Optimal System Research Articles

Learning-based production, maintenance, and quality optimization in smart manufacturing systems: A literature review and trends

With the introduction of manufacturing paradigms, including Industry 4.0, production research has shifted its focus to enabling intelligent manufacturing systems within industrial environments. These systems can efficiently schedule and control processes and operations using artificial intelligence methods, including machine learning and deep learning. Since 1995, relevant literature has presented several examples of such implementations, addressing topics, for example equipment fault diagnosis and quality inspections. To this end, the present paper strives to present a state-of-the-art review of the learning-based scheduling and control frameworks, which are exploited in the production research. The review is limited to the relevant research between the years 1995 and 2024, surveying approaches in the domains of manufacturing, maintenance, and quality control. To this end, the paper follows a meta-analysis method for the selection and evaluation of relevant research articles. Moreover, research questions are formulated to analyze the obtained findings and seek out insights on aspects of the relevant research, including the inclusion of decision-making models and dissemination of literature. The provided answers, among others, reveal trends and limitations of the state-of-the art research in relation to learning-based scheduling and control.

Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations

Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations

Assessing the water metabolism of coastal urban areas based on the water mass balance framework across time periods: A case study of Cape Town, South Africa

The status of water resources in many coastal cities has become increasingly fragile. In a changing climate, the amount and intensity of rainfall have continued to decline, causing some coastal regions to experience severe drought and deteriorated water supply situation. This article presents a comprehensive study of the urban water metabolism of Cape Town, South Africa. A water mass balance framework was utilized to examine the city's water system across four distinct periods. In addition, the water-energy nexus before and after the drought crisis was evaluated. The key findings include a large amount of surface runoff in the Cape Town area that was not utilized before the drought crisis, resulting in a natural loss potential of water supply that is 2.54 times greater than water used for supply systems. During the drought, per capita water consumption dropped by a substantial 25%; at the same time, the total rate of water loss experienced a substantial decrease of approximately 21%. Since the pandemic, Cape Town's water resources policy has shifted to diversifying water sources, and the use of wastewater and natural water losses will be optimized by more than 32% in 2040 to achieve a water-sensitive city. Future research should explore the temporal and spatial dynamics of urban water flows, the impact of socioeconomic factors, and the integration of water system optimization models for enhanced urban water management.

Effective Archival of Salesforce Stale Data: A Strategy for System Optimization and Cost Efficiency

This paper explores the importance of archiving stale data in Salesforce and how implementing an ongoing archival process helps maintain accurate, up-to-date information while optimizing system performance and reducing storage costs. As businesses generate vast amounts of data daily, the accumulation of outdated information can slow system operations, increase storage costs, and lead to data inaccuracies. The paper will outline best practices for Salesforce data archival, including soft archival strategies, hard deletion, data backup procedures, and integration precautions. Examples of account data archival, key performance indicators (KPIs), and cost-saving opportunities will be discussed, along with recommended tools and methods for successful data management. Keywords Salesforce, Data Archival, Stale Data, Data Backup, Soft Archival, Data Management, System Optimization, Cost Savings, AWS Backup, OwnBackup, Salesforce Queries

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Study on Dynamic Characteristics of Resilient Mount Under Preload.

Resilient mounts are essential for anti-vibration and shock absorption applications, making accurate predictions of their static and dynamic behaviors critical for effective design and mechanical performance. This study investigates static and dynamic characteristics of resilient mounts to predict their effects. Tension, compression, and shear tests were performed under quasi-static loading conditions to obtain stress-strain cycle curves. This study includes a review of the Yeoh hyperelastic model, which consists of three parameters, and discusses the calibration of these parameters to describe the hyperelastic material behavior. The parameters were validated through numerical analysis by comparing them with experimental results from quasi-static tests on the resilient mount. The dynamic behavior was further analyzed using modal analysis and frequency response simulations under various preload conditions. Results show that increasing preload significantly shifts the transmissibility curves and resonance peaks to lower frequencies. This study offers valuable insights into static and dynamic characteristics of resilient mounts, contributing to the design and optimization of vibration isolation systems for naval applications.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

An optimization method for deformable mirror configuration in multi-conjugate adaptive optics systems

