Search Results (134)

The use of biogas in Indonesia, derived from livestock manure, palm oil waste, and organic waste, remains limited to household-scale applications due to its inefficiency in transportation and storage. This limitation arises from the presence of CO₂ and H₂O in raw biogas, which results in a lower methane content compared to natural gas. Furthermore, raw biogas is not suitable for storage in cylinders or long-distance distribution without purification. This research aims to address these challenges by developing biogas into Bio-Compressed Natural Gas (Bio-CNG), a high-methane-content fuel suitable for industrial applications and power generation. Bio-CNG is produced through biogas purification, primarily using the water scrubbing method, to achieve methane concentrations exceeding 92%, followed by compression to 120 Bar for compact storage and ease of transport. The study focuses on designing and testing an industrial-scale effective water scrubber system for biogas purification, thereby enabling the broader utilization of renewable biogas energy beyond local reactor sites. The development of the biogas purification and compression system begins with the system modeling and the detailed design, which are then followed by the hardware fabrication in industrial-scale scenarios. The purification and compression of biogas consist of two main components: the purification system and the biogas compression system. The core of the purification system is a scrubber, designed as a vertical column measuring 6 m in height and 0.5 m in diameter. The designed and fabricated system for industrial-scale biogas purification and compression was then tested. The results showed a linear correlation between scrubber operating pressure and methane and CO₂ content. Based on the results of the pressure and water flow rate variation tests, an operating pressure of 2 bar is recommended for the water scrubber, as this condition yielded the lowest specific energy consumption of 0.3 kWh/Nm³. Meanwhile, in the biogas compression system, the energy required is exponentially proportional to the pressure between 75 and 105 bar. Full article

This study presents a comprehensive assessment and strategic framework for implementing Nature-Based Solutions (NBSs) to mitigate flooding in Lethem and Tabatinga, Region 9 of Guyana. The communities are increasingly vulnerable to flooding due to climate variability, hydrological dynamics, and socio-economic factors. A mixed-methods approach, comprising hydrological modelling and observation, a questionnaire survey with a sample of households in both communities, and interviews with municipal administrators, was utilised to acquire data for the study. The study utilised the Statistical Package for Social Sciences (SPSS) to analyse the socio-economic impacts of flooding in the two communities. The results revealed that recent events, such as the significant floods of 2022, have prompted an urgent need for sustainable management strategies. Community engagement efforts, supported by data analysis through remote sensing technology, identified flood-prone areas and vulnerable populations, including women, the elderly, and persons with disabilities. Chi-Square testing was conducted to determine mutual dependence between the communities’ livelihood activities and disruptions to income and working days, and their ability to deal with flooding. Based on the results, the farmers were the group that the highest inability to deal with flooding. Existing infrastructure, including drainage systems and emergency response initiatives led by the Civil Defence Commission, has contributed to improved flood management; however, limitations persist, particularly in urban planning and land use practices. This study underscores the detailed process of implementing and adopting NBS approaches, such as flood conveyance solutions and water storage and bio-retention solutions. These solutions can improve water quality, preserve ecosystems, and enhance community well-being while reducing flood risks. Applying these solutions in the targeted communities promises to bolster ecological resilience, support climate adaptation, and reduce the incidence and the impact of floods in the sampled communities. Full article

This paper presents a fast and robust approach to evaluate the singular values of small (e.g., $4\times 4$, $5\times 5$) matrices on single- and multi-Graphics Processing Unit (GPU) systems, enabling the modulation of the accuracy–speed trade-off. Targeting applications that require only computations of the SVs in electromagnetics (e.g., Multiple Input Multiple Output—MIMO link capacity optimization) and emerging deep-learning kernels, our method contrasts with existing GPU singular value decomposition (SVD) routines by computing singular values only, thereby reducing overhead compared to full-SVD libraries such as cuSOLVER’s gesvd and MKL’s desvg. The method uses four steps: interlaced storage of the matrices in GPU global memory, bidiagonalization via Householder transformations, symmetric tridiagonalization, and root finding by bisection using Sturm sequences. We implemented the algorithm in CUDA and evaluated it on different single- and multi-GPU systems. The approach is particularly suited for the analysis and design of multiple-input/multiple-output (MIMO) communication links, where thousands of tiny SVDs must be computed rapidly. As an example of the satisfactory performance of our approach, the speed-up reached for large matrix batches against cuSOLVER’s gesvd has been around 20 for $4\times 4$ matrices. Furthermore, near-linear scaling across multi-GPUs systems has been reached, while maintaining root mean square errors below $2.3\times {10}^{-7}$ in single precision and below $2.3\times {10}^{-13}$ in double precision. Tightening the tolerance from $\delta ={10}^{-7}$ to $\delta ={10}^{-9}$ increased the total runtime by only about 10%. Full article

