Search Results (7,658)

Background/Objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation and incomplete response to conventional therapies. Although biologics have advanced UC management, many patients with moderate-to-severe disease experience treatment failure, relapse, or adverse effects. This review evaluates the pharmacology, efficacy, and safety of oral Janus kinase (JAK) inhibitors—tofacitinib, upadacitinib, and filgotinib—to guide their clinical use in UC. Methods: A comprehensive literature review was conducted using the PubMed, Embase, Cochrane, and Web of Science databases to identify relevant studies on JAK inhibitors in UC. The review included Phase 3 randomized controlled trials (RCTs), real-world observational studies, and recent network meta-analyses. We assessed pharmacologic profiles, clinical efficacy, and safety data for tofacitinib, upadacitinib, and filgotinib. Additionally, we reviewed emerging pipeline agents and future directions in oral immunomodulatory therapy for UC. Results: All three agents demonstrated efficacy in the induction and maintenance of remission. Upadacitinib showed superior performance, including rapid symptom control, high clinical remission rates, and favorable long-term outcomes in both biologic-naïve and -experienced patients. Tofacitinib offered strong efficacy, particularly in early response, but was associated with higher risks of herpes zoster and thromboembolic events. Filgotinib provided moderate efficacy with a favorable safety profile, making it suitable for risk-averse populations. Meta-analyses consistently ranked upadacitinib highest in clinical efficacy and onset of action. Conclusions: JAK inhibitors offer effective and convenient oral treatment options for moderate-to-severe UC. Upadacitinib emerges as a high-efficacy agent; tofacitinib and filgotinib remain valuable based on patient-specific risk profiles. Future studies are needed to clarify optimal sequencing, long-term safety, and the role of emerging agents or combination therapies. Full article

Horizontal directional drilling (HDD) can be utilized in a submarine cable landing operation to solve the problems of a deficient buried depth and a limited route. In this study, a numerical model of the pullback process of a submarine cable using HDD technology is established based on the commercial finite element method platform OrcaFlex 11.3, which is validated using the in situ measured data of an HDD operation project for a pipeline. The effects of the crossing length, incident angle, and pullback velocity of the cable on the effective tension in the cable are investigated and analyzed. The results indicate that an increase in the crossing length and incident angle can significantly enhance the tension in the cable. Under the specific conditions in the Zhoushan islands, the maximum crossing length and incident angle are 1700 m and 35°, respectively. The pullback velocity has a minor influence on the tension in the cable, and an extremely large velocity might lock the cable during its pullback operation. The permissible values derived in this study can provide valuable information to similar engineering cases and projects. Full article

This study investigates the thermal and residual stress development in multi-layer lined pipe welding through numerical simulation and experimental validation. The focus is on the weld overlay/liner transition region, a critical area prone to stress concentrations and fatigue crack initiation. Using finite element analysis (FEA) with the Goldak double-ellipsoidal heat source model, the research examines the temperature evolution, residual stress distribution, and deformation characteristics during the welding process. Key findings reveal that the peak temperature in the weld overlay region reaches 3045.2 °C, ensuring complete metallurgical bonding. Residual stresses are predominantly tensile near the three-phase boundary, with maximum von Mises stress observed in the base pipe at 359.30 MPa. This study also employs Response Surface Methodology (RSM) to optimize welding parameters, achieving a 20.5% reduction in residual axial stress and a 58.1% reduction in residual circumferential stress. These results provide valuable insights for optimizing welding processes, improving quality control, and enhancing the long-term reliability of bimetallic composite pipelines. Full article

Traffic sign classification (TSC) based on deep neural networks (DNNs) plays a crucial role in the perception subsystem of autonomous driving systems (ADSs). However, studies reveal that the TSC system can make dangerous and potentially fatal errors under adversarial attacks. Existing defense strategies, such as adversarial training (AT), have demonstrated effectiveness but struggle to generalize across diverse attack scenarios. Recent advancements in self-supervised learning (SSL), particularly adversarial contrastive learning (ACL) methods, have demonstrated strong potential in enhancing robustness and generalization compared to AT. However, conventional ACL methods lack mechanisms to ensure effective defense transferability across different learning stages. To address this, we propose a robust pre-training–fine-tuning algorithm for meta-adversarial defense (RPF-MAD), designed to enhance the sustainability of adversarial robustness throughout the learning pipeline. Dual-track meta-adversarial pre-training (Dual-MAP) integrates meta-learning with ACL methods, which improves the generalization ability of the upstream model to different adversarial conditions. Meanwhile, adaptive variance anchoring robust fine-tuning (AVA-RFT) utilizes adaptive prototype variance regularization to stabilize feature representations and reinforce the generalizable defense capabilities of the downstream model. Leveraging the meta-adversarial defense benchmark (MAD) dataset, RPF-MAD ensures comprehensive robustness against multiple attack types. Extensive experiments across eight ACL methods and three robust fine-tuning (RFT) techniques demonstrate that RPF-MAD significantly improves both standard accuracy ($SA$) by 1.53% and robust accuracy ($RA$) by 2.64%, effectively enhances the lifelong adversarial resilience of TSC models, achieves a 13.77% improvement in the equilibrium defense success rate ($EDSR$), and reduces the attack success rate ($ASR$) by 9.74%, outperforming state-of-the-art (SOTA) defense methods. Full article

