Time Resource Allocation Research Articles

Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service

The demand for public fast charging stations is increasing with the number of electric vehicles on roads. The charging queues and waiting times get longer, especially during the winter season and on holidays. Priority based service at charging stations can provide shorter delay times to vehicles willing to pay more and lower charging prices for vehicles accepting to wait more. Existing studies use classical feedback control and simulation based control methods to maintain the ratio of high and low priority vehicles’ delay times at the station’s target level. Reinforcement learning has been used successfully for real time control in environments with uncertainties. This study proposes a deep Q-Learning based real time resource allocation model for priority service in fast charging stations (DRL-EXP). Results show that the deep learning approach enables DRL-EXP to provide a more stable and faster response than the existing models. DRL-EXP is also applicable to other priority based service systems that act under uncertainties and require real time control.

Earth observation satellite imaging task scheduling with metaheuristics: Multi-level clustering and priority-driven pre-scheduling

Daily planning and scheduling of Agile Earth Observation Satellite (AEOS) observation tasks are enormously challenging due to their inherent complexity. This complexity stems from the vast number of observations that need to be coordinated and the multiple constraints that must be considered. In this study, we propose a comprehensive framework consisting of three steps to effectively tackle these challenges. To begin with, we employ a Multi-Level Clustering technique (MLC) to mitigate the problem complexity. Additionally, we integrate a Fixed High Priority First (FHPF) algorithm for tasks pre-scheduling. This algorithm effectively manages conflicts in time resource allocation for observation tasks at the lowest cluster level. Finally, we conduct a comparative analysis of four metaheuristic algorithms, integrating the pre-scheduled task clusters from the MLC-FHPF module as input for the main scheduling process. Our strategy aims to overcome the limitations of existing methods by incorporating continuous time modeling and accounting for “Fixed Time Maneuver” for time-dependent transition time constraints. Real-world evaluations highlight the resilience and effectiveness of our MLC-FHPF framework when integrated with various metaheuristic algorithms. Our approach significantly outperforms standard metaheuristics in terms of efficacy and efficiency. Notably, the GA MLC-FHPF algorithm consistently surpasses BDP-ILS and ALNS, especially in large-scale scenarios. It adapts effectively to different task densities and scales, maintaining optimized scheduling performance and efficient processing times.

Full-duplex dynamic TDMA scheme and clock synchronization and compensation method for the multi-user UWOC system.

In this Letter, we propose a full-duplex dynamic time division multiple access (FDD-TDMA) scheme for multi-user underwater wireless optical communication (UWOC). It supports full-duplex communication through wavelength division duplex (WDD) and enhances the system throughput using dynamic time resource allocation. Additionally, a clock synchronization and compensation method is proposed for precise clock synchronization without time-keeping. With the proposed FDD-TDMA scheme and the clock synchronization and compensation method, we establish a two-user UWOC system based on on-off keying (OOK) modulation. Experiments are conducted in a 10 m water pool environment. The experimental results show that the designed system can achieve a data rate of 25 Mbps without error codes and 40 Mbps with a bit error rate (BER) below the forward error correction (FEC) limit. The FDD-TDMA scheme notably improves the system throughput when communication demands among users are variable compared to the conventional time division multiple access (TDMA). Moreover, a reduction in phase deviations from microseconds to ten nanoseconds is achieved by the clock synchronization and compensation method.

Barriers and Facilitators of Using Standardized Outcome Measures in Stroke Rehabilitation in Saudi Arabia: A Cross-Sectional Study of Practice Among Neurophysiotherapists.

Clinical guidelines emphasize the use of standardized outcome measures (SOMs) in post-stroke rehabilitation. However, the extent of SOM utilization among physiotherapists in this context in Saudi Arabia remains unclear. 1) assess the current use of SOMs by physiotherapists involved in stroke rehabilitation in Saudi Arabia and 2) identify facilitators and barriers influencing the use of SOMs. An online survey was administered using a three-section questionnaire designed for this study. The first section collected demographic data, the second evaluated the use of SOMs recommended by the American Physical Therapy Association, and the third explored factors facilitating or hindering SOM use in clinical practice. Only highly recommended outcome measures capturing the three levels of the International Classification of Functioning, and Disability (ICF) model were considered: body structure and function, activities, and participation. Poisson regression analysis was used to investigate the association between SOMs utilization and educational level, work experience, type of work facility, and the number of patients treated per week. A total of 138 physiotherapists responded. Most participants (98.5%) used at least one outcome measure in clinical practice. Regression analysis showed that number of strokes treated per week and facility type were associated with the likelihood of using higher number of SOMs. Physiotherapists managing more than 10 stroke patients per week and working in private sector had significantly higher odds of using a greater number of SOMs. The most pronounced barriers were time restrictions and limited resources. Physiotherapists working with stroke patients in Saudi Arabia demonstrate a high awareness and positive attitude toward SOM utilization. Addressing barriers such as time management and resource allocation is crucial to enhancing SOM integration in clinical practice. Organizational support in terms of adequate time and resources is needed to enhance the use of SOMs among physiotherapists.

