A Wi-Fi Based Authentication Scheme for Voice Enabled Large Language Model Consumer Electronics Devices

The increasing environmental and economic drawbacks of fossil fuels have accelerated the global transition to renewable energy sources. In this context, the optimal design of hybrid renewable energy systems (HRES) that combine solar, wind, and energy storage technologies is critical for achieving sustainable and cost-effective power generation. This study addresses the problem of optimally sizing a grid-connected HRES composed of photovoltaic (PV) panels, wind turbine (WTs), batteries (BTs), and supercapacitors (SCs). A mathematical model is developed to minimize the annual cost of the system (ACS) while ensuring high renewable energy utilization and system efficiency. To solve this optimization problem, five advanced meta-heuristic algorithms-Hunger Games Search (HGS), Spider Wasp Optimizer (SWO), Kepler Optimization Algorithm (KOA), Fire Hawk Optimizer (FHO), and Coronavirus Disease Optimization Algorithm (COVIDOA)-were applied and statistically validated. The model was tested on real meteorological and load data from a university campus in Turkey. Results show that HGS achieved the most favorable performance, with an ACS of $603,538.44, a cost of energy (COE) of $0.23801/kWh, and a renewable energy fraction (REF) of 80.04%. This configuration offers significant economic advantages compared to purchasing electricity directly from the grid at $0.35/kWh. The proposed system proves commercially viable for large consumers and demonstrates the practical effectiveness of meta-heuristic methods in energy system design. MATLAB was used for simulation, while R programming was employed for statistical validation of the algorithmic performance. The study establishes a reproducible and validated framework that can guide future research and implementation in the field of hybrid energy optimization.

The increasing penetration of variable renewable energy and the volatility of demand have amplified the importance of uncertainty-aware planning in power systems. Traditional approaches to generation and network expansion predominantly emphasize technical uncertainties associated with wind, solar, and load forecasts, while treating planning decisions as centrally coordinated. Such assumptions overlook the heterogeneous objectives and interactions of multiple stakeholders—including regulators, grid operators, renewable energy developers, and large industrial consumers—that ultimately shape the feasibility and cost-effectiveness of system expansion. This study develops a novel multi-agent game-theoretic framework for electricity system planning under source–load uncertainty, embedding stakeholder strategies into a robust optimization model. The proposed framework conceptualizes power planning as a hierarchical game, where a entity sets regulatory signals, grid operators ensure system reliability, renewable producers decide on capacity investments, and large load users respond through consumption adjustments. Their strategic interactions are modeled through a multi-layer game formulation, with each agent optimizing its own welfare function subject to operational, economic, and policy constraints. To rigorously address uncertainty, a robust optimization approach is integrated into the game, ensuring that planning outcomes remain feasible against a wide range of renewable generation variability and demand fluctuations. The robust layer captures adverse realizations of uncertainty by embedding budget-of-uncertainty sets for both renewable production and load demand, thereby producing strategies that are resilient without being excessively conservative. Case studies based on a modified IEEE benchmark system with realistic renewable and demand data demonstrate the distinct planning trajectories produced by the model. Results reveal that under robust equilibrium, coal retirements accelerate by 15–20%, while storage investments increase by 30–40% compared to nominal baselines. Load-serving entities reduce exposure to high scarcity prices by reshaping demand during peak hours, cutting tail-event prices by 20–25%. -imposed carbon penalties translate into emission reductions of 45–55% within the planning horizon, with shortfall risks limited to less than 2 GW in extreme stress scenarios. The contributions of this work are fourfold: it redefines electricity planning as a multi-agent game rather than a centralized optimization, it systematically embeds robust optimization into the strategic equilibrium, it highlights the interplay between regulatory signals and market responses, and it demonstrates how robust equilibria mitigate both physical shortfalls and economic volatility.

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts.

ABSTRACT As environmental and geopolitical crises deepen and global production networks (GPNs) reorganize, countries proximate to large consumer markets are becoming more attractive investment destinations. This argument has recently gained ground but lacks theoretical and empirical validation. This article opens up space for addressing this gap in two ways. Theoretically, it argues for conceptualizing proximity in multi-dimensional and macro-regional terms in the GPN literature, as this allows for a broader range of proximity benefits that firms might seek to exploit through nearshoring to be identified. Empirically, it performs a country-level panel data analysis of Italian and German nearshoring to Central and Eastern Europe between 2005 and 2019. The analysis found a significant correlation between nearshoring and environmental certification, while the results for regulatory and institutional certainty are inconclusive. The article concludes with a mixed-methods research agenda for the study of nearshoring as de-risking in economic geography.