To address the current limitations of spaceborne solid-state storage systems that cannot effectively support the parallel storage of multiple high-speed data streams, the throughput bottleneck of NAND FLASH-based solid-state storage systems was analyzed in relation to the high-speed data input requirements of payloads. A four-stage pipeline operation and bus parallel expansion scheme was proposed to enhance the throughput. Additionally, to support the parallel storage of multichannel data and continuity of pipeline loading, the shortcomings of existing caching schemes were analyzed, leading to the design of a storage system based on Synchronous Dynamic Random Access Memory (SDRAM). Model simulations indicate that, under extreme conditions, the proposed scheme could continuously receive and cache multiple high-speed file data streams into the SDRAM. File data were dynamically written into FLASH based on the priority and status of each partition cache autonomously, without overflow during caching. The system eventually entered a regular dynamic balance scheduling state to achieve parallel reception, caching, and autonomous scheduling of storage for multiple high-speed payload data streams. The data throughput rate of the storage system can reach 4 Gbps, thus satisfying future requirements for multichannel high-speed payload data storage in spaceborne solid-state storage systems. Full article

This paper discusses the energy-saving control method of a pipeline network system based on reinforcement learning and a genetic algorithm and compares it with traditional control methods such as constant-pressure control and non-frequency conversion control. The purpose is to improve the operational efficiency of an offshore oil and gas field water injection system. This paper simulates and verifies the experimental platform of a water injection system pipe network in offshore oil and gas fields and evaluates the optimization effect of different control strategies under different flow rates. The experimental results reveal that under a varying flow rate, the water injection system harnessing the GA and RL exhibits a remarkable energy-saving advantage over traditional control methods. Specifically, the GA strategy achieves an average energy-saving rate of 22.51%, with a maximum energy-saving rate of 38.14% under low flow rate, while the RL strategy attains an average energy-saving rate of 18.39%. These methodologies not only furnish novel solutions for the real-time optimal scheduling of water injection systems in offshore oil and gas fields but also proffer practical guidance, thereby paving the way for technological advancement and sustainable development in the industry. Full article

Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives—especially in under-resourced languages. Full article

The rapid growth of genomic sequence datasets in a FASTQ format calls for efficient storage and transmission solutions. Compression–decompression algorithms for streaming applications offer a promising potential to address these challenges. In this paper, we present a novel Pipelined Lossless Reference-free Compression (PLORC) architecture designed specifically for streaming genomic data in a FASTQ format. The proposed PLORC architecture consists of several submodules optimized for the structure of FASTQ files, maintaining the balance between the compression ratio (CR) and throughput rate (TPR). To verify the PLORC architecture in hardware, we implemented the PLORC compressor and decompressor in FPGA (field-programmable gate array). The experimental results across various open-source genomic datasets reveal that our PLORC compressor achieved about a 440 MB/s throughput rate, which was higher than the tested Gzip, LZ4, and Zstd compressors. In addition, the PLORC decompressor achieved a throughput rate matching that of the compressor. Additionally, the PLORC achieved competitive compression ratios with some well-known non-streaming compression algorithms. Full article

Diffusion-based approaches have recently emerged as powerful alternatives to GAN-based virtual try-on methods, offering improved detail preservation and visual realism. Despite their advantages, the substantial number of parameters and intensive computational requirements pose significant barriers to deployment on low-resource platforms. To tackle these limitations, we propose a diffusion-based virtual try-on framework optimized through feature-level knowledge compression. Our method introduces MP-VTON, an enhanced inpainting pipeline based on Stable Diffusion, which incorporates improved Masking techniques and Pose-conditioned enhancement to alleviate garment boundary artifacts. To reduce model size while maintaining performance, we adopt an attention-guided distillation strategy that transfers semantic and structural knowledge from MP-VTON to a lightweight model, LiteMP-VTON. Experiments demonstrate that LiteMP-VTON achieves nearly a 3× reduction in parameter count and close to 2× speedup in inference, making it well suited for deployment in resource-limited environments without significantly compromising generation quality. Full article

The increasing demand for sustainable and autonomous monitoring solutions in critical infrastructure has driven interest in Green Internet of Things (G-IoT) systems. This paper presents an analytical and experimental framework for designing energy-efficient, self-sustaining pipeline monitoring systems that leverage renewable energy harvesting and low-power operation techniques. We propose a hybrid approach combining solar energy harvesting with energy-saving strategies such as adaptive sensing, duty cycling, and distributed computing to extend the lifetime of IoT nodes without human intervention. Using real-world irradiance data and energy profiling from a prototype testbed, we analyze the impact of solar panel sizing, energy storage capacity, energy-saving strategies, and energy leakage on the energy balance of IoT nodes. The simulation results show that, with optimal dimensioning, harvested solar energy can sustain pipeline monitoring operations over multi-year periods, even under variable environmental conditions. We investigated the influence of design parameters such as duty cycling, solar panel area, the capacity of the energy storage system, and the energy leakage coefficient on energy performance metrics such as the autonomy or lifetime of the node (time required to drain all the stored energy), which is an important design object. This framework provides practical design insights for the scalable deployment of G-IoT systems in energy-constrained outdoor environments. Full article

