Search Results (147)

Background/Objectives: Military Service Members (SMs) require optimal nutrition to support health, readiness, and job performance. However, they often fall short of meeting nutrition guidelines. This study aimed to determine the impact and feasibility of implementing the U.S. Marine Corps (USMC) “Fueled to Fight^®” (F2F) nutrition program in non-appropriated fund (NAF) food venues. Objectives included evaluating changes in Military Nutrition Environment Assessment Tool (mNEAT) scores, feasibility of implementing and maintaining F2F strategies, and influence on customer purchasing patterns. Methods: Researchers conducted a pre-post interventional study from January to December 2024 at three NAF food venues across two USMC bases. F2F strategies, including identifying items using a stoplight color coding system (Green = healthy, Yellow = less healthy, Red = least healthy), menu revisions, food placement, promotion, and marketing, were implemented. Data included mNEAT assessments, sales reports, and stakeholder focus groups. Generalized Estimating Equations models were used to analyze sales data. Results: mNEAT scores increased across all venues post-intervention. Availability and sales of Green items increased, while sales of Red items decreased in some venues. Profit increased at all three food venues. Focus groups revealed feasibility and provided insights for future interventions. Conclusions: F2F interventions in NAF food venues are feasible and can positively impact the food environment and customer purchasing patterns without negatively affecting profit. This study highlights the importance of integrating nutrition programs into all military food venues, not just government-funded dining facilities, to support the nutritional fitness and readiness of SMs. Full article

This study presents a graph knowledge-enhanced iterated greedy algorithm that incorporates dual directional decoding strategies, disjunctive graphs, neighborhood structures, and a rapid evaluation method to demonstrate its superior performance for the hybrid flowshop scheduling problem (HFSP). The proposed algorithm addresses the trade-off between the finite solution space corresponding to solution representation and the search space for the optimal solution, as well as constructs a decision mechanism to determine which search operator should be used in different search stages to minimize the occurrence of futile searching and the low computational efficiency caused by individuals conducting unordered neighborhood searches. The algorithm employs dual decoding with a novel disturbance operation to generate initial solutions and expand the search space. The derivation of the critical path and the design of neighborhood structures based on it provide a clear direction for identifying and prioritizing operations that have a significant impact on the objective. The use of a disjunctive graph provides a clear depiction of the detailed changes in the job sequence both before and after the neighborhood searches, providing a comprehensive view of the operational sequence transformations. By integrating the rapid evaluation technique, it becomes feasible to identify promising regions within a constrained timeframe. The numerical evaluation with well-known benchmarks verifies that the performance of the graph knowledge-enhanced algorithm is superior to that of a prior algorithm, and seeks new best solutions for 183 hard instances. Full article

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. In general, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. It is also difficult to manually create and implement the quantum algorithm for the swap circuit as the size of the system increases. Here, we propose a different approach: to use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB 2022a. Training is conducted for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that the scale-up is very effective and relatively easy, and the effects of noise and decoherence are $reduced$ as the size of the system increases. Full article

Energy-aware scheduling plays a critical role in modern computing and manufacturing systems, where energy consumption often increases with job execution order or resource usage intensity. This study investigates a scheduling problem in which a sequence of heterogeneous jobs—classified as either heavy or light—must be assigned to multiple identical machines with monotonically increasing slot costs. While the machines are structurally symmetric, the fixed job order and cost asymmetry introduce significant challenges for optimal job allocation. We formulate the problem as an integer linear program and simplify the objective by isolating the cumulative cost of heavy jobs, thereby reducing the search for optimality to a position-based assignment problem. To address this challenge, we propose a structured assignment model termed monotonic machine assignment, which enforces index-based job distribution rules and restores a form of functional symmetry across machines. We prove that any feasible assignment can be transformed into a monotonic one without increasing the total energy cost, ensuring that the global optimum lies within this reduced search space. Building on this framework, we first present a general dynamic programming algorithm with complexity $O\left(n^2{m}^2\right)$. More importantly, by introducing a structural correction scheme based on misaligned assignments, we design an iterative refinement algorithm that achieves global optimality in only $O\left(n{m}^2\right)$ time, offering significant scalability for large instances. Our results contribute both structural insight and practical methods for optimal, position-sensitive, energy-aware scheduling, with potential applications in embedded systems, pipelined computation, and real-time operations. Full article