Plant by-products have gained significant attention due to their rich content in bioactive compounds, which exhibit promising antioxidant, antimicrobial, and antitumor properties. In European countries, vegetable waste generation ranged from 35 to 78 kg per capita in 2022, highlighting both the scale of the challenge and the potential for valorization. This review provides an overview of key studies investigating the potential of plant residues in biomedicine, highlighting their possible contents of antioxidant compounds, their antimicrobial and antitumor properties, as well as their applications in dermocosmetics and nutraceuticals. However, despite their potential, several challenges must be addressed, such as the standardization of extraction protocols, as bioactive compound profiles can vary with plant source, processing conditions, and storage methods. Effective segregation and storage protocols for household organic waste also require optimization to ensure the quality and usability of plant by-products in biomedicine. Emerging 4.0 technologies could help to identify suitable plant by-products for biomedicine, streamlining their selection process for high-value applications. Additionally, the transition from in vitro studies to clinical trials is hindered by gaps in the understanding of Absorption, Distribution, Metabolism, and Excretion (ADME) properties, as well as interaction and toxicity profiles. Nonetheless, environmental education and societal participation are crucial to enabling circular bioeconomy strategies and sustainable biomedical innovation. Full article

This study defines evidence-based minimum internal floor areas required to support long-term residential use across different household types. It addresses the following question: what is the smallest viable floor area that supports sustained occupancy without persistent stress, conflict, or turnover? An integrative review method was employed, drawing from behavioural studies in environmental psychology, international regulatory standards, and real-world market data. The analysis focuses on essential domestic functions including sleep, hygiene, food preparation, storage, social interaction, and work. Quantitative findings from tenancy surveys, post-occupancy research, and market performance data indicate that residential units below 30 square metres for single occupants and 45 square metres for couples are consistently associated with reduced satisfaction and shorter tenancies. Regulatory minimums across diverse jurisdictions tend to converge near these same thresholds. The study proposes technical minimums of 30, 45, and 60 square metres for one-, two-, and three-person households, respectively. These values reflect functional lower bounds rather than ideal or aspirational sizes and are intended to inform performance-based housing standards. Full article

One of the methods for mitigating the duck curve phenomenon in photovoltaic (PV) energy systems is storing surplus energy in the form of hydrogen. However, there is a lack of studies focused on residential PV systems that assess the impact of hydrogen storage on the reduction of energy flow imbalance to and from the national grid. This study presents an analysis of hydrogen energy storage based on real-world data from a household PV installation. Using simulation methods grounded in actual electricity consumption and hourly PV production data, the research identified the storage requirements, including the required operating hours and the capacity of the hydrogen tank. The analysis was based on a 1 kW electrolyzer and a fuel cell, representing the smallest and most basic commercially available units, and included a sensitivity analysis. At the household level—represented by a single-family home with an annual energy consumption and PV production of approximately 4–5 MWh over a two-year period—hydrogen storage enabled the production of 49.8 kg and 44.6 kg of hydrogen in the first and second years, respectively. This corresponded to the use of 3303 kWh of PV-generated electricity and an increase in self-consumption from 30% to 64%. Hydrogen storage helped to smooth out peak energy flows from the PV system, decreasing the imbalance from 5.73 kWh to 4.42 kWh. However, while it greatly improves self-consumption, its capacity to mitigate power flow imbalance further is constrained; substantial improvements would necessitate a much larger electrolyzer proportional in size to the PV system’s output. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

