Dynamic Demand Research Articles

Performance comparison of dynamically controlled particle swarm optimization and grey wolf optimizer for dynamic power reserve scheduling in multi areas problems

The primary goal of Multi-Area Economic Dispatch (MAED) is to effectively meet the dynamic power demands of multiple interconnected regions while optimizing the economical generation dispatch strategy. This paper introduces a comprehensive investigation into the application of two advanced optimization techniques, namely Dynamically Controlled Particle Swarm Optimization (DCPSO) and thereafter Grey Wolf Optimizer (GWO), and their variant adaptations to address the complex challenges posed by MAED issue. The performance and efficacy of these strategies are rigorously assessed in three distinct scenarios: (a) Single-Area Power System with Three Generation Units: This scenario represents a fundamental single-area power network comprising three generation units. (b) Two-Area System with Four Generating Units: In this case, a two-area system with four generating units is analyzed. (c) Four-Area System with Forty Generation Units and Six Tie Lines. In the most intricate scenario, a four-area interconnected power system is scrutinized, featuring a total of forty generation units. GWO technique produces the cost saving of $ 0.1 for single area, 2 area cost saving is $ 0.11 likewise for 4 area the cost saving is $ 474.10 over the PSO. The execution time taken by GWO is 12.7 sec. less compared to DPSO.

Management Training Program and Leadership Mental Development for Field Staff of PT Intan Pertiwi Industri

Management Training and Leadership Mental Coaching Program at PT INTIWI aims to enhance managerial skills and the mental well-being of field personnel. Against the backdrop of understanding the complexity of fieldwork and the increasingly dynamic market demands, this program is designed to cultivate outstanding field leaders. Training methods involve discipline training, case-based training, pre-tests, and post-tests. Evaluation is conducted through pre-tests and post-tests, as well as participant feedback and field performance monitoring. The program is expected to have a positive impact, such as increased productivity, job satisfaction, and a reduction in stress levels. The success of this program will create a more balanced work environment and contribute to the strategic goals achievement of PT INTIWI.

Stable QoE-Aware Multi-SFCs Cooperative Routing Mechanism Based on Deep Reinforcement Learning

The explosive development of the Internet of Things (IOT) has stimulated the sudden and dynamic demand pattern of potential Service Function Chain (SFC) traffic. The virtual network built based on the requirements of Multiple SFCs (Multi-SFCs), sharing the resource, should provide better Quality of Experience (QoE) for Multi-SFCs according to the real-time network status. However, simultaneous Multi-SFCs in the routing process compete for the VNF resources of the same node and preempt the same link. The instability of QoE caused by node invalidation and link faults is highlighted. Therefore, to provide a more efficient and stable service to Multi-SFCs, quantitative QoE utility and stability models and cooperative Multi-SFCs path allocation are needed. Considering the limited resources, this paper designs a stable QoE-aware Multi-SFCs Cooperative Routing Mechanism (CRM) based on Deep Reinforcement Learning (DRL). Firstly, we model the problem of Multi-SFCs cooperative routing aiming at optimizing diverse QoEs of utility, stability and delay, and formulate it as a multi-objective optimization problem. Moreover, the QoE stable queueing model based on node and link availability is formulated by Lyapunov drift, which can potentially mitigate the QoE fluctuations. Then we associate triple Dueling Deep Q-Networks (DDQNs) and propose a cooperative computing multi-task DRL to obtain the optimal path allocation policy. Experiments are conducted to verify the effectiveness and the results show that our method outperforms its peers on QoE, delay, throughput and stability.

Genetic structuring and species boundaries in the Atlantic stony coral Favia (Scleractinia, Faviidae)

AbstractScleractinian corals are the main modern builders of coral reefs, which are major hot spots of marine biodiversity. Southern Atlantic reef corals are understudied compared to their Caribbean and Indo‐Pacific counterparts and many hypotheses about their population dynamics demand further testing. We employed thousands of single nucleotide polymorphisms (SNPs) recovered via ezRAD to characterize genetic population structuring and species boundaries in the amphi‐Atlantic hard coral genus Favia. Coalescent‐based species delimitation (BFD* – Bayes Factor Delimitation) recovered F. fragum and F. gravida as separate species. Although our results agree with depth‐related genetic structuring in F. fragum, they did not support incipient speciation of the ‘tall’ and ‘short’ morphotypes. The preferred scenario also revealed a split between two main lineages of F. gravida, one from Ascension Island and the other from Brazil. The Brazilian lineage is further divided into a species that occurs throughout the Northeastern coast and another that ranges from the Abrolhos Archipelago to the state of Espírito Santo. BFD* scenarios were corroborated by analyses of SNP matrices with varying levels of missing data and by a speciation‐based delimitation approach (DELINEATE). Our results challenge current notions about Atlantic reef corals because they uncovered surprising genetic diversity in Favia and rejected the long‐standing hypothesis that Abrolhos Archipelago may have served as a Pleistocenic refuge during the last glaciations.

