Demand System Research Articles

Energy storage performance of sandwich structure dielectric composite by BNNS/TiO2 Co-doping for the high electric field capacitor

Dielectric capacitors are crucial for power systems and hybrid vehicles. However, the polymer dielectric owns low energy density and cannot meet the demands of high-power and energy storage systems. The synergistic improvement of balance between dielectric constant (εr) and breakdowon field strength (Eb), along with better energy storage performance, is a primary focus of this study. This research employs a high εr of P(VDF-TrFE-CFE) blended with linear PMMA to create a sandwich-structured composite via high-speed electrostatic spinning. Boron Nitride Nanosheets (BNNSs) for insulation and TiO2 for polarization are used as fillers, aiming to achieve polymer-based composite with large energy storage density and minimal energy loss. Results show that the sandwich-structured composite with 0.5 vol.% BNNSs in outer layer and 3 wt.% TiO2 in middle layer achieves the largest Ue of 21.8 J/cm3 at the η of 74 %. This study broadens the application of novel dielectric materials in the fields of energy.

Operation Strategy and Matching of Supply and Demand of CCHP System with Various Building Types

Operation Strategy and Matching of Supply and Demand of CCHP System with Various Building Types

Analysis of Accountability and Transparency of Village Fund Allocation Management in Sebani Village, Pandaan District, Pasuruan Regency

The village as the smallest government system demands renewal to support improved village development and the standard of living of village communities that are far from poverty. The various problems in the village are very complex, making it a reason for the village to develop. The development progress in each village is no less important. This research aims to analyze accountability and transparency of village fund allocation management in Sebani Village, Pandaan District, Pasuruan Regency. The method in this study is a descriptive qualitative method by means of direct interviews with informants. The location of this accountability and transparency research is in Sebani Village, Pandaan District, Pasuruan Regency. This research was carried out for a period of 6 months, starting from January 2024 to June 2024. The data is processed using data analysis techniques with stages, namely data reduction, data presentation, and drawing conclusions. The results of the study based on the Regulation of the Minister of Home Affairs Number 20 of 2018 show that the management of village fund allocations in Sebani Village is accountable and transparent in the stages of administration, reporting, and accountability.

Scalable Metadata-Hiding for Privacy-Preserving IoT Systems

Modern cloud-based IoT services comprise an integrator service and several device vendor services. The vendor services enable users to remotely control their devices, while the integrator serves as a central intermediary, offering a unified interface for managing devices from different vendors. Although such a model is quite beneficial for IoT services to evolve quickly, it also creates a serious privacy concern: the vendor and integrator services observe all interactions between users and devices. Toward this, we propose Mohito, a privacy-preserving IoT system that hides such interactions from both the integrator and the vendors. In Mohito, we protect both the interaction data and the metadata, so that no one learns which user is communicating with which device. By utilizing oblivious key-value storage as a primitive and leveraging the unique communication graph of IoT services, we build a scalable protocol specialized in handling large concurrent traffic, a common demand in IoT systems. Our evaluation shows that Mohito can achieve up to 600x more throughput than the state-of-the-art general-purpose systems that provide similar security guarantees.

From Sensors to Standardized Financial Reports: A Proposed Automated Accounting System Integrating IoT, Blockchain, and XBRL

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance.

Multilayer decoupling control of photovoltaic integrated heat pump based on expected value

Abstract In this paper, a multilayer decoupling control method for photovoltaic heat pumps based on expected values is proposed, which aims to achieve the optimal operation of the photovoltaic heat pump system and photovoltaic heat pump system. This method predicts the output energy of photovoltaic photovoltaic system and realizes multilayer decoupling control based on the load demand of the heat pump system. In order to adapt to the change in environment, the adaptive fuzzy PID compound control method is adopted to optimize the control strategy. Experimental results show that this method can effectively improve the system efficiency, reduce energy consumption, and demonstrate good adaptability and stability.