Intensive farming in urban suburbs often causes habitat loss, soil erosion, wastewater discharge, and agricultural productivity decline, threatening long-term benefits for the local community. We developed a nature-based solution for sustainable land restoration by establishing “Green Treasure Island” (GTI). The aim of this study is to evaluate the ecological restoration effectiveness of GTI and explore its feasibility and replicability for future applications. The core eco-functional zone of GTI—a 7 hm² forested wetland—embedded a closed-loop framework that integrates land consolidation, ecological restoration, and sustainable land utilization. The forested wetland efficiently removed 65% and 74% of dissolved inorganic nitrogen and phosphorus from agricultural runoff, raised flood control capacity by 22%, and attracted 48 bird species. Additionally, this biophilic recreational space attracted over 3400 visitors in 2022, created green jobs, and promoted local green agricultural product sales. Through adaptive management and nature education activities, GTI evolved into a landmark that represents local natural–social characteristics and serves as a publicly accessible natural park for both rural and urban residents. This study demonstrates the feasibility of creating GTI for improving ecosystem services, providing a practical, low-cost template that governments and local managers can replicate in metropolitan rural areas worldwide to meet both ecological and development goals. Full article

The implementation and practice of palliative medicine have numerous boundaries in low- and middle-income countries (LMICs), stemming from various cultural, legal, and religious concerns. Additionally, professional education in palliative care medicine in these countries is severely lacking, especially when compared with developed countries. Background/Objectives: To enhance and demystify palliative medicine practice to health care providers in LMICs. Methods: We developed a novel and comprehensive course in palliative care medicine and end-of-life (EOL) care, specifically within the context of management of patients with traumatic brain injury (TBI). We performed both immediate pre-course and post-course analysis of course participant comprehension and feedback, as well as a one-year post-course analysis and small group discussion. Results: The comprehension of the course material was strong, as participants scored an average of 13.9 points better on the post-test compared to the pre-test (49.6% vs. 35.7%, p < 0.001). Participants in the one-year follow-up session reported long-term applicability of the course material in their respective practice settings, with all participants reporting that they utilize the course material often. Small group discussion responses indicated a strong level of comprehension of the course material. Conclusions: Providing education in palliative medicine to health care professionals in LMICs is feasible, and likely to be both well-received and strongly influential to local medical practice. Local cultural and religious practices may be less of a barrier to the provision of palliative medicine than previously considered. Practicing palliative medicine, particularly at EOL, may strengthen patient–provider relationships, improve job satisfaction among health care providers, and improve the perception of medical care provided in LMIC medical settings. Full article

The development of offshore wind energy has emerged as a key driver of economic growth and energy transition in Europe, with the Baltic Sea region playing a crucial role in this transformation. This paper examines the impact of offshore wind farm (OWF) supply chains on regional economic development, focusing on the strategic role of ports in facilitating these operations. Particular emphasis is placed on Port Ustka, which has been identified as a potential logistics and service hub for offshore wind projects in Poland’s Exclusive Economic Zone. The study explores the integration of local ports into offshore wind supply chains and their contribution to industrial development, job creation, and infrastructure modernization. Through an analysis of infrastructure parameters, logistical capabilities, and economic impacts, the paper assesses the feasibility of Port Ustka as a service base for major offshore wind projects, such as Baltica 2 and 3. The findings highlight the importance of port infrastructure investments, supply chain optimization, and policy support in maximizing the economic benefits of offshore wind development. By evaluating the challenges and opportunities associated with offshore wind logistics, this paper provides strategic insights for policymakers, investors, and industry stakeholders. The research underscores the necessity of modernizing port facilities, improving transport connections, and fostering local supply chain participation to enhance the efficiency and sustainability of offshore wind operations in the Central Baltic Region. Full article

Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations. Full article

The concept of a four-day workweek (4DWW) has gained traction as organisations explore ways to improve employee well-being and productivity. This study investigates the opportunities, challenges, and perceived feasibility of adopting a 4DWW in the UK, from the perspective of employees on five-day schedules. A qualitative research design was employed, involving semi-structured interviews with 14 participants from diverse sectors, including education, finance, healthcare, and manufacturing. A thematic analysis identified the key benefits, concerns, and contextual influences at an employee, organisational, and wider system level. Switching to a 4DWW was seen as an opportunity to enhance work–life balance and promote employees’ well-being and job satisfaction. Concerns were raised about increased workload pressure, coordination difficulties, and financial viability, particularly in roles requiring continuous operations. Findings highlighted the role of strong leadership and clear governmental policy frameworks to support 4DWW adoption and sustained implementation. Full article

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate. Full article

Environmental conditions, job tasks, and clothing choices influence the thermal comfort of workers. While it is impossible to control outdoor environmental conditions, selecting appropriate clothing for workers is feasible. Personal protective equipment often does not completely protect outdoor workers at high air temperatures. In such cases, cooling garments can help dissipate body heat and be worn with standard work clothes or uniforms. Currently, there is a lack of consensus in the literature regarding the characterisation of this type of garment. This study employed an innovative laboratory testing method that integrates a thermal manikin with a computer simulation program to assess the thermal sensation and comfort of various garment types in different activities and environmental conditions. This advanced approach enables a thorough evaluation of cooling garments that considers their physical properties and interactions with the human body. The findings confirm the efficacy of the tested cooling mechanisms, highlighting that the air circulation mechanism was the only one that consistently maintained user comfort across low, medium, and high metabolic activity levels. This study aims to assist users in selecting the most suitable cooling mechanism for the market based on the type of work or activity being performed. Full article

