Search Results (9,062)

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

Background and Objectives: Hysterectomy is a common non-obstetric procedure. Minimally invasive techniques, such as laparoscopy and robot-assisted surgery, have replaced open surgery for benign gynecologic conditions. Robotic surgery offers reduced blood loss and shorter hospital stays but is limited by high costs. Articulating laparoscopic instruments aim to replicate robotic dexterity cost-effectively. However, comparative data on these two approaches in hysterectomy are limited. Materials and Methods: This multicenter study analyzed the outcomes of hysterectomies for benign gynecological diseases using articulating laparoscopic instruments (prospectively recruited) and robot-assisted surgery (retrospectively reviewed). The surgeries were performed by minimally invasive gynecological surgeons in South Korea, Japan, and Taiwan. The baseline characteristics, operative details, and outcomes, including operative time, blood loss, complications, and hospital stay, were compared. Statistical significance was set at p < 0.05. Results: A total of 151 patients were analyzed, including 67 in the articulating laparoscopy group and 84 in the robot-assisted group. The operating times were comparable (114.9 vs. 119.9 min, p = 0.22). The articulating group primarily underwent dual-port surgery (79.1%), whereas the robot-assisted group required four or more ports in 71.4% of the cases (p < 0.001). Postoperative complications occurred in both groups, without a significant difference (9.0% vs. 3.6%, p = 0.17). No severe complications or significant differences in the 30-day readmission rates were observed. Conclusions: Articulating laparoscopic instruments provide outcomes comparable to robot-assisted surgery in hysterectomy while reducing the number of ports required. Further studies are needed to explore the learning curve and long-term impact on surgical outcomes. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

This study optimizes the production of activated carbons from hydrothermally carbonized (HTC) biomass using potassium hydroxide (KOH) and phosphoric acid (H₃PO₄) as activating agents. A 2³ factorial experimental design evaluated the effects of agent-to-precursor ratio, dry impregnation time, and activation duration on mass yield and iodine adsorption capacity. KOH-activated carbons achieved superior iodine numbers (up to 1289 mg/g) but lower mass yields (18–35%), reflecting enhanced porosity at the cost of material loss. Conversely, H₃PO₄ activation yielded higher mass retention (up to 54.86%) with moderate iodine numbers (up to 1117.3 mg/g), balancing porosity and yield. HTC pretreatment at 190 °C reduced the ash content, thereby enhancing the stability of hydrochar. These findings highlight the trade-offs between adsorption performance and process efficiency, with KOH suited for high-porosity applications (e.g., water purification) and H₃PO₄ for industrial scalability. The study advances biomass waste valorization, aligning with circular economy principles and offering sustainable solutions for environmental and industrial applications, such as water purification and energy storage. Full article

Early blight, caused by the pathogen Alternaria solani, is a major fungal disease impacting potato production globally, with reported yield losses of up to 40% in susceptible varieties. As one of the most common diseases affecting potatoes, its incidence has been steadily increasing year after year. This study aimed to elucidate the molecular mechanisms underlying resistance to early blight by comparing gene expression profiles in resistant (B1) and susceptible (D30) potato seedlings. Transcriptome sequencing was conducted at three time points post-infection (3, 7, and 10 dpi) to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) and pathway enrichment analyses were performed to explore resistance-associated pathways and hub genes. Over 11,537 DEGs were identified, with the highest number observed at 10 dpi. Genes such as LOC102603761 and LOC102573998 were significantly differentially expressed across multiple comparisons. In the resistant B1 variety, upregulated genes were enriched in plant–pathogen interaction, MAPK signaling, hormonal signaling, and secondary metabolite biosynthesis pathways, particularly flavonoid biosynthesis, which likely contributes to biochemical defense against A. solani. WGCNA identified 24 distinct modules, with hub transcription factors (e.g., WRKY33, MYB, and NAC) as key regulators of resistance. These findings highlight critical molecular pathways and candidate genes involved in early blight resistance, providing a foundation for further functional studies and breeding strategies to enhance potato resilience. Full article