Holistic energy procurement planning for large industrial consumers through energy resources expansion and participation in multiple electricity markets and contracts

The increasing power demand, transmission line congestion, and increasing electricity traffic necessitate the effective implementation of demand-side management (DSM) strategies to improve energy efficiency and sustainability. This research presents an optimized DSM framework for large power consumers in the Western Cape municipality, utilizing particle swarm optimization (PSO) integrated with machine learning improved prediction algorithms to achieve peak clipping and reduce peak load power demand under real-time pricing conditions. The developed algorithms were validated using actual energy consumption data from large industrial customers in the Western Cape province. Simulation results indicate that the PSO-driven DSM framework significantly reduces peak demand, improves the load factor, and offers substantial cost savings compared to conventional load management techniques. This study highlights the potential of intelligent optimization methods to support municipalities and major energy users in adopting more flexible, affordable, and sustainable energy consumption practices.

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions.

This study was conducted to investigate consumer preference toward dragon fruit in Coimbatore city, India. The primary objective of the study was to identify and analyse the consumer preferences, factors influencing the purchase behaviour and constraints faced by consumers when purchasing the dragon fruit. With the help of primary data collected from 120 consumers through structured questionnaires and analysed with descriptive statistics, factor analysis and Garett ranking, this study identifies three main components impacting consumer behaviour: consumer exploration which signifies the importance of digital influence and recommendations, consumer satisfaction mostly consist of taste experience and health benefits and finally the last component is market affordability which emphasizes the significance of price and availability. The results shows that the majority of the consumer for dragon fruit is middle-aged (26-45 years old), well-educated and average income consumers. In this study, 77 % of respondents reported buying dragon fruits on occasionally or on very rare events. The biggest constraints for purchasing the fruit were price, which is followed by availability of fruit in the market. Most consumers like free samples to get to know the fruit indicating that large consumers are yet to try the fruit and then followed by discounts and offers as an effective strategy selected by the respondents. This research provides valuable insights for the stakeholders across the dragon fruit value chain to design marketing strategies, optimize pricing and enhance market penetration for this exotic fruit in Coimbatore city.

This study examined how age, gender, and consumption frequency influence texture perception and overall acceptability of cooked rice, using a large consumer panel (N = 692) across four replicated trials. Age significantly influenced ratings for hardness, chewiness, and especially overall liking, with older adults (70-79years) giving higher scores and showing less variability. In contrast, younger participants (20-49years) exhibited greater perception sensitivity and more diverse acceptability. Gender had a significant impact on ratings for hardness, stickiness, chewiness and overall liking, with males consistently giving higher ratings. Consumption frequency affected perceived hardness but had no effect on other texture attributes or overall acceptability, suggesting long-term consumption patterns may overshadow short-term variations in habituated consumers. Both frequentist and Bayesian regression analyses identified chewiness as the strongest predictor of overall liking, whereas the impacts of hardness and stickiness varied by demographic group.

Research on wind-thermal-energy storage optimization configuration method for large consumer loads

ABSTRACT This study examines customers’ perception of electric vehicles (EVs) in the Coimbatore. EVs, powered by one or more electric motors and producing zero tailpipe emissions, are increasingly viewed as sustainable alternatives to conventional vehicles. Growing environmental concerns, government incentives, and rising fuel prices have accelerated consumer interest in electric mobility. India’s expanding EV market, supported by a large consumer base, skilled labour, and comparatively lower production costs, has also attracted global manufacturers to establish operations in the country. Adopting a descriptive research design, this study collected primary data from 250 respondents in Coimbatore using a structured questionnaire. The analysis highlights both positive and negative factors shaping consumer perception, including environmental awareness, cost considerations, technological acceptance, and purchase intentions. The findings provide useful insights for policymakers and automobile manufacturers to enhance strategies for promoting EV adoption in emerging urban markets. Keywords: Electric Vehicles, Consumer Perception, Environmental Awareness, Technology, Purchase Intentions