Distributed Time Resource Allocation Algorithm for Multi-Beam Directional Ad-Hoc Networks

Distributed Time Resource Allocation Algorithm for Multi-Beam Directional Ad-Hoc Networks

The Role of Expectancy Theory in the Organizational Management Process in Innovative Entrepreneurial Enterprises

The expectancy theory, originally put forth by psychologist Viktor Frankl, is a psychological framework that offers insights into human motivation and conduct. This theory suggests that the amount of effort individuals exert is directly influenced by their anticipation of particular outcomes and the degree to which those outcomes are personally appealing. Essentially, individuals adjust their behavior based on their assessment of the effort required to achieve specific outcomes and the value they attach to those outcomes. Within the realm of innovative entrepreneurship, organizational management faces a range of challenges. Among these, the rapidly evolving business environment stands out, necessitating constant adaptability and proactive planning. Additionally, attracting and maintaining talent is crucial, as the success of any enterprise heavily relies on its ability to recruit and retain key individuals. Furthermore, the establishment of an organizational culture has a profound impact on employee satisfaction, loyalty, and their capacity for innovation. Resource management also plays a pivotal role, particularly in the efficient allocation of funds, time, and technological resources. Lastly, competitive market forces and the need for strategic adjustments are realities that innovative entrepreneurial ventures must navigate, demanding continuous strategic realignment to maintain competitiveness. Against this backdrop of complexity and dynamism, the application of expectancy theory offers valuable insights for organizational management. By understanding and applying this theory, innovative entrepreneurial ventures can more effectively stimulate employee motivation, refine incentive systems, enhance goal management, and foster employee growth and engagement. To a certain extent, these functions can assist enterprises in overcoming various challenges in organizational management processes, thereby bolstering overall organizational performance.

Large language model based agent for process planning of fiber composite structures

Process planning is a crucial activity, connecting product development and manufacturing of fiber composite structures. Recently published Large Language Models (LLM) promise more flexible and autonomous workflows compared to state of the art automation methods. An autonomous agent for process planning of fiber composite structures is implemented with the LangChain framework, based on OpenAI’s GPT-4 language model. The agent is equipped with deterministic tools which encode a-priori process planning knowledge. It can handle different process planning problems, such as cycle time estimation and resource allocation. Combinations thereof are solved through executing a multi-step solution path.

Practical guideline to efficiently detect insurance fraud in the era of machine learning: A household insurance case

AbstractIdentifying insurance fraud is a difficult task due to the complex nature of the fraud itself, the diversity of techniques employed, the rarity of fraud cases observed in data sets, and the relatively limited allocation of human, financial, and time resources to carry out investigations. The aim of this paper is to provide a clean and well structured study on modeling fraud on home insurance contracts, using real French data from 2013 to 2017. Several methods are developed to identify risk factors and unusual customer behaviors. Traditional econometric models as well as new machine‐learning algorithms with good predictive performance and high operational efficiency are tested, while maintaining method interpretability. Each methodology is evaluated on the basis of adequate performance measures and the issue of imbalanced databases is also addressed. Finally, specific methods are applied to interpret the results of the machine‐learning methods.

Optimal layout planning for human robot collaborative assembly systems and visualization through immersive technologies

