Search Results (6,604)

Student dropout remains a persistent challenge in higher education, with substantial personal, institutional, and societal costs. We developed a modular dropout prediction pipeline that couples data preprocessing with multi-model benchmarking and a governance-ready explainability layer. Using 17,883 undergraduate records from a Moroccan higher education institution, we evaluated nine algorithms (logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), gradient boosting, Extreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and multilayer perceptron (MLP)). On our test set, XGBoost attained an area under the receiver operating characteristic curve (AUC–ROC) of 0.993, F1-score of 0.911, and recall of 0.944. Subgroup reporting supported governance and fairness: across credit–load bins, recall remained high and stable (e.g., <9 credits: precision 0.85, recall 0.932; 9–12: 0.886/0.969; >12: 0.915/0.936), with full TP/FP/FN/TN provided. A Shapley additive explanations (SHAP)-based layer identified risk and protective factors (e.g., administrative deadlines, cumulative GPA, and passed-course counts), surfaced ambiguous and anomalous cases for human review, and offered case-level diagnostics. To assess generalization, we replicated our findings on a public dataset (UCI–Portugal; tables only): XGBoost remained the top-ranked (F1-score 0.792, AUC–ROC 0.922). Overall, boosted ensembles combined with SHAP delivered high accuracy, transparent attribution, and governance-ready outputs, enabling responsible early-warning implementation for student retention. Full article

Although total knee replacements have an insignificant impact on patients’ mobility and quality of life, real-time performance monitoring remains a challenge. Monitoring the load over time can improve surgery outcomes and early detection of mechanical imbalances. Triboelectric nanogenerators (TENGs) present a promising approach as a self-powered sensor for load monitoring in TKR. A TENG was fabricated with dielectric layers consisting of Kapton tape and 3D-printed thermoplastic polyurethane (TPU) matrix incorporating CNT and BTO fillers, separated by an air gap and sandwiched between two copper electrodes. The sensor performance was optimized by varying the concentrations of BTO and CNT to study their effect on the energy-harvesting behavior. The test results demonstrate that the BTO/TPU composite that has 15% BTO achieved the maximum power output of 11.15 $\mathsf{\mu }$W, corresponding to a power density of 7 $\mathrm{mW}/{\mathrm{m}}^2$, under a cyclic compressive load of 2100 N at a load resistance of 1200 M$\Omega$, which was the highest power output among all the tested samples. Under a gait load profile, the same TENG sensor generated a power density of 0.8 $\mathrm{mW}/{\mathrm{m}}^2$ at 900 M$\Omega$. By contrast, all tested CNT/TPU-based TENG produced lower output, where the maximum generated apparent power output was around 8 $\mu$W corresponding to a power density of 4.8 $\mathrm{mW}/{\mathrm{m}}^2$, confirming that using BTO fillers had a more significant impact on TENG performance compared with CNT fillers. Based on our earlier work, this power is sufficient to operate the ADC circuit. Furthermore, we investigated the durability and sensitivity of the 15% BTO/TPU samples, where it was tested under a compressive force of 1000 N for 15,000 cycles, confirming the potential of long-term use inside the TKR. The sensitivity analysis showed values of 37.4 mV/N for axial forces below 800 N and 5.0 mV/N for forces above 800 N. Moreover, dielectric characterization revealed that increasing the BTO concentration improves the dielectric constant while at the same time reducing the dielectric loss, with an optimal 15% BTO concentration exhibiting the most favorable dielectric properties. SEM images for BTO/TPU showed that the 10% and 15% BTO/TPU composites showed better morphological characteristics with lower fabrication defects compared with higher filler concentrations. Our BTO/TPU-based TENG sensor showed robust performance, long-term durability, and efficient energy conversion, supporting its potential for next-generation smart total knee replacements. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

In large-scale distributed storage simulations, disk simulation plays a critical role in evaluating system reliability, scalability, and performance. However, the existing virtual disk technologies face challenges in supporting ultra-large capacities and high-concurrency workloads under constrained physical resources. To address this limitation, we propose an overcapacity mapping (OCM) virtual disk technology that substantially reduces simulation costs while preserving functionality similar to real physical disks. OCM integrates thin provisioning and data deduplication at the Linux Device Mapper layer to construct virtual disks whose logical capacities greatly exceed their physical capacities. We further introduce an SSD-based tiered asynchronous I/O strategy to mitigate performance bottlenecks under high-concurrency random read/write workloads. Our experimental results show that OCM achieves substantial space savings in scenarios with data duplication. In high-concurrency workloads involving small-block random I/O, cache acceleration yields up to 7.8× write speedup and 248.2× read speedup. Moreover, we deploy OCM in a Kubernetes environment to construct a Ceph system with 3 PB logical capacity using only 8.8 TB of physical resources, achieving 98.36% disk cost savings. Full article

