Search Results (73)

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

An algorithm is proposed to recover out-of-service loads (OOSLs) in smart distribution systems (SDSs) after exposure to cyber-attacks (CAs) resulting in interruptions of in-service loads (INSLs). The proposed algorithm is implemented in three steps. The first step is based on building the SDS in matrix form to be data input to the proposed algorithm. The second step is concerned with classifying the SDS into three zones: the attacked zone, the primary neighbor zone, and the secondary neighbor zone. The third step is performing five maneuvering processes (MPs) to recover the OOSL without breaking the electric limitations (ELs). The ELs are related to the maximum branch current, the node voltage, the load priority, the radiality maintenance of the SDS, the minimum system total power loss, the instruction sequence of the automatic-communication-switches (ACS), and the minimum number of ACSs. The proposed algorithm was tested under a 70-bus SDS with four electric supply feeders. The proposed algorithm achieved supply recovery for all OOSLs with efficiency of 100% after the occurrence of a CA on a single or double ACS without breaking the ELs. The proposed algorithm succeeded in achieving supply recovery for 97.6%, 97.1%, and 96.4% of the OOSLs after the simultaneous occurrence of a CA on three, four, and five ACSs, respectively, without breaking the ELs. The advantages of the proposed algorithm are a lack of dependency on the system size, a short electric supply recovery time within the range of 190–199 ms, a lack of dependency on distributed generation (DG), and the achievement of self-healing in the SDS following a single and two simultaneous CAs, as well as almost achieving self-healing under exposure to three, four, and five simultaneous CAs. Full article

Approach sequencing is important for multiple unmanned electric vertical take-off and landing (eVTOL) vehicles landing in vertiport. In this study, the additional intermediate transition ring (AIR) approach procedure in a balanced traffic flow scenario, the single ring movement-allowed (SRMA) approach procedure in an imbalanced traffic flow scenario, and the additional ring and allowing of movement (ARAM) approach procedure in a mixed scenario are proposed and designed to improve the efficiency of approach sequencing. Furthermore, a priority loss classification method is proposed to consider the unmanned eVTOL flight priority difference. Finally, a multi-objective optimization model is constructed with the constraints of inflow, outflow, moment continuity, flow balance, and conflict avoidance. The objectives are minimizing the power consumption, total operation time, and priority loss. Comparison experiments are conducted, and the final results demonstrate that the ARAM approach procedure can reduce the average holding time by 8.4% and 7.6% less than the branch-queuing approach (BQA) and AIR in a balanced traffic flow scenario, respectively. The ARAM approach procedure can reduce the average holding time by 6.5% less than BQA in an imbalanced traffic flow scenario. Full article

Aiming at the efficiency bottleneck and error risk caused by the over-reliance on manual experience in traditional assembly sequence planning, the urgent demand for deep reuse of multi-source knowledge in complex products, and the growing demand for resource saving and sustainable development, this study focuses on the core problem of the lack of empirical knowledge modeling and reasoning mechanism in the assembly process of complex products, and proposes a three-phase assembly sequence intelligent planning method that integrates deep learning and ontology theory. Method: First, we propose an instance segmentation model based on the improved Mask R-CNN architecture, incorporate the ResNet50 pre-training strategy to enhance the generalization ability of the model, reconstruct the Mask branch, and add the attention mechanism to achieve high-precision recognition and extraction of geometric features of the assembly parts. Secondly, a multi-level assembly ontology semantic model is constructed based on the ontology theory, which realizes the structured expression of knowledge from three dimensions: product structure level (product–assembly–part), physical attributes (weight/precision/dimension), and assembly process (number of fits/direction of assembly), and builds a reasoning system with six assembly rules in combination with the SWRL language, which covers the core elements of geometric constraints, process priority, and so on. Finally, experiments are carried out with the example gearbox as the validation object, and the results show that the assembly sequence generated by the method meets the requirements of the process specification, which verifies the validity of the technology path. By constructing a closed-loop technology path of “visual perception–knowledge reasoning–sequence generation”, this study effectively overcomes the subjective bias of manual planning, integrates multi-source knowledge to improve the reuse rate of knowledge, and provides a solution of both theoretical value and engineering feasibility for the intelligent assembly of complex electromechanical products, which reduces the R&D cost and contributes to the sustainable development. Full article

