Search Results (2,369)

Taking the leap to the secondary and tertiary generations of the missions to Mars, a comprehensive outline was presented for a cluster of Martian Habitat Units (MHUs) designed for long-term settlements of research crew in Melas Chasma, Valles Marineris, Mars. Unlike initial exploration missions, where primary survival is ensured through basic engineering solutions, this concept targets later-stage missions focused on long-term human presence. Accordingly, the MHUs are designed not only for functionality but also to support the social and cultural well-being of scientific personnel, resulting in larger and more complex structures than those typically proposed for early-stage landings. To address the construction and structural integrity of the MHUs, the current work presents a comprehensive analysis of the feasibility of semi-3D-printed structural systems using in situ material to minimize the cost and engineering effort of logistics and construction of the units. Regolith-based additive manufacturing was utilized as the primary material, and the response of the structure, not only to the gravitational loads but also to those applied from the exterior flow field and wind pressure distributions, was simulated, as well as the considerations regarding the contribution of the extreme interior/exterior pressure differences. The full analyses and structural results are presented and discussed in this manuscript, as well as insights on manufacturing and its feasibility on Mars. The analyses demonstrate the feasibility of constructing the complex architectural requirements of the MHUs and their cost-effectiveness through the use of in situ resources. The manuscript presents an iterative structural optimization process, with results detailed at each step. Structural elements were modeled using FEM-based analysis in Karamba-3D to minimize near-yielding effects such as buckling and excessive displacements. The final structural system was integrated with the architectural design to preserve the intended spatial and functional qualities. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

Background: Compound lipophilicity is a fundamental physicochemical property for determining the pharmacokinetic and pharmacodynamic profiles of therapeutic substances. It is successfully used in the early stages of drug candidates’ design and development. Aim: Taking into account the importance of this parameter, we aimed to assess and compare the utility of a hybrid procedure based on calculation methods and an experimental one for rapid and simple estimation of the lipophilicity of selected neuroleptics such as fluphenazine, triflupromazine, trifluoperazine, flupentixol and zuclopenthixol and their potential new derivatives. Methods: Log P values of the studied compounds were predicted by means of different platforms and algorithms: AlogPs, ilogP, XlogP3, WlogP, MlogP, milogP, logPsilicos-it, logP_consensus, logP_chemaxon and logP_ACD/Labs. The experimental determination of lipophilicity was carried out by reverse-phase thin-layer chromatography (RP-TLC) using three types of stationary phases—RP-2F₂₅₄, RP-8F₂₅₄ and RP-18F₂₅₄—and mobile phases consisted of acetone, acetonitrile and 1,4-dioxane as organic modifiers. Results: Our results provide a confident proposal of optimal chromatographic conditions to experimentally determine the lipophilicity of neuroleptic drugs, including new derivatives. Conclusions: Additionally, for the first time, the paper shows the application of selected topological indices in determining lipophilicity factors and other ADMET parameters of neuroleptics and, in the future, the newly synthesized quinoline derivatives of the studied compounds. Full article

Code smells are code structures that indicate a potential issue in code design or implementation. These issues could affect the processes of code testing and maintenance, and overall software quality. Therefore, it is important to detect code smells in the early stages of software development to enhance system quality. Most studies have focused on detecting code smells of a single programming language. This article explores TL for cross-language code smell detection, where Java is the source, and both C# and Python are the target datasets, focusing on Large Class, Long Method, and Long Parameter List code smells. We conducted a comparison study across two transfer learning approaches—instance-based (Importance Weighting Classifier, Nearest Neighbors Weighting, and Transfer AdaBoost) and parameter-based (Transfer Tree, Transfer Forest)—with various base models. The results showed that the instance-based approach outperformed the parameter-based approach, particularly with Transfer AdaBoost using ensemble learning base models. The Transfer AdaBoost approach with Gradient Boosting and Extra Trees achieved consistent and robust results across both C# and Python, with an 83% winning rate, as indicated by the Wilcoxon signed-rank test. These findings underscore the effectiveness of transfer learning for cross-language code smell detection, supporting its generalizability across different programming languages. Full article