Salinization of irrigation areas is a key environmental challenge faced by arid and semi-arid regions worldwide, and the complexity of natural environment and human activities increases the uncertainty of salinization distribution. This study takes the Xiaohaizi Irrigation Area in Kashgar, Xinjiang as the research location. Soil samples were collected before sowing and after harvesting in 2023 and analyzed. Using geostatistics and digital soil mapping techniques, the spatial distribution and temporal evolution of soil salinization in the region were finely characterized. The results showed that the soil salinization in Xiaohaizi Irrigation District was moderate to high, with mean salt contents of 8.29 g/kg in the 0–30 cm layer, 6.16 g/kg at 30–60 cm, and 4.80 g/kg at 60–100 cm before sowing, all indicating moderate to high salinity levels. The salt content showed a surface aggregation distribution with significant differences between different depths. The main ions that affect salinization are SO₄²⁻, Ca²⁺, Mg²⁺, Cl⁻, K⁺, and Na⁺. The 0–30 cm soil layer is mainly composed of mildly saline soil, and the degree of soil salinization decreases with the depth of the soil layer. After harvesting, the overall degree of salinization in the irrigation area intensified, and the spatial distribution of salinization was uneven. The degree of salinization was higher in the northwest and lower in the south. The impact of human activities on surface soil salinization is greater than that on deep soil. The areas where the degree of salinization in the 0–30, 30–60, and 60–100 cm soil layers undergoes transformation account for 57.18%, 33.15%, and 26.9%, respectively. This study reveals the complex dynamics of soil salinization in the Xiaohaizi irrigation area, providing scientific support for soil management and irrigation strategies in the region, and is of great significance for achieving sustainable development of oasis agriculture. Full article

Tunnel excavation typically induces disturbance to the surrounding soil. Advance grouting using small-diameter pipes can effectively mitigate surface settlement. Taking the mine-method tunnel at the southern end of Xiancun Station on Guangzhou Rail Transit Line 18 as the research object, this paper uses the Midas GTS NX three-dimensional finite element (FE) software and adopts the upper-lower excavation method that prioritizes the formation of an upper support closed loop to simulate and analyze the impact of the CRD method on tunnel excavation under different grouting layer thicknesses. The research indicates that the surface settlement curve exhibits a “U”-shape. The settlement value decreases as the thickness of the grouting layer increases; when the thickness increases from 1.2 m to 2.0 m, the maximum surface settlement decreases from 12.87 mm to 9.09 mm, with successive reductions of 1.30 mm, 1.11 mm, 0.81 mm, and 0.56 mm, corresponding to rates of 10.10%, 9.59%, 7.67%, and 5.6%. Increasing the thickness of the grouting layer can effectively control surface settlement; however, when the thickness reaches 2.0 m, the stress distribution undergoes a change. Specifically, the compressive stress at the arch waist increases to 1683.01 kPa, and plastic failure occurs in the surrounding rock. By comparing the numerical results with field monitoring data, it is determined that when the grouting layer thickness is 1.4 m and the elastic modulus is increased by 30% based on that of the upper-soft soil, the model prediction shows the highest consistency with the actual effect. Furthermore, it is suggested that the grouting layer thickness be increased to 1.6 m. This study delivers a scientific foundation for the design of grouting parameters and the optimization of construction schemes for tunnels in composite strata and is of importance to improving tunnel construction technology in underground rail transit. Full article

