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Students' commitment to online collaborative learning poses a significant challenge for higher education institutions. This study explores the factors that affect students' commitment to Learning Management System (LMS) groups. A conceptual framework is developed that integrates social identity theory, DeLone and McLean's information systems success model, and Hofstede's cultural theory. By adopting a quantitative research design, 367 responses were gathered from university students. The data was analyzed using covariance-based structural equation modelling (CB-SEM). The findings reveal that factors such as LMS group identification, adherence to LMS group norms, perceived system quality, perceived service quality, and perceived information quality positively influence students' commitment to the LMS group. Interestingly, collectivism as a cultural variable moderates the positive relationship between compliance with LMS group norms and commitment to LMS groups. Furthermore, uncertainty avoidance amplified the positive relationships that perceived system quality and information quality had with LMS group commitment. The model presented in this study offers significant predictive power concerning commitment to an LMS group. These findings will provide valuable insights for systems developers, higher education institutions, and instructors aiming to bolster online group commitment within LMS.

Abstract Facial forgery technology is advancing rapidly, leading to significant social security concerns. In recent years, as forgery technologies and types continue to emerge, many methods struggle to strike a balance between accuracy and robustness. Most existing methods rely on CNN to extract high-quality forged face clues but often overlook inherent forgery traces. Consequently, they may overfit the training dataset and perform poorly on data from diverse sources or be subjected to various post-processing operations. To address this challenge, we propose leveraging multi-scale texture information to expose subtle artifacts in RGB space. To achieve this, we devise a two-stream detection architecture that integrates texture features and RGB features. We analyze forgery traces using global large texture information and local detailed texture information separately. Additionally, we design a feature pyramid to fuse these two texture features and employ an attention mechanism to enhance the features of both streams. By examining forgery traces from multiple perspectives, we have developed an adaptive feature fusion module to facilitate interactive feature fusion between the two streams. We conduct extensive experiments on various benchmark datasets and compare our method with recent state-of-the-art (SOTA) methods to demonstrate its effectiveness. Our code will be provided at https://github.com/hryyyy/MST .

Abstract Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications.

The main aim is to investigate the influence of hybrid fiber Slurry Infiltrated Fiber Concrete (SIFCON) strengthening of beams loaded under pure torsion. The volume percentage of fiber was constant at 5%. Seven beams underwent pure torsion testing after being cast., two beams were taken as a reference (The first one without torsional reinforcement and the second beam with torsional reinforcement), and five beams were strengthened by a SIFCON jacket, One beam fully jacketed, and four beams with three-side jacket. Different types of fiber (Steel, Glass, and Hybrid) used to cast SIFCON. It found that the increase in ultimate torsion by about (163 - 537) % for strengthened beams (no torsional reinforcement) and (46 - 254)% for beams with torsional reinforcement. Finally, the fully jacket-strengthening beam with 5% from steel fiber was the most effective among strengthening beams to improve torsion strength. In addition, the results of cracking torque, twist angle, Energy Absorption (Toughness), Torsional Stiffness, and torsional strength appear enhanced when using SIFCON different types of fiber in strengthened beams.

Abstract Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain‐based Privacy‐preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.

Abstract Treating lung and prostate cancer cells is a major health problem that may be solved through the interactions of laser beams with nanoparticles. In the paper, Au-H2Ti3O7 nanotubes (NTs) are proposed as a treatment agent and the interactions of different laser beams with the nanostructure are considered to solve the mentioned health problem. Also, the NTs are employed to treat the cancers in dark conditions. The results are motivating because Au-H2Ti3O7 NPs do not affect healthy cells, while they strongly affect cancer cells, and the viability percentage of LNCap cells reaches 16% for incubation times of 48 h. Furthermore, treating LNCap cells using the irradiated Au-H2Ti3O7 NTs by NIR beam at 808 nm has no cytotoxicity, while cytotoxicity of 92% is obtained using an irradiation laser beam at 532 nm. Also, applying the laser beam at 635 nm to the NTs leads to a cytotoxicity of ∼53% in lung cancer (A549 cells). In total, the Au-H2Ti3O7 NTs have a selective effect on cancer cells and greatly reduce the viability in the given dark and irradiation conditions, leading to the introduction of them as a promising agent for the non-invasive treatment of prostate cancer and a moderate candidate for lung cancer therapy.

