Search Results (61,658)

High-strength galvanized parallel steel wire (HSGPSW) is a primary load-bearing component in cable-supported bridge structures. However, due to both human and environmental factors, corrosion during its service life is often inevitable, and in severe cases, it may threaten the structural safety of the bridge. In this study, a novel method employing the organic corrosion inhibitor hydroxyethylidene diphosphonic acid (HEDP) is proposed to mitigate the corrosion of HSGPSW. First, electrochemical accelerated corrosion tests were conducted on 48 specimens immersed in HEDP solutions to investigate the effects of three key parameters—HEDP concentration, tensile stress, and inhibition duration—on the mass loss rate of the specimens. Subsequently, tensile tests were performed on the inhibited specimens to obtain their load–displacement curves, and the maximum tensile load under the influence of HEDP was comparatively analyzed. The results show that at an HEDP concentration of 0.12 mol·L⁻¹, the inhibition efficiency reached 40.31%, but it became saturated when the concentration exceeded 0.08 mol·L⁻¹. At a tensile stress of 7.5 kN, the inhibition efficiency decreased to 13.24%, with passive film breakdown identified as the primary cause of performance degradation. Energy-dispersive spectroscopy (EDS) analysis revealed that HEDP significantly stunts zinc layer dissolution, thereby enhancing initial corrosion protection, while mechanical tests indicated that its ability to slow the degradation of tensile performance diminishes after film rupture. The corrosion inhibition mechanism is attributed mainly to the synergistic effect of anodic suppression and interfacial coordination. This study provides a new method and novel insights for the corrosion protection of high-strength galvanized HSGPSW in cable-supported bridge structures. Full article

Driven by real-world demands of processing massive high-frequency data and achieving longer forecasting horizons in time series forecasting scenarios, a variety of deep learning architectures designed for time series forecasting have emerged at a rapid pace. However, this rapid development actually leads to a sharp increase in parameter size, and the introduction of numerous redundant modules typically offers only limited contribution to improving prediction performance. Although prediction models have shown a trend towards simplification over a period, significantly improving prediction performance, they remain weak in capturing dynamic relationships. Moreover, the predictive accuracy depends on the quality and extent of data preprocessing, making them unsuitable for handling complex real-world data. To address these challenges, we introduced Treeformer, an innovative model that treats the traditional tree-based machine learning model as an encoder and integrates it with a Transformer-based forecasting model, while also adopting the idea of time–feature two-dimensional information extraction by channel independence and cross-channel modeling strategy. It fully utilizes the rich information across variables to improve the ability of time series forecasting. It improves the accuracy of prediction on the basis of the original deep model while maintaining a low computational cost and exhibits better applicability to real-world datasets. We conducted experiments on multiple publicly available datasets across five domains—electricity, weather, traffic, the forex market, healthcare. The results demonstrate improved accuracy, and provide a better hybrid approach for enhancing predictive performance in Long-term Sequence Forecasting (LSTF) problems. Full article

This study investigates the influence of adaptive leadership on crisis management effectiveness in complex construction engineering projects in Saudi Arabia. Adaptive leadership was conceptualized through six core dimensions: Flexibility in Decision-Making, Emotional Intelligence, Leader-Follower Communication, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration. The study aimed to evaluate the impact of these leadership dimensions on crisis response effectiveness and safety outcomes within the high-risk, dynamic environment of the Saudi construction sector. A quantitative cross-sectional survey was conducted among managerial and supervisory personnel across major engineering and construction firms in Saudi Arabia. A total of 183 valid responses were obtained using a non-probability convenience sampling technique. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicated that five adaptive leadership dimensions—Flexibility in Decision-Making, Emotional Intelligence, Problem-Solving Adaptability, Resilience in Leadership, and Fostering Collaboration—had significant positive effects on crisis management effectiveness. However, Leader-Follower Communication did not demonstrate a statistically significant relationship with crisis outcomes. The findings contribute theoretical value by validating an adaptive leadership framework tailored to engineering project crises. Practically, the study underscores the importance of enhancing leadership flexibility, emotional intelligence, and collaborative engagement to strengthen crisis responsiveness and project continuity in Saudi construction firms. Recommendations include the development of targeted leadership training programs and the integration of digital technologies to support adaptive decision-making in real-time crisis conditions, resulting in better Safety and Crisis Management. Although, study limitations include reliance on self-reported data and the context-specific focus on the Saudi construction sector, which may affect generalizability, the findings are contextualized through comparison with international literature to support broader relevance. Full article

