Demand Management Research Articles

Exploring Water Supply Challenges to Selected Villages of Greater Giyani Municipality in Limpopo Province, South Africa

Limpopo Province faces significant water access challenges, with one-third of its population lacking sufficient domestic water supply. This study examines water access and supply-related challenges in selected villages in the Greater Giyani Municipality (GGM), which suffers from severe water shortages due to persistent drought. In 2008, water levels in the Nsami and Middle Letaba Dams dropped below 5% and 6%, respectively. An open-ended questionnaire survey of 638 households was conducted in 2021, and hydroclimatic data were used to achieve the aim of the study. The analysis revealed that the main water supply sources in GGM are boreholes, communal taps, bulk water through in-house connections, and water vendors. The estimated water demand for the selected villages is 3,230,600 L per day, while the total water supply is only 1,796,676 L per day, resulting in a 44.38% deficit. The questionnaire survey identified two key challenges: unreliable municipal water supply and a lack of access to water within 200 m of households. The study recommends prioritizing water management strategies, such as demand management, groundwater development, and supply innovations, to meet domestic water needs.

Enhancing the Performance of Layered Wood Composites Through the Non-Food Application of Dietary Fiber in Their Bonding Matrix

Rye flour is a commonly used filler in plywood production, made from finely ground rye grains. It enhances glue viscosity, ensuring even distribution and better adhesion, which improves the plywood’s mechanical properties, dimensional stability, and resistance to warping. Additionally, rye flour increases the plywood’s strength and durability, making it more resistant to mechanical damage and external factors. Its affordability and availability further support its widespread use in plywood production. However, the growing availability of new raw materials has sparked interest in alternative fillers, especially considering food waste challenges caused by low demand or poor household management. This study explores the potential of spirulina, bamboo flour, lupine flour, and coconut flour as alternative fillers to rye flour, being part of the food chain, in three-layer plywood production. Plywood panels were manufactured using birch and pine veneers, urea-formaldehyde resin, and varying filler contents (10, 15, and 20 parts by weight/pbw). Key mechanical properties were evaluated, including modulus of elasticity (MOE), modulus of rupture (MOR), shear strength, density profile, and filler water absorption. The highest MOE for hardwood plywood was observed with coconut flour (20 pbw, 17,228 N mm−2). Conversely, the lowest MOE values were recorded for coniferous plywood with spirulina (8440 N mm−2). For MOR, the best performance in softwood was achieved using lupine flour (10 pbw, 113 N mm−2), while coconut flour yielded the highest MOR in hardwood plywood (20 pbw, 177 N mm−2). Spirulina exhibited the lowest MOR (72 N mm−2, 15 pbw). Shear strength peaked with lupine and coconut flour. The filler composition determines adhesive properties and bond performance through water absorption, structural interactions, and filler content optimization. These findings emphasize the potential for fine-tuning alternative fillers to achieve desired mechanical performance, ensuring sustainable and efficient plywood production. These also demonstrate the potential of certain alternative fillers, particularly coconut and lupine flours, excluded from the food value chain, in improving specific properties of plywood.

Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing

Abstract Computing nonlinear functions over multilinear forms is a general problem with applications in risk analysis.
For instance in the domain of energy economics, accurate and timely risk management demands for
efficient simulation of millions of scenarios, largely benefiting from computational speedups. We develop a
novel hybrid quantum-classical algorithm based on polynomial approximation of nonlinear functions, computed
through Quantum Hadamard Products, and we rigorously assess the conditions for its end-to-end
speedup for different implementation variants against classical algorithms. In our setting, a quadratic quantum
speedup, up to polylogarithmic factors, can be proven only when forms are bilinear and approximating
polynomials have second degree, if efficient loading unitaries are available for the input data sets. We also
enhance the bidirectional encoding, that allows tuning the balance between circuit depth and width, proposing
an improved version that can be exploited for the calculation of inner products. Lastly, we exploit the
dynamic circuit capabilities, recently introduced on IBM Quantum devices, to reduce the average depth
of the Quantum Hadamard Product circuit. A proof of principle is implemented and validated on IBM
Quantum systems.

