Search Results (485)

Multi-agent coordination with task allocation, routing, and scheduling presents critical challenges when deploying heterogeneous robotic systems in constrained agricultural environments. These systems involve real-time sensing during their operations with various sensors, and having quick updates on coordination based on sensed data is critical. This paper addresses the specific requirements of harvesting automation through three heuristic approaches: (1) primal–dual workload balancing inspired by combinatorial optimization techniques, (2) greedy task assignment with iterative local optimization, and (3) LLM-based constraint processing through prompt engineering. Our agricultural application scenario incorporates robot size constraints for navigating narrow crop rows while optimizing task completion time. The greedy heuristic employs rapid initial task allocation based on proximity and capability matching, followed by iterative route refinement. The primal–dual approach adapts combinatorial optimization principles from recent multi-depot routing solutions, dynamically redistributing workloads between robots through dual variable adjustments to minimize maximum completion time. The LLM-based method utilizes structured prompt engineering to encode spatial constraints and robot capabilities, generating feasible solutions through successive refinement cycles. We implemented and compared these approaches through extensive simulations. Preliminary results demonstrate that all three approaches produce feasible solutions with reasonable quality. The results demonstrate the potential of the methods for real-world applications that can be quickly adopted into variations of the problem to offer valuable insights into solving complex coordination problems with heterogeneous multi-robot systems. Full article

Pharmaceutical care in European countries is at various stages of development. Although the problem of occupational burnout affects many professions, it is particularly relevant among healthcare workers, such as pharmacists. Studies assessing pharmacists’ life satisfaction and factors influencing the level of occupational burnout play an important role in social pharmacy. Therefore, the present study aimed to evaluate the impact of providing pharmaceutical care on the professional life satisfaction of pharmacists in Poland. This study was conducted as an anonymous online survey. It included pharmacists who are members of the professional self-government in Poland. A custom-designed questionnaire was used for data collection, and 91 completed questionnaires were obtained. The respondents were divided into four groups according to their professional experience: up to 5 years, 6–10 years, 11–20 years, and over 20 years. In response to questions regarding job satisfaction and the willingness to provide pharmaceutical care, the respondents gave affirmative answers. Pharmacists in Poland have a positive perception of the impact of pharmaceutical care on the prestige of their profession. Currently, the pharmaceutical care services most commonly provided are those financed by the State; however, pharmacists are willing to engage in such activities and expect an expansion of the scope of reimbursed services. Consequently, pharmacists express dissatisfaction with the current stage of pharmaceutical care implementation in Poland. Those who provide pharmaceutical care feel more appreciated in their profession, do not experience psychological strain, do not feel uncomfortable when communicating with patients, and are not afraid of the responsibility associated with providing such services. Nevertheless, they consider it an additional workload in their professional duties. Full article

Background: Patient education (PE) is an essential component of quality healthcare and chronic disease management. However, effective implementation often faces patient-, nurse-, and organization-related barriers. This is particularly relevant in multicultural healthcare settings such as Saudi Arabia, where a highly diverse nursing workforce may influence PE practices. Aim: To examine the barriers and facilitators influencing patient education practices among nurses working in multiple hospitals in Saudi Arabia. Methods: A descriptive cross-sectional study was conducted among 289 registered nurses recruited through convenience sampling from various hospitals in Saudi Arabia. Data were collected using a validated self-administered questionnaire consisting of demographic items and structured scales assessing PE barriers and facilitators. Descriptive statistics were used to analyze the data. Results: Language differences (64.3%) and cultural barriers (59.2%) were the most commonly reported patient-related obstacles. Among nurse-related barriers, staff shortages (72.4%), heavy workload (72.0%), and time constraints (59.9%) were prominent. Organizational barriers included limited educational resources (39.4%) and unsupportive environments (35.6%). Key facilitators identified by nurses included availability of policies and procedures (63.6%), provision of PE training (63.7%), and integration of PE into clinical workflow and nurse appraisals. Conclusions: Despite strong professional support for PE, multiple barriers hinder its implementation in Saudi hospitals. Addressing these challenges requires institutional strategies such as workforce reinforcement, policy integration, and resource allocation. Future efforts should also include integrating patient perspectives, developing culturally tailored education resources, and evaluating the impact of targeted interventions to strengthen PE delivery in diverse hospital settings. Full article