Cured-in-place pipe (CIPP) technology is a trenchless rehabilitation method for damaged pipelines in which a resin-saturated liner (often a fiber-reinforced type) is inserted into a host pipe and cured in situ, typically using a UV light beam or steam. This study investigates the influence of selected photoreactive diluents on the photopolymerization process of a styrene-free vinyl ester resin designed for the CIPP applications by evaluating the rheological properties, photopolymerization kinetics (photo-DSC), thermal characteristics (DSC), crosslinking density (gel content), and mechanical properties of thick (15 mm) UV-cured layers. The tested diluents included monofunctional (i.e., methyl methacrylate and vinyl neodecanoate), difunctional (1,6-hexanediol diacrylate, aliphatic urethane acrylates, and an epoxy acrylate), and trifunctional monomers (trimethylolpropane triacrylate, pentaerythritol triacrylate, and trimethylolpropane ethoxylate triacrylate). The key findings demonstrate that the addition of pentaerythritol triacrylate (the most attractive diluent) increases the flexural strength (+6%) and deflection at strength (+28%) at the unchanged flexural modulus value (ca. 2.1 GPa). The difunctional epoxy acrylate caused an even greater increase in the deflection (+52%, at a 5% increase in the flexural strength). Full article

Over the past few decades, numerous efforts have been made to increase the proportion of women in the construction industry, coupled with various calls for legislation and rules to prohibit gender discrimination. Despite these efforts, minimal progress has been noticed in the construction industry. While recruitment remains crucial, the current culture in construction reveals a knowledge gap in recruitment and retention in employment—a concept known as a ‘leaky pipeline’. Lack of awareness of career options and the challenges of working in a male-dominated, occasionally discriminatory workplace are some of the significant barriers to attracting and keeping women in the construction industry. Much of the research in South Africa shows that most construction companies employed few women but only in lower secretarial and administrative positions. Therefore, this study investigated the barriers facing women’s entry and retention in construction-related employment in South Africa using fuzzy synthetic evaluation (FSE) to understand and prioritise the barriers. Data were collected through the administration of online and paper-based questionnaires. The results of the analysis show that the barriers in the order of criticality include support and empowerment issues (SEs), educational/academic-related barriers (ABs), barriers from professional conditions and work attributes (BPs), social perception and gender stereotype barriers (SPs), professional perceptions and gender bias (PP), and individual confidence/interest/awareness/circumstance-related barriers (IBs), respectively. Based on the findings of the study, several recommendations, including on-the-job tutoring and flexible work arrangements, amongst others, were provided. Full article

This paper proposes a pipeline leakage detection technology that integrates machine learning algorithms with Dempster–Shafer (DS) evidence theory. By implementing five machine learning algorithms, this study constructs pipeline pressure and flow signal characteristics through wavelet decomposition. The data were normalized and processed using principal component analysis to prepare the algorithm for training. A new method for constructing basic probability functions using a confusion matrix and a simple support function is proposed and compared with the traditional triangular fuzzy number method. The basic probability function of the identification sample is refined by calculating a comprehensive discount factor. Finally, the results from multiple algorithms are fused using DS evidence theory. Experimental results demonstrate that after combining multiple algorithms, the average accuracy improves by 0.1565%, and the precision of the triangular fuzzy number method is enhanced by 0.091%. Full article

This work deals with the effects of a non-electrochemically deposited copper- or nickel–phosphorus-based coating on the resulting resistance of traditional X42 grade pipeline steel against hydrogen embrittlement (HE). The susceptibility to HE was determined by the evaluation of the hydrogen embrittlement index (HEI) from the results of conventional room-temperature tensile tests using cylindrical tensile specimens. Altogether, three individual material systems were studied, namely uncoated steel (X42) and two coated steels, specifically with either a copper-based coating (X42_Cu) or a nickel–phosphorus-based coating (X42_Ni-P). The HEI values were calculated as relative changes in individual mechanical properties corresponding to the non-hydrogenated and electrochemically hydrogen-precharged tensile test conditions. Both applied coatings considerably improved the hydrogen embrittlement resistance of the investigated steel in terms of decreasing the HEI values related to the changes in the yield stress, ultimate tensile strength, and reduction of area. In contrast, the hydrogenation of both coated systems had detrimental effects on the value of total elongation, which resulted in an increase in the corresponding HEI value. This behavior was likely related to the earlier onset of necking during tensile straining due to strain localizations induced by the coatings’ surface imperfections. The findings from fractographic observations indicated that both studied coatings acted like protective barriers against hydrogen permeation. However, the surface quality in terms of pores and other superficial defects in the considered coatings remains a challenging issue. Full article

The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents. Full article