Exposure to light is an important factor in regulating sleep and sleep quality, especially for elderly people with a high risk of sleep problems. A systematic literature review was conducted to explore the current understanding of the relationship between light and sleep quality of the elderly, and to identify methodological gaps and soundness of existing studies to inform the design of future experimental studies. Specific focus is given to healthcare centres and similar settings due to their controlled environment and the high prevalence of sleep disturbances. Out of 406 publications screened from four databases—namely Google Scholar, Semantic Scholar, Lens.Org, and Scopus—380 studies remained after removing duplicates, and 19 studies published after 2002 that were relevant to the review topic were selected based on the PRISMA 2020 guidelines. The selected studies were analysed using six key aspects, which reflect typical components of experimental design such as participants’ characteristics, experiment and exposure duration, mode of light exposure, lighting and light interventions, experiment procedure, and data collection methods. The results indicated that many studies have limitations in terms of the accuracy and generalisability of findings in representing the entire elderly population due to issues with experimental design or control of the participants’ attendance. The results suggest that future studies should increase the duration of light intervention to around 21–35 days and the number of participants to around 14 and 47. The issues identified from the experimental designs of the selected studies provide valuable insights for establishing guidelines and recommendations for future studies. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

The scarcity of high-quality wood encouraged the development of various technological processes for joining wood. The finger joint is one of the most widespread technological processes for wood joining. This study aimed to determine the effect of steaming and heat modification of beech wood, as well as the type of adhesive, on the mechanical characteristics of finger joints. Samples made from un-modified beech, steamed-treated, and heat-treated beech wood were bonded with polyvinyl acetate (PVAC), non-structural, and structural polyurethane (PUR) adhesives. Compressive tests on wood materials were used to evaluate their mechanical performance. The finger joint samples were tested for their bending performance. Modulus of rupture, modulus of elasticity, and compressive strength were calculated. An analysis of variance (ANOVA) was conducted to evaluate the impact of wood modification type and adhesive used on the mechanical characteristics of the finger joints. According to the results of this study, it can be concluded that the steaming process does not influence changes in the mechanical characteristics of the finger joints. Heat treatment of beech and the type of adhesive used significantly influence the tested mechanical characteristics of the finger joints and beech wood. Heat-treated beech had lower values of modulus of rupture (70 MPa) and density (690 kg/m³) and higher values of compression strength (59 MPa) in relation to un-modified (780 kg/m³) and steamed-treated (800 kg/m³) beech wood. Full article

Background and Objectives: Diagnosing the underlying cause of ascites remains complex, especially when cytology results are inconclusive. Measuring biomarkers directly in ascitic fluid may offer better diagnostic insight than serum testing alone. This review evaluated the clinical utility of tumor and inflammatory markers in ascitic fluid. Materials and Methods: A systematic search was conducted in PubMed and Scopus for studies published from January 2014 to December 2024, with the final search carried out in May 2025. The included studies were observational, comparative or biomarker validation studies evaluating ascitic fluid markers for diagnosing malignant and inflammatory ascites. The extracted outcomes included diagnostic accuracy metrics such as area under the curve (AUC), sensitivity and specificity. Risk of bias was evaluated using the ROBINS-I tool. Studies were excluded if they were case reports, animal studies, cytology-only analyses, or if they lacked biomarker data in ascitic or peritoneal fluid. Results: Forty-two studies met the inclusion criteria. CEA showed high diagnostic performance when measured in ascitic fluid. Combining markers or using ascitic-to-serum ratios improved diagnostic reliability. Inflammatory markers in ascitic fluid, such as CRP, IL-6 and VEGF added diagnostic value when cytology was inconclusive. Discussion and Conclusions: Evaluating biomarkers in ascitic fluid improved diagnostic accuracy. However, the included studies showed considerable methodological heterogeneity and moderate risk of bias. Full article

