Search Results (43,289)

With the continuous growth in the scale of engineering simulation and intelligent manufacturing workflows, more and more problem-solving tasks are migrating to cloud computing platforms to obtain elastic computing power. However, a core operational challenge for cloud platforms lies in the difficulty of stably obtaining high-quality scheduling solutions that are both efficient and free of symmetric redundancy, due to the coupling of multiple constraints, partial resource interchangeability, inconsistent multi-objective evaluation scales, and heterogeneous resource fluctuations. To address this, this paper proposes a Dual-Improved Whale Optimization Algorithm (DI-WOA) accompanied by a modeling framework featuring discrete–continuous divide-and-conquer modeling, a unified monetization mechanism of the objective function, and separation of soft/hard constraints; its iterative trajectory follows an augmented Lagrangian dual-rollback mechanism, while being rooted in a three-layer “discrete gene–real-valued encoding–decoder” structure. Scalability experiments show that as the number of tasks J increases, the DI-WOA ranks optimal or sub-optimal at most scale points, indicating its effectiveness in reducing unified billing costs even under intensified task coupling and resource contention. Ablation experiment results demonstrate that the complete DI-WOA achieves final objective values (OBJ) 8.33%, 5.45%, and 13.31% lower than the baseline, the variant without dual update (w/o dual), and the variant without perturbation (w/o perturb), respectively, significantly enhancing convergence performance and final solution quality on this scheduling model. In robustness experiments, the DI-WOA exhibits the lowest or second-lowest OBJ and soft constraint violation, indicating higher controllability under perturbations. In multi-workload generalization experiments, the DI-WOA achieves the optimal or sub-optimal mean OBJ across all scenarios with H = 3/4, leading the sub-optimal algorithm by up to 13.85%, demonstrating good adaptability to workload variations. A comprehensive analysis of the experimental results reveals that the DI-WOA holds practical significance for stably solving high-quality scheduling problems that are efficient and free of symmetric redundancy in complex and diverse environments. Full article

Pathogenic variants in the thyroid-stimulating hormone receptor gene (TSHR) contribute to a wide spectrum of thyroid dysfunctions, ranging from congenital hypothyroidism to thyrotropin resistance. With the advancement of bioinformatics algorithms for variant effect prediction, assessing the pathogenic potential of variants has become increasingly important. This study aimed to investigate the pathogenic effects of TSHR variants classified as variants of uncertain significance (VUSs) in the gnomAD v4.1.0 database. TSHR variants listed in gnomAD v4.1.0 were retrieved and filtered to select missense VUSs based on ClinVar classifications. Multiple bioinformatics tools were used to assess the secondary and three-dimensional structures of the TSHR, as well as protein stability, evolutionary conservation, and molecular dynamics simulations. A total of 2760 TSHR variants were found in gnomAD v4.1.0, including 75 frameshifts, 80 splice-sites, 265 in the 3′ and 5′ untranslated regions, 422 synonymous, 892 others, and 1026 missense variants. Among these, 68 missense VUSs were identified and selected for bioinformatics analysis. Three variants (p.Cys29Trp, p.Leu57Pro, and p.Phe97Ser) were consistently predicted to be pathogenic by all the bioinformatics tools used. All three variants were located within the leucin-rich repeat domain extracellular region of the TSHR and within a highly conserved region across species. Molecular dynamics simulations for mutant proteins (p.Cys29Trp, p.Leu57Pro, and p.Phe97Ser) reveal structural instability in comparison to the wild protein. Comprehensive bioinformatics analysis revealed that three TSHR missense VUSs exhibited pathogenic potential. These variants may contribute to thyroid dysfunction by affecting the receptor’s structural and signalling integrity. Full article

Single-cell RNA sequencing (scRNA-seq) provides a comprehensive understanding of cellular complexity; however, its requirement for fresh or frozen samples limits its flexibility. To overcome this limitation to effectively leverage clinical samples, Chromium Fixed RNA Profiling on formalin-fixed paraffin-embedded (FFPE) tissue blocks (scFFPE-seq) was developed to perform single-nucleus RNA sequencing from nuclei isolated from FFPE. In this study, we utilized fresh tissue samples from colon, ileum, and skin to assess the viability of scFFPE-seq compared to these fresh samples. We were able to recover unique cell types from challenging FFPE tissues and validated scFFPE-seq findings through Hematoxylin and Eosin (H&E) images. The results demonstrated that scFFPE-seq effectively captured the single-cell transcriptome in FFPE tissues, obtaining comparable cell abundance, cell type annotation, and pathway characterization to those in fresh tissues. Overall, the study presents strong evidence of the potential of scFFPE-seq to enhance scientific knowledge by enabling the generation of high-quality, sensitive single-nucleus RNA-seq data from preserved tissue samples. This technique unlocks the vast archives of FFPE samples for extensive retrospective genomic studies. Full article

