Operational Constraints Research Articles

Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.

Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning techniques applied to routinely collected data can enhance clinical tools and improve patient outcomes, but their effective deployment comes with significant challenges. While some tasks can be addressed by training machine learning models directly on the collected data, more complex problems require additional input in the form of data annotations. Data annotation is a complex and time-consuming problem that requires domain expertise and frequently, technical proficiency. With clinicians' time being an extremely limited resource, existing tools fail to provide an effective workflow for deployment in health care. This paper investigates the approach of intensive care unit staff to the task of data annotation. Specifically, it aims to (1) understand how clinicians approach data annotation and (2) capture the requirements for a digital annotation tool for the health care setting. We conducted an experimental activity involving annotation of the printed excerpts of real time-series admission data with 7 intensive care unit clinicians. Each participant annotated an identical set of admissions with the periods of weaning from mechanical ventilation during a single 45-minute workshop. Participants were observed during task completion and their actions were analyzed within Norman's Interaction Cycle model to identify the software requirements. Clinicians followed a cyclic process of investigation, annotation, data reevaluation, and label refinement. Variety of techniques were used to investigate data and create annotations. We identified 11 requirements for the digital tool across 4 domains: annotation of individual admissions (n=5), semiautomated annotation (n=3), operational constraints (n=2), and use of labels in machine learning (n=1). Effective data annotation in a clinical setting relies on flexibility in analysis and label creation and workflow continuity across multiple admissions. There is a need to ensure a seamless transition between data investigation, annotation, and refinement of the labels.

Experimental Validation of Offset-Free Model-Based Predictive Control in Voltage Source Inverters for Grid Connected and Microgrids Applications

This article presents the experimental validation of a model-based predictive control (MPC) strategy for the safe interconnection of voltage source inverters (VSI) with output LC filters for the grid connection of DC energy resources. The MPC is formulated as a quadratic programming (QP) problem and solved using the operator splitting quadratic programs (OSQP). The proposed approach incorporates integral action to achieve precise voltage magnitude reference tracking while accounting for modulated voltage limits and nominal current constraints within the control design. The effectiveness of the proposed strategy is validated through simulations conducted in MATLAB, demonstrating superior dynamic performance compared to the traditional hierarchical PI control. The implementation of the proposed MPC is experimentally verified on a VSI setup using the dSPACE MicroLabBox. The results confirm that the computational requirements are satisfied, establishing this approach as a practical alternative for modern power electronic systems. The proposed MPC for VSIs offers an effective approach to enforcing operational constraints, improving dynamic performance, and facilitating the robust integration of renewable energy sources in microgrids.

A Social Group Optimization Algorithm Using the Laplace Operator for the Economic Dispatch Problem

The economic dispatch (ED) problem focuses on the optimal scheduling of thermal generating units in a power system to minimize fuel costs while satisfying operational constraints. This article proposes a modified version of the social group optimization (SGO) algorithm to address the ED problem with various practical characteristics (such as valve-point effects, transmission losses, prohibited operating zones, and multi-fuel sources). SGO is a population-based metaheuristic algorithm with strong exploration capabilities, but for certain types of problems, it may stagnate in a local optimum due to a potential imbalance between exploration and exploitation. The new version, named SGO-L, retains the structure of the SGO but incorporates a Laplace operator derived from the Laplace distribution into all the iterative solution update equations. This adjustment generates more effective search steps in the solution space, improving the exploration–exploitation balance and overall performance in terms of solution stability and quality. SGO-L is validated on four power systems of small (six-unit), medium (10-unit), and large (40-unit and 110-unit) sizes with diverse characteristics. The efficiency of SGO-L is compared with SGO and other metaheuristic algorithms. The experimental results demonstrate that the proposed SGO-L algorithm is more robust than well-known algorithms (such as particle swarm optimization, genetic algorithms, differential evolution, and cuckoo search algorithms) and other competitor algorithms mentioned in the study. Moreover, the non-parametric Wilcoxon statistical test indicates that the new SGO-L version is more promising than the original SGO in terms of solution stability and quality. For example, the standard deviation obtained by SGO-L shows significantly lower values (6.02 × 10−9 USD/h for the six-unit system, 7.56 × 10−5 USD/h for the 10-unit system, 75.89 USD/h for the 40-unit system, and 4.80 × 10−3 USD/h for the 110-unit system) compared to SGO (0.44 USD/h for the six-unit system, 50.80 USD/h for the 10-unit system, 274.91 USD/h for the 40-unit system, and 1.04 USD/h for the 110-unit system).

