Demand Management Research Articles

Multifaceted approach to successful management of pediatric acute liver failure: High-volume plasma exchange as a pivotal intervention

Abstract: Pediatric acute liver failure (pALF) poses a formidable clinical challenge characterized by rapid hepatocyte necrosis and pro-inflammatory events. The urgency of pALF necessitates advanced therapeutic modalities, prompting the exploration of high-volume plasma exchange (HVPE) as a sophisticated intervention. This case report delves into the intricate management of a 16-year-old female with hyperacute liver failure secondary to hepatitis A, highlighting the critical need for nuanced and multidisciplinary approaches. The case unfolds through a detailed description of symptoms, clinical decision-making, and the therapeutic journey. The management includes a calculated deferral of liver transplantation in favor of HVPE, with a focus on the rationale for choosing HVPE over continuous renal replacement therapy. Technical aspects of the procedure, such as blood volume calculations and procedural details, are meticulously outlined. Laboratory investigations track the patient’s progress, showcasing the profound impact of HVPE on clinical and laboratory recovery. The patient’s response to HVPE is evident in the comprehensive clinical and laboratory recovery observed after the intervention. The resolution of encephalopathy, successful extubation, and eventual discharge highlight the positive outcomes achieved through the judicious application of HVPE. However, the procedure is not without challenges, as the emergence of hypertension during the second and third cycles and a lone episode of seizure require swift and careful management. The management of pALF demands a sophisticated and multidimensional approach, especially when liver transplantation is not immediately feasible. This case report underscores the nuanced decision-making involved in selecting extracorporeal therapies, with a focus on the successful application of HVPE. HVPE, as recommended by the American Society for Apheresis, emerges as a promising intervention in the context of pALF, offering a viable option when liver transplantation is deferred due to advanced encephalopathy. The favorable outcomes and considerations of cost-effectiveness and procedural simplicity further accentuate the potential of HVPE as a cornerstone in the critical care of pediatric patients with acute liver failure.

Operational optimization of demand management strategy for hybrid power system based on battery capacity fading characteristics

In the hybrid power system (HPS), taking battery capacity fading characteristics and demand management strategy (DMS) into consideration, an operational optimization method is developed in this work. From the energy utilization and economic perspectives, the minimum total renewable energy curtailment rate (TCR), minimum total outsourced electricity (TOE) from the grid and minimum total operating cost (TOC) are respectively taken as the optimization objectives. The corresponding mathematical models of the proposed method are established, which feature mixed integer nonlinear programming (MINLP) ones. A case study of a solar-wind-biomass-battery HPS is adopted to verify and illustrate the application and effectiveness of this proposed method. Results show that the optimal operating schemes and the DMSs of the HPS based on battery capacity fading characteristics can be determined by minimizing the TCR, TOE and TOC. The target values of TOE and TOC of the HPS with adopting DMS are 15,995.3 kWh and 18,112.1 $, which are about 70 % these of the HPS without DMS. Furthermore, the relationships between battery capacity fading characteristics and DMS are comprehensively investigated in this operational optimization of the HPS. Finally, the effects of different battery types, outsourced electricity price changes on the operation of the HPS are analyzed and discussed. This work provides a valuable method for the operational optimization of the HPS.

Improving the Leaf Nutrient and Mineral Elements of Constituents (Moringa oleifera L.) by Application of Certain Biostimulants and Biofertilizer

Increasing the need for food as a result of global population growth negatively impacted the ecology and soil. As a result of an increase in the demand for industrial chemical fertilizers and inefficient crop management. In this research, the effect of some biofertilizers, amino acids, and seaweed extracts on the chemical composition of the leaves of moringa plant was evaluated. The results showed that bio-fertilization with Phosphorein biofertilizer associated with amino acids and seaweed extracts at 700 mg/L (T25) was the most effective treatment for improving and maximizing the leaf nitrogen content. Also, Herein, T26 (soil bio-fertilized with cerealine plus bio-fertilizer with amino acid and seaweed extracts as foliar spray each at 700 mg/L) was the most effective treatment in improving the leaf contents of phosphorus, potassium, calcium, vitamin C, total carbohydrates which produced a better response than using each treatment separately.