Application of deep learning for automatic detection of table tennis balls from an intelligent serving machine

The Intelligent Table Tennis Serving Machine (TTSM) has revolutionized table tennis training by simulating the variability and precision of human opponents. However, current systems lack the capability to track and analyze ball landing points, limiting comprehensive performance evaluation. To address this, we propose a novel approach utilizing YOLOv8 as the benchmark model for detecting table tennis balls launched by a TTSM, tailored to meet the demands of intelligent training systems. Our key innovation involves integrating a Res2Net architecture enhanced with a Non-local Attention Module (NLAM) and a Dilated Atrous Spatial Pyramid Pooling (DASPP) unit into the neck network. This design improves the network’s ability to integrate multi-scale local features, thereby enhancing global information processing. The DASPP module, with adaptive dilation convolution, ensures thorough context comprehension. Additionally, we incorporate an Omni-dimensional Dynamic Convolution (ODConv) module in the detection head, employing parallel strategies to learn diverse and complementary attention cues, further enhancing detection accuracy and robustness. To validate our method, we conducted evaluations using the publicly available OpenTTGames dataset and a self-constructed dataset comprising over 10,000 table tennis ball images captured in various environments. Experimental results indicate that our method enhances mAP0.5, Precision, and Recall by 2.3%, 5.1%, and 0.9% on the OpenTTGames dataset, and by 3%, 2%, and 1.6% on the self-built dataset, respectively. Finally, we describe the optimization process of the intelligent TTSM system, which leverages keyframe analysis of ball landing points to provide feedback, thereby enabling continuous performance improvement in subsequent phases.

Materials for semiconductor nanowires in nanoscale photonic and electronic devices

Abstract Many aspects of civilization are impacted by electronic and optoelectronic equipment, including medical instruments, communications, computing, multimedia systems, and basic home appliances. the development of nanoscale devices has garnered significant attention due to the increasing demand for smaller, more powerful, and efficient systems across various industries. Nanoscale devices operate at the nanometer scale, typically involving structures and components with dimensions on the order of 1 to 100 nanometers. As a potent class of materials, semiconductor nanowires are creating a lot of new possibilities for innovative nanoscale photonic and electrical devices through carefully regulated development and organization. the exploration of electronic and optoelectronic nanodevices, along with integrated arrays, opens up a rich landscape of possibilities across various technological domains. This research not only pushes the boundaries of miniaturization but also paves the way for innovative applications in electronics, photonics, sensing, and emerging fields like quantum and neuromorphic computing that hold the potential for numerous applications in the future.

Bridging Experimental Neuroscience and Clinical Neuropsychology: Fluid Intelligence in Frontal Lobe Assessments

This viewpoint explores the gap between theoretical frameworks in experimental neuroscience and clinical neuropsychology. It highlights how John Duncan's theory of the Multiple Demand (MD) system, which links the frontal lobe to fluid intelligence (g), helps explain general performance on classical executive tests. However, it also discusses how traditional scores often fail to capture the complexity of behaviours associated with frontal lobe damage, and we suggest that developing improved scoring methods could be useful for integrating experimental and clinical neuropsychology insights.

Knowledge-Data Driven Optimal Control for Nonlinear Systems and Its Application to Wastewater Treatment Process.

Optimal control is developed to guarantee nonlinear systems run in an optimum operating state. However, since the operation demands of systems are dynamically changeable, it is difficult for optimal control to obtain reliable optimal solutions to achieve satisfying operation performance. To overcome this problem, a knowledge-data driven optimal control (KDDOC) for nonlinear systems is designed in this article. First, an adaptive initialization strategy, using the knowledge from historical operation information of nonlinear systems, is employed to dynamically preset parameters of KDDOC. Then, the initial performance of KDDOC can be enhanced for nonlinear systems. Second, a knowledge guide-based global best selection mechanism is used to assist KDDOC in searching for the optimal solutions under different operation demands. Then, dynamic optimal solutions of KDDOC can be obtained to adapt to flexible changes in nonlinear systems. Third, a knowledge direct-based exploitation mechanism is presented to accelerate the solving process of KDDOC. Then, the demand response speed of KDDOC can be improved to ensure nonlinear systems with optimal operation performance in different states. Finally, the performance of KDDOC is validated on a simulation and a practical process. Several experimental results illustrate the effectiveness of the proposed optimal control for nonlinear systems.

US household demand system analysis for dairy milk products and plant‐based milk alternatives

AbstractUsing the QUAIDS model, we analyze interrelationships among dairy milk and plant‐based milk alternatives (PBMA) for US households from 2018 to 2020 using NielsenIQ. We adopt the Shonkwiler–Yen methodology to account for censored observations. PBMA demand is inelastic, while milk products show elastic demand. PBMA substitutes for traditional white milk and organic milk. Traditional flavored milk and PBMA as well as lactose‐free and organic milk are complements. PBMA and lactose‐free milk are independent goods. Demographic factors like income, household size, and education significantly affect budget share, alongside women, infants, and children participation and the pandemic.