To address the substantial disparities in operational characteristics between social vehicles and dedicated bus lanes, as well as the sub-optimal coordination control effects, a comprehensive approach is proposed. This approach integrates social vehicle arterial coordination with bus priority control in dedicated bus lanes. Initially, an analysis of the differences in travel time distribution on both types of roads is conducted. The likelihood of buses passing through upstream and downstream intersections without stopping is also assessed. This analysis aids in determining the correlated traffic states and the corresponding signal adjustment strategies for arterial coordination. Subsequently, an incentive mechanism is established by quantitatively analyzing vehicle delay losses and bus priority benefits based on the signal adjustment strategy. Finally, a deep reinforcement learning framework is proposed to solve, in real-time, the optimal signal adjustment strategy. Simulation experiments indicate that, in comparison to the arterial coordination of social vehicles and dedicated bus arterial coordination control, this method significantly reduces the average per capita delay by 38.63% and 27.43%, respectively, under conventional traffic flow scenarios. This is in contrast to the separate arterial coordination for social vehicles and dedicated bus lanes. Furthermore, it leads to a reduction of 52.17% in the number of bus stops at intersections when compared solely with the arterial coordination of social vehicles. In saturated traffic flow scenarios, this method achieves a reduction in average per capita delay by 29.7% and 9.6%, respectively, while also decreasing the number of bus stops at intersections by 39.5% and 8.7%, respectively. Full article

Gene replacement using adeno-associated viral (AAV) vectors has become a major therapeutic avenue for neurodegenerative diseases (NDD). In single-gene diseases with loss-of-function mutations, the objective of gene therapy is to express therapeutic transgenes abundantly in cell populations that are implicated in the pathological phenotype. X-ALD is one of these orphan diseases. It is caused by ABCD1 gene mutations and its main clinical form is adreno-myelo-neuropathy (AMN), a disabling spinal cord axonopathy starting in middle-aged adults. Unfortunately, the main cell types involved are yet poorly identified, complicating the choice of cells to be targeted by AAV vectors. Pioneering gene therapy studies were performed in the Abcd1^-/y mouse model of AMN with AAV9 capsids carrying the ABCD1 gene. These studies tested ubiquitous or cell-specific promoters, various routes of vector injection, and different ages at intervention to either prevent or reverse the disease. The expression of one of these vectors was studied in the spinal cord of a healthy primate. In summary, gene therapy has made promising progress in the Abcd1^-/y mouse model, inaugurating gene replacement strategies in AMN patients. Because X-ALD is screened neonatally in a growing number of countries, gene therapy might be applied in the future to patients before they become overtly symptomatic. Full article

Blasting operations in surface mining pose a risk of flyrock, which is a critical safety concern for both personnel and infrastructure. This study presents the use of unmanned aerial vehicles (UAVs) and photogrammetric techniques to improve the accuracy of blast design, particularly in relation to controlling burden values and reducing flyrock. The research was conducted in a basalt quarry in Lower Silesia, where high rock fracturing complicated conventional blast planning. A DJI Mavic 3 Enterprise UAV was used to capture high-resolution aerial imagery, and 3D models were created using Strayos software. These models enabled precise analysis of bench face geometry and burden distribution with centimeter-level accuracy. The results showed a significant improvement in identifying zones with improper burden values and allowed for real-time corrections in blasthole design. Despite a ten-fold reduction in the number of images used, no loss in model quality was observed. UAV-based surveys followed software-recommended flight paths, and the application of this methodology reduced the flyrock range by an average of 42% near sensitive areas. This approach demonstrates the operational benefits and enhanced safety potential of integrating UAV-based photogrammetry into blasting design workflows. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