Design of Dual-Channel Supply Chain Network Based on the Internet of Things Under Uncertainty

In this paper, a mathematical model of a dual-channel supply chain network (DCSCN) based on the Internet of Things (IoT) under uncertainty is presented, and its solution using algorithms based on artificial intelligence such as genetic algorithm (GA), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and gray wolf optimizer (GWO). The main goal of this model is to maximize the total DCSCN profit to determine the amount of demand accurately, price in direct and indirect channels, locate distribution centers, and equip/not equip these centers with IoT devices. The results show that with the increase in the uncertainty rate, the amount of demand and corresponding transportation costs have increased. This issue has led to a decrease in the total DCSCN profit. By analyzing the mathematical model, it was also observed that deploying IoT equipment in distribution centers has increased fixed costs. Examining this issue shows that by increasing the savings factor by 0.2, the total DCSCN profit has increased by 6.5%. By ranking the algorithms with the TOPSIS method, the GA was ranked as the most efficient algorithm, followed by PSO, ICA, and GWO. This IoT-enhanced dual-channel supply chain model not only aims to optimize traditional supply chain metrics but also introduces advanced, data-driven strategies for improving demand management, pricing, and infrastructure allocation, ultimately driving profitability in uncertain environments.

Development of a Low-Cost Automated Demand Response Controller for Home Energy Management

This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions.

Data Migration and security in cloud

Data migration and security in the cloud" refers to the process of transferring data from an on-premises system to a cloud environment while prioritizing security measures to protect sensitive information during transit and once stored in the cloud, including implementing strong access controls, encryption, and regular monitoring to prevent data breaches and ensure compliance with relevant regulations. In this time of digital evolution, cloud migration and its security have become essential topics that cannot be avoided. With more companies turning to cloud services for support, the demand for large-scale data storage and data management has also increased. Along with this rising need, the risk of cyber attacks on cloud data has also increased. To better protect yourself from these risks, it’s essential to ensure that you and your organization are up-to-date on the latest security technologies and safety precautions. Keywords : Data Migration, Data Security, cloud, ETL, Data Integration

Estabilidad financiera y volatilidad de los mercados criptoactivos

The global financial crisis made it clear that financial stability should be considered an objective of economic policy. In this context, traditional economic policies of demand management encounter serious limitations to correct the problems of financial instability, requiring the use of regulatory and macroprudential policies. Thus, we have since witnessed the emergence of a new objective (financial stability) and a new instrument (macroprudential policy) in economic policy. The development of cryptoassets and their ecosystem constitute a challenge for financial stability, as their excessive volatility introduces significant risks of destabilizing financial systems. To avoid this, it is not a matter of banning them or limiting their use, but of regulating and supervising them appropriately.

Study on Instability Mechanism and Compensation Strategy for Distributed Energy Storage Systems

Distributed energy storage systems (DESSs), which would become key components in a new power system, can flexibly deliver peak load shaving and demand management. With the popularization of distributed renewable energy generation in a distribution network, the grid impedance varies and DESSs thus have to face stability issues. In order to enhance the system’s stability, a compensation strategy is proposed for the inverter in a DESS. First, a stability analysis model is developed to show the main factors that affect system stability. Then, an improved compensation strategy is proposed for the phase-locked loop (PLL) in a DESS, in which control parameters are adaptively tuned on-line according to real-time conditions to improve the stability of a grid-tied DESS. Simulation and hardware-in-the-loop (HIL) experimental results are given to validate the effectiveness of the proposed strategy. Simulation and experimental results show that the proposed strategy significantly increases the system’s tolerance to grid impedance variations, maintains total harmonic distortion (THD) below 5% during normal operation, and effectively reduces low-order harmonic content caused by impedance fluctuations. Moreover, the strategy is demonstrated to enhance system stability under low state-of-charge (SOC) conditions, showcasing its robustness and adaptability across various operating scenarios.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Improving the Structure of the Electricity Demand Response Aggregator Based on Holonic Approach

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services.