In recent years, Location-Based Augmented Reality (LAR) systems have been increasingly implemented in various applications for tourism, navigation, education, and entertainment. Unfortunately, the LAR content creation using conventional desktop-based authoring tools has become a bottleneck, as it requires time-consuming and skilled work. Previously, we proposed an in-situ mobile authoring tool as an efficient solution to this problem by offering direct authoring interactions in real-world environments using a smartphone. Currently, the evaluation through the comparison between the proposal and conventional ones is not sufficient to show superiority, particularly in terms of interaction, authoring performance, and cognitive workload, where our tool uses 6DoF device movement for spatial input, while desktop ones rely on mouse-pointing. In this paper, we present a comparative study of authoring performances between the tools across three authoring phases: (1) Point of Interest (POI) location acquisition, (2) AR object creation, and (3) AR object registration. For the conventional tool, we adopt Unity and ARCore SDK. As a real-world application, we target the LAR content creation for pedestrian landmark annotation across campus environments at Okayama University, Japan, and Brawijaya University, Indonesia, and identify task-level bottlenecks in both tools. In our experiments, we asked 20 participants aged 22 to 35 with different LAR development experiences to complete equivalent authoring tasks in an outdoor campus environment, creating various LAR contents. We measured task completion time, phase-wise contribution, and cognitive workload using NASA-TLX. The results show that our tool made faster creations with 60% lower cognitive loads, where the desktop tool required higher mental efforts with manual data input and object verifications. Full article

Borehole sealing materials have drawn significant research attention for their applications in mine disaster prevention, efficient utilization of coalbed methane resources and green mine construction. However, it is still an enormous challenge to simultaneously achieve sealing materials with lower material consumption, lower expense, and lower labor intensity for high-performance long-term borehole sealing. Meanwhile, drilling cuttings (DC) possess large production amounts, low granularity, a large workload for cleaning out the alley, high labor intensity, and high transportation cost. Herein, a composite with universal applicability to DC has been developed, which can be combined with different DC to produce a low-cost sealing material with adjustable strength, fulfilling the sealing requirements of various boreholes. The properties of the sealing material can be adjusted as required by regulating the water/cement ratio and DC content to meet the sealing requirements of different boreholes. Consequently, the DC-derived materials, featuring adjustable strengths and lower usage, can reduce cement usage, material costs, and labor intensity dramatically, displaying great promise in high-performance borehole sealing, coalbed methane extraction and utilization, timely mining waste reutilization, gas disaster prevention, and green mine construction. Full article

Cardiac inflammation and hypertrophy develop as a pathologic response to an array of insults, such as myocardial infarctions, chronic systemic hypertension, and valvular defects. Due to the high prevalence of such conditions, there is an increasing need to prevent and halt cardiac hypertrophy. Because cardiac damage and subsequent remodeling can lead to arrhythmias, heart failure, and even sudden cardiac death, inhibition of cardiac hypertrophy is key to reducing cardiovascular-related mortality. The immune system is the driving force behind inflammatory reactions. All three pathways of complement system activation—classical, lectin, and alternative—are implicated in developing cardiac damage, inflammation, and hypertrophy due to infectious and non-infectious causes, autoimmune diseases, genetic polymorphisms, and forms of complement dysregulation. Of interest in this review is the role of the complement system, a collection of soluble and membrane-bound proteins that mediate inflammatory processes through interactions with signaling molecules and immune cells. This review comprehensively discusses the roles of these complement pathways in contagious, chronic inflammatory, genetic, and metabolic diseases. An overview of the completed and terminated clinical trials aimed at preventing cardiovascular mortality by targeting various aspects of the complement system and inflammatory reaction is included. Most current treatments for cardiac inflammation and remodeling primarily target the renin–angiotensin–aldosterone system (RAAS), which prevents further remodeling by reducing myocardial workload. However, moving forward, there may be a place for emerging anti-complement therapeutics, which impair the inflammatory response that generates hypertrophy itself. Full article