Reducing Operational Costs in Sulselbar’s 150 kV System with Electromagnetic Field and Sine-Cosine Optimization

Dynamic economic dispatch (DED) is a crucial task in modern power systems, requiring efficient optimization to minimize generation costs while satisfying operational constraints. This study introduces a Hybrid Electromagnetic Field Optimization with Sine-Cosine Algorithm (EMFO-SCA) tailored to address the unique challenges of the Sulselbar 150 kV power system. Specifically, the algorithm is designed to handle non-linear cost functions, complex constraints, and dynamic load variations across a 24-hour scheduling period. EMFO-SCA achieves a balanced integration of global exploration (via Electromagnetic Field Optimization) and local exploitation (via the Sine-Cosine Algorithm), resulting in robust optimization performance. Applied to a system with seven active generator buses, EMFO-SCA demonstrates an average operational cost reduction of 0.27% compared to the Kho-Kho Algorithm (KKA). This improvement translates to measurable cost savings while maintaining strict adherence to generation limits, ramp rate constraints, and power balance at all intervals. For instance, during peak demand at 521.52 MW (hour 12), the method effectively minimizes costs without compromising operational reliability. The dual-phase design of EMFO-SCA enables faster convergence and higher accuracy than conventional methods, making it a scalable solution for real-world DED challenges. By optimizing power generation schedules dynamically and reliably, this study establishes EMFO-SCA as a significant advancement in energy system optimization, with clear potential for practical deployment in similar power systems.

Containment Control for High-Order Heterogeneous Continuous-Time Multi-Agent Systems with Input Nonconvex Constraints

This article investigates containment control for high-order heterogeneous continuous-time multi-agent systems (MASs) with input nonconvex constraints, bounded communication delays and switching topologies. Firstly, we introduce a scaling factor for the constraint operator to obtain an equivalent unconstrained system model. Following equivalent model transformations, we analyze the maximum distance from all agents to the convex hull spanned by leaders using norm-based differentiation. It is demonstrated that, within high-order heterogeneous continuous-time MASs subject to control input nonconvex constraints, the convergence of each agent into the convex hull spanned by leaders is guaranteed, provided that there exists at least one directed path from any leader to each agent within the union of communication topologies. Simulation examples are presented to validate the theoretical findings.

Active Eavesdropping Attack Scheduling for Cyber–Physical Systems With Operation Constraints

Active Eavesdropping Attack Scheduling for Cyber–Physical Systems With Operation Constraints

Patient preferences for gender-concordant physiotherapists in stroke rehabilitation: a cross-sectional study

ObjectivesTo investigate patient perspectives on the gender of physiotherapists in stroke rehabilitation. MethodsA cross-sectional study was conducted among a diverse group of patients with stroke (N=143) who required manual assistance with transfer and ambulation. Participants were asked about their preferences for the gender of their physiotherapist and responses were categorized as male physiotherapist, female physiotherapist, or gender doesn't matter. ResultsA significant proportion of participants (24-38%) showed a preference for gender-concordant physiotherapists (p < 0.01). Women had a higher preference for gender-concordant physiotherapists compared to men. Specifically, 3 to 4 out of 10 women (32-38%) preferred physiotherapists of the same gender, while 5 to 6 out of 10 (51-57%) indicated that the gender of the physiotherapist didn’t matter to them (p < 0.01). ConclusionsThis study highlights the diverse perspectives on the importance of physiotherapist gender among patients with stroke. While gender preferences may influence patient comfort and engagement in therapy, these preferences are not universal and should be considered within a broader context of patient-centered care.Practice Implications:Rehabilitation programs should strive to be sensitive to patient preferences while balancing these preferences with the availability of skilled therapists and the operational constraints of clinical settings