Introduction Delta-8 Tetrahydrocannabinol (THC) has become increasingly popular in the United States. Many online stores sell Delta-8 THC products, yet little is known about how they are marketed to consumers. Methods We selected a sample of popular Delta-8 online stores, captured them on one day, and coded their main pages for marketing and advertising strategies. The primary marketing strategies coded for were grouped into four main themes: 1) product claims; 2) engagement strategies; 3) user testimonials and other information; and 4) regulations and warnings. Descriptive statistics were calculated to estimate percentages of each strategy used. Results A total of 134 online stores were identified and coded. With respect to claims, over one-third stated that a product was natural or organic (35.1%), changed energy levels (37.3%) or mood (36.5%). Engagement strategies were used by most stores, with the most common feature being a link to their social media page (79.9%) and sales and discounts on products (over 50%). Over half of websites included product reviews (60.5%) or blogs (53.7%). Nearly three-quarters (72.4%) stated that the products were not regulated by the FDA and over half (59%) required an age verification. Discussion Surveillance is an essential public health service. For Delta-8, it is important to understand the marketing practices that retailers are using online because this setting caters to a large consumer base. Moreover, online stores can evade state regulations. A deeper investigation into Delta-8 marketing strategies could yield regulatory policies to protect public health.

BackgroundCare coordination is commonly employed to assist individuals with mental health challenges [MHCs]. However, its implementation in mental health contexts is inconsistent. The term, ‘care coordination’ is also used interchangeably with integrated care and case management. This review aims to (1) consolidate the literature on how, and in what contexts, care coordination has been used to help adults with MHCs access care from more than one service and (2) describe the challenges and benefits of implementing care coordination for adults with MHCs from the perspective of service providers, care coordinators and service users.MethodsThis is a scoping review that adopted methodological aspects of Arksey and O’Malley and those proposed by the Joanna Briggs Institute.ResultsCare coordination has been employed in a range of clinical and non-clinical settings. Eligibility for care coordination was predominantly restricted to individuals at high risk of deterioration or those unable to access multiple services independently. Care coordinators worked individually or as part of a team and were mostly mental health nurses or social workers. Care coordination was reported to include both clinical and non-clinical tasks. Clinical tasks included medication management, preparing coordinated treatment plans and implementing crisis triage. Non-clinical tasks included acceptance of referrals, identification of service user needs, developing a plan for service involvement, implementation of the said plan, and monitoring of outcomes. Benefits of care coordination included improved access to services, reduced consumer distress, and self-harming behaviour, a team approach to care, decrease in psychiatric hospitalisations, emergency room visits and arrests, and better interservice collaboration. Challenges to care coordination included continuing unmet needs, lack of service availability and health insurance, unclear processes causing confusion, difficulties in engaging with some service users, administrative complications, large consumer load and staff shortages, incompatibility of technology between systems, insufficient funding and limited community support agencies.ConclusionA better understanding of care coordination is needed that includes indications, eligibility criteria, coordination tasks, expected outcomes, as well as organizational and service system requirements.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13033-025-00679-5.

The construction industry is a large consumer of natural resources and a significant contributor to environmental deterioration. Sustainable construction practices, particularly in brick manufacture, have become important for mitigating these consequences. The use of agro-industrial waste as an alternative raw material in brick manufacture is an innovative approach to waste management and resource conservation. This research investigates the viability of using agro-industrial wastes such as rice husk ash, fly ash, bagasse, and other byproducts to make bricks. It assesses their environmental, economic, and technological advantages while confronting problems such as material variability, processing methods, and regulatory adherence. This thorough research seeks to establish a framework for using agro-industrial waste into conventional construction methods to attain sustainability objectives.

Abstract The use of renewable energy sources to meet the demand of large consumers has been increasing over the last few years. The current American energy load is being met with a mix of about 60% fossil fuels, 19% nuclear, and 21% renewable sources. However, when compared to isolated American communities, the landscape is a lot different. For example, the island of Hawaii meets its energy consumption with an energy mix consisting of 64% petroleum and oil imports, 6% coal, 24% solar and wind, 3% geothermal, and others. Because of the high reliance on imports for energy generation in island communities, there has been interest in the wider adoption of higher efficiency and renewable energy sources. This push allows for the communities to have a higher level of energy security and has the potential to reduce the cost of energy generation. This study seeks to model how the cost of electricity could be affected due to the adoption of renewable sources for microgrids found on island communities, including Hawaii and Puerto Rico. The key energy production methods being added are solar, wind, and energy storage. This is done by modeling local energy loads and adding generation sources, treating them like a microgrid with no export or import options. Solar is to be added in the form of photovoltaic panels and wind as onshore wind turbines. Adding storage to the mix also affects the techno-economic and allows for the reduction of curtailed resources. The modeling is performed using pyz, an in-house developed software used for techno-economic analysis for different levels of renewable energy generation, including the use of load time series. The renewable systems were varied with and without energy storage. Results show that the addition of local renewable generation can reduce the energy cost for a microgrid in these locations by up to 40% in addition to reducing the reliance on imports. The addition of energy storage, while presenting a higher initial capital cost, can further reduce the electricity cost and maximize the renewable penetration. Results show that with the addition of storage, a microgrid can reduce its emissions by 80% before incurring additional costs.