Effects of Socio-Economic and Demographic Factors on Meat Consumption Pattern in Iran: A Demand System Approach

Meat as one of the most important resources of protein has a special role in human nutrition. Understanding the meat consumption structure of households is essential for planning and policymaking in this regard. In this research, we studied consumption patterns of meat products including chicken, veal, lamb, and fish for households in Iran (Mashhad city) using demand system estimation. The hypothesis of this study is that chicken is a necessary goods and other types of meat are luxury goods. Given the cross-sectional nature of the data and presence of zero expenditure for some households, we used the censored demand model based on a consistent two-step approach. For this purpose, at first, four Probit models were estimated to determine the factors affecting the probability of purchasing each selected meat product. After that, the probability density function (PDF) and the cumulative distribution function (CDF) were calculated for each selected meat product, and the Almost Ideal Demand System (AIDS) considering PDF and CDF was estimated for all types of meat using a non-linear seemingly unrelated regression. Also, the effect of demographic variables on meat consumption pattern was considered in demand system. The results of expenditure elasticities confirmed the hypothesis. The highest own-price elasticity was related to veal. Based on compensated price elasticities, all types of meat were net substitutes for chicken and chicken was also a net complement for all types of meat. On the other hand, the only substitute for lamb and chicken was veal, but with compensating income effect fish also became a substitute for them. So, in the event of an increase of the price of lamb and chicken, we recommend subsidizing the consumers with low purchasing power in order to increase the diversity of consumption of protein products. This can increase the consumption of fish.

두뇌 유입(Brain Gain) 관점에서 외국인 유학생의 국내 고용 활성화를 위한 IPA 분석 : 정부출연연구기관 이공계 석·박사과정을 중심으로

With Korea's total fertility rate recorded at 0.72 in 2023, there is even a pessimistic prospect that it will become the 'first country to extinguish the population'. In the face of serious demographic challenges such as a population decline, regions disappear, and a lack of high-quality human resources in the industry due to a rapid decline in the fertility rate, the Korean government and each local government are pouring out various policies to alleviate these problems by utilizing the capabilities of international students. This study analyzed the importance of domestic employment promotion activities and differences in the degree of implementation of the employment support system for international students studying as master's and doctoral students at government-funded research institutes using the importance-performance analysis(IPA) methodology. As a result of the analysis, 'recommendation of supervisors' and 'industrial cooperation projects' were evaluated as the most important employment promotion activities, and in the employment support system, 'flexible visa system' and 'simplification of immigration procedures' for researchers were recognized as important. In addition, it was found that there is a difference in perception between the importance and performance of international students depending on the region of residence and country of origin, so a strategy for promoting employment tailored to the region of residence and country of origin is necessary. This study is limited to international students in science and engineering who are enrolled in U graduate university, so there is a limit to the generalization of research results. In future research, extended research is needed including international students from science and technology specialized universities and base national universities. In addition, it is proposed to expand the study on how the expectations and demands of the employment support system of international students differ according to the cultural context by comprehensively considering the country and cultural background and economic conditions that affect the employment activation of international students.

Aircraft Ducted Heat Exchanger Aerodynamic Shape and Thermal Optimization

Abstract Interest in aircraft electrification and hydrogen fuel cells is driving demand for efficient waste heat management systems. Ultimately, most of the heat must be rejected to the freestream air. Ducted heat exchangers, also called ducted radiators, are the most common and effective way to do this. Engineers manually design ducted heat exchangers by adjusting the duct's shape and heat exchanger's configuration to reduce drag and transfer sufficient heat. This manual approach misses potential performance improvements because engineers cannot simultaneously consider all of the complex interactions between the detailed duct shape, heat exchanger design, and operating conditions. To find these potential gains, we apply gradient-based optimization to a three-dimensional ducted heat exchanger computational fluid dynamics (CFD) model. The optimizer determines the duct shape, heat exchanger size, heater exchanger channel geometry, and coolant flowrate that minimize the ducted heat exchanger's power requirements while rejecting enough heat. Gradient-based optimization enables the use of nearly 100 shape design variables, creating a large design space and allowing fine-tuning of the optimal design. When applied to an arbitrary, poorly performing baseline, our method produces a nuanced and sophisticated ducted heat exchanger design with five times less cruise drag. Employing this method in the design of electric and fuel cell aircraft thermal management could uncover performance not achievable with manual design practices.

