Search Results (89,155)

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. Full article

As people spend extended periods of time indoors, stress and negative emotions caused by work have become increasingly difficult to ignore. Observing window views is widely considered an effective method to alleviate stress and promote mental health. However, the specific visual elements within these views that contribute to stress reduction and the differential restorative benefits across varying compositions remain insufficiently understood. This study focuses on four major visual elements commonly seen through windows: sky, buildings, greenery, and roads. Using a horizontal layering approach, nine window views were created based on different proportions of these elements. Participants were exposed to these views, and their responses were evaluated through the positive and negative affect scale (PANAS), as well as electroencephalographic (EEG) data acquisition. The findings indicate that greenery exhibits the most pronounced positive effect on stress mitigation and the enhancement of positive affect, while the presence of roads is more likely to elicit negative emotional responses. Additionally, the visual richness and structural completeness of the window scenes are found to significantly impact restorative outcomes. These findings provide empirical insights for landscape and architectural design aimed at improving psychological well-being. Full article

Lost circulation, a prevalent challenge in drilling engineering, poses significant risks including drilling fluid loss, wellbore instability, and environmental contamination. Conventional plugging materials often exhibit an inadequate performance under high-temperature, high-pressure (HTHP), and complex formation conditions. To address that, this study developed a high-performance gel–resin composite plugging material resistant to HTHP environments. By optimizing the formulation of bisphenol-A epoxy resin (20%), hexamethylenetetramine (3%), and hydroxyethyl cellulose (1%), and incorporating fillers such as nano-silica and walnut shell particles, a controllable high-strength plugging system was constructed. Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) confirmed the structural stability of the resin, with an initial decomposition temperature of 220 °C and a compressive strength retention of 14.4 MPa after 45 days of aging at 140 °C. Rheological tests revealed shear-thinning behavior (initial viscosity: 300–350 mPa·s), with viscosity increasing marginally to 51 mPa·s after 10 h of stirring at ambient temperature, demonstrating superior pumpability. Experimental results indicated excellent adaptability of the system to drilling fluid contamination (compressive strength: 5.04 MPa at 20% dosage), high salinity (formation water salinity: 166.5 g/L), and elevated temperatures (140 °C). In pressure-bearing plugging tests, the resin achieved a breakthrough pressure of 15.19 MPa in wedge-shaped fractures (inlet: 7 mm/outlet: 5 mm) and a sand-packed tube sealing pressure of 11.25 MPa. Acid solubility tests further demonstrated outstanding degradability, with a 97.69% degradation rate after 24 h in 15% hydrochloric acid at 140 °C. This study provides an efficient, stable, and environmentally friendly solution for mitigating drilling fluid loss in complex formations, exhibiting significant potential for engineering applications. Full article

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

The Mycoplasmataceae are a family of bacteria that typically cause respiratory, arthritic, and genitourinary disease in humans. Mycoplasma spp. of animal origin are also the causative agents of porcine wheezing disease, chronic respiratory disease and arthritis in chickens and other conditions. These diseases have a significant impact on public health and the economic development of livestock breeding. Clinical prevention and treatment of mycoplasma infections is primarily dependent on the use of antibiotics. However, inappropriate and excessive use of antimicrobials has enabled resistance development that has become a significant clinical concern. Mycoplasma are also robust biofilm producers, and this process is a major factor for the persistence of these infections, especially in conjunction with common antibiotic resistance mechanisms, including target gene mutations and the action of efflux pumps. A mycoplasma biofilm refers to a structured and stable microbial community formed by Mycoplasma spp. adhering to biological or non-biological surfaces under suitable conditions and secreting extracellular polymers (EPS) such as polysaccharides. This process allows the microorganisms to adapt to their surrounding environment and survive during the growth process. These biofilms render bacteria more resistant to antimicrobials than planktonic bacteria, resulting in biofilm-associated infections that are more challenging to eradicate and more likely to recur. The current study reviews progress from the fields of biofilm formation, structure and identification, correlations between biofilms and drug resistance and virulence as well as methods of biofilm prevention and control. Our aim was to provide a reference basis for the subsequent in-depth understanding of the research of mycoplasma biofilms. Full article