In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. It integrates the scheduling of the workpieces, machines, and maintenance personnel to improve the response efficiency of emergency equipment maintenance. To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. Further, the generated solutions are locally adjusted via the feasible solution optimization strategy to ensure that the solutions satisfy all the constraints and ultimately generate a Pareto optimal solution set with high quality. Simulation results based on standard examples and real cases show that the ACODDQN algorithm exhibits significant optimization effects in several tests, which verifies its superiority and practical application potential in dynamic scheduling problems. Full article

The dual-resource constrained flexible job shop scheduling problem with variable sublots (DRCFJSP-VS) can be decomposed into four subproblems: the sublot splitting subproblem, the sublot sequencing subproblem, the machine assignment subproblem, and the worker assignment subproblem, which are difficult to solve efficiently using conventional methods. The introduction of variable-size batch splitting and the constraints of multiple levels and skills of workers further increase the complexity of the problem, making it difficult to solve efficiently using conventional methods. This paper proposes a mixed-integer linear programming (MILP) model to solve this complex problem and introduces a two-stage multi-objective evolutionary algorithm (TSMOEA). In the first stage of the algorithm, an improved multi-objective discrete difference evolutionary algorithm is used to optimize the dual-resource constrained flexible job shop scheduling problem; in the second stage, an adaptive simulated annealing algorithm is used to search for variable-size batch splitting strategies. To validate the feasibility of the model, the solution results are obtained using the CPLEX solver and compared with the results of TSMOEA. The performance of TSMOEA is compared with NSGA-II, PSO, DGWO, and WOA on improved instances. The results show that TSMOEA outperforms the other algorithms in both IGD and HV metrics, demonstrating its superior solution quality and robustness. Full article

This study examines the socio-technical challenges and public acceptance of hybrid pumped-hydropower storage (HPHS) technology within the broader context of energy transition in two European coal-mining regions: Western Macedonia, Greece, and the Łódzkie Region, Poland. These regions, deeply rooted in lignite mining, face profound socio-economic transformations driven by the EU Green Deal and its commitment to achieving net-zero emissions by 2050. The transition from coal dependency to renewable energy represents not only a critical environmental goal but also a significant socio-economic challenge for local communities, necessitating innovative and inclusive strategies to mitigate impacts and ensure equitable outcomes. The research integrates desk studies with stakeholder surveys (n = 129) to assess public awareness, perceived impacts, and acceptance of HPHS technology as a repurposing solution for decommissioned open-pit lignite mines. Results reveal that while awareness of the energy transition process is high (90% in Western Macedonia and 80% in Łódzkie Region), familiarity with HPHS technology varies significantly (76% and 48%, respectively). Support for implementing HPHS in former mining sites is stronger in Western Macedonia (73%) compared to Łódzkie Region (63%), with knowledge of HPHS correlating positively with acceptance (r = 0.83, p < 0.01). Both regions recognize the environmental benefits of HPHS, including improved air quality and biodiversity; yet, socio-economic challenges such as job losses, reduced income levels, and limited opportunities for reskilling persist, particularly in Łódzkie Region, where 77% of respondents view the energy transition as negatively impacting the labor market. By focusing on regions historically dependent on mining, this study highlights the critical role of addressing the unique needs of communities undergoing systemic transformation. The repurposing of former lignite mines into HPHS facilities offers a promising pathway for balancing environmental objectives with local socio-economic revitalization. However, success requires region-specific strategies, including transparent communication, stakeholder involvement, and targeted investment in workforce adaptation and infrastructure. These findings contribute to the growing discourse on how socially inclusive and technically feasible solutions can drive equitable energy transitions in post-mining regions. Full article

This paper explores a development strategy for peripheral economies by advocating for a paradigm shift from traditional economic models that rely on accumulating foreign reserves. It proposes the job guarantee (JG) policy, an automatic stabilizer based on a reserve pool of employed individuals, as a cornerstone for fostering sustainable and inclusive growth. Grounded in modern monetary theory (MMT), this study critiques the conventional approach that prioritizes external reserves and highlights the potential of MMT in offering a more autonomous development path for developing countries. A systematic review of the literature, using the PRISMA methodology, reveals significant divergence between MMT advocates and critics, particularly regarding monetary sovereignty and the feasibility of implementing macroeconomic policies in peripheral economies. This study emphasizes that while external constraints remain, the MMT perspective calls for flexible exchange rates, low interest rates, and capital controls as part of a broader strategy to reduce dependency on foreign currencies. The proposed approach prioritizes full employment, the mobilization of domestic resources, and structural transformation through policies like import substitution. Although the shift may involve the slower accumulation of capital, it offers a more equitable and stable development path. Ultimately, this analysis underscores the potential of MMT to expand the external constraint and enable sustainable development, despite challenges in implementation and political resistance. Full article