The purpose of this study is to systematically examine the impact of technological innovation on the coordinated development of regional economies and its internal mechanism. It is aimed at revealing whether and how technological innovation promotes the coordinated development of regional economies, and further identifying its heterogeneity characteristics and boundary conditions in the space–time dimension. The research was conducted using panel data for 258 prefecture-level cities in China from 2011 to 2021. This study found that technological innovation significantly promoted the coordinated development of regional economies; this effect was more prominent in China’s eastern region and the Yangtze River Economic Belt. The mechanism test shows that technological innovation can optimize regional resource allocation and narrow the development gap by promoting industrial structure upgrades and rationalization. Further analysis shows that the level of marketization has a nonlinear regulatory effect on the coordination effect of technological innovation, with two threshold levels. A heterogeneity analysis reveals significant differences in the effects of technological innovation in different regions in China, especially in the western region and the northwest side of the Hu Changyong line. The research leads to four key policy recommendations. First, it is important to strengthen the core driving role of technological innovation and implement regionally differentiated innovation support policies. Second, industrial structure upgrades should be encouraged through industrial chain coordination. The third recommendation is to improve the market-oriented institutional environment and minimize barriers to factor flow. Fourth, supporting coordinated policies, such as optimizing human capital and introducing high-quality foreign capital, is necessary to establish a sustainable long-term mechanism for regional coordinated development. Full article

Recent advances in large multimodal models (LMMs) have opened new opportunities for multitask recognition from remote sensing images. However, existing approaches still face challenges in effectively recognizing the complex geospatial characteristics of regions such as Japan, where its location along the seismic belt leads to highly diverse urban environments and cityscapes that differ from those in other regions. To overcome these challenges, we propose the GeoJapan Fusion Framework (GFF), a multimodal architecture that integrates a large language model (LLM) and a vision–language model (VLM) and strengthens multimodal alignment ability through an in-context learning mechanism to support multitask recognition for Japanese remote sensing images. The GFF also incorporates a cross-modal feature fusion mechanism with low-rank adaptation (LoRA) to enhance representation alignment and enable efficient model adaptation. To facilitate the construction of the GFF, we construct the GeoJapan dataset, which comprises a substantial collection of high-quality Japanese remote sensing images, designed to facilitate multitask recognition using LMMs. We conducted extensive experiments and compared our method with state-of-the-art LMMs. The experimental results demonstrate that GFF outperforms previous approaches across multiple tasks, demonstrating its promising ability for multimodal multitask remote sensing recognition. Full article

As the fourth-largest global food crop, the quality and functional characteristics of processed potato products are closely linked to endogenous sugar metabolism in tubers, with the trehalose–sucrose metabolism playing a key role in processing adaptability. This study analyzed 333 accessions from a tetraploid potato natural population. The trehalose and sucrose content of potato tubers at harvest was quantified using the high-performance liquid chromatography (HPLC) method. Combined with whole-genome resequencing, a genome-wide association study (GWAS) was conducted to map regulatory loci and identify candidate genes. The results showed that relative trehalose content in tubers was 20.38–24.78, while relative sucrose content was 10.32–19.50. Frequency histograms for both sugars exhibited normal distributions characteristic of quantitative traits, and a positive correlation was observed between them. GWAS for trehalose identified 111 significant SNP loci, mainly on chromosomes 10 and 12, leading to the identification of 88 candidate genes. Kyoto encyclopedia of genes and genomes analysis (KEGG) revealed that trehalose-related genes were primarily involved in pathways such as ABC transporters, tricarboxylic acid (TCA) cycle, and cysteine and methionine metabolism. Candidate genes potentially regulating tuber trehalose content included GH10, GH28, GH127, UXS, UGT, PMEI, and MYB108. For sucrose, GWAS identified 279 significant SNP loci, mainly on chromosomes 5, 6, and 12, resulting in 111 candidate genes. KEGG enrichment analysis showed that sucrose-related genes were enriched in pathways including starch and sucrose metabolism, cyanoamino acid metabolism, and phenylpropanoid biosynthesis. Candidate genes potentially regulating tuber sucrose content included GH17, GH31,GH47, GH9A4, SPP1, BGLU12, GSA1, TPS8, cwINV4, HXK, UST, MYB5, MYB14, and WRKY11. Therefore, this study provides marker loci for trehalose and sucrose metabolism research, aiming to clarify their regulatory mechanisms and support potato variety improvement and superior germplasm development. Full article