Multi-conjugate adaptive optics (MCAO) is a crucial technology for achieving high-resolution imaging over a wide field of view with modern ground-based optical telescopes. The configuration of deformable mirrors (DMs) is a key component in the analysis and optimization of MCAO performance. Currently, the search for the optimal DM configuration often relies on iterative and time-consuming Monte Carlo simulations. This issue arises from the lack of an appropriate optimization method for DM configurations. The primary objective of this paper is to establish an optimization method for DM configurations in MCAO systems. We established a quantitative criterion for evaluating DM configurations by analyzing their correction capabilities for turbulence aberrations at different altitudes. Then, we optimized the DM configurations based on this criterion. This method provides a new theoretical foundation and practical tool for the design and performance optimization of MCAO systems. Based on the pupil phase structure function, we established a DM configuration evaluation criterion, namely the non-conjugate correction index (NCCI). Using NCCI as the optimal criterion, combined with the particle swarm optimization algorithm, we searched for the optimal solution across different DM configuration spaces. We conducted simulations based on the turbulence profiles of typical telescope sites. We validated our proposed theoretical model against Monte Carlo simulation models and find that the NCCI error ranges from 0.05 to 0.1. For optimizing DM conjugate heights, the results of our optimization algorithm differ by less than 1 km from those obtained via Monte Carlo simulations. Regarding the performance of the DM optimization algorithm, the average convergence accuracy error is less than 0.1 km, and the average convergence speed is approximately ten iterations. Additionally, our optimization method runs in just a few minutes; Monte Carlo simulations, in comparison, require several dozen hours.

Optimization of renewable energy-based integrated energy systems: A three-stage stochastic robust model

The integration of renewable energy into an integrated energy system (REIES) represents a promising approach to achieving clean and low-carbon energy consumption. However, the inherent uncertainty and variability of renewable energy sources and load demands present significant challenges to the operational efficiency and stability of REIES. To address these challenges, we propose a three-stage stochastic robust optimization (TRSRO) model. This model accounts for market electricity prices, source-load scenario probabilities, and output uncertainties to optimize system configurations in REIES. It also includes the optimization of power exchanges with the regional grid and strategic operational scheduling. Initial scenarios of uncertainty variables are generated using the spectral normalized conditional Wasserstein generative adversarial network (SNCWGAN), forming polyhedral uncertainty sets. Comprehensive norm constraints are then applied to capture probability distribution uncertainties within these sets. To improve the efficiency of solving the model, we introduce a two-layer column constraint generation algorithm, considering variable alternate iterations (TLCCG-VAI). The results demonstrate that the proposed method can achieve a 12.16 % reduction in total cost compared to the baseline method. Furthermore, the TLCCG-VAI algorithm reduces runtime by 82.97 % while maintaining the same level of solution accuracy.

Multi‐objective optimization of inductive power transfer system with reconfigurable topology for misalignment tolerance

AbstractAt present, most of the analyses or studies about inductive power transfer (IPT) with constant current (CC) output and constant voltage (CV) output are carried out without considering misalignment conditions or different gaps. An IPT system satisfying battery charging demand and anti‐misalignment requirements simultaneously is infrequent. This paper proposes a multi‐objective particle swarm optimization method of IPT reconfigurable topology to realize CC and CV modes at varying resistance conditions and wide coupling ranges. The output characteristics of an inductor–capacitor–capacitor (LCC)–LCC compensation circuit have been explored, and it is found that the secondary‐side compensated capacitors have a greater impact on the output power, which can be used to improve power regulation ability accompanied by coupling varying. Eight optimization compensated parameters of the reconfigurable topology are obtained from the Pareto front to achieve the required CC and CV charging outputs. By switching the compensated capacitors, the selected parameters can make the current and voltage fluctuation less than 9.3% and 7.9%, respectively, during the coupling charging range from 0.3 to 0.22. Moreover, primary zero voltage switching operation is achieved to enable high efficiency. The simulation and the experimental verification are carried out to verify the validity of the proposed method.

Advanced supply chain optimization for emerging market healthcare systems

The complexities of pharmaceutical supply chains in emerging markets present significant challenges that directly impact healthcare outcomes, particularly in underserved regions. This research focuses on the optimization of supply chain systems for the healthcare sector, with an emphasis on enhancing pharmaceutical delivery. By leveraging data analytics and innovative procurement strategies, the study seeks to improve efficiency, reduce operational costs, and ensure the timely distribution of essential medications. Drawing on practical experiences at eMedic Technologies Africa, the research highlights the need for advanced models that integrate real-time data to optimize inventory management, forecast demand, and address bottlenecks in last-mile delivery. The study proposes a multi-faceted approach to addressing common challenges in emerging market supply chains, including unreliable infrastructure, fluctuating demand, and limited access to technology. By utilizing predictive analytics, the proposed models aim to improve accuracy in demand planning, reducing stockouts and overstock situations. Additionally, innovative procurement strategies, such as centralized procurement and strategic partnerships with local suppliers, are explored as key methods for enhancing cost-effectiveness and streamlining supply chain operations. A critical component of the research is the focus on last-mile delivery, which remains a significant hurdle in rural and underserved areas. The study advocates for the use of digital platforms and mobile technologies to enhance delivery tracking and coordination, ensuring that essential medications reach their destination efficiently. Furthermore, it emphasizes the importance of fostering collaboration between governments, non-governmental organizations, and private sector stakeholders to build resilient supply chain systems capable of adapting to local conditions. By providing actionable insights and scalable models for supply chain optimization, this research aims to contribute to improved healthcare delivery in emerging markets. The proposed strategies not only reduce costs and improve efficiency but also ensure a reliable and consistent supply of medications, ultimately leading to better health outcomes in regions most in need. Keywords: Supply Chain Optimization, Data-Driven Procurement, Last-Mile Delivery, Healthcare Efficiency, Emerging Markets