Advancing Lifelong Learning in the Digital Age: A Narrative Review of Singapore’s SkillsFuture Programme

Amidst the fourth industrial revolution, marked by swift technological advancements and changing economic environments, lifelong learning has risen as an essential cornerstone for developing people and society. Adult education, with a particular focus on skills learning, is vital in equipping individuals with the necessary competencies to navigate the dynamic demands of the modern workforce. This paper provides a qualitative analysis and commentary on the case study of Singapore’s SkillsFuture movement, an exemplary national initiative to promote skills learning among adults. Intending to reach a wide audience in educational science, we investigate the effectiveness and impact of this comprehensive programme and its implications for other countries. This article contributes to educational science and policy development by illustrating the importance of investing in adult education and skills development. By comprehensively studying the SkillsFuture experience, we offer valuable insights into establishing effective and inclusive lifelong learning ecosystems to foster a culture of continuous learning, equipping individuals to adapt and thrive in a volatile, uncertain, complex, and ambiguous global landscape.

Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin

AbstractTo meet the requirement of product variety and short production cycle, reconfigurable manufacturing system is considered as an effective solution in addressing current challenges, such as increasing customisation, high flexibility and dynamic market demand. Dynamic factory layout design and optimisation are the crucial factors in response to rapid change in the mechanical structure, software and hardware integration, as well as production capability and functionality adjustment. Nevertheless, in the current research, the layout design for reconfigurable manufacturing systems is usually simplified with autonomous devices being regarded as 2D shapes. Issues such as overlapping and transportation distance are also addressed in an approximate form. In this paper, we present a novel multi-agent cooperative swarm learning framework for dynamic layout optimisation of reconfigurable robotic assembly cells. Based on its digital twin established in the proposed learning environment (constructed in Visual Components and controlled by TWINCAT), the optimisation framework uses 3D digital representation of the facility models with minimal approximation. Moreover, instead of using a traditional centralised learning manner, multi-agent system could provide an alternative way to address the layout issues combined with the proposed decentralised multi-agent cooperative swarm learning. In order to verify the application feasibility of the learning framework, two aerospace manufacturing use cases were implemented. In the first use case, the layout compactness is reduced by 3.8 times compared with the initial layout setting, the simulated production time is reduced by 2.3 times, and the rearrangement cost decreased by 33.4$$\%$$ % . In addition, all manufacturing activity within the cell can be achieved with a feasible robot path, meaning without any joint limits, reachability or singularity issue at each key assembly point. In the second use case, we demonstrated that with the proposed dynamic layout optimisation framework, it is possible to flexibly adjust learning objectives by selecting various weight parameters among layout compactness, rearrangement cost and production time.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Understanding the Determinants of Lane Inefficiency at Fully Actuated Intersections: An Empirical Analysis

As urban traffic challenges intensify, the growing interest for fully actuated control systems in intersection management is on the rise due to their capacity to adapt to dynamic traffic demands. These systems play a crucial role in sustainable traffic solutions, significantly reducing delays and emissions and enhancing overall system efficiency. The optimal performance of these systems relies on effectively facilitating vehicle discharge at the saturation flow rate throughout the green period. This study introduces a new parameter, lane inefficiency, evaluating vehicle discharge effectiveness by comparing saturation flow rate with instantaneous discharge for each green period. It provides a comprehensive assessment of green utilization for specific lanes. This study also explores the impact of signal control system parameters and traffic flow characteristics on lane inefficiency using principal component analysis (PCA) and multiple linear regression models. This approach holistically evaluates how both signal control system and traffic flow parameters collectively influence efficient green period utilization. The findings emphasize the impact of critical factors on lane inefficiency, including green time, the proportion of total unused green time to green time, total unused green time, the percentage of heavy vehicles in departing traffic, the ratio of effective green time to cycle time, the total time headways of the first four vehicles, and queue length. Decision makers need to pay due attention to these parameters to enhance intersection performance and foster a more sustainable urban transportation network.