Advanced Control Strategies for Continuous Stirred Tank Reactors:

This paper investigates the control of a Continuous Stirred Tank Reactor (CSTR), with a primary emphasis on achieving temperature stability and optimizing reactant conversion. The CSTR poses challenges due to its nonlinear and exothermic behavior. To address these challenges, Model Predictive Control (MPC) is employed, a powerful strategy for handling complex systems. Additionally, Particle Swarm Optimization (PSO) is introduced to fine-tune MPC parameters, ensuring optimal performance. By integrating PSO with MPC, this study enhances control capabilities, specifically targeting the intricate demands of CSTR systems.

Posisi Pasar Karet Indonesia Di Pasar Internasional

Indonesia's position as the largest rubber producing country in the world allows them to become the main exporter of natural rubber. Indonesia exports the majority of its natural rubber production to countries like Japan, India, Brazil, the United States, South Korea, China, Turkey, and others. The competition between Indonesia and other producing countries in exporting rubbers to international markets is relatively tight. The aim of this research is to analyze the position of Indonesia’s technically specified natural rubbers (TSNR) in the international market compared to other exporting countries. The analytical method used is the Almost Ideal Demand System (AIDS) Model. The data used in this research is secondary data from Indonesia, Thailand, Malaysia, Ivory Coast, Vietnam, Belgium, and Nigeria over the last 10 years (2012 – 2022). The results show that there is a tight competition between Indonesia and Thailand, Ivory Coast, Belgium, Nigeria and the Rest of the World in competing for market share of natural rubber in the international market because the relationship between Indonesia's natural rubber and these countries are substitutional. Meanwhile, Indonesia's natural rubber relationship with Malaysia is complementary. Indonesian natural rubber is a normal good, so if there is an increase in prices it will cause a decrease in demand. If there is an increase in demand for rubber exports in the international market, then the country that will benefit the most is Vietnam. Meanwhile, Indonesia is the fourth ranked country that will benefit the most if it happens.

A Bi-Level Peak Regulation Optimization Model for Power Systems Considering Ramping Capability and Demand Response

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system.

Interruption Cost Estimation for Value-Based Reliability Investment in Emerging Smart Grid Resources

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities.

Наноструктурные материалы для хранения энергии: инновации и применение

Background: As the world shifts towards renewable energy, there is a growing demand for efficient energy storage systems. Because of their unique features, nanostructured materials have emerged as key to improving the performance and efficiency of these storage systems. Objective: This research aims to assess the most recent discoveries and uses of nanostructured materials in energy storage technologies, emphasizing its disruption in the industry. Methods: A detailed assessment of recent advances was undertaken, focusing on diverse nanostructured materials utilized in batteries, supercapacitors, and other energy storage systems. Synthesis and characterization techniques for these materials and their incorporation into energy storage devices were investigated. Results: The nanostructured materials considerably increase energy density, charge/discharge speeds, and durability of energy storage devices. Graphene, carbon nanotubes, and metal-oxide nanostructures have significantly improved performance metrics across various applications. Conclusion: Nanostructured materials have to revolutionize energy storage systems. Continued research and development in this sector are critical for developing high-performance, long-term energy storage technologies capable of meeting the growing demands of contemporary technology and renewable energy systems. Further investigation into these materials' scalability and environmental effects is necessary to ensure their practicality and sustainability.

Gellan gum as a promising transplantation carrier for differentiated progenitor cells in ophthalmic therapies

Stem cell-based therapies for various ocular conditions are increasingly gaining traction in ophthalmic treatments, with hydrogel-based polymers playing a pivotal role. Current stem cell delivery methods face challenges such as limited cell retention, immunological rejection, and uneven dispersion. Hence, there is a critical demand for innovative delivery systems to enhance the viability, localization, and integration of transplanted stem cells while minimizing adverse effects. Central to this advancement is the meticulous selection of appropriate materials. Among the promising options, gellan gum, a versatile polysaccharide, is emerging as a potential carrier for differentiated progenitor cells in regenerative medicine, particularly in ophthalmology. This study explores the utilization of gellan gum hydrogels as carriers, focusing on their biocompatibility, customizable gelation properties, and ability to encapsulate, transplant, and biofunctionalize cells. Through a review of literature, the impact of gellan gum hydrogels on cell viability parameters is investigated, revealing their potential for promoting tissue regeneration and functional recovery in ocular diseases. Furthermore, this study compares gellan gum systems utilizing natural and synthetic polymers, discerning differences in efficacy, biocompatibility, and suitability for diverse applications in regenerative ophthalmology. This review highlights the promising role of gellan gum in ophthalmic therapies, providing valuable insights into future directions and hurdles in this evolving field.