Mosquito-disseminated pyriproxyfen for mosquito-borne disease control in Belo Horizonte, Brazil: a pragmatic, before–after control–intervention paired-series trial

Mosquitoes transmit important human pathogens, including dengue virus, but are notoriously hard to control. Mosquito-disseminated pyriproxyfen (MDPPF) uses the mosquitoes themselves to transfer particles of pyriproxyfen, a potent larvicide and pupicide, from lure dissemination stations to untreated larval habitats. MDPPF can reduce mosquito densities, but possible epidemiological effects remain to be measured. We aimed to investigate whether MDPPF can help curb mosquito-borne disease transmission. In this pragmatic, before-after control-intervention paired-series (BACIPS) trial conducted in Belo Horizonte, Brazil, municipal vector-control staff deployed, then serviced monthly (from November, 2017, to December, 2019), 2481 pyriproxyfen dissemination stations in a nine-neighbourhood cluster with a history of high dengue endemicity; nine adjacent neighbourhoods were designated as a buffer area, and the remaining 258 city neighbourhoods as the control area. The primary epidemiological outcome of the trial was dengue incidence. Based on official dengue-notification records broken down by week and neighbourhood (ie, week-neighbourhood case counts; N=265 162 cases in total) from Jan 1, 2016, to Dec 31, 2019, we estimated intervention effects on incidence using a BACIPS approach and negative-binomial generalised linear mixed models (GLMMs). Zika and chikungunya cases were too rare to be assessed with confidence. Week-neighbourhood dengue incidence ranged from 0 to 379·5 cases per 10 000 residents, with epidemic outbreaks recorded in 2016 and 2019. Intention-to-treat, BACIPS-GLMM adjusted estimates indicate that MDPPF deployment was associated with a net 29% (95% CI 21-36; p=4·7 × 10-10) average decrease of dengue incidence in intervention neighbourhoods and a net 21% (12-30; p=2·7 × 10-5) average decrease in buffer neighbourhoods. In contrast, and due in part to larger uncertainties, average incidence rates were statistically indistinguishable across areas before the intervention (intervention area p=0·47; buffer area p=0·11) and across trial periods in control neighbourhoods (p=0·74). Hence, in the all-too-common scenario of a 100 000-case outbreak, public health managers could expect MDPPF to reduce the strain on the health-care system by at least about 29 000 (21 000-36 000) symptomatic cases. Our results suggest that MDPPF can help prevent dengue under the many operational constraints of real-world vector-control interventions and despite incomplete coverage and potential dilution of intervention effects. MDPPF holds promise as an additional tool for dengue control. Coordenação-Geral de Vigilância de Arboviroses, Secretaria de Vigilância em Saúde e Ambiente, Ministry of Health, Brazil, Secretaria Municipal de Saúde de Belo Horizonte, Brazil, and Fundação de Amparo à Pesquisa do Estado do Amazonas, Brazil.

A simulation-based framework for quantifying potential demand loss due to operational constraints in automated mobility services

A simulation-based framework for quantifying potential demand loss due to operational constraints in automated mobility services

A heuristic algorithm to improving the coil slitting process in the steel industry

Abstract The steel industry is constantly facing problems and challenges that require optimisation to improve the production process. We present an algorithm to address a major challenge, the slitting problem, for a specific Spanish company. This problem arises when large steel coils need to be cut into smaller strips. Given the highly heterogeneous stock (coils come from previous operations), selecting the most suitable coils and defining the cutting patterns become very complicated due to operational and customer constraints. The company aims to reduce the leftovers and increase the service level (the difference between the weight requested by the customer and the weight supplied). The algorithm is currently in production and was validated using the company’s data and compared with an exact model. Results significantly improved the company’s operations, achieving a 50% reduction in leftovers and a much better service level in minutes, as opposed to the hours the company previously required. Although there are Mixed Integer Linear Optimization models that provide an optimal solution in small cases, they are not a viable alternative for the company because they require excessive computational time (even, in some cases, to obtain feasible solutions) and use overly expensive commercial solvers.