This capacity development (CD) report assesses the fiscal implications of fuel subsidies in the Republic of Congo and proposes actionable measures to sustainably reduce their large and negative footprint on the country’s public finances. With an estimated cost of 4.5 percent of gross domestic product (GDP) in 2022 and 2.5 percent of GDP in 2023, fuel subsidy spending is higher than public expenditure on health (2.3 percent of GDP) and comparable to that on education (3.6 percent of GDP). The majority of fuel price subsidies do not directly benefit Congolese households but rather businesses with high consumption of diesel and aviation fuel. In addition to consumer subsidies, public companies of the downstream fuel sector benefit from direct subsidies estimated at over 2 percent of GDP per year on average between 2018 and 2023. Against this background, short- and medium-term measures can be taken to reach three objectives: i) rapidly reduce subsidy expenditures by focusing first on closing price gaps for large consumers while temporarily shielding households from large price increases, ii) effectively increase tax revenues from fuel consumption, and iii) strengthen the downstream fuel sector governance and strategic planning.

Water scarcity persists as a pressing issue in Harare, exacerbated by undetected leaks within the municipal water distribution networks, which significantly contribute to non-revenue water (NRW) losses. A considerable volume of water fails to reach end-users due to these inefficiencies, intensifying supply shortages and elevating operational expenditures for water utility providers. These challenges stem primarily from deteriorating infrastructure, reliance on conventional leak detection methodologies, and inadequate management systems. As noted by Dinar (2024), effective water resource management remains a critical concern, particularly in regions facing water deficits amid escalating demand for potable water. This research project proposes the development of an IoT-enabled real-time water leakage detection system to optimise monitoring capabilities and mitigate NRW losses. The system architecture incorporates a network of sensors, microcontrollers, and IoT communication frameworks to facilitate continuous, real-time data acquisition. Strategically positioned sensors will monitor key hydraulic parameters, including flow rate, pressure, and water levels, with collected data transmitted to a cloud-based analytical platform. A dedicated web interface will be implemented to deliver instantaneous leakage alerts, dynamic graphical representations, and comprehensive diagnostic reports, thereby enabling preemptive maintenance interventions and minimising water loss. The system's efficacy will be empirically validated through prototype testing, ensuring precise leak localisation and rapid response mechanisms. In this context, the proposed method will reduce the non-revenue water in Harare by at least 20% within a year of deployment, as well as reduce the response time to leaks by at least 50%. Ultimately, this initiative aims to advance water conservation efforts, improve operational efficiency, and promote sustainable water management practices that can be deployed to large consumers of fresh water, such as industrial complexes

Saudi Arabia, the world’s largest oil exporter, aims to reach net zero emissions (NZE) by 2060 and heads to a drastic transformation of its energy sector amid a changing international oil market. This paper presents least-cost NZE trajectories for Saudi Arabia, consistent with alternative international oil prices and availability of carbon capture solutions. We use a hybrid forward-looking general equilibrium model where current and future mitigation technologies are represented explicitly. The results show that domestic price reforms alone can cut emissions by 13 percent to 27 percent below a baseline no-policy scenario by 2060. Lower international oil prices reduce the opportunity cost of fossil fuels and increase the implicit CO 2 price needed to reach NZE. Along with energy efficiency, renewables and clean hydrogen; NZE would need to rely on a large deployment of carbon capture technologies, whose scalability is still uncertain. In NZE, direct air capture (DAC) would need to offset residual emissions and would become a large consumer of electricity and gas. The massive energy sector investments needed for NZE tend to crowd out non-energy investments. In NZE, the GDP is 3 percent to 10 percent below the baseline, depending on whether the international oil price is high or low and on the scale of carbon capture availability. JEL Classification: C61, C68, H23, Q41, Q48

Data centers are specialized facilities designed to computing resources such as servers, storage, and networking equipment. They provide the infrastructure needed to store, process, and distribute large amounts of data efficiently and securely. Data centres are large electricity consumers, so monitoring their consumption is important for studying and monitoring the environmental impacts of digitalization. Such an industry is evolving rapidly, driven by demand for faster, more efficient, and sustainable solutions. A results of survey on the energy consumption of data centres in Slovenia for year 2023 were dealt within a paper. The survey, identification of the reporting units and questionnaire testing are presented, as well. Key words. Data centres, energy consumption, survey, digitalization.