progressive upsampling and returns lightweight updates for encoder refinement. FF-ResUNet is evaluated on the Liver Tumor Segmentation (LiTS) Challenge dataset, with cross-domain testing on 3D-IRCADb and AMOS-CT to assess robustness under distribution shifts and simulated multi-institution collaboration. On LiTS, the proposed framework achieves a liver Dice score of 0.952 ± 0.015 and a tumor Dice score of 0.737 ± 0.060, with a tumor HD95 of 10.9 ± 4.1 mm. Cross-domain experiments demonstrate stable generalization to unseen datasets, while multi-client simulations show improved performance as the number of participating institutions increases before saturation. Compared with skip-based collaborative U-Net architectures, FF-ResUNet reduces communication payload by 92–98% per training iteration while maintaining competitive segmentation accuracy. These results indicate that FF-ResUNet provides an effective balance between segmentation performance, communication efficiency, and privacy preservation evaluated under simulated multi-institution collaborative settings, supporting practical multi-center clinical deployment in bandwidth- and policy-constrained healthcare environments. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

Flotation circuits typically incorporate grinding stages, yet mathematical models for these processes often operate on different principles, leading to misalignment in circuit design. Building on a previously established grinding model for flotation performance, this research introduces significant advances to develop a more comprehensive and industrially relevant framework. The primary innovation is the integration of mechanical entrainment and gangue recovery into the kinetic model, distinguishing between species captured by true flotation and those carried to the surface despite being non-hydrophobic. We developed a robust set of grinding-mill equations based on first-order kinetics to describe the mass-fraction transformation of both true-flotation and entrainment species. To ensure practical applicability, a systematic experimental and modeling methodology for parameter adjustment is introduced, providing a clear sequence for identifying breakage rate constants and flotation kinetic parameters. The proposed strategy was validated using two distinct case studies: an expanded analysis of a copper sulfide ore (ore A) and a new case involving significant gangue entrainment (ore B). The results demonstrate that the model accurately predicts species kinetics, providing a high-fidelity, cost-effective tool to optimize mineral recovery and prevent economic losses from overgrinding in industrial processing plants. Full article

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article

This study presents a comparative analysis of classroom engagement effects on the academic achievement of senior secondary school physics students, focusing on the replication of prior research and contrasting the impacts of guided and minimally guided constructivist instructional approaches. Drawing on established frameworks of inquiry-based instruction, particularly Cognitively Guided Instruction (CGIS) and Cubing Instruction (CIS), the research investigates their relative efficacy in enhancing student learning outcomes. The clustered quasi-experimental pretest–posttest design, involving the Cognitively Guided Instructional Strategy (CGIS) and the Cubing Instructional Strategy (CIS), was adopted by the study. The intact classroom groups of schools purposively selected participated in the study. An achievement test was administered before and after instruction, and the Analysis of Covariance (ANCOVA) and t-tests were used to determine the effects of the intervention while controlling for baseline achievement and mathematical ability. The findings show that the treatment had a significant effect on the students’ achievement (p = 0.030). The t-test result demonstrated that students exposed to the CGIS recorded higher posttest mean scores than those in the CIS group. These outcomes suggests that guided inquiry may offer pedagogical advantages in supporting classroom and conceptual learning. However, the evidence should be cautiously interpreted. The study contributes to the literature as a conceptual replication by providing evidence regarding the effects of guided and minimally guided constructivist approaches in a different instructional setting. The outcomes underscore the importance of balancing instructional guidance and learner autonomy in physics classrooms, as well as the need for further research involving larger samples and diverse contexts to strengthen causal inference. Full article

