Search Results (101,967)

The RPN-based Siamese tracker has achieved remarkable performance with real-time speed but suffers from a lack of robustness in complex motion tracking. Especially when the target comes into an abrupt motion scenario, the assumption of motion smoothness may be broken, which will further compromise the reliability of tracking results. Therefore, it is important to develop an adaptive tracker that can maintain robustness in complex motion scenarios. This paper proposes a novel tracking method based on the interval spatio-temporal constraints and a region proposal method over a pixel-spatial hierarchy. Firstly, to cope with the limitations of a fixed-constraint strategy for abrupt motion tracking, we propose a question-guided interval spatio-temporal constraint strategy. Based on the consideration of tracking status and the degree of penalty expansion, it enables the dynamic adjustment of the constraint weights, which ensures a match between response scores and true confidence values. Secondly, to guarantee the coverage of a target using candidate proposals in extreme motion scenarios, we propose a region proposal method over the pixel-spatial hierarchy. By combining visual common sense with reciprocal target-distractor information, our method implements a careful refinement of the primary proposals. Moreover, we introduce a discriminative-enhanced memory updater designed to ensure effective model adaptation. Comprehensive evaluations on five benchmark datasets: OTB100, UAV123, LaSOT, VOT2016, and VOT2018 demonstrate the superior performance of our proposed method in comparison to several state-of-the-art approaches. Full article

The growing integration of renewable energy sources, such as photovoltaic and wind systems, into energy grids has underscored the need for reliable control mechanisms to mitigate the inherent intermittency of these sources. According to the Brazilian grid operator (ONS), there have been cascading disconnections in renewable energy distributed systems (REDs) in recent years, highlighting the need for robust control models. This article addresses this issue by presenting the validation of an active power ramp rate control (PRRC) function for a PV plant coupled with a Battery Energy Storage System (BESS) using WECC generic models. The proposed model underwent rigorous validation over an extended analysis period, demonstrating good accuracy using the Root Mean Squared Error (RMSE) and an R-squared (R²) metrics for the active power injected at the Point of Connection (POI), PV active power, and BESS State of Charge (SOC), providing valuable insights for medium and long-term analyses. The ramp rate control module was implemented within the plant power controller (PPC), leveraging second-generation Renewable Energy Systems (RES) models developed by the Western Electricity Coordination Council (WECC) as a foundational framework. We conducted simulations using the Anatem software, comparing the results with real-world data collected at 100 ms to 1000 ms intervals from a PV plant equipped with a BESS in Brazil. The proposed model underwent rigorous validation over an extended analysis period, with the presented results based on two days of measurements. The positive sequence model used to represent this control demonstrated good accuracy, as confirmed by metrics such as the Root Mean Squared Error (RMSE) and R-squared (R²). Furthermore, the article underscores the critical role of accurately accounting for the power sampling rate when calculating the ramp rate. Full article

Over the last few years, the hospitality industry has increasingly embraced green practices. Indeed, tourists pay more attention to the sustainability actions of accommodations nowadays, demonstrating positive attitudes towards those implementing sustainable initiatives. Against this backdrop, “greening” a hotel has become a key driver of success for hotel operators to attract and retain these emerging eco-friendly travelers. Accordingly, many hotels worldwide have started implementing green management practices and adopting new business models. However, this is particularly difficult for luxury hotels, where combining luxury characteristics and sustainability is a challenging and ongoing issue. This research aims to investigate the unique business model of a luxury eco-hotel sited in a WWF-affiliated reserve and belonging to a social foundation group. Our findings not only demonstrate that it is possible to combine sustainability and luxury but also to depict a new form of business model that integrates social and environmental dimensions. Full article