The deployment of Industry 4.0 emerging technologies such as Augmented reality (AR), Virtual reality (VR), and collaborative Robots enhances flexibility and precision in the manufacturing systems. A flexible, and collaborative manufacturing system with robots and human integrates the prominent attributes of customized automation and fortify the safety of the human workforce. The proposed work involves the approach of AR-assisted assembly layout configurations for the Human-Robot Collaboration (HRC) production system, which is a typical example of the smart manufacturing system. The reconfiguration/configuration of the assembly layout is triggered with respect to the demand of new variants into the market and the upgradation of new technologies in the existing system. A multi-objective algorithm is proposed to maximize the production outlet with effective utilization of floor space, minimum idle time and optimum resource allocation. The selected work is branched into three phases: To begin with, the approach of task allocation is constructed to identify the suitable resources (human, robot) for the appropriate assembly tasks in the HRC system. The outcomes such as resource allocation, number of resources, and footprint of resources are considered as input to the optimization model. Further, modified particle swarm optimization (MPSO) is deployed to generate a feasible assembly layout for the HRC manufacturing system. Additionally, the breath-first approach has been utilized to generate the optimal aisle as a conveyor in the layout. Notably, the comparisons studies have been carried out in this work, which facilitates to explore the robustness of proposed modified PSO algorithms for the selected applications. Subsequently, the virtual and AR tool is deployed for the layout planer to explore and validate the obtained layout from the PSO model in both the virtual and physical environment of the industrial workspace. Finally, an industrial case study of rotary gear pump assembly system is adopted for the implementation of the proposed framework.

The mental health impact of the COVID-19 pandemic on post-secondary students: A longitudinal study

This prospective longitudinal study measured sex-specific changes in depression, anxiety, and stress scores using, validated Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the Perceived Stress Scale (PSS) in a cohort of 1445 post-secondary students (500 males, 945 females) assessed at three time points from December 2020 to January 2022. Participants were ascertained from a population of 15,585 students with in-person activities on campus at baseline and recruited from December 2020 to January 2021. We also assessed how sociodemographic characteristics influenced students' mental health outcomes. Inverse probability weighting was used to account for missing data and attrition. Linear mixed effects models were used to analyze the relationship between the mental health scores in each questionnaire, demographic and academic data, and public health stringency measured by the local stringency index. No change was observed in questionnaire scores over time for males and females, but the stringency index was significantly associated with increased stress. Being in a non-health-related-field or being white affected males and females differently for stress and anxiety, but not depression. Demographics tended to be more influential on females’ mental health than males. In conclusion, mental health resource allocation in time of emerging pandemic could benefit from targeted interventions.

Challenges in Designing Blended Learning: the Experience of the “Schools of Instructional Design”

The concept of “blended learning” has been widely discussed in Russian higher education. However, there are different interpretations of the concept and its status is not completely clear in the Russian regulatory environment. Although the effectiveness of this mode of education has been repeatedly recognised, systematic measures for the introduction of blended learning by universities are not being implemented. One of the factors for the success of such an implementation is the high-quality instructional design for blended learning. Difficulties in instructional design use can lead to the abandonment of blended learning or the adoption of less efficient practices. This paper explores the challenges faced by representatives of Russian universities involved in designing and teaching courses in a blended mode during the COVID-19 pandemic. The method used in the study is an online survey which was conducted in 2020-2021. The respondents were participants in the professional development program “School of Instructional Design for Blended Learning”. The results of the study showed that the respondents’ professional deficits in the design of blended courses were associated with the development of new ways of designing and teaching, growth of the course quality and student-centeredness, and more efficient allocation of time resources and use of online tools. The development stage, which involves the creation or updating of digital learning resources, turned out to be the most challenging for designing. Most respondents used LMS but were not satisfied with the quality of its application; also, the majority faced the problem of retaining student engagement in blended learning. The respondents would like to improve their formative and summative assessment practices and strengthen the course quality assessment practices. Commitment to overcome the identified difficulties can contribute to the creation of more effective practice-oriented professional development programs on teaching the blended learning design for educators, instructional designers and academic advisors in Russian higher education, leading to an increase in its quality.

ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi Networks

Charging processes are the key to promoting electric taxis and improving their operational efficiency due to frequent charging activities and long charging time. Nevertheless, optimizing charging resource allocation in real time is extremely challenging because of uneven charging demand/supply distributions, heuristic-based charging behaviors of drivers, and city-scale of the fleets. The existing solutions have utilized real-time contextual information for charging recommendation, but they do not consider the much-richer fleet information, leading to the suboptimal individual-based charging recommendation. In this paper, we design a data-driven fleet-oriented charging recommendation system for charging resource allocation called ForETaxi for electric taxis , which aims to minimize the overall charging overhead for the entire fleet, instead of individual vehicles. ForETaxi considers not only current charging requests but also possible charging requests of other nearby electric taxis in the near future by inferring their status in real time. More importantly, we implement ForETaxi with multiple types of sensor data from the Chinese Shenzhen city including GPS data, and taxi transaction data from more than 13,000 electric taxis, combined with road network data and charging station data. The data-driven evaluation results show that compared to the state-of-the-art individual-based recommendation methods, our fleet-oriented ForETaxi outperforms them by 16% in the total charging time reduction and 82% in the queuing time reduction.