Background: Disease-related malnutrition, with or without inflammation, in older adults is currently emerging as a public health priority. The use of Foods for Special Medical Purposes, including Oral Nutritional Supplements, and supplements is crucial to support patients in achieving their nutritional needs. Therefore, this article aims to comprehensively provide an analysis of the adequacy of FSMPs in meeting the nutritional requirements of different age-related diseases and takes into account the emerging role of inflammation. Moreover, it provides an identikit of the ideal products, following the pathology-specific guidelines. Methods: Data on 132 products were gathered through face-to-face meetings with companies’ consultants. Specifically, information on energy, macronutrient, and micronutrient contents were collected, as well as on texture and flavors, osmolarity, cost, and packaging. Results: Most FSMPs met the daily recommendations for energy and protein intake. Nonetheless, few products contained β-hydroxy-β-methylbutyrate, optimal Branched-Chain Amino Acids ratios, arginine, glutamine, and omega-3 fatty acids. Furthermore, a marked predominance of FSMPs with a high osmolarity (85.7%), sweet taste (72%), and only animal protein content (79.5%) was observed. Cost analysis of FSMPs revealed a mean cost of EUR 5.35/portion. Products were mostly adequate for cancer, neurodegenerative diseases, diabetes, inflammatory bowel disease, end-stage kidney disease, dysphagiam and chronic obstructive pulmonary disease. However, gaps have been found for sarcopenia and abdominal surgery. Conclusion: In light of the current market landscape, there is a need for a comprehensive regulation that indicates the optimal composition of FSMPs and the production of such products to tackle disease-related malnutrition. Full article

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) has been widely adopted in commercial processors to reduce Icache misses, our analysis reveals that FDIP still suffers from Icache misses caused by branch mispredictions and late prefetch, leaving considerable opportunity for performance optimization. Priority-Directed Instruction Prefetching (PDIP) has been proposed to reduce Icache misses caused by branch mispredictions in FDIP. However, it neglects Icache misses due to late prefetch and suffers from high storage overhead. In this paper, we proposed a branch-triggered instruction prefetcher (BTIP), which aims to prefetch Icache lines that FDIP cannot efficiently handle, including the Icache misses due to branch misprediction and late prefetch. We also introduce a novel Branch Target Buffer (BTB) organization, BTIP BTB, which stores prefetch metadata and reuses information from existing BTB entries, effectively reducing storage overhead. We implemented BTIP on the Champsim simulator and evaluated BTIP in detail using traces from the 1st Instruction Prefetching Championship (IPC-1). Our evaluation shows that BTIP outperforms both FDIP and PDIP. Specifically, BTIP reduces Icache misses by 38.0% and improves performance by 5.1% compared to FDIP. Additionally, BTIP outperforms PDIP by 1.6% while using only 41.9% of the storage space required by PDIP. Full article

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared ($R_a^2$), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha⁻¹. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies. Full article

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management. Full article

The apple tree (Malus × domestica Borkh.) belongs to the Rosaceae. Due to its adaptability and tolerance to different soil and climatic conditions, it is cultivated worldwide for fresh consumption. The priorities of apple growers are high-quality fruits and stable yield for high production. About 90 to 95 percent of fruits should fall or be eliminated from apple trees to avoid overcropping and poor-quality fruits. Apple trees engage in a complex biological process known as yield self-regulation, which is influenced by several internal and external factors. Apple buds develop in different stages along the branches, and they can potentially give rise to new shoots, leaves, flowers, or fruit clusters. The apple genotype determines how many buds will develop into fruit-bearing structures and the capacity for yield self-regulation. Plant hormones such as ethylene, cytokinins, auxins, and gibberellins play a crucial role in regulating the fruit set, growth, and development, and the balance of these hormones influences the flowering intensity, fruit size, and fruit number on the apple tree. Apple growers often interfere in the self-regulation process by manually thinning fruit clusters. Different thinning methods, such as by hand, mechanical thinning, or applying chemical substances, are used for flower and fruit thinning. The most profitable in commercial orchards is the use of chemicals for elimination, but more environmentally sustainable solutions are required due to the European Green Deal. This review focuses on the biological factors and genetic mechanisms in apple yield self-regulation for a better understanding of the regulatory mechanism of fruitlet abscission for future breeding programs targeted at self-regulating yield apple varieties. Full article