Low-carbon optimal dispatch of campus integrated energy system considering flexible interactions between supply and demand

To solve the problem of coupled and coordinated dispatch of multi-energy between campus integrated energy system operator and users. A bi-level Stackelberg low-carbon economic dispatch model with horizontal and vertical integrated demand response and dynamic supply and demand multi-energy pricing (DSDMEP) is proposed. Firstly, the DSDMEP is established to effectively coordinate the interests of the upper CIESO and lower users. Secondly, the combined heat and power containing the power to gas and carbon capture system and the reward and penalty stepped carbon trading mechanism (SCTM) are introduced on the energy supply side to minimize carbon emissions. Then, the HVIDR is introduced to the users to increase the energy coupling and adjustable price characteristics of supply and demand. Finally, the results show that the proposed model compared to other scenarios has its carbon emissions reduced by 21.72%, the benefits of CIESO are increased by 27.86%, and the cost of the users is reduced by 6.9%. This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the SCTM on the operation of the campus integrated energy system (CIES), as well as a sensitivity analysis of the HVIDR and peak and valley electricity price difference.

Identifying Students’ English Language Needs: Digital Business Students' Perspectives

This study examined the English language needs of students studying digital business, aiming to clarify their difficulties and provide recommendations for educators and policymakers. English is essential for communication between multinational corporations, start-ups, and freelancers in the digital business sector, making collaboration, communication, and information sharing easier. Students from various linguistic backgrounds may struggle to assimilate to English language requirements for their academic and professional tasks. The lack of knowledge on the specific English language requirements of students studying digital business was the cause of the current research gap. This study reveals that digital business students prioritize speaking skills, particularly in phone conversations and meeting discussions, as they are crucial for real-time interactions in the digital business environment. Writing skills are deemed less important, with 15% deemed very important. This suggests a shift towards skills better suited for immediate, interpersonal exchanges. The students also rank the functions of English language proficiency most frequently employed in their daily tasks, such as answering meeting questions, participating in discussions, speaking to foreigners by phone, answering working interview questions, scheduling appointments, writing CVs and application letters, writing reports, and responding to emails. The most prominent issues identified were presenting project/term papers orally, executing simulation tasks, attending group talks, and accurately speaking words. Listening skills were also identified as significant, with 55% of respondents stating their difficulty in capturing lecture notes via listening. Reading and analytical skills were identified as the most challenging, with 55% identifying key parts in texts and 35% predicting new vocabulary interpretations from context. Writing skills were identified as the most challenging, with 40% stating their difficulty. These findings emphasized the need for tailored approaches to enhance academic success and prepare students for the dynamic communication demands of the digital business world.

Exploring the alignment between paramedicine's professional capabilities and competency frameworks for current and evolving scopes of practice: a literature review

BackgroundTo adequately prepare graduates for the dynamic demands of paramedic practice, adopting a contemporary educational approach is essential. This involves collaborating to identify crucial competencies through input from industry stakeholders, experienced practitioners, and discipline-specific experts. Accreditation assumes a central role within this framework, serving as a cornerstone to ensure that paramedicine curricula align with paramedics' diverse and evolving professional roles.MethodsA narrative review of the literature and a directed search of grey literature were performed to identify specific developments in paramedicine competencies and scope of practice and mapped to the professional capabilities published by the Paramedicine Board of Australia. In determining a competency map and accreditation’s role in a competency framework specific to current and evolving paramedic practice, key documents were analysed using a qualitative approach based on content analysis to identify common traits among documents, countries and other professions.ResultsThe review process identified 278 themes that were further allocated to 22 major analytical groupings. These groupings could further be mapped to previously reported cognitive, technical, integrative, context, relationship, affective/moral competencies and habits of mind. At the same time, the highest-rated groupings were key competencies of intellectual skills, safety, accountability, clinical decision-making, professionalism, communications, team-based approach and situational awareness. Two groups were represented in the literature but not in the professional capabilities, namely Health and Social continuum and self-directed practice.ConclusionsThis review highlights the importance of measuring and validating the professional capabilities of Paramedicine Practitioners. The study explores various metrics and competency frameworks used to assess competency, comparing them against national accreditation schemes' professional capability standards. The findings suggest that accreditation frameworks play a crucial role in improving the quality of paramedicine practice, encompassing intellectual skills, safety, accountability, clinical decision-making, professionalism, communication, teamwork, and situational awareness.