Catechol-grafted chitosan-based antioxidant hydrogel with rapid self-healing property for wound healing.

Chronic wounds, particularly those with non-healing ulcers, show high levels of oxidant stress, such as excessive reactive oxygen species (ROS), which delays the healing process. Bacterial infections in wounds increase the need for antibiotics, which brings up serious concerns regarding antibiotic resistance. The innovative antibiotic-free strategies to endow the hydrogels with antibacterial and antioxidant features including the grafting of ROS-scavenging moieties onto the hydrogel structure are in high demand for wound management. Herein, an antibacterial and antioxidant hydrogel with rapid self-healing performance was fabricated via the dynamic borate ester cross-linkages between catechol-grafted chitosan (CS-CA) and polyvinyl alcohol in the presence of borax with the incorporation of ellagic acid (EA). Benefiting from the catechol groups from EA and CS-CA, the resulting hydrogel exhibited desirable adhesive property, excellent antioxidant and intensified antibacterial dual functions. Furthermore, the hydrogel demonstrated remarkable biocompatibility and pH-responsive behavior for the controlled release of EA, attributed to the reversible characteristics of borate ester linkages. Notably, these multifunctional hydrogels exhibited substantial regenerative capabilities in wound healing, as evidenced by in vivo assessments conducted on a full-thickness skin defect model, highlighting their considerable potential as wound dressings for effective wound management.

Air pollution reduction co-benefits associated with low-carbon transport initiatives for carbon neutrality in China by 2060

Low-carbon transport policy measures will contribute to greenhouse gas emission reductions, as well as improvements in air quality via air pollutant emission reductions. Here, we developed a regional transport energy model by integrating a transport demand model and an energy system model to explore the associated air pollutant emission reductions achieved by low-carbon transport initiatives when projecting a path towards carbon neutrality. Several scenarios were created to identify the effectiveness and feasibility of low-carbon strategies using the Avoid–Shift–Improve (A-S-I) framework including “Technology”, “Regulation”, “Information”, and “Economy” instruments. Significant reductions in air pollutant emissions alongside transport decarbonization in China could be realized by implementing policy measures to establish effective transport demand management, a modal shift in transport use, and technological improvements. In particular, the “Improve” strategy was more effective in delivering significant reductions in air pollutant emissions than were the “Avoid” and “Shift” strategies, but the policy effects of each A-S-I pillar varied greatly for different pollutants and across different regions. An air pollution reduction strategy for the transport sector should be designed in an integrated manner based on a series of different strategies and tools.

Identification of the degree of greenness of the proposed skills with the requirements of green jobs based on fuzzy logic

The deepening climate crisis development have prompted most countries to recognize the importance of transition to a green economy. This causes a rapid growth of “green” jobs in various spheres of human activity and, accordingly, the need for “green” skills necessary to use new technologies. New professions and specialties requiring the acquisition of green skills create a discrepancy between the demand for human resources of the required profile and their supply. In this context, it was clarified what the terms green jobs and green skills mean. Considering the content of these two concepts, studies dedicated to determining the relationship between the green transition and relevant skills were reviewed. In this regard, the selection of personnel with the necessary skills determines the relevance of developing methods for assessing the supply and demand for specialists, providing an opportunity to assess the compliance of the proposed skills with the requirements of green jobs. This article proposes a methodological approach to intelligent management of supply and demand in the green segment of the labor market. It also develops decision support methods to identify of compliance the greenness degree of the proposed skills with the level of the requirements of green jobs. Decision making methods were based on fuzzy situational analysis and pattern recognition: reference situations are determined (fuzzy reference pattern), real situations are determined (fuzzy real pattern); degree of green similarity of the reference situation to each real situation is calculated in accordance with the chosen measure of assessment of similarity between two fuzzy situations

AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.