Group A rotavirus (RVA) is a leading causative agent of diarrhea in both young animals and humans. In China, multiple genotypes are commonly found within the bovine population. In this study, we investigated 1917 fecal samples from calves with diarrhea between 2022 and 2025, with 695 testing positive for RVA, yielding an overall detection rate of 36.25%. The highest positivity rate was observed in Hohhot (38.98%), and annual detection rates ranged from 26.75% in 2022 to 42.22% in 2025. A bovine rotavirus (BRV) strain, designated 0205HG, was successfully isolated from a fecal sample of a newborn calf. Its presence was confirmed through cytopathic effects (CPEs), the indirect immunofluorescence assay (IFA), electron microscopy (EM), and high-throughput sequencing. Genomic characterization identified the strain as having the G6-P[¹]-I2-R2-C2-M2-A3-N2-T6-E2-H3 genotype constellation. The structural proteins VP2 and VP7, along with nonstructural genes NSP1–NSP4, shared high sequence identity with Chinese bovine strains, whereas VP1, VP4, and NSP5 clustered more closely with human rotaviruses, and VP3 was related to feline strains. These findings highlight the genetic diversity and interspecies reassortment of BRVs in China, underlining the importance of continued surveillance and evolutionary analysis. Full article

With the continuous increase in the proportion of distributed energy output in the distribution network and the limited equipment on the management side of the active distribution network, it is very important to give full play to the regulating role of the dispatchable potential of large-scale electric vehicles for the economic operation of the distribution network. To deal with this issue, this paper proposes an optimal dispatching model of the distribution network considering the combination of the dispatchable potential of electric vehicle clusters and demand response. Firstly, the active distribution network dispatching model with the demand response is introduced, and the equipment involved in the active distribution network dispatching is modeled. Secondly, the bidirectional long short-term memory network algorithm is used to process the historical data of electric vehicles to reduce the uncertainty of the model. Then, the shared energy-storage characteristics based on the dispatchable potential of electric vehicle clusters are fully explored and the effect of peak shaving and valley filling after the demand response is fully explored. This approach significantly reduces the network loss and operating cost of the active distribution network. Finally, the modified IEEE-33 bus test system is utilized for test analysis in the case analysis, and the test results show that the established active distribution network model can reduce the early construction cost of the system’s energy-storage equipment, improve the energy-utilization efficiency, and realize the economic operation of the active distribution network. Full article

Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance. Full article

The southern margin block of the Xinjiang Oilfield represents a typical ultra-high-pressure condensate gas field. Existing surface throttling practices rely heavily on empirical experience, with the underlying throttling mechanisms remaining unclear and lacking systematic theoretical support. In this study, the TW1 Well is selected as the research subject. Based on the principle of equal total enthalpy before and after throttling—and with particular attention to the effects of condensate gas heavy components and water on enthalpy calculations—a mathematical model for throttling-induced temperature drop, tailored to ultra-high-pressure condensate gas, is developed. The model enables a systematic analysis of temperature variations throughout the throttling process. Results indicate that the pre-throttle temperature is the primary factor controlling the magnitude of temperature change, and that post-throttle temperature rise may occur due to the Joule–Thomson coefficient becoming negative under ultra-high-pressure conditions. By integrating hydrate-formation prediction with differential pressure calculations across the throttling valve, a rational production scheme is proposed. This study provides a theoretical basis for understanding the mechanisms of ultra-high-pressure condensate gas well throttling and delivers critical technical support for the scientific design and optimization of surface throttling operations. Full article