The human placenta is a complex organ that supports fetal development and is rich in extracellular matrix proteins and growth factors, making it suitable as a biomaterial in wound care. Placenta-derived amnion-only allografts have traditionally been used in the clinic, but they lack the structural and biochemical complexity of the full three-layer placental membrane, which includes the amnion, intermediate, and chorion layers. Advances in tissue engineering have enabled preservation of multiple layers, giving rise to multilayer placental-based Cellular and Acellular Matrix-like Products (CAMPs) such as Full-Thickness (FT; amnion, intermediate, chorion) and ACA (amnion, intermediate, chorion, amnion). Although these advanced CAMPs are increasingly applied clinically, their molecular composition has not been comprehensively defined. This study presents a global proteomic analysis of FT and ACA, complemented by targeted multiplex analysis of soluble proteins and an in vitro angiogenesis assay. Proteomic profiling identified 8908 structural and bioactive components, with 32.5% of proteins associated with tissue repair and remodeling pathways. Multiplex analysis confirmed accessibility of biologically relevant soluble factors. Endothelial tube formation assays further supported biological relevance, demonstrating that soluble proteins in FT and ACA support angiogenesis. These data provide a molecular characterization of multilayer CAMPs and underscore their potential to deliver durable wound coverage while supporting the local microenvironment. Full article

The widespread presence of bisphenol A (BPA), a persistent endocrine-disrupting pollutant, in aquatic environments poses significant ecological and health risks, necessitating its effective removal. However, conventional remediation technologies are often hampered by catalysts with narrow pH adaptability and poor stability. In this study, a novel catalyst, Zeolite-supported Cu₃Mn-layered double hydroxide (LDH), was fabricated using the co-precipitation method. The synthesized catalyst was applied to activate peroxymonosulfate (PMS), effectively enabling decomposition of BPA by advanced oxidation processes. The composite material was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and transmission electron microscopy (TEM), which confirmed the successful synthesis of the zeolite-supported Cu₃Mn-LDH. The catalyst exhibited high activity in both neutral and strongly alkaline environments, achieving complete degradation of 10 mg⋅L⁻¹ bisphenol A (BPA) within 40 min and a 98% total organic carbon (TOC) removal rate when both the PMS and catalyst were dosed at 0.15 g⋅L⁻¹. Singlet oxygen was detected as the primary reactive species responsible for BPA degradation, as verified by quenching experiments and EPR analysis, which also identified the presence of sulfate (SO₄^•−), hydroxyl (•OH), and superoxide (•O₂⁻) radicals. The catalyst exhibited excellent reusability, maintaining high catalytic efficiency over two consecutive cycles with minimal performance loss. Gas chromatography-mass spectrometry (GC-MS) analysis revealed five intermediate products, enabling the proposal of potential BPA degradation pathways. This work not only presents a novel synthetic approach for zeolite-supported LDH composites, but also offers a promising strategy for the efficient removal of BPA from aqueous systems through AOPs. Full article

This review article critically examines the fire performance of cross-laminated timber (CLT), a key structural material for sustainable construction, by synthesising recent advancements in both experimental and numerical research. It identifies a critical gap between experimental findings and numerical models, offering insights to refine future fire-safe design and research. The article assesses fire design strategies across major international standards and reviews experimental fire testing of CLT elements, highlighting how adhesives, protective cladding, layer thickness, load levels, and support conditions affect fire resistance. This article also summarises CLT compartment tests, focusing on how openings, ventilation size, and protective cladding affect fire dynamics and CLT degradation. A literature review of numerically modelled CLT specimens under fire load is compiled and evaluated based on several criteria, including material characterisation, mesh characteristics, and modelling procedures. Subsequently, the outcomes of two distinct approaches are evaluated, emphasising the disparities in the techniques employed and the difficulties inherent in performing more precise numerical simulations. The article will bridge and inform the gap between experimental tests and numerical analysis, focusing on identifying suitable approaches for such simulations. The study aims to provide a broader understanding of the topic and promote the development of fire-safe design and modelling of engineered timber construction using CLT. Full article

This study analyzes winds over the Red Sea (17 N, 39.5 E) and consequences for the northeast African climate in early summer (May–July). As the Indian SW monsoon commences, NNW winds > 6 m/s are channeled over the Red Sea between 2000 m highlands, forming a low-level jet. Although sea surface temperatures of 30C instill evaporation of 8 mm/day and surface humidity of 20 g/kg, the air mass above the marine layer is dry and dusty (6 g/kg, 100 µg/m³). Land–sea temperature gradients drive afternoon sea breezes and orographic rainfall (~4 mm/day) that accumulate soil moisture in support of short-cycle crops such as teff. Statistical analyses of satellite and reanalysis datasets are employed to reveal the mesoscale structure and temporal response of NE African climate to marine winds via air chemistry data alongside the meteorological elements. The annual cycle of dewpoint temperature often declines from 12C to 4C during the Indian SW monsoon onset, followed by dusty NNW winds over the Red Sea. Consequences of a 14 m/s wind surge in June 2015 are documented via analysis of satellite and meteorological products. Moist convection was stunted, according to Cloudsat reflectivity, creating a dry-east/moist-west gradient over NE Africa (13–14.5 N, 38.5–40 E). Diurnal cycles are studied via hourly data and reveal little change for advected dust and moisture but large amplitude for local heat fluxes. Inter-annual fluctuations of early summer rainfall depend on airflows from the Red Sea in response to regional gradients in air pressure and temperature and the SW monsoon over the Arabian Sea. Lag correlation suggests that stronger NNW winds herald the onset of Pacific El Nino. Full article