In this manuscript, we present a unique cogeneration system that combines diesel power generators with thermoelectric generators (TEGs), using the comparatively cool water in an integrated tank and hot exhaust gases to produce additional heat and power. By connecting a series of TEGs and a cold water tank to the diesel generator, the design enables the water to gradually warm up and become appropriate for usage in both domestic and commercial settings. We did a parametric study and created detailed thermal models to verify the viability of our idea. The findings indicate that the temperature differential across each TEG module and the total power output increase roughly linearly, based on the ratio of the diesel generator's power to the thickness of TEG to thermal conductivity. Additionally, the total power output increases linearly with the length of the TEG plate and declines exponentially with the height of the exhaust gas duct. The system may produce up to 3,223 W more electricity under certain circumstances, such as an exhaust gas duct height of 0.05 m, a TEG plate length of 1 m, and a generator output of 150 kW. This improvement could result in a 2.58 % reduction in fuel consumption and an equivalent increase in total power production when compared to conventional systems. The heater system recovers up to 22.4 kW/m² and can heat 100 liters of water in 0.2 h. According to these results, diesel generators and eventually diesel power plants might use less fuel and have more energy efficiency thanks to the suggested cogeneration technology.

BackgroundThe emergence of new SARS-CoV-2 variants makes it difficult to forecast potential epidemiological changes. This study investigates Saudi citizens’ perceptions of COVID-19 during the Omicron wave.MethodsWe conducted a cross-sectional study using an online survey and a convenience sample of 746 participants. The survey included questions about demographics, anxiety levels, and perception of COVID-19 during the Omicron wave.ResultsOur findings revealed that 27.3% of the participants believed that COVID-19 cases would decrease, while 30.2% believed that cases would increase; the remaining 42.5% were uncertain. When asked about the primary reasons for expecting a rise in COVID-19 cases, the two most frequently cited causes were non-adherence to prevention measures (74.7%) and the high transmissibility of the virus (66.7%). Conversely, when asked about the primary reasons for expecting a decrease in COVID-19 cases, participants cited the availability of free vaccines (60.3%), government measures (59.9%), compliance with preventive measures (57.4%), and health awareness programs (44.1%). Multivariate logistic regression analysis indicated that anxiety about COVID-19 (AOR = 1.23, 95% CI: 1.15–1.32) and education level (AOR = 1.58, 95% CI: 1.11–2.25) were significant predictors of respondents’ expectations of increases or decreases in COVID-19 cases (p

Despite recent rapid developments in computer programs used to improve buildings construction sector cost estimation techniques, costing models utilizable in a building construction project’s early planning stages remain scarce due to the lack of a simplified, integrated method that can operate with the limited data available. This research aimed to help buildings construction project stakeholders in Iraq to improve the accuracy of preliminary buildings project cost estimations based on the initial information available and historical buildings project data to evaluate a future project’s feasibility. A literature review of topics related to preliminary cost estimation, particularly buildings projects and factors affecting their cost, informed development of an integrated mathematical model based on a support vector machine to facilitate preliminary cost estimation during early buildings construction project planning utilizing relevant factors from both old and new projects (i.e., total floor area of buildings, construction duration, total number of floors, average floor height, location index, project quality standards, project complexity, and facilities provision). To enhance accuracy, the final cost forecasting model was modified using an inflation rate impact scenario to account for any future economic effects on anticipated building costs. This study concluded that the dominant factors behind cost variations between similar projects over different periods, and thus the primary factors for building cost estimation, are building area, average floor height, and number of floors. Thus, this study contributes to enhancing best practice in cost estimating for building construction projects in the pre-design, early planning phase, enabling decision makers to plan alternative options to enhance decisions on feasibility within the project’s constraints. Novelty arises from the features of the input factors used for the developed model to determine the preliminary budget required in the early planning stage taking into account construction sector inflation values for the following period.

IntroductionExcessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.MethodThe study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.ResultThe proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.DiscussionThese results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.

In hot climates, excessive heat gain through building roofs significantly increases cooling loads and energy consumption. This study aims to reduce roof heat gain by developing an innovative hybrid active slab with phase change material (PCM) panels and a geothermal well (HASP-GW). The HASP-GW system incorporates PCM panels and an active concrete slab embedded with water pipes as heat exchangers. Water at a constant temperature of 22 °C, sourced from a geothermal well, flows through these pipes to stabilize indoor temperatures. This research investigates the thermal performance of the HASP-GW with varying PCM transition temperatures and water flow rates using numerical simulations, validated against experimental results. The experimental setup involved two well-insulated test rooms (each 0.85 m × 0.85 m × 1 m); one equipped with the HASP-GW system and the other as a conventional comparison. Results showed that using the HASP-GW with a PCM transition temperature of 38–41 °C and a water flow rate of 1 L/min reduced the heat flux on inner surface of the roof, decrement factor, and inner surface temperature by 90.13 %, 0.127, and 16.24 °C, respectively, compared to a traditional roof. These findings indicate the potential of HASP-GW for energy-efficient roof cooling in hot climates.

