Search Results (4,487)

The biotechnological conversion of CO₂ to biomethane represents an energy-efficient, environmentally friendly, and sustainable approach within the waste-to-energy cycle. This process, in which CO₂ and H₂ are converted to biomethane in anaerobic bioreactors, is referred to as hydrogenotrophic biomethane production. While several studies have investigated hydrogenotrophic biomethane production, there is a lack of research comparing flocculent and granular sludge inoculum in continuously operated systems fed with a gas substrate. Both granular and flocculent sludge possess distinct advantages: granular sludge offers higher density, stronger microbial cohesion, and superior settling performance, whereas flocculent sludge provides faster substrate accessibility and more rapid initial microbial activity. In this study, two UASB (Upflow Anaerobic Sludge Blanket) reactors operated under mesophilic conditions were continuously fed with synthetic off-gas composed of pure H₂ and CO₂ in a 4:1 ratio and were compared in terms of microbial community shifts and their effects on hydrogenotrophic biomethane production. Biomethane production reached 75 ± 2% in the granular sludge reactor, significantly higher than the 64 ± 1.3% obtained with flocculent sludge. Although hydrogen consumption did not differ significantly, the granular sludge reactor exhibited higher CO₂ removal efficiency. Microbial analyses further revealed that granular sludge was more effective in supporting methanogenic archaea under conditions of gas substrate feeding. These findings offer advantageous suggestions for improving biogas production, enhancing waste gas management, and advancing sustainable energy generation. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time–Frequency Distributions (TFDs) to seismic signals to identify diagnostic precursory patterns of major earthquakes. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for the identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new possible precursory pattern of major earthquakes, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, the Philippines, New Zealand, and Japan. Precursors in the time–frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in the time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis. To the best of the authors’ knowledge, no similar approach for consistently identifying earthquake diagnostics precursors has been proposed, and, therefore, we propose a novel approach in reliable earthquake prediction using TFD analysis. Full article

The increasing awareness of the need for sustainable solutions to secure future energy supplies has spurred the search for innovative approaches. Energo-Efekt Sp. z o.o. has prepared a project for the green transformation of the energy system at a producer of spirits through the rectification of raw alcohol. An installation was conceptualised to develop the system to convert energy from biomass fuels into electricity and heat. The innovation of the installation is the use of an expander—a Heliex system which is the twin-screw turbine generator converting energy in the form of wet steam into electrical power integrated with pressure-reducing valve. This system captures all or part of the available steam flow and reduces the steam pressure, not only delivering steam at the same, lower pressure but also generating rotary energy that can be used to produce electricity with the power output range of 160 to 600 kWe. Currently, the company utilises natural gas as a fuel source and acquires electricity from the external grid. Implementing the system could reduce the carbon footprint associated with the production of vodka at the plant by 97%, to 102 t CO₂ annually. This reduction would account for approximately 21% of the total carbon footprint of the entire alcohol production process. The system could also be applied to other low-power systems that produce < 250 kW, making it a viable option for use in distributed energy networks, and can be used as a model solution for other distillery plants. The transformation project dedicated to Polmos Żyrardów involves a comprehensive change in both the energy source and its management. The fossil fuels used until now are being replaced with a renewable energy source in the form of biomass. The steam and electricity cogeneration system meets the rectification process’s energy demand and can supply the central heating node. Heat recovery exchangers recuperate heat from the boiler room exhaust gases and the rectification cooling process. Potentially, all of these changes lead to the company’s energy self-sufficiency and reduce its overall environmental impact with almost zero CO₂ emissions. Full article

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

Desalination plays a critical role in addressing global water scarcity, yet brine disposal remains a significant environmental challenge. This study evaluates a minimal liquid discharge (MLD) membrane-based system integrating high-pressure reverse osmosis (HPRO) and membrane distillation (MD) for brine treatment, with a focus on the Eastern Mediterranean. A techno-economic assessment (TEA) was conducted to analyze the system’s feasibility, water recovery performance, energy consumption, and cost-effectiveness. The results indicate that the hybrid HPRO-MD system achieves a high water recovery rate of 78.65%, with 39.65 m³/day recovered from MD and 39 m³/day from HPRO. The specific energy consumption is 23.2 kWh/m³, with MD accounting for 89% of the demand. The system’s cost is USD 0.99/m³, generating daily revenues of USD 228 in Cyprus and USD 157 in Greece. Compared to conventional brine disposal methods, MLD proves more cost-effective, particularly when considering evaporation ponds. While MLD offers a sustainable alternative for brine management, challenges remain regarding energy consumption and the disposal of concentrated waste streams. Future research should focus on renewable energy integration, advanced membrane technologies, and resource recovery through brine mining. The findings highlight the HPRO-MD MLD system as a promising approach for sustainable desalination and circular water resource management. Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

The article examines the application of controlling in energy and heating (E&H) companies, with particular emphasis on diagnosing the extent to which reporting and management controlling are implemented, as well as identifying barriers that limit the development of their managerial functions. The aim of the study was to determine the extent to which management controlling is applied in the managerial practice of the E&H sector and how its use differs from practices observed in other sectors of the economy. The research employed a mixed methods approach, including a literature review, a case study of controlling implementation in a selected energy company, and a quantitative analysis based on the Managerial Controlling Index (MCI). The central research question addressed the impact of legal, market, and organisational conditions on the scope of controlling in the E&H sector. The findings indicate that E&H companies record lower MCI scores than companies in other industries, regardless of their size, age, or business profile. The article concludes with a set of managerial recommendations outlining directions for the development of management controlling as a tool for supporting decision-making and enhancing integration with the overall management system. Full article