Background: Pressure algometry is commonly used in sports, exercise, and rehabilitation settings to assess pain sensitivity and monitor neuromuscular status. Reliable and accessible devices are required for consistent assessment. This study evaluated the accuracy, agreement, repeatability, and test–retest reliability of the AMF-500 digital pressure algometer. Methods: Three independent studies were conducted. Study A assessed the agreement between the AMF-500 and a three-axis AMTI force plate during 30 controlled pressure trials. Study B compared pressure pain thresholds (lumbar paravertebral muscles, tibialis anterior, and thenar eminence) and lumbar pressure pain tolerance between the AMF-500 and the MED.DOR algometer in 27 healthy adults. Study C (n = 27 healthy adults) evaluated test–retest reliability across two sessions separated by 48 h. Agreement was assessed using Bland–Altman analyses, and intraclass correlation coefficients (ICC) was also applied. Standard error of measurement (SEM) and minimal detectable change (MDC) were also calculated. All pressure values were expressed in N/cm². Results: In Study A, the AMF-500 slightly overestimated pressure compared with the force plate (19.34 ± 2.44 vs. 18.71 ± 2.49 N/cm²), with a mean bias of 0.63 N/cm² and limits of agreement from 0.21 to 1.05 N/cm², corresponding to a mean difference of approximately 3.4%. Despite this small systematic bias, agreement between devices was excellent (ICC = 0.99; 95% CI: 0.96–0.99), and no proportional bias was detected, indicating a small and consistent overestimation of pressure by the AMF-500. In Study B, no significant differences were observed between AMF-500 and MED.DOR for lumbar threshold, lumbar tolerance, tibialis anterior threshold, or thenar eminence threshold (all p > 0.05). Agreement between devices was good to excellent (ICC = 0.82–0.91). Bland–Altman analyses showed small mean biases (1.05–7.51 N/cm²), with proportional bias detected only for lumbar tolerance. In Study C, test–retest reliability for the AMF-500 ranged from moderate to good across sites (ICC = 0.69–0.88), comparable to MED.DOR (ICC = 0.63–0.88). SEM values for the AMF-500 ranged from 9.31 to 25.15 N/cm², with higher variability observed for lumbar tolerance. Conclusions: The AMF-500 demonstrated acceptable accuracy, agreement, and reliability when compared with a laboratory force plate and an established clinical algometer. These findings can support its use as a low-cost tool for pressure pain assessment in sports, exercise, and rehabilitation contexts. Full article

Balancing structural safety and economic efficiency in super-tall building design remains a formidable challenge. To address this issue, this study proposes a genetic-algorithm-based multi-variable, multi-objective optimization method. The design variables include the member sizes and vertical layout positions of outrigger and belt trusses, as well as the cross-sectional dimensions of mega-columns. Total structural weight and maximum inter-story drift ratio are adopted as objective functions, while code-specified constraints, such as shear-weight ratio, stiffness-weight ratio, and axial compression ratio, are incorporated to formulate the fitness evaluation for optimization. Taking a 300 m baseline structure designed for 6-degree seismic intensity and equipped with two outrigger trusses and three belt trusses as an example, single-variable sensitivity analyses are first performed. The results show that optimizing any single parameter can yield certain local improvements, yet it cannot overcome the weight–deformation trade-off induced by strong variable coupling. By selecting representative feasible solutions from the multi-variable solution set that match the “optimal” values identified by single-variable optimization as benchmarks, the multi-variable optimum reduces the total structural weight by approximately 6.5–18.4% relative to these representative designs. Moreover, optimal layout strategies of outrigger and belt trusses are investigated for two typical building heights (200 m and 300 m) and two seismic intensity levels associated with design ground motions having a 10% exceedance probability in 50 years, namely 6-degree (0.05 g) and 8-degree (0.20 g). Finally, the proposed method is validated through a case study of a super-tall financial center in Chongqing, where the total structural weight is reduced by 12.3% after optimization while the inter-story drift ratio still satisfies relevant code requirements. The results demonstrate that the proposed framework can generate competitive feasible solutions and provide a systematic means to achieve a balanced trade-off between structural safety and economic efficiency for outrigger–belt-truss super-tall buildings. Full article

Log anomaly detection in modern distributed systems is challenging. Anomalous behaviors are rare. Manual labeling is expensive. Session boundaries are often set by fixed heuristics before model training. This fixed-boundary assumption is problematic because segmentation errors propagate into representation learning and cannot be corrected during optimization. To address this, this paper proposes BASN (Boundary-Aware Sessionization Network), a boundary-aware contrastive learning framework that jointly learns session boundaries and anomaly representations using a differentiable soft-reset mechanism. BASN does not treat sessionization as a separate step. Instead, it predicts boundary probabilities from event semantics and temporal gaps, then modulates end-to-end session-state updates. The session representations are optimized with self-supervised contrastive learning, enabling effective zero-shot anomaly detection and few-shot adaptation. Experiments on four benchmark datasets (BGL, HDFS, OpenStack, SSH) show strong zero-shot performance (area under the receiver operating characteristic curve, AUROC 0.935–0.975) and boundary alignment with expert-validated proxy segmentation (boundary F1 0.825–0.877). Comparative gains over baselines are reported in the article after bibliography correction, baseline verification, and expanded statistical analysis. BASN is also computationally efficient, requiring less than 10 ms per session on a Graphics Processing Unit (GPU) and less than 45 ms on a Central Processing Unit (CPU). This is compatible with real-time inference needs in the evaluated settings. However, cross-system transfer AUROC (0.735–0.812) remains below in-domain performance. Domain-specific adaptation is still needed for deployment in environments that differ greatly from the training domain. Full article