Assessing Road Users’ Preferences for Various Travel Demand Management Strategies for Adoption in Accra, Ghana

The rise in population density, vehicle ownership, and urban development has significantly influenced travel demand and altered travel patterns, leading to traffic congestion in rapidly growing urban centers such as Accra, Ghana. The traditional approach of expanding roadways to accommodate rising traffic is no longer environmentally sustainable. Instead, the emphasis has shifted toward travel demand management (TDM) strategies as a more sustainable solution. This study aimed to investigate a range of TDM strategies that can be adopted in Accra to improve traffic flow through the lenses of everyday road users. The study employed a questionnaire survey and a stratified sampling technique to gather data from 615 respondents for relative importance index (RII) ranking and Chi-square statistical analysis. The findings revealed that the topmost preferred strategies were mass transit improvements, walking and cycling improvements, and alternative work schedules. Notably, mass transit improvements emerged as the most preferred strategy. Conversely, strategies like efficient parking pricing, congestion pricing, and higher fuel and road taxes for private vehicles ranked lowest, garnering minimal preference. The study also revealed a statistically significant correlation between variables such as age, education level, marital status, income level, and mode of transportation and all the selected TDM preferences. However, no significant relationship was found between gender or car ownership and any of the selected TDM preferences. The study provides valuable insights into road users’ preferences for TDM strategies that can aid in planning future urban mobility systems to proactively manage travel demand, alleviate congestion, and promote sustainable transportation options for the city’s residents.

Integrated optimization of train stop planning and seat allocation for high-speed railways based on active management for dynamic demand

ABSTRACT Proactive demand management methods can achieve a trade-off between supply and demand for high-speed railways. Focusing on multi-type demand configurations, four active demand management methods were designed, two single-scenario and two multi-scenario methods. An optimization model of stop planning and seat allocation was constructed to minimize the difference between demand and supply, number of trains, and train-stop costs. The number of train stops, transport capacity, and train occupancy rates were considered. Particle swarm optimization and CPLEX were combined to handle this significant linear programming problem. A real case study of different demand scenarios based on the Hohhot-Beijing high-speed railway in China verified the feasibility of active demand management methods. The results indicate that integrated optimization can be justified with mixed load patterns. Compared with the original scheme, the optimized scheme reduced the number of trains by 23.4% and increased train occupancy rate by more than 36.7%.

Impact of Technological Adoption, Innovation Management, and Market Demand on Competitive Advantage

Impact of Technological Adoption, Innovation Management, and Market Demand on Competitive Advantage

Development of an intelligent building management systembased on BIM technology

The construction of smart cities has led to new demands for intelligent building management. This paper proposes an innovative building management system that integrates BIM, IoT, cloud computing, big data, and artificial intelligence technologies. The system architecture is rational, and its functional modules are comprehensive, achieving breakthroughs in key areas such as BIM visualization, fault diagnosis, and energy consumption optimization. Through practical testing, the system demonstrates outstanding capabilities in data processing, real-time performance, and fault recognition accuracy, providing solid technical support for the operation of green intelligent buildings and contributing to sustainable urban development.

Law Enforcement of Environmental Agreement Violations by Business Entities

In the increasing demands for environmental protection and management which are currently increasing and require attention from every child of the nation as both policy makers and the community in general, the environmental law since the enactment of Law Number 4 of 1982 to Law Number 32 of 2009 which was then codified in the main ideas of Law Number 6 of 2023 concerning Job Creation, this is the answer to the government's efforts in enforcing environmental law to be better and more beneficial. In the application of criminal law sanctions as a result of criminal acts by reviewing and highlighting the application of law to the implementation of environmental management as an effort for environmentally sustainable development, problems regarding the consequences of criminal acts committed by Business Entities so that they can result in additional criminal penalties must be based on legal norms by paying attention to the level of public awareness and global environmental developments and international legal instruments related to the environment as well as the awareness and life of the community in relation to environmental management have developed in such a way that they need to be refined to achieve the goals of environmentally sustainable development.

Individual and group emotional intelligence measurement of sex differences and invariance for individual (WLEIS-S) and group (WEIP-S) emotional intelligence measurement scales

Different international research groups focus on the study of socio-emotional resources and tools to facilitate adequate management of daily challenges and demands among teachers. This allows for better personal and professional performance. One of these resources that has attracted most attention in recent decades has been that of emotional intelligence. In this regard, the present study set out to analyse the psychometric properties of trait emotional intelligence as a construct. For this purpose, the study focused on two EI measurement instruments. One of the instruments (WLEIS-S) measures individual emotional intelligence and the other instrument (WEIP-S) measures group emotional intelligence. Against this background, the possible invariance of both scales with respect to the sex variable is analysed, as well as possible differences in the variables measured by the scales between sexes. To this end, 452 primary education teachers from Southern Spain filled in the WLEIS-S and WEIP-S tests. The results found in the factor analyses carried out with both instruments showed an excellent fit, as well as good internal consistency. Moreover, both instruments showed robust invariance, which indicates that both scales measure the emotional intelligence construct consistently for both women and men. No differences were found in the assessed latent variables between sexes. To conclude, this study shows psychometric evidence supporting the suitability of the WLEIS-S and WEIP-S scales for the accurate assessment of individual and group emotional intelligence in women and men in the teacher population.