Understanding water-energy-carbon nexus in English and Welsh water industry by assessing eco-productivity of water companies

Understanding the water-energy-carbon nexus in water supply is essential for water regulators and utilities. This study employs a non-radial Data Envelopment Analysis (DEA) model to assess eco-productivity (ecoP) change, a synthetic indicator that integrates carbon emissions, energy costs, and water delivered. It also evaluates its components—eco-efficiency change and eco-technological change—across water companies in England and Wales from 2011 to 2018. The analysis reveals an annual improvement in ecoP of 1.1%, driven by a 2.1% gain in eco-efficiency but offset by a 1.0% decline in technological advancement. The reduction in GHG emissions emerged as the most significant positive contributor, enhancing ecoP by 3.22% annually, while energy costs detracted ecoP by –0.09%. The results underscore the negative impacts of increased water delivery (–1.74%) and the number of connected properties (–1.27%) on ecoP, highlighting the need for demand management policies.

Optimizing Large-Scale Demand and Capacity Balancing in Air Traffic Flow Management Using Deep Neural Networks

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving.

Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU

Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.

Systematic Analysis of Commuting Behavior in Italy Using K-Means Clustering and Spatial Analysis: Towards Inclusive and Sustainable Urban Transport Solutions

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development.

GESTÃO DA CADEIA DE SUPRIMENTOS USANDO IA

Supply Chain Management (GCS) integrates and coordinates all activities from obtaining materials to final delivery, aligning objectives to maximize efficiency, reduce costs and improve quality. Planning in the chain is crucial to guarantee resources, minimize uncertainties and add value to the customer. Logistics is essential in efficient distribution, and inventory management balances supply and demand. Performance indicators (KPIs) and process mapping are used to evaluate and improve operations, while production planning and control (PCP) ensure efficiency in production and delivery. These aspects form the basis of GCS, essential for customer success and satisfaction. Therefore, the objective of this work is to analyze the use of artificial intelligence of supply chain management in a contemporary context, identifying challenges, strategies and impacts for organizations, with a focus on optimizing processes and improving business competitiveness.

Body fat variation and redistribution across different stages of life measured by dual-energy x-ray absorptiometry.

The global obesity epidemic of all ages has increased the demand for accurate management of body fat in each stage of life. The primary aim of this study was to provide reference centiles of body fat indices for Chinese children and adults and compare those with ethnicities from USA using dual-energy x-ray absorptiometry (DXA). The samples were drawn from two nationwide cross-sectional surveys of China Body Composition Life-course Study (2013-2023) and the US National Health and Nutrition Examination Survey (2011-2018). Age- and sex-specific centile curves were generated for a set of fat measurements, including total fat mass (FM), fat mass index (FMI), body fat percentage (BF%), trunk-to-leg fat ratio (TLR), android-to-gynoid fat ratio (AGR) and visceral-to-subcutaneous fat ratio (VSR), using the general additive model for location scale and shape method. The age-related variations from childhood to adulthood were generally similar among Chinese, Non-Hispanic Whites, Non-Hispanic Blacks and Mexican American population, with distinct levels across races and ethnicities. For whole-body fat (FM, FMI, and BF%), Mexican American population consistently presented the highest levels before 40 years old, followed by Non-Hispanic White, Non-Hispanic Black and Chinese individuals. For central fats indices, although the TLR and AGR levels in Chinese males were second to Mexican American counterparts in most stages of life, the VSR was much higher in Chinese than other races and ethnicities from eight years old. DXA-based centiles for body fat quantity and distribution in Chinese population aged 3-60 years old were presented, and their differences with other race and ethnicity were noted across periods from early childhood to middle adulthood. Our findings will promote age-, sex- and ethnic-specific assessment of life-course body fat and obesity-related risks in clinical practice.