In recent years, the deterioration of social infrastructure such as bridges has become a serious issue in many countries around the world. To maintain the functionality of aging bridges over the long term, it is necessary to conduct regular inspections, detect damage at an early stage, and perform timely repairs. However, inspections require significant cost and time, and ensuring the safety of inspectors remains a major challenge. As a result, inspection using robots has attracted increasing attention. This study focuses on a wire-driven bridge inspection robot designed to inspect the underside of bridge girders. To use this robot, wires must be installed in the space beneath the girders. However, it is difficult to install wires over areas such as rivers. To address this problem, we developed a robotic arm capable of throwing a spear attached to a string. In order to throw the spear accurately to the target location, a three-dimensional dynamic model of the spear in flight was constructed, considering the tension of the string. Using this model, we accurately estimated the required throwing conditions and confirmed that the robotic arm could successfully throw the spear to the target location. Full article

The consistent quality control of ultrapure water (UPW) in semiconductor manufacturing depends on removing trace organonitrogen compounds such as urea. Due to its high solubility, chemical stability, and neutral polarity, urea is inadequately removed by conventional processes. Even at low concentrations, it elevates total organic carbon (TOC) and reduces electrical resistivity. The use of reclaimed water as a sustainable feed stream amplifies this challenge because its nitrogen content is variable and persistent. Conventional methods such as reverse osmosis, ultraviolet oxidation, and ion exchange remain limited in treating urea due to its uncharged, low-molecular-weight nature. This review examines the performance and limitations of these processes and explores electrochemical oxidation (EO) as an alternative. Advances in EO are analyzed with attention to degradation pathways, electrode design, reaction selectivity, and operational parameters. Integrated systems combining EO with membrane filtration, adsorption, or chemical oxidation are also reviewed. Although EO shows promise for selectively degrading urea, its application in UPW production is still in its early stages. Challenges such as low conductivity, byproduct formation, and energy efficiency must be addressed. The paper first discusses urea in reclaimed water and associated removal challenges, then examines both conventional and emerging treatment technologies. Subsequent sections delve into the mechanisms and optimization of EO, including electrode materials and operational parameters. The review concludes with a summary of main findings and a discussion of future research directions, aiming to provide a comprehensive foundation for validating EO as a viable technology for producing UPW from reclaimed water. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article

The tight sandstone gas reservoirs of the Xujiahe Formation are critical targets for tight gas exploration and development in the Sichuan Basin. While Class I reservoirs have been successfully developed using staged volume fracturing technology, efforts are being increasingly directed toward Class II and III matrix-type blocks. These reservoirs are characterized by a low permeability, high geo-stress differentials, strong heterogeneity, and limited fracture development. These properties result in several challenges, including ambiguous gas production sources, low reservoir utilization rates, significant variability in horizontal well performance, and rapid early-stage production decline—all of which hinder the effective development of matrix-type reservoirs. This study examines two representative fractured wells, Xin 8-5H and Xinsheng 204-1H, located in Class II and III blocks of the Xujiahe Formation gas reservoir. To identify gas production sources, we establish full-fracturing-section productivity models. Furthermore, accounting for variations in geological characteristics, we develop distinct productivity models for three key zones, the matrix area, fracture area, and fault area, to evaluate the productivity controls. The findings reveal that well Xin 8-5H primarily produces gas from the matrix and fault zones, whereas well Xinsheng 204-1H derives most of its production from the matrix and natural fractures. In matrix-dominated zones, generating complex fracture networks enhances productivity. An optimal cluster spacing of approximately 14 m ensures broad pressure sweep coverage while maintaining effective inter-cluster fracture connectivity. Additionally, natural fractures in the Xu-2 matrix reservoirs play a vital role in fluid communication. To maximize reservoir contact, well trajectories should be designed such that natural fractures are oriented either parallel or perpendicular to the wellbore, thereby improving lateral and vertical development. Near fault zones, adjusting cluster spacing to 14–25 m—while keeping the distance between faults and fracturing stages below 50 m—effectively connects faults and substantially increases production. This study introduces a systematic methodology for identifying gas sources in matrix reservoirs and optimizes key productivity-influencing parameters. The results provide both theoretical insights and practical strategies for the efficient development of Xu-2 matrix reservoirs. Full article