Cellulite, a multifactorial condition affecting approximately 98% of women, is characterised by dermal and subcutaneous architectural changes that compromise skin texture and elasticity. Its progression is closely linked to hormonal, vascular, and inflammatory factors, as well as ageing-related extracellular matrix degradation. This review critically evaluates bioactive compounds incorporated into topical and minimally invasive formulations for the management of cellulite and skin rejuvenation. A comprehensive literature search was conducted across major scientific databases and cosmetic ingredient repositories, focusing on active ingredients with demonstrated efficacy in enhancing skin structure. Key compounds include capsaicin, forskolin, L-carnitine, caffeine, retinol, and extracts from plants such as Centella asiatica, which act via lipolysis, improved circulation, and antioxidant effects. Minimally invasive agents, such as deoxycholic acid and poly-L-lactic acid, complement these strategies by inducing adipocytolysis and neocollagenesis, thereby improving skin firmness and contour. Evidence indicates that multi-active formulations combining lipolytic agents with antioxidants and collagen-stimulating molecules yield synergistic benefits, reducing adipose protrusion and improving skin firmness. However, heterogeneity in study design and the lack of standardised evaluation methods limit firm conclusions. Further studies should validate efficacy and optimise delivery. Integrated topical and injectable therapies represent a promising, multifunctional approach to addressing cellulite and age-related skin changes. Full article

With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development. Full article

Background and Objective: Male individuals diagnosed with metabolic dysfunction-associated steatotic liver disease (MASLD) frequently present with decreased blood testosterone concentrations concomitant with increased levels of hepatic cholesterol, the fundamental substrate for testosterone synthesis; however, the mechanistic relationship between these phenomena remains inadequately elucidated. This study aimed to examine the involvement of hepatic cholesterol biosynthesis and testosterone metabolism in the pathogenesis of MASLD. Methods: An MASLD model was established in male C57BL/6J mice subjected to a high-fat diet (HFD). Comprehensive analyses, including hepatic transcriptomics, metabolomics, enzyme-linked immunosorbent assay, Western blotting, and quantitative polymerase chain reaction, were conducted. Additionally, in vitro experiments were performed using AML-12 hepatocytes treated with oleic acid and testosterone, with or without the presence of a uridine diphosphate-glucuronosyltransferase family 2 member B (UGT2B) enzyme inhibitor. Results: The HFD elevated cholesterol levels and activated cholesterol synthesis and testosterone metabolic pathways, notably characterized by upregulation of UGT2B enzymes and their transcriptional regulator, the aryl hydrocarbon receptor (AHR). Blood testosterone increased initially but decreased after 24 weeks of HFD. In vitro, testosterone alone did not affect oleic acid-induced lipid accumulation, but inhibiting UGT2B enabled testosterone levels to reduce lipid deposition and downregulate lipid uptake and synthesis pathways. Conclusions: The HFD induces dynamic, UGT2B-mediated hepatic testosterone metabolism. Compensatory early testosterone increase is offset by enhanced UGT2B-mediated clearance, resulting in eventual testosterone depletion and the loss of its protective effects against hepatic lipid accumulation. This explains the clinical paradox and suggests targeting the hepatic UGT2B enzymes as a potential MASLD treatment. Full article

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research. Full article

Phase unwrapping is the decisive factor for achieving dimensional accuracy in phase-shifting profilometry, yet unavoidable phase jumps occur at discontinuities. Existing dual-frequency heterodyne techniques suffer from a narrow measurement range and overly coarse projected fringes due to grating superposition requirements, leading to large errors when scanning objects with hole-like features. To address these issues, this paper proposes an edge-oriented phase-unwrapping error-compensation method based on fringe projection phase profilometry. First, the wrapped phase of the measured object is acquired via phase-shifting profiling. The wrapped phase map is then smoothed at multiple scales using Gaussian filters, and parallel Canny edge detection combined with phase gradient thresholding is applied to comprehensively capture both coarse and fine discontinuities. Morphological closing fills in breakpoints, followed by skeleton thinning and connectivity reconstruction to generate an edge band of defined width. Within this band, edge-preserving smoothing is performed using guided filtering or bilateral filtering, and the result is fused with the original phase through Gaussian weighting based on the distance to the skeleton. Finally, an ordered multi-frequency heterodyne unwrapping restores the absolute phase, maximally preserving true discontinuities while effectively correcting noise and detection errors. Experiments show that this method overcomes edge-induced phase jumps—with jump-error correction rates exceeding 96.7%—exhibits strong noise resilience under various conditions, and achieves measurement precision better than 0.06 mm. Full article