Postmenopausal osteoporosis is estrogen-dependent and results in an imbalance between bone formation and resorption. The approved therapy is intended to reduce the risk and consequences of fractures, but still has a number of contraindications and associated adverse effects. Recently, oxytocin has been shown to have an anabolic effect on bone tissue, increasing the production of osteoblasts and inhibiting the activity of osteoclasts. Thus, this study aimed to examine the potential of oxytocin as a biomarker and therapeutic agent for postmenopausal osteoporosis. A PubMed search yielded 16 articles upon analysis of the inclusion and exclusion criteria. The results showed that, compared to women in the same age group without bone loss, those diagnosed with osteoporosis exhibited lower blood oxytocin levels, possibly related to a greater tendency towards fractures. The administration of oxytocin could be a promising strategy to enhance bone quality and, consequently, to reduce the incidence of fragility fractures; however, no human studies have been conducted regarding its use as a possible treatment. Thus, it is essential to increase the number of clinical trials in women with ovarian dysfunction and bone loss, in which oxytocin could become a viable therapeutic alternative. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

Background/Objectives: Anti-tumour necrosis factor (anti-TNF) medications were historically commonly prescribed as the first-line biologic treatment for chronic inflammatory pouch conditions. However, their use in these conditions is mainly based on retrospective studies of relatively small numbers of patients with short follow up periods. We aimed to describe the long-term outcomes of first-line anti-TNF therapy in a large, multi-centre, multi-national patient cohort with chronic inflammatory pouch conditions. Methods: This was an observational, retrospective, multi-centre, multi-national study. We included patients with chronic inflammatory pouch conditions initially treated with anti-TNF drugs infliximab (IFX) or adalimumab (ADA), who had a follow up of at least 1 year. The primary outcome was anti-TNF treatment persistence, defined as continuation of anti-TNF throughout the study period. The secondary outcome was pouch failure, defined by the need for a defunctioning ileostomy or pouch excision. Results: We recruited 98 patients with chronic inflammatory pouch conditions initially treated with anti-TNF medications—63 (64.3%) treated with IFX and 35 (35.7%) treated with ADA. Average follow up length was 94.2 months (±54.5). At the end of the study period only 22/98 (22.4%) patients were still on anti-TNF treatment. In those in whom the first-line anti-TNF was discontinued, the median time to discontinuation was 12.2 months (range 5.1–26.9 months). The most common cause for anti-TNF discontinuation was lack of efficacy despite adequate serum drug levels and absence of anti-drug antibody formation (30 patients, 30.6%). Loss of response due to anti-drug antibody formation was the cause for discontinuation in 18 patients (18.4%), while 12 patients (12.2%) stopped treatment because of adverse events or safety concerns. Out of the 76 patients discontinuing anti-TNF treatment, 34 (34.7% of the cohort) developed pouch failure, and 42 (42.8% of the cohort) are currently treated with a different medical therapy. Conclusions: First-line anti-TNF therapy for chronic pouch inflammatory conditions is associated with low long-term persistence rates. This is due to a combination of lack of efficacy and adverse events. A significant percentage of patients initially treated with anti-TNF therapy develop pouch failure. Full article

Hurricane Helene made landfall near Perry (Latitude 30.1 N) in the Big Bend area of Florida with a central pressure of 939 hPa. It moved northwards creating devastating damage and loss of life; however, the greatest damage and number of fatalities occurred well to the north around the City of Ashville (Latitude 35.6 N) where extreme rainfall fell and some of the strongest wind gusts were reported. This paper describes the change in the hurricane’s structure as it tracked northwards, how it gathered tropical moisture from the Atlantic and a turning wind profile between the 850 hPa and 500 hPa elevations, which led to such extreme rainfall. This turning wind profile is shown to be associated with extreme rainfall and loss of life from drowning and landslides around the globe. The area around Ashville suffered 157 fatalities, which is a considerable proportion of the 250 fatalities so far recorded in the whole United Stares from Helene. This is of extreme concern and should be investigated in detail as the public expect the greatest impact from hurricanes to be confined to coastal areas near the landfall site. It is another example of increased death tolls from tropical cyclones moving inland and generating heavy rainfall. As the global population increases and inland centres become more urbanised, run off from such rainfall events increases, which causes greater devastation. Full article