The isomerization of geraniol using natural, acid-modified minerals such as vermiculite presents a promising approach aligned with the principles of green chemistry. Vermiculite, a naturally abundant layered silicate mineral, was subjected to the acid activation and thoroughly characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). These methods allowed the evaluation of crystallinity, structural stability, and surface morphology, which are critical parameters in the heterogeneous catalysis. The catalytic performance of the modified vermiculite was examined in the transformation of geraniol under mild conditions. The study systematically investigated the influence of key process parameters—temperature, reaction time, and catalyst content—on the conversion of geraniol and products selectivities. Optimization using the response surface methodology (RSM), enabled the identification of conditions leading to high conversion of geraniol (up to 85%) and allowing us to obtain favorable selectivities toward linalool, thunbergol, and 6,11-dimethyl-2,6,10-dodecatrien-1-ol. The results indicate that the acid-treated vermiculite exhibits sufficient surface acidity to effectively catalyze isomerization and cyclization reactions, without requiring additional promoters or metal-based systems. Moreover, the use of RSM provided the efficient framework for optimization reaction conditions, reducing experimental workload, and enhancing process efficiency. This study demonstrates the viability of natural, low-cost minerals as environmentally friendly catalysts and supports their integration into sustainable and “green” chemical technologies. Full article

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z^0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation. Full article

Testicular stiffness is a potential indicator of spermatogenic activity. Herein, we investigated the relationship between testicular stiffness and intratesticular morphology in Syrian hamsters by using a robotic system with a micro-force sensor. Animals were divided into control, sham-operated, and surgically induced cryptorchidism groups. Testicular stiffness, testis weight and size, and Johnsen score data for sham and crypt groups were partially derived from our previous study and reanalysed. Testicular stiffness and histological parameters were analysed, including tunica albuginea thickness, seminiferous tubule occupancy, tubule diameter, intratubular cell-layer thickness, peritubular lamina propria thickness, and Leydig cell numbers. Compared with those of sham and normal controls, cryptorchid testes showed significantly lower stiffness and marked morphological changes, such as reduced tubule occupancy and diameter, thinner intratubular cell layers, thickened tunica albuginea and peritubular lamina propria, and increased numbers of Leydig cells. Decreased testicular stiffness and the Johnsen score, a standard index of spermatogenic function, were strongly related to these structural changes. These findings indicate that structural changes in the testes caused by impaired spermatogenesis are related to measurable differences in tissue stiffness. This study supports using mechanical properties as non-invasive quantitative indices to evaluate testicular function in animal models, offering a novel approach for future research in experimental andrology. Full article

The fracturing and driving oil technology used in shale oil provides a new approach for the development of offshore low-permeability reservoirs. However, the main control role of technical parameters is unclear, resulting in unsatisfactory accuracy and effectiveness of the enhanced oil recovery plan. For this reason, this study is based on the production and process data of five wells in the WZ oilfield. Fuzzy analytic hierarchical process analysis method (FAHP) was used to evaluate the parameter weights. Combined with numerical simulation technology, the evaluation results were verified by geological-engineering integration. The results show that in offshore low-permeability oilfields, the reservoir pressure coefficient has the greatest influence on the fracturing and oil repelling effect. The comprehensive weight reaches 0.450 compared to not adopting hydraulic fracturing oil displacement technology. This improves the recovery rate by 10.19% in 5 years. The surfactant concentration and the residual oil saturation of the reservoir rank are second, with a comprehensive weight of 0.219. Finally is the effective thickness of the reservoir, with a comprehensive weight of 0.113. In this study, the key parameters of fracturing and oil repelling in offshore low-permeability reservoirs are clarified. It provides theoretical basis and practical support for improving the success rate of well selection, layer selection and recovery capacity. Full article

The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts—room and door—implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments. Full article