Electrification as a factor in replacing hydrocarbon fuel

The article looks at the role of electrification that is expanding as a key factor in transitioning to a low-carbon economy. The study is based on a systemic approach and aims to provide a comprehensive understanding of the necessary transformations in energy production and consumption directed at phasing out fossil fuels and advancing the climate agenda. The novelty of the study resides in the development of conceptual foundations of the fuel-saving aspect of electrification which makes it possible to not only significantly reduce the negative environmental effects in regions, but also to ensure intensive economic growth through the application of advanced energy technologies and minimization of the use of a scarce energy carrier - natural gas - in the power industry and industry. The article clarifies the conceptual apparatus, defines the characteristics and patterns of the new stage of electrification, assesses the impact of electrification on the economic growth and environmental safety of the region, gives recommendations on optimizing the technological structure of the electric power industry in the context of the changing requirements for the parameters of electricity supply. The authors propose a methodology for accounting for the environmental factor in the electrification of the region, which includes ranking and selecting facilities to be electrified according to the minimum “electric fuel replacement ratio”; using the associated energy saving effect for financial compensation of the environmental burden, as well as introducing electricity demand management programs that improve the environmental situation in the region. The article defines the sequence of the implementation of the fuel-saving aspect of electrification and formulates a set of progressive solutions that would invigorate the relevant projects.

Key determinants to enhance the competitiveness of tourist destinations

Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.

"Life with Diabetes": A Pilot Study on an Experiential Continuous Glucose Monitoring Curriculum for Resident Physicians.

The use of technology in diabetes mellitus (DM) management has been growing. The indications and coverage for continuous glucose monitoring (CGM) have increased. Primary care (PC) clinics, including resident continuity clinics, are the frontline for DM management; however, they struggle to adopt CGM. To implement a CGM curriculum to resident physicians to improve knowledge and confidence. An internal medicine (IM) resident PC clinic in an urban academic medical institution. Twenty-four IM residents. We designed a curriculum that included a lecture about CGM indications, interpretation, ordering, and insurance consideration; and a voluntary, experiential learning module in which the residents wore a CGM. We conducted a retrospective pre-post survey with a 4-point Likert scale. Average self-reported scores in knowledge increased for CGM (1) indications from 1.85 to 3.45, (2) ordering from 1.35 to 3.05, (3) functioning from 2.20 to 3.50, and (4) data interpretation from 1.85 to 3.25 (all p < 0.0001). Confidence for "describing CGM monitoring" and "fielding questions about CGM" increased from 2.25 to 3.65 (p < 0.0001) and 1.90 to 3.30 (p < 0.0001). Given the demand for DM management in the PC setting, this targeted CGM curriculum has promise to help residents adopt CGM into their practice.

A new methodology for reducing carbon emissions using multi-renewable energy systems and artificial intelligence

Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396 % per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437 % per day. From the obtained results the proposed algorithm gives superior performance.

Optimal robust online tracking control for space manipulator in task space using off-policy reinforcement learning

This study addresses the demands for adaptability, uncertainty management, and high performance in the control of space manipulators, and the inadequacies in achieving optimal control and handling external uncertainty in task space in previous research. Based on off-policy reinforcement learning, a model-free and time-efficient method for online robust tracking control in task space is devised. To address the complexity of dynamic equations in task space, a mixed-variable approach is adopted to transform the multivariable coupled time-varying problem into a single-variable problem. Subsequently, the optimal control policy is derived with the disturbance convergence, stability, and optimality of the control method being demonstrated. This marks the first instance of achieving robust optimal tracking control in task space for space manipulators. The efficacy and superiority of the presented algorithm are validated through simulation.

SecuDB: An In-Enclave Privacy-Preserving and Tamper-Resistant Relational Database

With the escalation in the demand for privacy-preserving and tamper-resistant data management and processing on the public cloud, an increasing number of mainstream databases start to provide always-encrypted and blockchain-like features, including Microsoft SQL Server, MongoDB, and Alibaba PolarDB. The recent progress in Trusted Execution Environment (TEE) technology has enabled the deployment of the complete database engine within TEE. This implementation ensures that data stored in memory, cache, and registers is encrypted, thereby maintaining the confidentiality of information. In this paper, we present SecuDB, a multi-granularity privacy-preserving and tamper-resistant relational database by placing the entire RDBMS in Intel TDX. We propose a novel visibility control mechanism incorporating column masking, log masking, and statistics masking to realize fine-grained privacy preservation and devise an isolated TEE-endorsed temporal table method to support efficient data and query verifiability, without affecting insertion and selection performance. We evaluate SecuDB using Sysbench, TPC-C and TikTok copyright workloads. The results show that compared with a system without an enclave, SecuDB hits 84.7% and 94.7% of the performance when providing coarse-grained and fine-grained privacy preservation, respectively. While the overhead for tamper-resistance is less than 22.6%.