Regarding a simple graph $ \Gamma $ possessing $ \nu $ vertices ($ \nu $-vertex graph) and $ m $ edges, the vertex-weight and weight of an edge $ e = uv $ are defined as $ w(v_{i}) = d_{ \Gamma}(v_{i}) $ and $ w(e) = d_{ \Gamma}(u)+d_{ \Gamma}(v)-2 $, where $ d_{ \Gamma}(v) $ is the degree of $ v $. This paper puts forward a novel graphical matrix named the edge-weighted adjacency matrix (adjacency of the vertices) $ A_{w}(\Gamma) $ of a graph $ \Gamma $ and is defined in such a way that, for any $ v_{i} $ that is adjacent to $ v_{j} $, its $ (i, j) $-entry equals $ w(e) = d_{ \Gamma}(v_{i})+d_{ \Gamma}(v_{j})-2 $; otherwise, it equals 0. The eigenvalues $ \lambda_{1}^{w}\ge \lambda_{2}^{w}\ge\ldots\ge \lambda_{\nu}^{w} $ of $ A_w $ are called the edge-weighted eigenvalues of $ \Gamma $. We investigate the mathematical properties of $ A_{w}(\Gamma) $'s spectral radius $ \lambda_{1}^{w} $ and energy $ E_{w}(\Gamma) = \sum_{i = 1}^{\nu}|\lambda_{i}^{w}| $. Sharp lower and upper bounds are obtained for $ \lambda_{1}^{w} $ and $ E_{w}(\Gamma) $, and the respective extremal graphs are characterized. Further, we employ these spectral descriptors in structure-property modeling of the physicochemical properties of polycyclic aromatic hydrocarbons for a set of benzenoid hydrocarbons (BHs). Detailed regression analysis showcases that edge-weighted energy outperforms classical adjacency energy in structure-property modeling of the physicochemical properties of BHs.

Deep beams are crucial for construction projects due to their load-carrying capacity, shear resistance, and architectural adaptability. Ultra-high strength concrete and ultra-high-performance concrete (UHPC) are used in their production. Basalt fiber is used as an alternative due to its corrosion resistance, tensile strength, and thermal stability. This study investigates the behavior of UHPC deep beams reinforced with basalt fibers. Three sets of 11 specimens were constructed without transverse reinforcement and reinforced with either fibers or steel fibers. The study also analyzes the impact of parameters like shear strength capacity, crack development, and load-deflection behavior on UHPC deep beams. The study discovered that the inclusion of basalt fibers in UHPC deep beam can effectively postpone the onset of diagonal cracks. Incorporating basalt fiber at concentrations of 0.5, 0.75, and 1.0% led to respective increases of 48.17, 70.07, and 86.66% in the diagonal fracture force, as compared to the inclusion of steel fibers which resulted in increases of 18.24, 56.93, and 98.54% in diagonal fracture loads. The ideal ratio for enhancing the maximum shear capacity was found to be 0.75% of basalt. This specific percent resulted in the highest measured force out of the three percentages that were examined. The addition of basalt fibers at concentrations of 0.5, 0.75, and 1.0% resulted in respective improvements of 11.62, 30.08, and 28.69% in the ultimate shear capacities. During that period, steel fibers significantly enhanced the ultimate shear capacity, resulting in an increase of 19.83, 34.49, and 55.24% compared to specimens without fiber reinforcement. Regarding the second parameter of this investigation, a drop in the shear span ratio is linked to an augmentation in shear capacity and a reduction in mid-span deflection to varying extents for both the utilization of basalt and steel fibers.

Low-frequency ultrasound waves (LFUWs) are applied in various medical treatments, but their effects on blood proteins’ molecular structure are not well understood. This study explores how LFUWs alter blood protein structures, utilizing ultraviolet-visible (UV–vis), Raman, and Fourier transform infrared (FTIR) spectroscopies. Blood samples from five volunteers were subjected to LFUWs for periods of 0, 5, 10, 15, and 20 min. Multivariate analyses, including hierarchical cluster analysis (HCA) and principal components analysis (PCA), were performed to distinguish between the spectroscopic data of control samples and those exposed to LFUWs. Results from UV–vis spectroscopy indicated hemolysis and changes in hemoglobin (Hb) and amino acids after more than 10 min of LFUW exposure. Raman spectroscopy showed a negative correlation between LFUW exposure time and intensity ratio, hinting at Hb deoxygenation and structural changes. FTIR spectroscopy revealed an increase in α-helices and a decrease in random coils, β-sheets, and turns in samples exposed to 10 min or more of sonication. These findings suggest that LFUW exposure could cause blood protein denaturation, likely through localized hyperthermia induced by ultrasound waves. This study highlights the potential of LFUWs to induce protein denaturation and demonstrates the effectiveness of UV–vis, Raman, and FTIR spectroscopy in investigating the impacts of ultrasound on biomolecular structures.