Deep Learning Application in Sales Automation and Customer Experience Personalization in Small and Medium-Sized Business: A Hybrid Approach Using Transformer-Based Large Language Models and Reinforcement Learning

Due to limited resources and fragmented technological infrastructures, omnichannel small and mediumsized businesses (SMBs) often face challenges in automating sales processes and delivering personalized customer experiences. This paper proposes a hybrid AI framework that integrates transformer-based large language models (LLMs) and reinforcement learning (RL) to address these challenges effectively. By combining LLMs' natural language understanding capabilities with RL’s dynamic decision-making, the framework aims to optimize customer engagement and sales automation in SMB contexts. The research employs LLMs to analyze customer behavior and deliver real-time conversational assistance through an AI concierge. This system provides personalized product recommendations, navigates shoppers to checkout, and collects data for customer insights. RL enhances this functionality by optimizing decision-making policies, such as dynamic pricing and resource allocation, based on long-term reward structures [4]. Key methodologies include multi-task learning to handle diverse customer interactions and offline simulators like Pseudo Dyna-Q to reduce deployment risks. Simulations based on retail scenarios demonstrated significant improvements: customer satisfaction scores increased by 20%, sales efficiency rose by 15%, and average order values grew by 40%. These findings highlight the potential of hybrid AI frameworks to empower SMBs by delivering scalable, resourceefficient solutions tailored to their unique operational constraints. The study also addresses ethical considerations, including fairness and transparency, by incorporating fairness-aware RL and explainable AI techniques to mitigate biases and build trust [15]. These measures ensure that the system promotes equitable outcomes, maintains user autonomy, and adheres to data protection standards. This research bridges the technological gap for SMBs and contributes to the democratization of advanced AI tools, enabling smaller enterprises to compete in increasingly customer-centric markets. The findings provide a robust foundation for further exploration of scalable and ethical AI-driven solutions in sales automation and personalization.

Studying Repeat Victimization: A Consideration of Measurement Issues

Decades of research have recognized the phenomenon of repeat victimization and its policy relevance. Although there are continuing efforts to explain the theoretical underpinnings of repeat victimization, there are still measurement-related issues that limit our understanding of the topic and ability to inform interventions, including varying operational definitions, data constraints, and sampling and nonsampling error. In this article, we review theoretical advances in the literature over the past decade, propose operationalizations that can foster greater consistency across studies, comprehensively assess the data constraints around commonly used public data sources to study repeat victimization, and empirically demonstrate how one of these constraints—missing data—can be considered. Recommendations for future research in the area of repeat victimization are provided.

ITER NBI operational window and power availability constraints due to shine-through losses

Abstract This paper explores the operational boundaries and power availability of the neutral beam injection (NBI) system in ITER, with a specific focus on shine-through loss prevention. Shine-through, a phenomenon where part of the injected neutral beam remains un-ionized in the plasma and directly impacts the first wall components, poses a significant risk to the lifetime of ITER’s plasma-facing components. The operational window for NBI is consequently constrained by these losses, which are influenced by factors such as plasma density, beam energy, and injection geometry. Leveraging advanced numerical simulations, we investigate these dependencies across various ITER plasma scenarios, particularly for the DT-1 phase, which will mark the first NBI operations. In light of recent ITER blanket design changes, our analysis refines previous estimates of the maximum acceptable shine-through power on plasma-facing components. We then present a new heuristic formula which permits the calculation of the shine-through fraction and the minimum plasma density that permits ITER NBI operations as a function of global variables. This allows for establishing operational limits for Hydrogen and Deuterium NBI in Hydrogen, Deuterium, and Deuterium-Tritium plasmas. Additionally, we compare commonly used beam ionisation codes for ITER and tokamak simulations, evaluating their reliability in the investigated parameter space. The findings of this study are crucial for ensuring the efficient operation of the NBI system during ITER's experimental phases. They define the conditions under which beam power can be fully utilised without compromising operational lifetime, thereby informing future plasma operation plans and contributing to the success of ITER's scientific objectives.