The advancement of sensor technologies enables the measurement of high-quality continuous blood pressure signals, which has become an important area in healthcare. The development of such application-specific sensors can be time-consuming, expensive, and difficult to test or validate with known and consistent waveforms. In this manuscript, an open-source blood pressure waveform simulator with a Python validation package is described. The core part, a 3D-printed cam, can be generated based on real blood pressure waveforms. The validation software framework compares in detail the waveform used to design the cam with the time series from the sensor being validated. The simulator was validated using a 3D force sensor. The RMSE of accuracy was 1.94 (44)–2.74 (63)%, and the Pearson correlation with the nominal signal was 99.84 (13)–99.39 (18)%. As for precision, the RMSE of the repeatability of cam rotations was 1.53 (71)–2.13 (116)% and the Pearson correlation was 99.85 (16)–99.59 (57)%. The presented simulator proved to be robust and accurate in short- and long-term use, as it produced the signal waveform reliably and with high fidelity. It reduces development costs for early-stage sensor development and research, offering a solution that is easy to manufacture yet capable of continuously outputting human arterial blood pressure waveforms spanning multiple consecutive cardiac cycles. Full article

Background: Latent tuberculosis infection (LTBI) is a major global health concern, particularly among individuals with chronic kidney disease (CKD), who are at increased risk of reactivation due to impaired immunity and frequent exposure to immunosuppressive therapies. Despite growing reliance on interferon-gamma release assays (IGRAs) such as QuantiFERON-TB Gold In-Tube (QFT-GIT) in BCG-vaccinated populations, data on IGRA performance across CKD stages remain limited in resource-limited settings. Objective: To determine the prevalence of LTBI and indeterminate IGRA results across CKD stages in a Thai population and assess the clinical utility of IGRA in this context. Materials and Methods: We conducted a cross-sectional study among 785 Thai adults receiving care at a national infectious disease institute, including diabetes clinic patients, hospital staff, and individuals on hemodialysis. Each participant underwent QFT-GIT testing, and the CKD stage was classified using the estimated glomerular filtration rate (eGFR) closest prior to testing. Results: Overall IGRA positivity was 22.2%, peaking in CKD stage G3 (31.6%) and declining in stage G5 (11.0%), where indeterminate results were also highest (6.8%). Limitations: Single-center design and lack of confirmatory testing may limit generalizability. Conclusions: IGRA performance is reliable in early-to-moderate CKD but less so in advanced stages. LTBI is prevalent in CKD stages G2–G4, supporting stage-specific approaches to LTBI screening and caution against overreliance on IGRA in advanced renal impairment. Full article

Capsule-based self-healing technologies offer a promising solution to extend pavement service life without requiring external activation. The effect of the capsule content on the mechanical behaviour of self-healing asphalt mixtures still needs to be understood. This study presents a numerical evaluation of the isolated effect of incorporating capsules containing encapsulated rejuvenators, at different volume contents, on the stiffness and strength of asphalt mixtures through a three-dimensional discrete-based programme (VirtualPM3DLab), which has been shown to predict well the experimental behaviour of asphalt mixtures. Uniaxial tension–compression cyclic and monotonic tensile tests on notched specimens are carried out for three capsule contents commonly adopted in experimental investigations (0.30, 0.75, and 1.25 wt.%). The results show that the effect on the stiffness modulus progressively increases as the capsule content grows in the asphalt mixture, with a reduction ranging from 4.3% to 12.3%. At the same time, the phase angle is marginally affected. The capsule continuum equivalent Young’s modulus has minimum influence on the overall rheological response, suggesting that the most critical parameter affecting asphalt mixture stiffness is the capsule content. Finally, while the peak tensile strength shows a maximum reduction of 12.4% at the highest capsule content, the stress–strain behaviour and damage evolution of the specimens remain largely unaffected. Most damaged contacts, which mainly include aggregate–mastic and mastic–mastic contacts, are highly localised around the notch tips. Contacts involving capsules remained intact during early and intermediate loading stages and only fractured during the final damage stage, suggesting a delayed activation consistent with the design of healing systems. The findings suggest that capsules within the studied contents may have a moderate impact on the mechanical properties of asphalt mixtures, especially for high-volume contents. For this reason, contents higher than 0.75 wt.% should be applied with caution. Full article