Contaminated sediments may induce long-term risks to humans and ecosystems due to the accumulation of priority and emerging inorganic and organic pollutants having toxic and bio-accumulation properties that could become a secondary pollution source. This study focused on the screening of novel bio-based materials to be used in the decontamination of marine sediments considering technical and environmental criteria. It aimed to compare the environmental impacts of cellulose-based adsorbents produced at lab scale by using different syntheses protocols that involved cellulose functionalization by oxidation and branching, followed by structuring of an aerogel-like material via Soxhlet extraction and freeze-drying or their combination. As model pollutants, we used 4-nitrobenzaldehyde, 4-nitrophenol, methylene blue, and two heavy metals, i.e., cadmium and chromium. When comparing the three materials obtained by only employing the Soxhlet extractor with different solvents (without freeze-dying), it was observed that the material obtained with methanol did not have a good structure and was rigid and more compact than the others. A Life Cycle Assessment (LCA) was conducted to evaluate the environmental performance of the novel materials. Apart from the hierarchical categorization of the materials based on their technical and environmental performance in eliminating organic pollutants and heavy metal ions, it was demonstrated that the cellulose-based material obtained via Soxhlet extraction with ethanol was a better choice, since it had lower environmental impacts and highest adsorption capacity for the model pollutants. LCA is a useful tool to optimize the sustainability of sorbent materials alongside lab-scale experiments and confirms that the right direction to produce new performant and sustainable adsorbent materials involves not only choosing wastes as starting materials, but also optimizing the consumption of electricity used for the production processes. The main results also highlight the need for precise data in LCA studies based on lab-scale processes and the potential for small-scale optimization to reduce the environmental impacts. Full article

The use of pedestrian footbridges plays a critical role in urban mobility, particularly in university areas with high pedestrian traffic. Despite its importance, footbridge utilization often remains suboptimal due to various influencing factors that are not well understood. This study aims to identify and analyze the factors affecting footbridge utilization among university students in Amman City. By surveying students from four universities—Islamic Sciences University (ISUF), Balqa Applied University (BAUF), and two branches of Jordan University (JUF1 and JUF2)—we explore how accessibility, design aesthetics, safety, emotional and psychological factors, and environmental considerations influence their decision to use footbridges. The findings reveal significant differences in the impact of these factors across universities. Notably, environmental considerations and design aesthetics are pivotal for ISUF students, while JUF1 and BAUF students prioritize convenience and time-saving. Furthermore, our research identifies a crucial difference in safety concerns, with female students exhibiting a higher focus on safety compared to their male counterparts. This highlights the need for gender-sensitive safety interventions in footbridge design. Environmental considerations consistently emerge as a priority for all, reflecting a collective concern for the eco-friendly features of bridge design. This study is considered the first of its kind in Amman City and the region. The results align with global findings, establishing a basis for wider implications concerning the development of theoretical knowledge of urban mobility. This research fills a crucial gap by providing empirical evidence of the diverse factors impacting footbridge usage. It offers valuable insights for urban planners seeking to design and maintain user-friendly and gender-sensitive footbridges, ultimately promoting sustainable urban mobility. Full article

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time. Full article

Fiscal policy stands as a crucial pillar of economic development through its economic financing function. The regulatory effects of fiscality have been shown to reduce the ripple effects of uncertainties on economic growth within the EU. Unlike the average European economy, the Romanian economy has exhibited particularities concerning economic growth (ranking highly in economic growth among European nations in absolute terms), partly due to a more assertive fiscal policy applied to a consumption-based economy affected by hyperinflation (especially in the last five calendar years). The research issue stems from the premise of the lack of predictability in Romanian fiscal policy and its implications for the business environment. Our aim is to develop an econometric model of the fiscal effects of VAT on the business performance of the construction sector in Romania for the period 2010–2021. The methods employed involve empirical analysis and the development of consolidated industry-level databases followed by econometric modeling using the multiple linear regression method. The results of the research demonstrate that financial independence and solvency promote excessive taxation in emerging markets and developing countries, such as Romania, being correlated with the macroeconomic evolution of the respective state. Additionally, the results indicate that tax pressure can constitute a barrier to the sustainable development of firms, with direct repercussions for consumers. Attractiveness to investors is also affected, remaining a priority for companies. The study’s findings will enable the identification of the main impediments and opportunities brought about by VAT taxation on branch-level performance, proving useful for construction sector managers and fiscal policy makers in fostering sustainable industry development and establishing a sustainable fiscal regime to safeguard investors. Full article

Mission scheduling is an effective method to increase the value of satellite missions and can greatly improve satellite resource management and quality of service. Based on the priority-based task scheduling model, this paper proposes a CubeSat scheduling method that takes operational reliability into account, considering the impact of scheduling results on reliable operation. In this method, the available energy and the time window are used as scheduling resources, and the average state of charge of the lithium battery and the number of task start-ups are defined as two indices to measure its reliability. To meet the mission requirements and energy availability of photovoltaic (PV) solar panel and battery constraints, the scheduling model is constructed with an objective function that includes mission priority and reliability index. The branch and bound (BB) method and analytical hierarchy process (AHP) method are used to solve the scheduling problem. The example analysis compares different scheduling results and verifies the effectiveness of the proposed scheduling method. Compared with the existing methods, it comprehensively considers the mission value and operational reliability of the CubeSat, improves the energy reserve level of the CubeSat, and reduces the surge current caused by the start-up of tasks. Full article