Efficient Bike-sharing Repositioning with Cooperative Multi-Agent Deep Reinforcement Learning

As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in BSS because of the inefficiency of the existing bike repositioning strategy, which reallocates bikes according to a pre-defined periodic schedule without considering the highly dynamic user demands. While reinforcement learning has been used in some repositioning problems for mitigating demand-supply unbalance, there are significant barriers when extending it to BSS due to the dimension curse of action space resulting from the dynamic number of workers and bikes in the city. In this paper, we study these barriers and address them by proposing a novel bike repositioning system, namely BikeBrain, which consists of a demand prediction model and a spatio-temporal bike repositioning algorithm. Specifically, to obtain accurate and real-time usage demand for efficient bike repositioning, we first present a prediction model ST-NetPre, which directly predicts user demand considering the highly dynamic spatio-temporal characteristics. Furthermore, we propose a spatio-temporal cooperative multi-agent reinforcement learning method (ST-CBR) for learning the worker-based bike repositioning strategy in which each worker in BSS is considered an agent. Especially, ST-CBR adopts the centralized learning and decentralized execution way to achieve effective cooperation among large-scale dynamic agents based on Mean Field Reinforcement Learning (MFRL), while avoiding the huge dimension of action space. For dynamic action space, ST-CBR utilizes a SoftMax selector to select the specific action. Meanwhile, for the benefits and costs of agents’ operation, an efficient reward function is designed to seek an optimal control policy considering both immediate and future rewards. Extensive experiments are conducted based on large-scale real-world datasets, and the results have shown significant improvements of our proposed method over several state-of-the-art baselines on the demand-supply gap and operation cost measures.

A Bilevel EV Charging Station and DC Fast Charger Planning Model for Highway Network Considering Dynamic Traffic Demand and User Equilibrium

The rapid electric vehicle (EV) adoption and relatively short EV driving range urge city planners to expand charging infrastructure (i.e., EV charging stations (EVCSs) and direct current fast chargers (DCFCs)) in the highway network for supporting long-distance intercity EV travels. To properly address distinct roles as well as interactions of the EVCS planner, traffic network, power distribution networks (PDNs), and drivers, this paper explores a bilevel planning model in which the upper level determines EVCS and DCFC construction plans and the two lower-level subproblems describe the user equilibriumbased traffic assignment model of the traffic network and the operation model of PDNs. Moreover, the statistical charging time is modeled in the traffic network subproblem as a function of statistical charging demand and the number of DCFCs, leveraging the macro traffic pattern and micro charging behavior of individual drivers with balanced accuracy and computational complexity. The proposed model also captures the impacts of social factors, charging infrastructure, and driver behaviors on dynamic EV traffic demands. Reformulation and linearization techniques are applied to convert the proposed bilevel problem into a tractable single-level mixed-integer linear programming model for effective computation. The effectiveness of the proposed approach is tested on an intercity highway network with multiple PDNs. The findings from numerical case studies highlight the important interplay among the construction plans of EVCSs and DCFCs, the PDN constraints, the user equilibrium traffic network, and the EV drivers. Case studies illustrate the feasibility and necessity of considering the number of DCFCs to calculate the macro-perspective charging time in the user equilibrium traffic network model. The dynamic traffic demand, induced by social factors and traffic demand elasticity, was also found to be a crucial factor in reshaping EV traffic. Thus, properly considering these important factors would lead to a more accurate quantification of traffic demands and ultimately result in a more reasonable charging facility construction plan.

A review on Machine Learning Enhanced Predictive Maintenance for Electric Vehicle Power Electronics: A Pathway to Improved Reliability and Longevity

The emergence of electric vehicles (EVs) as a mainstream mode of transportation presents new challenges in the realm of power electronics, particularly concerning reliability and longevity. Power electronics are the cornerstone of EV performance, dictating efficiency, durability, and overall vehicle health. Traditional maintenance strategies fall short in addressing the dynamic operational demands and complex failure mechanisms inherent in EV power systems. This paper introduces a machine learning (ML)-enhanced predictive maintenance framework designed to revolutionize the upkeep of EV power electronics. By harnessing advanced ML algorithms, the framework predicts potential system failures and degradation patterns, enabling preemptive maintenance actions. A robust data-driven approach is employed, utilizing operational data and failure modes to train the predictive models. The efficacy of the proposed method is demonstrated through extensive simulation and real-world EV power system analyses, showcasing significant improvements in fault identification accuracy and maintenance scheduling optimization. The result is a substantial extension of component lifespan and a reduction in unplanned downtimes, propelling EV power electronics towards higher reliability standards. This work not only contributes a novel predictive maintenance methodology but also paves the way for adaptive maintenance regimes, tailored to the unique demands of EV power electronics systems in the pursuit of sustainable and resilient transportation solutions.