With the deepening of oilfield development, logging data proliferate, and their complexity makes manual stratigraphic division both difficult and time-consuming. Aimed at the current network model widely used to solve the problem of stratigraphic delineation, which has problems such as not considering the multi-scale features of logging curves and insufficient accuracy, the YOLOv8x target detection algorithm in deep learning is utilized to detect the target strata, which has the ability to characterize the multi-scale features and can improve the efficiency and accuracy of the division. In order to better localize and identify targets, this paper proposes a new stratigraphic automatic division method, YOLOv8x-CAMDP, which introduces a CA (Coordinate Attention) mechanism module into the original YOLOv8x model to improve the model’s ability to identify stratigraphic interval boundaries. In addition, the CIOU loss function in the original YOLOv8x network model was replaced using the MDPIOU loss function to effectively improve the accuracy and efficiency of bounding box regression. Based on the logging data from the Xing 10 area pure oil zone, a thorough comparison of the YOLOv8x-CAMDP and YOLOv8x models’ training results is presented. The YOLOv8x-CAMDP model achieves a mean Average Precision (mAP) value of 98.7%, outperforming the YOLOv8x model by one percentage point. Moreover, the YOLOv8x-CAMDP model demonstrates greater precision in boundary division for each stratigraphic interval. The application of the YOLOv8x-CAMDP model to project implementation achieved significant results in stratigraphic division, reduced workload, and optimized manual division. These results not only confirm the practical value of the YOLOv8x-CAMDP model but also demonstrate the prospect and potential of its wide application. Full article

COVID-19 vaccines have reduced the negative health and economic impact of the COVID-19 pandemic by preventing severe disease, hospitalizations and deaths. In the new socio-economic normality, the COVID-19 vaccination strategy can be universal or high-risk and seasonal or not seasonal, and different vaccines can be used. The universal vaccination strategy can achieve greater health and herd immunity effects and is associated with greater costs than the high-risk vaccination strategy. In each country, the optimal COVID-19 vaccination strategy must be decided by considering the advantages and disadvantages and assessing the costs, health effects and cost-effectiveness of the universal and high-risk vaccination strategies. The universal vaccination strategy should be implemented when the objective of the vaccination program is to achieve the greatest health benefits from COVID-19 vaccination and when its incremental cost-effectiveness ratio is lower than EUR 30,000−50,000 per QALY or LYG. The use of adapted vaccines targeting currently circulating variants of SARS-CoV-2 is necessary to avoid the immune escape of emerging variants. Full article

This study explores the automation enhancement in the assembly process of wiring harnesses for automotive applications, focusing on manually inserting fuses and relays into boxes—a task known for quality and efficiency challenges. This research aimed to address these challenges by implementing a robotic arm integrated with API technology for digital twin. The methods used included the development of a digital twin model to simulate and monitor the assembly process, supported by real-time adjustments and optimizations. The results showed that the robotic system significantly improved the accuracy and speed of fuse insertion, reducing the insertion errors typically seen in manual operations. The conclusions drawn from the research confirm the feasibility of using robotic automation supported by digital twin technology to enhance assembly processes in automotive manufacturing, promising substantial improvements in production efficiency and quality control. Full article

The utilization of oxygen transport membranes enables the production of high-purity hydrogen by the thermal decomposition of water below 1000 °C. This process is based on a chemical potential gradient across the membrane, which is usually achieved by introducing a reducing gas. Computational fluid dynamics (CFD) can be used to model reactors based on this concept. In this study, a modelling approach for water splitting is presented in which oxygen transport through the membrane acts as the rate-determining process for the overall reaction. This transport step is implemented in the CFD simulation. Both gas compartments are modelled in the simulations. Hydrogen and methane are used as reducing gases. The model is validated using experimental data from the literature and compared with a simplified perfect mixing modelling approach. Although the main focus of this work is to propose an approach to implement the water splitting in CFD simulations, a simulation study was conducted to exemplify how CFD modelling can be utilized in design optimization. Simplified 2-dimensional and rotational symmetric reactor geometries were compared. This study shows that a parallel overflow of the membrane in an elongated reactor is advantageous, as this reduces the back diffusion of the reaction products, which increases the mean driving force for oxygen transport through the membrane. Full article

Pancreatic ductal adenocarcinoma (PDAC) has earned a notorious reputation as one of the most formidable and deadliest malignant tumors. Within the tumor microenvironment, cancer cells have acquired the capability to maintain incessant expansion and increased proliferation in response to hypoxia via metabolic reconfiguration, leading to elevated levels of lactate within the tumor surroundings. However, there have been limited studies specifically investigating the association between hypoxia and lactic acid metabolism-related lactylation in PDAC. In this study, multiple machine learning approaches, including LASSO regression analysis, XGBoost, and Random Forest, were employed to identify hub genes and construct a prognostic risk signature. The implementation of the CERES score and single-cell analysis was used to discern a prospective therapeutic target for the management of PDAC. CCK8 assay, colony formation assays, transwell, and wound-healing assays were used to explore both the proliferation and migration of PDAC cells affected by CENPA. In conclusion, we discovered two distinct subtypes characterized by their unique hypoxia and lactylation profiles and developed a risk score to evaluate prognosis, as well as response to immunotherapy and chemotherapy, in PDAC patients. Furthermore, we indicated that CENPA may serve as a promising therapeutic target for PDAC. Full article