To overcome the challenges of conventional low-carbon retrofits for existing buildings—such as high construction volume, cost, and implementation difficulty—this study proposes a minimally invasive design and optimization method for Photovoltaic–Energy Storage–Direct Current–Flexible (PEDF) systems. The goal is to maximize energy savings and economic benefits while minimizing physical intervention. First, the minimally invasive retrofit challenge is decomposed into two coupled problems: (1) collaborative PV-ESS layout optimization and (2) flexible energy management optimization. A co-optimization framework is then developed to address them. For the layout problem, a model with multiple constraints is established to minimize retrofitting workload and maximize initial system performance. A co-evolutionary algorithm is employed to handle the synergistic effects of electrical pathways on equipment placement, efficiently obtaining an optimal solution set that satisfies the minimally invasive requirements. For the operation problem, an energy management model is developed to maximize operational economy and optimize grid interactivity. A deep reinforcement learning (DRL) agent is trained to adaptively make optimal charging/discharging decisions. Case simulations of a typical office building show that the proposed method performs robustly across various scenarios (e.g., office, commercial, and public buildings). It achieves an energy saving rate exceeding 20% and reduces operational costs by 10–15%. Moreover, it significantly improves building–grid interaction: peak demand is reduced by 33%, power fluctuations are cut by 75%, and voltage deviation remains below 5%. The DRL-based policy outperforms both rule-based strategies and the DDPG algorithm in smoothing grid power fluctuations and increasing the PV self-consumption rate. Full article

Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it is common that users cannot accurately specify their applications’ low-level requirements, and they tend to overestimate them—a problem further intensified by their lack of detailed knowledge on the infrastructure’s characteristics. In this context, resource orchestration mechanisms perform allocations based on the provided worst-case assumptions, with a direct impact on the performance of the whole infrastructure. In this work, we propose a resource orchestration mechanism based on intents, in which users provide their high-level workload requirements by specifying their intended preferences for how the workload should be managed, such as prioritizing high capacity, low cost, or other criteria. Building on this, the proposed mechanism dynamically assigns resources to applications through a Reinforcement Learning method leveraging the feedback from the users and infrastructure providers’ monitoring system. We formulate the respective problem as a discrete-time, finite horizon Markov decision process. Initially, we solve the problem using a tabular Q-learning method. However, due to the large state space inherent in real-world scenarios, we also employ Deep Reinforcement Learning, utilizing a neural network for the Q-value approximation. The presented mechanism is capable of continuously adapting the manner in which resources are allocated based on feedback from users and infrastructure providers. A series of simulation experiments were conducted to demonstrate the applicability of the proposed methodologies in intent-based resource allocation, examining various aspects and characteristics and performing comparative analysis. Full article

Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot workload. Existing multi-objective evolutionary algorithms (MOEAs) often struggle with slow convergence and insufficient diversity when tackling the combinatorial complexity of large-scale MRTA instances. This paper introduces the Collaborative Swarm-Differential Evolution (CSDE) algorithm, a novel MOEA designed to overcome these limitations. CSDE’s core innovation lies in its deep, operator-level fusion of Differential Evolution’s (DE) robust global exploration capabilities with Particle Swarm Optimization’s (PSO) swift local exploitation prowess. This is achieved through a unique fused velocity update mechanism, enabling particles to dynamically benefit from their personal experience, collective swarm intelligence, and population diversity-driven knowledge transfer. Comprehensive experiments on various MRTA scenarios demonstrate that CSDE consistently achieves superior performance in terms of convergence, solution diversity, and Pareto front quality, significantly outperforming standard multi-objective algorithms like Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Differential Evolution (MODE), and Multi-Objective Genetic Algorithm (MOGA). This study highlights CSDE’s substantial contribution to the MRTA field and its potential for more effective and efficient multi-robot system deployment. Full article