Ensuring the safety of building occupants during emergency evacuations is a critical aspect of building design. The spatial configuration and functional layout of buildings significantly influence overall evacuation efficiency. However, accurately assessing evacuation performance based on spatial characteristics remains challenging. This study proposes a weighted network analysis approach to evaluate the evacuation efficiency of buildings. It establishes the “Space-to-Network” diagram translation principles for converting spatial configurations into graph-based representations, defines analytical indicators for evacuation-weighted networks, and introduces a systematic methodology and workflow. A case study demonstrates the effectiveness of this approach, showing that the average relative deviation from evacuation simulation results is less than 10%. The method is particularly well suited for evaluating designs during the early stages. This research offers a novel perspective for evacuation analysis and provides a concise and reliable tool for the quantitative evaluation and performance optimization of building evacuation space. Full article

This study critically examines the integration of artificial intelligence (AI) into education through the lens of Marx’s theory of alienation, engaging with contemporary critiques of digital capitalism and academic labour. Drawing on an exploratory survey of 395 educators in Northern Cyprus, a context of early-stage AI adoption, the paper identifies four distinct forms of alienation exacerbated by AI: from the product of academic labour, from the educational process, from professional identity (species-being), and from interpersonal relations. Findings suggest that while educators who view AI more positively tend to report lower levels of alienation, particularly with respect to their pedagogical outputs, this association is tentative due to the low reliability of the AI perception scale (Cronbach’s α = 0.42). The results, therefore, serve as hypothesis-generating rather than conclusive. Situating the empirical findings within broader critiques by Noble, Hall, Preston, and Komljenovic, the study highlights how algorithmic governance, commercial platform logics, and data-driven performance regimes threaten teacher autonomy, creativity, and relationality. The paper concludes with a call for participatory governance, ethical oversight, and human-centred design to ensure that AI integration supports, not supplants, educators. In doing so, it contributes to critical debates on the ethical sustainability of digital education under conditions of intensifying automation. Full article

This study proposes an improved formulation of the blade element momentum theory (BEMT) to enhance its robustness and versatility for urban/advanced air mobility (UAM/AAM) applications. A new velocity factor was introduced to eliminate numerical singularity issue under low inflow velocity conditions. The BEMT framework was further extended and modified to account for non-axial inflow and descent flight conditions. The proposed approach was validated for an isolated propeller case by comparing the results with wind tunnel test data and the computational fluid dynamics (CFD) based on both the overset mesh and sliding mesh methods. The improved BEMT provided reliable accuracy even in low inflow velocity conditions where basic BEMT fails to converge, and yielded reasonable performance predictions with respect to the sliding mesh results. The practicality of the method was confirmed through further application studies such as analyzing on the tilt propeller of single-seated UAM along its mission profile and constructing a propeller performance database for the lift and propulsion propellers of a lift and cruise type 5-seated UAM. The improved BEMT exhibited satisfactory engineering-level accuracy for various flight conditions, with prediction errors within 14% of the CFD results. The results and observations indicate that the proposed BEMT framework is suitable for use in the early design stages, performance analysis, and construction of a performance database, for distributed propulsion aircraft, such as eVTOL and UAM/AAM. Full article

Canopy coverage is a key indicator for judging maize growth and production prediction during the seedling stage. Researchers usually use deep learning methods to estimate canopy coverage from maize images, but fully supervised models usually need pixel-level annotations, which requires lots of manual labor. To overcome this problem, we propose ASLNet (Automatic Scribble Labeling-based Semantic Segmentation Network), a weakly supervised model for image semantic segmentation. We designed a module which could self-generate scribble labels for maize plants in an image. Accordingly, ASLNet was constructed using a collaborative mechanism composed of scribble label generation, pseudo-label guided training, and double-loss joint optimization. The cross-scale contrastive regularization can realize semantic segmentation without manual labels. We evaluated the model for label quality and segmentation accuracy. The results showed that ASLNet generated high-quality scribble labels with stable segmentation performance across different scribble densities. Compared to Scribble4All, ASLNet improved mIoU by 3.15% and outperformed fully and weakly supervised models by 6.6% and 15.28% in segmentation accuracy, respectively. Our works proved that ASLNet could be trained by pseudo-labels and offered a cost-effective approach for canopy coverage estimation at maize’s seedling stage. This research enables the early acquisition of corn growth conditions and the prediction of corn yield. Full article