Sustainability of health outcomes of patients with type-2 diabetes mellitus after completing 6 months of remote tele-monitoring: Two-year results from a randomised controlled trial (OPTIMUM).

Meta-analysis shows that home tele-monitoring (HTM) improves glycaemic control in patients with type-2 diabetes mellitus (T2DM) up to 12 months, but their health outcomes after HTM cessation remains unclear. This study aimed to determine the health outcomes of these patients 18 months after completing 6 months of HTM, compared to standard care. Patients with T2DM were enrolled in an open-labelled randomised controlled trial, aged 26 to 65 years, and suboptimal glycaemic control (HbA1c = 7.5%-10%). Patients in the intervention group (n = 165) undertook HTM using the OPTIMUM (Optimising care of Patients via Telemedicine In Monitoring and aUgmenting their control of diabetes Mellitus) HTM system for 6 months followed by usual care for another 18 months, while control group (n = 165) had usual care for 24 months. The OPTIMUM HTM system includes in-app video-based tele-education, tele-monitoring of the blood pressure (BP), capillary glucose and weight via Bluetooth devices and mobile applications, followed by algorithm-based telecare by the investigators. They were assessed using the Self-Care Inventory Scale (SCIR) and medication adherence (Medication Adherence Report Scale 5) at baseline, 6-month and 24-month time-points. The data from 146 (intervention) and 152 (control) patients, with comparable baseline demographic profiles were eventually analysed. The decrease in HbA1c over 24 months was comparable between intervention and control group. Those in the intervention group were more likely to maintain their glycemic control (HbA1c ≤ 8%) (adjusted odds ratio (AOR) = 1.9, 95%confidence interval (CI) = 1.1-3.2; p = 0.028), had higher SCIR score (p = 0.004), and less likely to "never forget" (p = 0.022), or "stop medications" (p = 0.048), at 24-month time-point as compared to subjects in the control group. The glycaemic control of patients with T2DM continued to be maintained for another 18 months after 6 months of HTM, which were attributed to sustained self-care behaviour and medication adherence.

Spatial Analysis of Middle-Mile Transport for Advanced Air Mobility: A Case Study of Rural North Dakota

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

Effects of Inosine-5′-monophosphate Dehydrogenase (IMPDH/GuaB) Inhibitors on Borrelia burgdorferi Growth in Standard and Modified Culture Conditions

Borrelia burgdorferi’s inosine-5′-monophosphate dehydrogenase (IMPDH, GuaB encoded by the guaB gene) is a potential therapeutic target. GuaB is necessary for B. burgdorferi replication in mammalian hosts but not in standard laboratory culture conditions. Therefore, we cannot test novel GuaB inhibitors against B. burgdorferi without utilizing mammalian infection models. This study aimed to evaluate modifications to a standard growth medium that may mimic mammalian conditions and induce the requirement of GuaB usage for replication. The effects of two GuaB inhibitors (mycophenolic acid, 6-chloropurine riboside at 125 μM and 250 μM) were assessed against B. burgdorferi (guaB+) grown in standard Barbour–Stoenner–Kelly-II (BSK-II) medium (6% rabbit serum) and BSK-II modified to 60% concentration rabbit serum (BSK-II/60% serum). BSK-II directly supplemented with adenine, hypoxanthine, and nicotinamide (75 μM each, BSK-II/AHN) was also considered as a comparison group. In standard BSK-II, neither mycophenolic acid nor 6-chloropurine riboside affected B. burgdorferi growth. Based on an ANOVA, a dose-dependent increase in drug effects was observed in the modified growth conditions (F = 4.471, p = 0.001). Considering higher drug concentrations at exponential growth, mycophenolic acid at 250 μM reduced spirochete replication by 48% in BSK-II/60% serum and by 50% in BSK-II/AHN (p < 0.001 each). 6-chloropurine riboside was more effective in both mediums than mycophenolic acid, reducing replication by 64% in BSK-II/60% serum and 65% in BSK-II/AHN (p < 0.001 each). These results demonstrate that modifying BSK-II medium with physiologically relevant levels of mammalian serum supports replication and induces the effects of GuaB inhibitors. This represents the first use of GuaB inhibitors against Borrelia burgdorferi, building on tests against purified B. burgdorferi GuaB. The strong effects of 6-chloropurine riboside indicate that B. burgdorferi can salvage and phosphorylate these purine derivative analogs. Therefore, this type of molecule may be considered for future drug development. Optimization of this culture system will allow for better assessment of novel Borrelia-specific GuaB inhibitor molecules for Lyme disease interventions. The use of GuaB inhibitors as broadcast sprays or feed baits should also be evaluated to reduce spirochete load in competent reservoir hosts.