Real-time resource allocation in the emergency department: A case study

Overcrowding is a phenomenon that affects Emergency Departments (EDs) worldwide determining a harmful impact on the healthcare provided. Because of the wide variety of different patient paths, in the literature the ED processes are usually modeled making significant assumptions, and neglecting fundamental aspects. Such assumptions could make sense for strategic or tactical decisions but nowadays the objective most frequently required by practitioners is the optimization of already available resources. To deal with this problem, we need to act at the operational level, with a particular attention to resource allocation in real time since arrivals and activities to be performed are known only over time. In this paper we present the case study of an Italian ED to investigate if an online allocation algorithm based on prioritization combined with a prediction tool can improve the ED performance, alleviating the overcrowding. Then, we propose several policies for the online allocation of the ED resources, which take into account the real-time state of the ED and the prediction of the next activities provided by an ad hoc process mining model. The proposed approach is validated and analyzed on the case study through a fine-grained simulation model. Results suggest that if the decisions of allocating resources for the execution of activities take into consideration the probably subsequent activities, then there is a room for improvement for the average door-to-doctor time, ED Length-of-Stay, and resource utilization.

Equilibrated and Fast Resources Allocation for Massive and Diversified MTC Services Using Multiagent Deep Reinforcement Learning

Massive and diversified machine type communication (MTC) service is one of the development trends of MTC in Internet of Things (IoT). Meanwhile, realizing network functions virtualization (NFV) is inseparable from reasonable virtual network function (VNF) scheduling and resource allocation. For VNF scheduling and resource allocation of MTC services, recently, deep reinforcement learning (DRL) has become one of the feasible solutions. However, existing DRL solutions have problems of inapplicability to the environment with both discrete and continuous variables, long training, time and nonequilibrium resource allocation. In this article, we first model the end-to-end (E2E) VNF scheduling and resource allocation of core network nodes, links, and access network subcarriers with different strategies, respectively, and propose a compound variable optimization problem aiming at maximizing the net income of the network provider. Then, we propose the mapping scheme of the absolute value of the signum function (ASgn mapping scheme) to simplify the compound variables into continuous variables of the optimization problem, so that the DRL algorithm is applicable. Moreover, we propose a model paralleling multiagent twin delayed deep deterministic (MPMA-TD3) policy gradient algorithm to handle massive services, reduce training time, and action space of agents. Finally, we improve the MPMA-TD3 algorithm to handle diversified services, solve the problem of nonequilibrium resources allocation, and realize the reasonable resource allocation for each service. Simulation results show that the proposed algorithms are better than other algorithms in reward, delay, cost, and training time for massive services. Further, the Improved MPMA-TD3 algorithm has the best service equilibrating ability.

Variable False Alarm Rate Detection Framework for Phased Array Radar

This paper considers a low signal-to-noise ratio (SNR) target detection problem for phased array radar, where classical constant false alarm rate (CFAR) detection framework would cause missed detection. To overcome this problem, we propose a variable false alarm rate (VFAR) detection framework for phased array radar to ensure the detection performance for low-SNR targets in multi-stage target detection. First, exploiting the previous detected results, the searching area of interest is divided into multiple sub-areas, while automatically reducing the false alarm probability of each range section of each frame. Then, a joint allocation strategy of beam and time resources accounting for maximizing the weighted sum of SNRs of spatial sectors in the whole surveillance area of the phased array radar along with some practical coupled constraints, is proposed. Next, the fuzzy analytic hierarchy process and convex optimization methods are, respectively, developed to obtain beam and time resource allocation scheme. Finally, the effectiveness and superiority of the proposed framework are highlighted and evaluated by numerical simulations.

Determination of Importance of Key Decision Points in the Technology Commercialization Process: Attitude of the US and German Experts

With the help of commercialization, inventions become marketable commodities that find new ways of solving problems. Turning technology into reality requires an excellent understanding of the development process from idea to market of the technology. The primary purpose of this article was to examine the commercialization process of inventions and divide the commercialization process into stages that culminate in decision points. Opinions of different authors and representatives of R&D organizations were compared concerning the content of technology commercialization, which is understood and named differently in the scientific and practical literature. Later, with the help of two groups of experts from the US and Germany, the importance of key decision points was determined. The research results were summarized using the MCDM method: the integrated Fuzzy Delphic–Eckenrode Likert-type Scale-based Rating Technique (FDELSRT). The results of this study can be applied in practice to making strategic decisions related to the allocation of efforts, limited time, and financial resources based on the determined importance of key decision points. Research in different countries and the comparison of results will identify areas and opportunities for further mutual learning and more intensive, mutually beneficial international cooperation in technology development.