Custom, High Density Power Modules Enabled by Thermal Integration Technologies

Researchers at the University of Arkansas are achieving significant strides in power electronic packaging, focusing on the critical needs for high power density and integrated thermal management. They have introduced an innovative non-metallic jet impingement cooler, produced via advanced additive manufacturing techniques, which has proven to be effective in lowering thermal loads. Specifically, it reduces junction temperatures by as much as 30°C and attenuates electromagnetic interference, a crucial factor for the efficiency of high-power silicon carbide MOSFET-based inverters. In response to the 2021 PowerAmerica Institute challenge, the team engineered a high-voltage power module, achieving the specifications of 1.7-kV/1600-A within a confined volume of 100 cm³. This module exemplifies the successful integration of thermal and electrical systems in a space-efficient package, highlighting the practical resolution of high-density packaging challenges. The process has provided insights into the behavior of thermal resistance in such compact modules and the associated operational constraints. Further extending their research, the team has examined advanced cooling techniques employing dielectric fluids, targeting applications surpassing 1.2 kV. They have balanced the turbulence within fluid movement and the fluid’s voltage blocking capacity, essential for the stability of thermal management systems in high-voltage electronics. Moreover, the group has developed an empirical model for direct jet impingement cooling using dielectric fluids, optimizing for enhanced cooling performance with minimal flow rates. They are also researching in the area of two-phase immersion cooling, with a focus on maintaining electrical isolation, especially during phase changes, and conducting assessments of breakdown voltages in boiling dielectric fluids. These efforts are pivotal in guiding the next generation of efficient, compact power electronic packaging solutions, contributing significantly to the field’s future advancements.

Multi-Objective Power Supply Restoration in Distribution Networks Based on Graph Calculation and Information Collected by Multi-Source Sensors

With the increasing complexity of the distribution network structure, enhancing the efficiency and reliability during fault restoration has become a focal point. Based on the multi-source information collected by traditional sensors, such as CT and PT, and intelligent sensors, such as D-PMU, and the graph calculation model, the fault recovery problem of a multi-objective distribution network is studied. Firstly, a power flow calculation model and operation constraint adaptable to topology changes are proposed under the graph calculation framework. The minimum spanning tree theory is utilized to define the blackout range and recovery path set. Secondly, the intelligent sensor D-PMU is configured to collect fault information to ensure that at least one of any two connected load vertices is configured with D-PMU. Thirdly, a topological evolution model is established that considers repeated primary and secondary transfer in outage areas while exploring possible recovery strategies deeply. Finally, a distribution network in Shaanxi Province is taken as an example to verify the model. The experiment shows that the strategy in this paper dynamically adjusts the recovery strategy through four means—one transfer, one repeat transfer in the outage area, two transfers, and cutting off part of the outage load—and the overall recovery rate is increased by more than 20%.

Designing a Chatbot Application Using the Flask Framework and Rule-Based Algorithm

This research aims to develop a website based chatbot application implemented at PT. International Hardware Indo using the Flask framework and Rule-Based algorithm. The application is designed to improve customer service efficiency by providing quick answers to frequently asked questions, reducing the workload of customer service, and addressing operational time constraints. The chatbot development process follows the Waterfall methodology, which includes requirements analysis, design, implementation, testing, and maintenance. The Rule-Based algorithm is employed to map user input to the company dataset through logical "IF-THEN" rules. The research results demonstrate a chatbot accuracy rate of 90%, as measured through black box testing, despite limitations in handling input outside the trained dataset. The chatbot is designed with hybrid navigation, a simple interface, and cross-platform device compatibility. This research contributes to the development of chatbot based information systems that are widely accessible to customers, thereby enhancing user experience and improving operational efficiency for the company.