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

The rapid development of digital technology is reshaping the global economic landscape. However, its impact on firms’ capacity utilization rate (CUR), particularly through technological innovation, remains unclear. This study investigates this issue by developing an endogenous growth model that connects digital technology to CUR. The empirical analysis is based on data from Chinese A-share manufacturing firms. The methods employed include quantile regression, instrumental variable techniques, and various tests to explore underlying mechanisms. CUR is calculated using a special model that looks at random variations, and digital transformation is assessed using text analysis powered by machine learning. The findings indicate that digital transformation significantly enhances CUR, especially for firms with average capacity utilization levels, but has a limited effect on low- and high-end firms. Moreover, technological innovation mediates this relationship; however, factors like “double arbitrage” (involving policy and capital markets) and “herd effects” tend to prioritize quantity over quality, which constrains innovation potential. Improvements in CUR lead to enhanced firm performance and productivity, generating industry spillovers and demonstrating the broader economic externalities of digitalization. This study uniquely applies endogenous growth theory to examine the role of digital transformation in optimizing CUR. It introduces the “quantity-quality” technology innovation paradox as a crucial mechanism and highlights industry spillovers to address overcapacity while offering insights for fostering sustainable economic and social development in emerging markets. Full article

Background/Objectives: Older adults (65+) are the fastest growing age group in Canada, comprising 18.8% of the country’s population. During the COVID-19 pandemic, use of virtual care, including telehealth and tele-medicine, increased dramatically among older adults in Canada who often face higher health risks, mobility limitations, and many barriers to accessing healthcare. Despite the rapid expansion in virtual care, no systematic review has focused specifically on virtual care among older adults in Canada. This review aims to explore the factors influencing virtual care adoption and the experiences of older Canadians during the pandemic through a systematic review. Methods: Conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the review involved a comprehensive search of PubMed, Scopus, ESCBOHost, and Web of Science on 2 May 2025, yielding 281 unique citations. After screening and applying eligibility criteria, 15 studies employing quantitative, qualitative, or mixed-methods designs, with sample sizes ranging from 15 to 2,282,798, were included and appraised using the Mixed Methods Appraisal Tool (MMAT). Results: The review identified three domains of factors and the ways in which each factor shapes older adults’ virtual care experiences: (1) personal factors influencing virtual care use and demand (e.g., age, education, language, income, immigration status, community sizes), (2) resource factors impacting virtual care adoption (e.g., technology access, support), and (3) varying virtual care experiences among older adults (e.g., in assessment and communication efficacy, privacy, care quality, convenience, safety, and costs). Conclusions: This review highlights the complexities of virtual care engagement among older adults and underscores the need for inclusive, tailored strategies to improve the accessibility and effectiveness of virtual care delivery in both pandemic and post-pandemic contexts. Full article

Histone post-translational modifications (PTMs) have emerged as promising epigenetic biomarkers with increasing forensic relevance. Unlike conventional genetic markers such as short tandem repeats (STRs), histone modifications can offer additional layers of biological information, capturing individual-specific regulatory states and remaining detectable even in degraded forensic samples. This review highlights recent advances in understanding histone PTMs in forensic contexts, focusing on three key domains: analysis of degraded biological evidence, differentiation of monozygotic (MZ) twins, and postmortem interval (PMI) estimation. We summarize experimental findings from human cadavers, animal models, and typical forensic samples including bone, blood, and muscle, illustrating the stability and diagnostic potential of marks such as H3K4me3, H3K27me3, and γ-H2AX. Emerging technologies including CUT&Tag, MALDI imaging, and nanopore-based sequencing offer novel opportunities to profile histone modifications at high resolution and low input. Despite technical challenges, these findings support the feasibility of histone-based biomarkers as complementary tools for forensic identification and temporal analysis. Future work should prioritize methodological standardization, inter-laboratory validation, and integration into forensic workflows. However, the forensic applicability of these modifications remains largely unvalidated, and further studies are required to assess their reliability in casework contexts. Full article

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article