A Blended Approach to Improve Reliability and Efficiency of Active EDN via Dynamic Feeder Reconfiguration, Demand Response, and VVO

A Blended Approach to Improve Reliability and Efficiency of Active EDN via Dynamic Feeder Reconfiguration, Demand Response, and VVO

Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands

Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands

Capacity requirements and demand management strategies in meal delivery

Online restaurant aggregators, which connect diners with restaurants and organize the delivery of ordered meals, have experienced significant growth in recent years. Meal delivery logistics is quite challenging, primarily due to the difficulty in managing the supply of delivery resources, i.e., crowdsourced couriers, to satisfy dynamic and uncertain customer demand under very tight time constraints. In this paper, we study several questions in meal delivery operations focused on matching the correct levels of supply with demand. To ensure excellent customer service, delivery aggregators may, for example, decide to temporarily decrease demand during an operating day by temporarily reducing the delivery area for one or more restaurants. We show that such simple demand restriction strategies allow a significantly smaller fleet to meet service requirements. To simplify analysis, we focus on problem geometries that enable the use of stylized mixed integer programs to optimally deploy a fleet of couriers serving large numbers of orders. Applying the proposed framework to several scenarios with one and two depots, we conduct an extensive experimental study of the effects on system performance of (i) allowing courier sharing between multiple depots, (ii) relaxing the delivery deadlines of placed orders, and (iii) restricting demand through limited adjustment of the coverage of restaurants. The results demonstrate the potential effectiveness of different dispatch control and demand management mechanisms, in terms of both the required courier fleet size to serve requests and the coverage level of orders.

Recursive Learning Based Smart Energy Management With Two-Level Dynamic Pricing Demand Response

Recursive Learning Based Smart Energy Management With Two-Level Dynamic Pricing Demand Response

Mapping the educational Frontier: Unleashing the Potential of artificial intelligence talents through cooperative planning in the Guangdong-Hong Kong-Macao greater bay area

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has become an important hub for technological innovation and economic development in China. With the growing demand for artificial intelligence (AI) and big data technology talents, it is essential to develop educational cooperation within the GBA to develop a talent pool that can meet the changing needs in the region. This paper focuses on the development of dynamic demand for AI talents and proposes a strategic planning framework for educational cooperation in the GBA. We use the research idea of common attributes and key chain clustering-factor association selection-analysis of the driving force and subordination among factors-the key characteristics of AI talents. Using collinear analysis of citations and grounded theory methods, an operational definition of the influencing factors of AI talent literacy characteristics is constructed. Using the Interpretative Structural Modeling(ISM) and MICMAC (Matrice d’Impacts Croises-Multipication Applique A Classement), analyze and identify the driving force and subordination of the influencing factors of key traits of talents, and present the combined effect of multi-level factors of key traits of talents. Combined with the educational differences and complementary advantages in the GBA, five strategies and seven implementation suggestions for the GBA's AI talent education cooperation plan are formulated to establish a collaborative ecosystem that promotes the growth and integration of AI in the GBA.

BODY WEIGHT OF CHERAX QUADRICARINATUS AND INDICATORS OF HS-GROUPS IN THEIR LIVER AFTER INCLUSION OF DIFFERENT DOSES OF VERMICULTURE BIOMASS IN THE DIETS

Crustacean products have a positive dynamic demand among the population of the country. Crayfish meat is an effective source of essential amino acids and other biologically active substances for the human body. An important element in the technology of crayfish farming is their feeding. The growth rate of crayfish Сherax quadricarinatus depends on the content and origin of protein in their diets. An effective source of protein for crayfish can be the biomass of a hybrid of red California worms. However, the issue of determining the effectiveness of using vermiculture biomass grown on a substrate containing broiler manure fermented with active aeration in crayfish diets remains unexplored. The aim of the study is to investigate the effect of the optimal content of vermiculture biomass in diets on the growth of crayfish and the content of HS groups in their liver. To achieve the planned goal, in the conditions of the experimental farm of the Bila Tserkva National Agrarian University, crayfish from the I, II and III experimental groups were fed standard diets containing 10.0, 15.0 and 20.0 % of California worm biomass. Crayfish from the control group were fed standard diets (complete feed + oak leaves). The content of sulfhydryl groups in the homogenate from the liver of Cherax quadricarinatus was determined using the method of Ellman G.L. It was proved that feeding diets containing 20.0 % of vermiculture biomass increased the body weight of crayfish by 7.0 % compared to the control. The highest body weight of Cherax quadricarinatus at the end of the experiment was found in the group where the diet containing 15.0 % of California worm biomass was used. The difference with the control was 8.2 %. An increase in the preservation of crayfish by 8.0 % was found when feeding them 15.0 and 20.0 % of vermiculture biomass. The use of worm biomass increases the average daily and absolute growth of crayfish and does not cause a decrease in the content of sulfhydryl groups in their liver. The study of the chemical composition of muscle tissue of crayfish when fed diets containing vermiculture biomass is promising.