As a key connection technology in prefabricated buildings, offshore wind power, and bridge engineering, the performance and environmental sustainability of grouted sleeve connections are essential for the long-term development of civil infrastructure. To address the environmental burden of conventional high-strength cement-based grouts, an eco-friendly sleeve grouting material incorporating industrial solid waste was developed. In this study, silica fume (15%) and fly ash (5%) were employed as supplementary cementitious materials, while nanosilica (NS) was introduced to enhance the material properties. Mechanical testing, microstructural characterization, and half-grouted sleeve uniaxial tensile tests were conducted to systematically evaluate the effect of NS content on grout performance. Results indicate that the incorporation of NS significantly accelerates the hydration of silica fume and fly ash. At an optimal dosage of 0.4%, the 28-day compressive strength reached 105.5 MPa, representing a 37.9% increase compared with the control group without NS. In sleeve tensile tests, specimens with NS exhibited reinforcement necking failure, and the load–displacement response closely aligned with the stress–strain behavior of the reinforcement. A linear relationship was observed between sleeve wall strain and reinforcement stress, confirming the cooperative load-bearing behavior between the grout and the sleeve. These findings provide theoretical guidance and technical support for developing high-strength, low-impact grouting materials suitable for sustainable engineering applications. Full article

The increased complexity of buildings has led to rigorous performance demands from materials and building envelopes. As markets for low-carbon, renewable construction materials grow, cross-laminated timber and wood fiber insulation have emerged as promising alternatives to meet these rigorous demands. However, an investigation into the performance and interaction of materials within high-performance systems is necessary to determine the durability risks associated with increased complexity and the introduction of new materials. This is important in order to ensure that these materials can meet the required functions of the building while taking advantage of their environmental benefits. To do so, this case study investigated a building constructed of cross-laminated timber and wood fiber insulation in a cold climate (Zone 6A) (Belfast, ME, USA). During construction, the building was instrumented with temperature, relative humidity, and moisture content monitoring instrumentation through the envelope, i.e., wall and roof assemblies. The conditions within the envelope were monitored for a two-year period and used to calibrate a hygrothermal model, along with measured material properties. The calibrated model was used to conduct a 5-year simulation and mold risk assessment. Findings demonstrated that there was no moisture or mold risk throughout the monitoring period or simulation. This supports the integration of cross-laminated timber and wood fiber insulation in sustainable building practices, particularly in cold climates where moisture management is critical. Full article

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article

The saline–alkali soil environment profoundly influences the diversity and composition of soil microbial communities, reshaping their ecological network structures. As a vital staple crop, rice (Oryza sativa L.) plays a crucial role in global food security, highlighting the urgent need to improve its cultivation efficiency in saline–alkali soils. However, the mechanisms by which rice roots recruit beneficial microorganisms from native soils under prolonged saline–alkali stress remain largely unclear, and limited research has been conducted on the effectiveness of microbial inoculants in enhancing rice salt tolerance. This study investigated microbial communities in a saline field subjected to over a decade of continuous rice cultivation. Plant growth-promoting microorganisms were isolated and screened from the rhizosphere. The findings revealed long-term salt stress significantly altered microbial diversity and community composition, although the overall microbial network structure remained resilient. A total of 21 plant growth-promoting strains were identified, indicating that rice roots under sustained salt stress selectively recruit beneficial microbes that contribute to plant growth and stress adaptation. Further experimental validation demonstrated that synthetic microbial communities outperformed individual strains in promoting rice seedling growth under high-salinity conditions, likely due to synergistic microbe and microbe–plant interactions. In conclusion, while saline–alkali conditions disrupt native microbial communities, rice exhibits adaptive capacity by selectively enriching growth-promoting microorganisms. The application of synthetic microbial consortia presents a promising strategy to enhance rice resilience and productivity in saline–alkali environments. Full article

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article