Background/Objectives: Mechanical circulatory support (MCS) is increasingly utilized for the management of acute decompensated heart failure (HF) and cardiogenic shock (CS). The primary goals of MCS are to restore systemic perfusion, reduce cardiac workload, and support end-organ function. A thorough understanding of cardiovascular physiology in patients supported by MCS is essential for clinical decision-making. This review summarizes current evidence on the physiological effects of various MCS devices, key monitoring techniques, patient management, and explores the emerging role of artificial intelligence (AI) in this field. Main Text: Short-term MCS devices include intra-aortic balloon pumps (IABP), percutaneous left-sided devices such as Impella (Abiomed, Danvers, MA, USA) and TandemHeart (LivaNova, London, UK), percutaneous right-sided support devices like Protek Duo (LivaNova, London, UK) and Impella RP Flex (Abiomed, Danvers, MA, USA), and veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Long-term support is mainly provided by left ventricular assist devices (LVADs), including the HeartMate 3 (Abbott Laboratories, Chicago, IL, USA). Optimal MCS application requires an understanding of device-specific cardiovascular interactions and expertise in appropriate monitoring tools to assess device performance and patient response. The choice of device, timing of initiation, and patient selection must be individualized, with careful consideration of ethical implications. The integration of AI offers significant potential to advance clinical care by improving complication prediction, enabling real-time optimization of device settings, and refining patient selection criteria. Conclusions: MCS is a rapidly evolving field that requires a comprehensive understanding of cardiovascular interactions, careful selection of monitoring strategies, and individualized clinical management. Future research should address current device limitations, clarify device-specific clinical applications, and assess the validity of AI-driven technologies. Full article

In a Kubernetes environment, the resource allocation for Pods has a direct impact on both performance and cost. When resource sizes are determined based on user experience, under-provisioning can lead to performance degradation and execution instability, while over-provisioning can result in resource waste and increased costs. To address these issues, this study proposes an adaptive scheduling method that employs the Design of Experiments (DoE) approach to determine the optimal resource size for each application with minimal experimentation and integrates the results into a custom Kubernetes scheduler. Experiments were conducted in a Kubernetes-based cloud environment using five applications with diverse workload characteristics, including CPU-intensive, memory-intensive, and AI inference workloads. The results show that the proposed method improved the performance score—calculated as the harmonic mean of execution time and cost—by an average of approximately 1.5 times (ranging from 1.15 to 1.59 times) compared with the conventional maximum resource allocation approach. Moreover, for all applications, the difference in mean scores before and after optimal resource allocation was statistically significant (p-value < 0.05). The proposed approach demonstrates scalability for achieving both resource efficiency and service-level agreement (SLA) compliance across various workload environments. Full article

Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve performance and increase availability to manage cloud computing resources. The combination of ML and cloud architectures balances the workload and ensures reliability. This research discusses cloud architectures that use ML to run different algorithms to predict the improvement in the cloud architectures by using a cloud computing resource dataset. The dataset is used with different classifiers with the same ML framework that is discussed in this paper; the ML framework has a sequence to provide the steps of the model training and testing and uses different techniques and methods for the better performance of the cloud architectures. The researchers used various ML techniques to create a model for predicting the workload. To enhance the model’s performance and flexibility, we used a regression-based dataset that was recently updated, which was used with different ML approaches to predict better performance in the cloud architectures. By using the Generalized Linear Model, we achieved the highest performance. The R² value refers to the goodness of the model and its performance. Using cloud datasets and machine learning with cloud architectures enhances performance using the different techniques in this paper, resulting in a more generalizable model with overfitting risk. This study focuses on refining the execution of cloud architectures with the help of ML. Full article