Sarcopenia is a prevalent and disabling complication of chronic kidney disease (CKD), associated with frailty, diminished quality of life, and increased morbidity and mortality. Despite its clinical significance, no pharmacological treatments are currently approved to address muscle wasting in this population. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), widely used in the management of type 2 diabetes and obesity, have shown potential to support muscle mass and function through pleiotropic mechanisms. These include anti-inflammatory and antioxidant actions, improvements in insulin sensitivity and energy metabolism, and mitochondrial support. Given the high burden of sarcopenia in CKD and the frequent overlap with metabolic and cardiovascular comorbidities, GLP-1RAs may offer a novel therapeutic approach. This review examines the biological plausibility and emerging evidence supporting the role of GLP-1RAs in preserving muscle health in CKD, highlighting the need for targeted clinical trials and mechanistic investigations to establish their efficacy in this high-risk group. Full article

Sustainability within the rice and wheat supply chain is integral to attaining the UN’s Sustainable Development Goals (SDGs), as they are the two most consumed grains as food. Rice and wheat cultivation significantly impacts the environment, with the agricultural sector employing 27% of the global workforce and contributing 4% to the world’s GDP, thereby affecting social and economic sustainability. Developing a sustainability index for the wheat and rice supply chain is a complex endeavor, as it depends on various factors such as the location of growers, farming methods, the target audience, and the stakeholders involved. This index must be derived from an optimal selection of indicators to avoid information overload while covering all essential sustainability aspects. There are different methods, such as life cycle assessment, energy analysis, ecological footprint, and carbon footprint, being used to assess sustainability, with indicator-based assessment emerging as a comprehensive approach. This study utilised the Triple Bottom Line (TBL) to identify optimal sustainability indicators in the wheat and rice supply chain. A systematic literature review was initially conducted, followed by an expert opinion survey to determine the required indicators. The literature review unveiled a wide array of indicators used across studies, often contingent on each study’s specific objectives. While some consistency existed in environmental indicators, discussions on social and economic dimensions within the wheat and rice supply chain were limited. Analysis of the expert opinion survey revealed a consensus on most selected indicators, albeit with variations based on experts’ geographical locations. The final set of optimal indicators identified can serve as a foundation for developing a sustainability index, implementing a sustainability information management system, and formulating policy initiatives in the rice and wheat supply chain. Full article

Wastewater treatment plants (WWTPs) are critical infrastructure that lessen the environmental impacts of human activity by stabilizing wastewaters laden with organics, chemicals, and nutrients. WWTPs face an increasing global population, greater wastewater volumes, stricter environmental regulations, and additional societal pressures to implement more sustainable and energy-efficient waste management strategies. WWTPs are energy-intensive facilities that generate significant GHG emissions and involve high operational costs. Therefore, improving the process efficiency can lead to widespread environmental and economic benefits. One promising approach is to integrate anaerobic digestion (AD) with hydrothermal carbonization (HTC) to enhance sludge treatment, optimize energy recovery, create valuable bio-based materials, and minimize sludge disposal. This study employs an LCA to evaluate the environmental impact of coupling HTC with AD compared to conventional AD treatment. HTC degrades wastewater sludge in an aqueous medium, producing carbon-dense hydrochar while reducing sludge volumes. HTC also generates an aqueous byproduct containing >30% of the original carbon as simple organics. In this system model, the aqueous byproduct is returned to AD to generate additional biogas, which then provides heat and power for the WWTP and HTC process. The results indicate that the integrated AD + HTC system significantly reduces environmental emissions and sludge volumes, increases net energy recovery, and improves wastewater sludge valorization compared to conventional AD. This research highlights the potential of AD + HTC as a key circular bioeconomy strategy, offering an innovative and efficient solution for advancing the sustainability of WWTPs. Full article

The urgent need for sustainable food systems in Indonesia is hindered by urban planning policies that are disconnected from food security priorities. Smart city planning policies in Indonesia have been subject to numerous misconceptions compared to successful implementations in developed countries. This study examines the relationship between urban planning policies and architectural design in fostering sustainable food systems, employing a mixed-methods approach that combines multiple linear regression analysis with a sample of 75 smart cities, correlation analysis, and case studies from six representative cities that demonstrate best practices. Key findings reveal that food security is significantly undermined by the Gross Regional Domestic Product (GRDP), indicating distributional inequalities, high food expenditure, and a lack of clean water, while access to electricity improves resilience. Case study analysis showed that Semarang is the city with the highest readiness level (97%), followed by Makassar (91%), which employs a Holistic Benchmark approach, Jakarta (91%), which follows a Technological—fragmented approach, Samarinda (86%) and Medan (79%), which are in a Developing Transition phase, and Surabaya (66%), which utilizes a Community and Local Initiatives approach. Each city adopted a different approach, which means the national strategy for developing Smart Cities will also differ; however, they must prioritize equitable infrastructure and architectural innovation, such as urban farming integration and a water–energy–food nexus system. Smart cities extend beyond technological innovations, encompassing integrated urban planning policies and architectural practices that foster sustainable food systems through infrastructure management and environmental sustainability. Full article