Adaptive weight adjustment technology for reinforcement learning reward indicators based on the new-type power system

Current RL-based scheduling methods struggle with designing effective reward functions that balance cost reduction, safety and renewable energy integration. To tackle these issues, this paper proposes a novel hybrid optimization framework that combines Particle Swarm Optimization and Reinforcement Learning (PSO-RL), focusing on three key aspects: (1) Minimizing Generation Costs: Systematically reducing operational costs for economic efficiency. (2) Enhancing System Safety: Improving reliability by incorporating operational constraints. (3) Increasing Renewable Energy Proportion: Emphasizing the integration of renewable energy sources. The comprehensive mathematical model encapsulates intricate constraints and multi-objective characteristics of the new-type power system, featuring reward functions tailored for dynamic responsiveness. Empirical validations using the Grid2Op scheduler affirm the efficacy of our framework. Results highlight superior convergence speed and optimization precision of the PSO-RL approach over traditional RL methods. In summary, this study advances scheduling methodologies for the new-type power system, fostering renewable integration and smart grid technology evolution.

Considerations and expectations for the administration of dispersible arpraziquantel to young children: a landscape analysis.

Schistosomiasis is a serious public health problem in many African countries and beyond. Preventive chemotherapy with praziquantel is a successful public health intervention that is recommended for all communities at risk, commonly reached through large-scale mass drug administration campaigns. However, preschool-age children are currently not routinely targeted for treatment due to operational challenges related to dosing and administration with the standard drug formulation. In response to the need for a suitable, child-friendly treatment, the multistakeholder Pediatric Praziquantel Consortium has developed a novel dispersible tablet with improved taste and smaller size. To prepare the introduction and inform future uptake of the novel paediatric formulation in endemic countries, we conducted a landscape analysis to explore perspectives, opinions and experiences of key stakeholders on operational and practical issues in relation to drug dosing and administration. Our findings confirm that child-friendly drug formulations mitigate several operational constraints related to the dosing and administration in young children. The introduction of this novel child health intervention into routine platforms and programs requires solid training and careful communication by engaging with communities, caregivers, healthcare workers and decision-makers to ensure acceptance and future uptake of treatment.

Energy-Efficient Deployment of Laser Illumination for Rotating Vertical Farms

As the global population grows, vertical farming offers a promising solution by using vertically stacked shelves in controlled environments to grow crops efficiently within urban areas. However, the shading effects of farm structures make artificial lighting a significant cost, accounting for approximately 67% of total operational expenses. This study presents a novel approach to optimizing the deployment of laser illumination in rotating vertical farms by incorporating structural design, light modeling, and photosynthesis. By theoretically analyzing the beam pattern of laser diodes and the dynamics in the coverage area of rotating farm layers, we accurately characterize the light conditions on each vertical layer. Based on these insights, we introduce a new criterion, cumulative coverage, which accounts for both light intensity and coverage area. Then, an optimization framework is formulated, and a swarm intelligence algorithm, Differential Evolution (DE) is used to solve the optimization while considering the structural and operational constraints. It is found that tilting lights and placing them slightly off-center are more effective than traditional vertically aligned and center-aligned deployment. Our results show that the proposed strategy improves light coverage by 4% compared to the intensity-only optimization approach, and by 10% compared to empirical methods. This study establishes the first theoretical framework for designing energy-efficient artificial lighting deployment strategies, providing insights into enhancing the efficiency of vertical farming systems.

Multi-Period Operational Modelling and Optimization for Large-Scale Natural Gas Networks Considering Linepack Functions in Long-Distance Transmission Pipelines

As a promising energy resource, offshore natural gas is primarily used for power generation. The comprehensive offshore gas-to-power system, which includes extraction, treatment, compression, pipeline transmission, and power generation, is extensive and operates within various regulatory, operational, and financial constraints. This complexity offers opportunities to optimize one or more system operations to enhance profitability while fulfilling user demands and environmental considerations. In this research, we present a model-based, computer-aided framework that intuitively connects upstream natural gas operations with downstream power generation and distribution. We develop a multi-period Mixed-Integer Nonlinear Programming (MINLP) model that integrates gas treatment, compression, and long-distance transmission with power generation. The model combines first-principle mechanistic process models with a linepack model that calculates the gas volume storable in long-distance pipelines for transmission. The linepack model facilitates gas storage and withdrawal across different periods to accommodate demand scheduling. We apply this framework using the MINLP model in three scenarios: profit maximization, cost minimization, and supply-demand balancing using linepack. The results demonstrate improved economic performance for offshore natural gas-based power generation in China under varying periodic power demands.