Optimization of an Innovative Design System for Han Embroidery Dresses Based on Image Matching Method

With cultural confidence emerging as a significant trend in national development, this study underscores the importance of comprehending the cultural essence of Han embroidery attire. Understanding and advancing Han embroidery not only enriches its heritage but also elevates its cultural prominence and fosters the training of future custodians. Through an examination of the historical trajectory of Han embroidery, this paper introduces an innovative design approach grounded in multimedia information gathering techniques. By advocating for increased attention to Han embroidery elements in attire, the study aims to stimulate broader interest in its design and application. Embracing cultural confidence as a guiding principle, the research delves into the nuances of innovative Han embroidery gown design, spanning color, pattern, and style considerations. Through this inquiry, the paper seeks to offer insights that expedite and enhance the evolution of Han embroidery design.

Comparative performance analysis of recuperative helium and supercritical CO₂ Brayton cycles for high-temperature energy systems

This study presents a comprehensive comparative analysis of recuperative Brayton cycles utilizing helium (He) and supercritical carbon dioxide (sCO2) as working fluids for high-temperature power generation, focusing on applications in next-generation concentrated solar power (CSP) systems. Thermodynamic cycle simulations were conducted across a temperature range of 1000 K–2000 K, exploring the effects of low-side pressure (1 bar–100 bar) and expansion ratio (1–5) on key performance indicators. These indicators included thermal efficiency, net specific work output, mass flow rate, volumetric flow rate, and system irreversibilities. Results demonstrate that helium-based Brayton cycles exhibit superior thermal efficiency at high temperatures, reaching up to 65 %, compared to sCO2 cycles, which achieve efficiencies up to 55 %. This advantage stems from helium's higher specific heat capacity and lower pressure drops within the cycle. Conversely, sCO2 cycles demonstrate higher net specific work output due to the fluid's higher density, leading to more compact turbomachinery. The study further investigates the impact of irreversibilities, including pressure losses, leakage losses, and heat transfer losses, on cycle performance. A detailed analysis of multi-stage Brayton cycles incorporating intercoolers and reheaters reveals the potential for further efficiency enhancements in both helium and sCO2 systems. This research provides valuable insights for the design and optimization of high-temperature power generation systems, particularly for next-generation CSP plants. The findings highlight the trade-offs between working fluid properties and cycle configurations, guiding the selection of optimal solutions based on specific application requirements and operating conditions.

Optimization of Distance Learning Systems Using Artificial Intelligence and the Internet of Things in Improving the Quality of Education in the Post-Pandemic Era

The COVID-19 pandemic forced a sudden change from face-to-face learning to Distance Learning (PJJ), posing challenges in the quality of learning and student interaction. This study examines the application of Artificial Intelligence (AI) and the Internet of Things (IoT) to optimize the PJJ system in the post-pandemic era. The goal is to evaluate the impact of AI in personalizing learning and IoT in improving student interactivity and collaboration, as well as identify the challenges of implementing this technology. The research uses a mixed-methods approach by combining qualitative and quantitative analysis. Data were obtained from 50 educators, 100 students, and 20 administrators through questionnaires, in-depth interviews, and participatory observations. Correlation tests and logistic regression are used to assess the influence of AI and IoT on learning quality. The results show that 85% of students and 78% of educators agree that AI helps in customizing learning materials. IoT also increases student engagement, with 76% of students feeling more engaged and the likelihood of student engagement increasing 2.35 times greater with IoT. Key challenges include limited technological infrastructure and lack of training for educators. In conclusion, AI and IoT have great potential in improving the quality of education, but support is needed for infrastructure and educator training so that the implementation of these technologies is optimal.