Evaluating a web-based visual acuity and refractive error self-assessment tool in myopic children.

Demands for myopia management are rising. A web-based tool that allows home-performed self-assessments of visual acuity (VA) and refractive error may enable hybrid care pathways and aid in identifying those with deteriorating visual performance. The tool has been validated in adult populations, but has yet to be evaluated in children. This study compared home-performed VA and refraction self-assessments to conventional measurements obtained at the clinic in a population of myopic children. Myopic children aged ≥6 years old were invited to perform web-based eye tests at home, assisted by a parent. At two myopia control clinics, they also underwent measurements of VA using a Snellen chart and refractive error using cycloplegic autorefraction. Agreement between the tests, repeatability of the web-based test and associations between clinical characteristics and web-based test accuracy were evaluated. A total of 147 children were enrolled, of whom 116 (51% male; mean age 13 ± 3 years; mean spherical equivalent refraction (SEQ) -5.58 ± 3.05) performed the web-based tests at home. Overall, the home-performed VA self-assessment and the Snellen chart assessment at the clinic agreed well (mean difference 0.03 ± 0.11 logMAR). A significant proportional bias was identified (β 0.65, p < 0.001), indicating underestimated web-based VA scores when the child's vision declined. The sensitivity to detect VA poorer than 0.10 logMAR was 94%; the specificity was 71%. The web-based refractive error algorithm measured more myopia progression compared to clinic observations (mean difference SEQ 0.40 ± 0.51 dioptres). Age, sex or use of atropine drops were not significantly associated with test accuracy. The web-based test for self-assessing vision, performed at home by children with assistance from their parents, yielded VA scores with a precision similar to Snellen chart testing conducted in a clinical setting. However, the web-based refractive error algorithm overestimated myopia progression and requires recalibration for this specific age group.

Customer enrollment and participation in building demand management programs: A review of key factors

Increasing the efficiency and flexibility of electricity demand is necessary for ensuring a cost-effective and reliable transition to zero-carbon electricity systems. Such demand-side management (DSM) resources have been procured by utilities for decades via energy efficiency and demand response programs; however, the key drivers of program enrollment and customer participation levels remain poorly understood — even as governments and grid planners seek to scale up the deployment of DSM assets to meet climate targets. Here we systematically review the evidence on multiple factors that may influence customer enrollment and participation in building DSM programs, focusing primarily on residential and commercial buildings. We examine the contexts in which relationships between DSM factors and outcomes are most often explored and with which methods; we also score the strength, direction, and internal consistency of each factor's reported impact on the enrollment and participation outcomes. We find that studies most commonly assess the effects of economic incentives for load flexibility on program participation levels, often using simulation-based methods in lieu of measured data. Few studies focus on program enrollment outcomes or regulatory drivers of either enrollment or participation, and gaps are also evident in the coverage of emerging DSM opportunities like load electrification. Removal of structural barriers (e.g., the lack of controls infrastructure) and the use of third party services (e.g., load aggregators) are the factors with the largest positive impacts on DSM outcomes, but no single factor emerges as clearly most impactful. For a given factor, the range of reported impacts typically varies widely across the relevant studies reviewed. Our findings provide a snapshot of the state of knowledge about building DSM and customer decision-making, and they expose key gaps in understanding that must be filled if building DSM is to expand as a critical resource for operating clean power grids.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Perancangan Proses Bisnis Untuk Mengurangi Waste Overproduction dengan Metode Business Process Improvement pada Proses Produksi Konveksi di PT. XYZ

PT XYZ is a company engaged in the field of convection. There is a problem related to the excess of the actual production amount of 899 with a production request of 732 which indicates the occurrence of waste overproduction. This research aims to minimize waste overproduction in the production process caused by poor demand management and the process of recording the amount of production every day by workers is done manually so it is necessary to design related records in the information category. Problem solving is done by improving business processes using the Business Process Improvement method. The results of this improvement obtained a proposed business process by making improvements to the initial business process. The process of making google spreadsheet as a recording media is done using the waterfall method. The results of the google spreadsheet design can display the number of requests, cut results, and remaining products continuously so that the recording process can be done more accurately and the collected data will be safely stored in the system so that it can minimize the occurrence of overproduction