Low-temperature stress significantly limits wheat growth and productivity. Poly-γ-glutamic acid (γ-PGA) is an environmentally friendly green molecular material that plays an important role in plant growth and regulation; however, its protective mechanisms against cold stress in wheat remain poorly understood. In this study, the effect of γ-PGA on both chilling (4 °C) and freezing (−18 °C) resistance in wheat seedlings and its underlying mechanisms were comparatively studied. The results showed that the γ-PGA-treated seedlings exhibited a 128.81% higher survival rate after freezing stress and maintained significantly greater biomass accumulation under both stress conditions (62.44% and 26.56% higher dry weight under chilling and freezing stress, respectively). A physiological analysis revealed that γ-PGA enhanced osmoprotectant (proline and soluble sugars) accumulation and activated key antioxidant enzymes (SOD, POD, and APX). Then, an RNA-seq analysis identified 11,401 and 7721 differentially expressed genes under chilling and freezing stress, respectively, with 3598 common genes constituting a core cold-response network. KEGG and GO analyses demonstrated significant enrichment in pathways related to carbon metabolism, glutathione metabolism, phenylpropanoid–flavonoid biosynthesis, fatty acid metabolism, and cell wall organization. Notably, γ-PGA strongly upregulated key genes in phenylpropanoid–flavonoid metabolism (TraesCS2B02G615000 and TraesCS2B02G624400), glutathione metabolism (TraesCS1B02G127900), and lipid metabolism (TraesCS1B02G018700). These results provide comprehensive molecular insights into γ-PGA-mediated cold tolerance and support its potential application in sustainable wheat production under low-temperature stress conditions. Full article

The accurate estimation of shield attitude deviation is related to the quality of tunnel construction. However, the existing recurrent neural network (RNN)-based methods are unable to efficiently capture the spatial correlation between different timestamps (DT) and have poor prediction performance when handling drastically changing attitude data, which makes it difficult to estimate attitude deviation when attitude changes are frequent. This study proposes a shield machine attitude prediction model (ST-GC-GRU) based on a spatial–temporal graph. Different from the traditional attitude prediction methods, the method firstly introduces an improved GCN (ST-GCN: spatial–temporal graph) and the time decomposition technique to enhance its representation of the attitude change information, thus more rationally modeling the comprehensive spatial–temporal dependence of the shield structure operation data. The method demonstrates better prediction performance than previous methods under a large number of real data tests and effectively improves the low-confidence predictions of the prediction model when dealing with large attitude changes. The results indicate that the proposed method is better than the other seven prediction models in four attitude deviation values. The model and the research results can provide a reference for developing adaptive control technology in shield tunnel construction. Full article

Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the urban heat island effect and enhancing stormwater management, alongside benefits for public health and biodiversity. Effective GI implementation remains challenging, particularly in dense, rapidly urbanized mid-Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied trans-scalar methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project to bridge the theory–practice gap and enable pilot implementations in multiple Italian mid-Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, trafficked urban area projected to face “extremely strong heat stress” by 2050. Design interventions included spatial optimization, strategic species replacement, the creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area (+194%), a measurable reduction in the UTCI index (average ENVI-MET simulated reduction of 1.17 °C by 2030), and a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces. Full article

Large-scale mining of graphite, a crucial strategic mineral, generates substantial amounts of graphite tailings (GT). The stockpiling of this solid waste occupies vast land resources and poses persistent environmental risks due to potential heavy metal leaching. Repurposing GT into construction materials presents a promising solution, with its use as a partial replacement for fine aggregates in cementitious composites being one of the most effective methods. This review systematically consolidates current research on graphite tailings cement mortar (GTCM) and graphite tailings concrete (GTC). Due to its physicochemical properties comparable to natural sand, GT is suitable for producing building materials. Studies consistently demonstrate that a substitution level of 10% to 20% optimizes overall performance. This optimal range enhances particle packing, promotes cement hydration via pozzolanic activity, and refines the microstructure, leading to improved workability, superior mechanical strength, and enhanced durability, including resistance to permeability, freeze–thaw cycles, and chemical attacks. Moreover, the inherent carbon content imparts electrical conductivity to GTC, enabling functional applications like de-icing and structural health monitoring. The successful utilization of GT also extends to lightweight foamed and autoclaved aerated concrete. However, research on the structural behavior of GTC components remains limited. Preliminary findings on beams and columns are encouraging, but comprehensive studies on their seismic performance and design methodologies are urgently needed to facilitate the widespread engineering application of this sustainable material and mitigate the environmental impact of tailings accumulation. Full article