Many organizations execute multiple projects simultaneously, competing for limited resources, including specialized and expensive equipment. Managing such multi-project environments requires advanced planning and decision-making. An additional difficulty is taking into account the possibility and profitability of using internal and external resources. The construction industry is a particularly demanding example of this scenario, where simultaneously executed projects must share high-value equipment with limited availability. Project management planning with resource allocation is supported by various types of IT tools. ERP (enterprise resource planning) systems are particularly useful in this regard, as they use the organization’s transaction data directly, but only offer basic project support. Therefore, it is necessary to extend their functionality in order to fulfill the expected functional requirements of business users, with particular emphasis on the provision of a consistent, graphically supported interface. This article proposes an algorithm to support decision-making on equipment allocation in a multi-project environment, taking into account the use of own and third-party equipment. A case study is presented demonstrating the practical implementation of the proposed solution in the IFS Applications ERP system. The developed extension supports users through graphical and numerical presentation of machine workloads across multiple projects. Full article

Background: In light of the concerning increase in burnout among healthcare professionals, it is essential to identify the specific factors that contribute to this phenomenon and can be addressed. This narrative review synthesizes evidence on the relationship between burnout and emotional intelligence (EI) among healthcare professionals, alongside additional factors that may influence both concepts. Methods: A structured search in OVID, PubMed, Medline, Scopus, and Web of Science (2000–2024) was conducted. The inclusion criteria were English language and peer-reviewed studies assessing both burnout and EI in healthcare professionals. The exclusion criteria were non-English papers, studies without EI–burnout correlation, or involving non-healthcare populations. Thirty-one eligible studies were included in this analysis. Results: The findings suggest a consistent inverse correlation between EI and burnout across various healthcare professionals, including doctors, nurses, and residents. Higher EI was associated with reduced levels of emotional exhaustion and depersonalization and a greater sense of personal accomplishment. Burnout was found to be prevalent among younger healthcare workers, particularly residents, with contributing factors including exposure to workplace violence, high workload, and diminished psychological ownership. In contrast, associations that suggest protective influences on emotional intelligence included spiritual intelligence, self-control, income, and healthy habits, such as sufficient sleep and physical activity. Conclusions: This narrative review highlights a consistent inverse association between EI and burnout in healthcare professionals. Given that both burnout and EI are affected by adjustable individual and organizational elements, specific interventions aimed at enhancing EI and improving workplace conditions may provide effective techniques to boost clinician occupational well-being and performance. Full article

In HPC systems, job scheduling plays a critical role in determining resource allocation and task execution order. With the continuous expansion of computing scale and increasing system complexity, modern HPC scheduling faces two major challenges: a massive decision space consisting of tens of thousands of computing nodes and a huge job queue, as well as complex temporal dependencies between jobs and dynamically changing resource states.Traditional heuristic algorithms and basic reinforcement learning methods often struggle to effectively address these challenges in dynamic HPC environments. This study proposes a novel scheduling framework that combines GTrXL with PPO, achieving significant performance improvements through multiple technical innovations. The framework leverages the sequence modeling capabilities of the Transformer architecture and selectively filters relevant historical scheduling information through a dual-gate mechanism, improving long sequence modeling efficiency compared to standard Transformers. The proposed SECT module further enhances resource awareness through dynamic feature recalibration, achieving improved system utilization compared to similar attention mechanisms. Experimental results on multiple datasets (ANL-Intrepid, Alibaba, SDSC-SP2) demonstrate that the proposed components achieve significant performance improvements over baseline PPO implementations. Comprehensive evaluations on synthetic workloads and real HPC trace data show improvements in resource utilization and waiting time, particularly under high-load conditions, while maintaining good robustness across various cluster configurations. Full article