As malware samples grow in complexity and employ advanced evasion techniques, traditional detection methods are insufficient for accurately classifying large volumes of sophisticated malware variants. To address this issue, image-based malware classification techniques leveraging machine learning algorithms have been developed as a more optimal solution to this challenge. However, accurately classifying content distribution-based features with unique pixel intensities from grayscale images remains a challenge. This paper proposes an enhanced image-based malware classification system using convolutional neural networks (CNNs) using ResNet-152 and vision transformer (ViT). The two architectures are then compared to determine their classification abilities. A total of 6137 benign files and 9861 malicious executables are converted from text files to unsigned integers and then to images. The ViT examined unsigned integers as pixel values, while ResNet-152 converted the pixel values into floating points for classification. The result of the experiments demonstrates a high-performance accuracy of 99.62% with effective hyperparameters of 10-fold cross-validation. The findings indicate that the proposed model is capable of being implemented in dynamic and complex malware environments, achieving a practical computational efficiency of 47.2 s for the identification and classification of new malware samples. Full article

This study addresses the critical need for improved sustainability reporting in the construction sector, focusing on the integration of Life Cycle Sustainability Assessment (LCSA) indicators to enhance reporting quality and promote standardization. The increasing regulatory pressure from the European Commission, particularly in sustainability reporting, has intensified the demand for corporate transparency. Despite these efforts, many companies still face challenges in implementing robust sustainability performance measures. This research employs a systematic literature review alongside the case studies of three leading German construction companies to critically assess the current reporting practices and explore the integration potential of LCSA indicators. The findings highlight a significant gap between the existing sustainability disclosures and LCSA indicators, with only 7–19% of the assessed indicators being integrated into the current reporting practices. Although some consistency in reporting themes and qualitative disclosures is evident, the misalignment with LCSA indicators underscores the need for further integration of standardized, life cycle-based metrics. This study concludes that collaborative efforts among companies, policymakers, and LCSA researchers are required to bridge this gap, ensuring the adoption of the existing, scientifically robust indicators that enhance the precision, comparability, and transparency of sustainability reporting in the construction sector. Full article

Background: Warehousing operations, crucial to logistics and supply chain management, often seek innovative technologies to boost efficiency and reduce costs. For instance, AR devices have shown the potential to significantly reduce operational costs by up to 20% in similar industries. Therefore, this paper delves into the pivotal role of smart glasses in revolutionising warehouse effectiveness and efficiency, recognising their transformative potential. However, challenges such as employee resistance and health concerns highlight the need for a balanced trade-off between operational effectiveness and human acceptance. Methods: This study uses scenario and regression analyses to examine data from a German logistics service provider (LSP). Additionally, structured interviews with employees from various LSPs provide valuable insights into human acceptance. Results: The findings reveal that smart glasses convert dead time into value-added time, significantly enhancing the efficiency of order picking processes. Despite the economic benefits, including higher profits and competitive advantages, the lack of employee acceptance due to health concerns still needs to be addressed. Conclusions: After weighing the financial advantages against health impairments, the study recommends implementing smart glass technology in picking processes, given the current state of technical development. This study’s practical implications include guiding LSPs in technology adoption strategies, while theoretically, it adds to the body of knowledge on the human-technology interface in logistics. Full article