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces stochastic perturbations into the step-size evolution; (2) a mirror opposition-based learning strategy to actively inject structured population diversity; and (3) an adaptive adjustment mechanism for the Lévy flight parameter $\beta$ to enable phase-sensitive optimization behavior. The effectiveness of EPIMO is validated through a multi-stage experimental framework. Systematic evaluations on the CEC 2017 and CEC 2022 benchmark suites, alongside four classical engineering optimization problems (Himmelblau function, step-cone pulley design, hydrostatic thrust bearing design, and three-bar truss design), demonstrate its comprehensive superiority. The Wilcoxon rank-sum test confirms statistically significant performance improvements over its predecessor (PIMO) and a range of state-of-the-art and classical algorithms. EPIMO exhibits exceptional performance in convergence accuracy, stability, robustness, and constraint-handling capability, establishing it as a highly reliable and efficient metaheuristic optimizer. This research contributes a systematic, adaptive enhancement framework for projection-based metaheuristics, which can be generalized to improve other swarm intelligence systems when facing complex, constrained, and high-dimensional engineering optimization tasks. Full article

Background/Objectives: Plantar fasciitis (PF) is a leading cause of heel pain and functional impairment in adults. Laser therapy, in its low-intensity laser therapy (LLLT), high-intensity laser therapy (HILT), and photobiomodulation (PBMT) modalities, has been proposed as a non-invasive alternative, although its clinical effectiveness remains a subject of debate. The aim of this systematic review and meta-analysis was to evaluate the efficacy of laser therapy in reducing pain, improving function, and modifying fascial thickness in patients with PF. Methods: A comprehensive search was conducted in PubMed, Embase, and PEDro (last search: August 2025). Randomized controlled trials comparing laser therapies versus placebo or alternative physical interventions were included. Two reviewers performed study selection, data extraction, and risk of bias assessment using the PEDro scale. Random-effects meta-analyses were performed for pain (VAS), heel tenderness (HTI), function (FFI, AOFAS, ASQoL, SF-36), and fascial thickness, expressing effects as standardized mean differences (SMDs) or mean differences (MDs). Results: Thirteen trials with 784 participants were included. Laser therapy showed a significant improvement in heel tenderness (SMD = −0.40; 95% CI −0.71 to −0.09; I² = 0%). No significant differences were observed in overall pain (SMD = −0.18), function (SMD = 0.20), or fascial thickness (MD = −0.18 mm). Pain and function analyses showed high heterogeneity. Conclusions: Laser therapy may reduce heel tenderness in plantar fasciitis, but it does not consistently improve overall pain, function, or fascial thickness. Its use should be considered as a therapeutic adjunct and not as a primary intervention. Larger trials with standardized protocols are needed. Full article

The extensive use of prefabricated large-diameter steel-reinforced concrete (SRC) hollow tubular columns in major infrastructure projects creates a critical demand for efficient and reliable column-to-foundation connections with satisfactory seismic performance. To address this, three novel prefabricated connection details are proposed herein. A refined three-dimensional nonlinear finite element model was developed using ABAQUS to assess their mechanical behavior under quasi-static cyclic loading. The model was established based on widely accepted constitutive models, contact algorithms, and loading protocols consistent with relevant codes and international research. The results demonstrate that the proposed prefabricated connections significantly outperform conventional cast-in-place connections in terms of ultimate bearing capacity, with an increase of approximately 79%. A comprehensive parametric analysis was conducted, identifying an optimal design configuration comprising a socket depth of 600 mm, six embedded steel sections, an axial compression ratio of 0.1, and a hollow core radius of 600 mm, which achieves an optimal balance between mechanical performance and cost-effectiveness. These findings provide a reliable theoretical basis and practical guidance for designing and implementing high-performance prefabricated connections in engineering practice. Full article