Implementation of an Advanced Nursing Practice to advance transcultural healthcare: A practical development project

Implementation of an Advanced Nursing Practice to advance transcultural healthcare: A practical development project Abstract. Background: Patients and staff with different cultural backgrounds encounter each other in different roles at the children's hospital. Cultural differences are at the heart of misinterpretations and misunderstandings that can result in errors and prolong hospitalisation. Aim: To meet the challenges of reaching a common understanding in trans-cultural care, improve empowerment and ensure adequate treatment of children and family alike we introduced an ANP trans-cultural care team (ANP-TKV). Methods: The role of the ANP-TKV team was conceived as a project, encompassing a stakeholder and risk analysis, defining of the organisation, the setting of milestones, resources and controlling, as well as establishing an information concept. Results: The project concept successfully integrated and consolidated the APN-TKV team into our hospital activity. The team is now regularly consulted to manage complex trans-cultural situations, sensitises diverse professional groups and initiates professional inputs. It also provides helpful material and is able to anticipate potential current trans-cultural issues. Discussion: The evidence-based work of the ANP-TVK team has exerted positive effects on therapeutic teams as well as patients and their families and initiates a cultural change within the hospital. It is now an operative addition to the hospital's strategic commission. Further development of the meaning of trans-cultural and the question of allocation of financial and time resources to the APN-TKV team are presently under review.

Performance Analysis of Distance-Based D2D Matching Mechanism

The traditional cellular architecture where devices connect to their service base station (BS) may cause poor performance especially for edge users. Device-to-device (D2D) communication enables nearby user as a relay to help BS forward information, thereby improving the network coverage and quality of service (QoS) of edge users. This paper proposes a distance-based D2D matching mechanism for general cellular networks, where a relay user who successfully connects to its targeted BS can transmit data to its closest user for D2D communication. A link of BS to D2D pair contains two sublinks, which occur at different time phases in each cell. Assuming a nonsynchronous system, we consider that there exists cross-layer interference for D2D links. Based on the techniques of stochastic geometry, we develop the performance of coverage probability and ergodic rate of the D2D network. A key intermediate step in this analysis is the derivation of the interference expressions for D2D links caused by BSs and cochannel D2D users. Then, we derive the meta distribution of the signal-to-interference ratio (SIR) to capture the performance changes of individual links. Simulation results demonstrate that our matching mechanism based on the appropriate time resource allocation favors the edge users with a higher probability of successful communication and transmission rate.

Edge intelligence assisted resource management for satellite communication

Satellite communication has been seen as a vital part of the sixth generation communication, which greatly extends network coverage. In satellite communication, resource management is a key problem attracting many research interests. However, previous study mainly focuses on throughput improvement via power allocation and spectrum assignment and the proposed approaches are mostly model-based and dedicated to specific problem structures. Fortunately, with the trend of edge intelligence, complex resource management problems can be efficiently resolved in a model-free manner. In this paper, a joint beam activation, user-beam association and time resource allocation approach is proposed. The core idea is using stochastic learning at the ground station to identify active user-link beams to meet user rate demand. In addition, the convergence, optimality and complexity of our proposal are rigorously discussed. By simulation, it is shown that the rate goal of most of the users can be met and meanwhile satellite energy is saved owing to much less active beams.

Hedge Funds: Resolving Myths about ESG Integration

Despite the widespread acceptance of the ESG concept, its adoption by the hedge fund industry has been relatively slower than in other asset classes. A series of misconceptions and myths seems to hinder the conviction level in the hedge fund universe. This article examines the top misconceptions about ESG integration and hopefully clears a path for increased adoption in the hedge fund investment management process. It argues that ESG integration can be adopted in all hedge fund strategies and provides meaningful benefits to both hedge funds and their clients. By incorporating ESG considerations early in the initial underwriting process, a hedge fund manager can potentially improve the future risk-adjusted return for the fund without necessarily creating a social tilt in the portfolio. Importantly, senior leadership should champion its firm-wide adoption by being intentionally strategic about the time commitment and resource allocation.