Background: In the transition to a professional learning environment, healthcare professionals in their first year of specialized postgraduate clinical training (known as residents in Spain) are suddenly required to handle confidential information with little or no prior training in the safe and appropriate use of digital media with respect to confidentiality issues. The aims of this study were: (1) to explore the usefulness of an advanced clinical simulation program for educating residents from different healthcare disciplines about confidentiality and the dissemination of clinical data or patient images; (2) to explore the use of social networks in healthcare settings; and (3) to explore participants’ knowledge and attitudes on current regulations regarding confidentiality, image dissemination, and the use of social networks; Methods: This was a cross-sectional study. Data were collected from all 49 first-year residents of different health professions at a Spanish hospital between June and August 2022. High-fidelity clinical simulation sessions designed to address confidentiality and health information dissemination issues in hospital settings, including the use of social networks, were developed and implemented. Data were assessed using a 12-item ad hoc questionnaire on confidentiality and the use of social media in the healthcare setting. Descriptive of general data and chi-square test or Fisher’s exact test were performed using the SPSS 25.0 software; Results: All the participants reported using the messaging application WhatsApp regularly during their working day. A total of 20.4% of the participants stated that they had taken photos of clinical data (radiographs, analyses, etc.) without permission, with 40.8% claiming that they were unaware of the legal consequences of improper access to clinical records. After the course, the participants reported intending to modify their behavior when sharing patient data without their consent and with respect to how patients are informed; Conclusions: The use of advanced simulation in the training of interprofessional teams of residents is as an effective tool for initiating attitudinal change and increasing knowledge related to patient privacy and confidentiality. Further follow-up studies are needed to see how these attitudes are incorporated into clinical practice. Full article

Urban green infrastructure plays a significant role in sustainable development and requires proper land management during planning. This study develops a valuation model for urban green infrastructure in land management, focusing on Zagreb’s 17 city districts. The fuzzy AHP method was used to calculate the weighting coefficients for a suitable set of criteria, and the TOPSIS method was used to select the priority city districts for implementing green infrastructure. The research results are relevant to decision makers, who can utilize them to prioritize areas for the development and implementation of green infrastructure. The green infrastructure index calculated in this study can be compared with other spatial and land data for effective spatial planning. Full article

Cancer prehabilitation is the process between the time of cancer diagnosis and the beginning of the active acute treatment; prehabilitation consists of various need-based interventions, e.g., physical activity, a nutritional program, and psychological support. It can be delivered as unimodal or multimodal interventions. Physical activity, including resistant exercise and aerobic activities, has to be tailored according to the patient’s characteristics; nutritional support is aimed at preventing malnutrition and sarcopenia; while psychological intervention intercepts the patient’s distress and supports specific intervention to address it. In addition, multimodal prehabilitation could have a potential impact on the immune system, globally reducing the inflammatory processes and, as a consequence, influencing cancer progression. However, many challenges are still to be addressed, foremost among them the feasibility of prehabilitation programs, the lack of adequate facilities for these programs’ implementation, and the fact that not all prehabilitation interventions are reimbursed by the national health system. Full article

Background: The Second Victim Phenomenon (SVP) significantly impacts the well-being of healthcare professionals and patient safety. While the SVP has been explored in various healthcare settings, there are limited data on its prevalence and associated factors among nurses in Austria. This study investigates the prevalence, symptomatology, and preferred support measures for SVP among Austrian nurses. Methods: A nationwide, cross-sectional, anonymous online survey was conducted September to December 2023 using the SeViD questionnaire (Second Victims in German-speaking Countries), which includes the Big Five Inventory-10 (BFI-10). Statistical analyses included binary logistic regression and multiple linear regression using the bias-corrected and accelerated (BCa) bootstrapping method based on 5000 bootstrap samples. Results: A total of 928 participants responded to the questionnaire with a response rate of 15.47%. The participants were on average 42.42 years old and were mainly women (79.63%). Among the respondents, 81.58% (744/912) identified as Second Victims (SVs). The primary cause of becoming an SV was aggressive behavior from patients or relatives. Females reported a higher symptom load than males, and higher agreeableness was linked to increased symptom severity. Notably, 92.47% of SVs who sought help preferred support from colleagues, and the most pronounced desire among participants was to process the event for better understanding. Conclusions: The prevalence of SVP among Austrian nurses is alarmingly high, with aggressive behavior identified as a significant trigger. The findings emphasize the necessity for tailored support strategies, including peer support and systematic organizational interventions to mitigate the impact of SVP on nurses and to improve overall patient care. Further research should focus on developing and implementing effective prevention and intervention programs for healthcare professionals in similar settings. Full article