Background: Food and nutrition security are key social determinants of cardiometabolic health. While food security reflects access to sufficient food, nutrition security incorporates the quality, consistency, and usability of food that supports long-term health. However, few studies have examined how household-level barriers to food utilization shape these relationships. Objective: This study assessed whether tangible (e.g., equipment, storage) and intangible (e.g., time, knowledge) food utilization barriers modify the associations between food and nutrition security and cardiometabolic outcomes in low-income adults. Methods: A cross-sectional survey was conducted among 486 low-income adults across five U.S. states. Participants reported household food security (USDA 18-item module), nutrition security (four-item scale), and utilization barriers (eight-item scale, categorized into tangible and intangible subscales). Self-reported diagnoses of hypertension, hyperlipidemia, and diabetes were combined into a cardiometabolic outcome. Mixed-effects logistic regression models, adjusted for sociodemographic and program participation factors, were used to assess associations and effect modification. Results: Higher nutrition security was associated with lower odds of cardiometabolic conditions (AOR = 0.59; 95% CI: 0.41–0.83). Tangible barriers significantly modified the relationship between nutrition security and hypertension (p-interaction = 0.04), with stronger protective effects observed in households without such barriers. No significant moderation effects were found for intangible barriers or for food security. Conclusions: Tangible household barriers influence the protective association between nutrition security and cardiometabolic outcomes. Public health strategies should address not only food access but also the practical resources required to store, prepare, and consume healthy foods effectively. Full article

Food loss and waste are critical challenges in Taiwan’s agri-food supply chain, deteriorating security and resource efficiency. By employing the best–worst method (BWM), a multi-criteria decision-making model was developed in this study to evaluate the severity of losses and wastes. Combining literature review results with expert survey analysis results, key loss points, and mitigation strategies were identified to enhance sustainability and efficiency in Taiwan’s agricultural food system. Among the seven stages of the agricultural food supply chain, supermarket waste (16.95%) was identified as the severest, followed by government policies (16.63%), restaurant waste (15.35%), processing loss (14.71%), production site loss (13.64%), household waste (11.93%), and logistics/storage/distribution loss (10.79%). In the subcategories of each supply chain stage, the eight severe issues were identified as “Inadequate planning and control of overall production and marketing policies” under government policies, “Adverse climate conditions” and “Imbalance in production and marketing” under production site loss, “Inaccurate market demand forecasting” and “Poor inventory management at supermarkets” under supermarket waste, and “Improper storage management of ingredients leading to spoilage” as well as “Inability to accurately forecast demand due to menu diversity” under restaurant waste. The least severe issues included “Poor production techniques” under production site loss. Other minor issues included “Inefficient use of ingredients due to poor cooking skills”, “Festive culture and traditional customs”, and “Suboptimal food labeling design”, all of which contributed to household waste. Based on these findings, we proposed recommendations to mitigate food loss and waste in Taiwan’s agricultural food supply chain from practical, policy, and academic perspectives. The results of this study serve as a reference for relevant organizations and stakeholders. Full article

Food waste poses critical environmental, economic, and social challenges, with consumer behavior recognized as a key leverage point for intervention. Packaging plays a vital role in preserving food quality and reducing waste, yet its behavioral influence on household food waste (HFW) remains underexplored. This review systematically examines 52 studies investigating the impact of food packaging—excluding storage/date labeling—on consumer food waste (CFW) behaviors. Using a structured methodology, we classified studies by methodological design, geographic coverage, food types, and focal packaging features. The analysis reveals a dominant reliance on consumer surveys and short-duration diaries, with limited application of rigorous experimental methods. Geographically, the English-language literature is skewed toward high-income countries, particularly Australia and Europe, with notable gaps in regions such as Asia and Africa. Moreover, despite U.S. households discarding approximately 40% of their food, research coverage remains limited. The findings also expose a misalignment between research focus and consumer-perceived importance of packaging features; attributes such as transparency, grip/shape, and dispensing mechanisms are frequently rated as important by consumers but are under-represented in the literature. This review contributes by identifying these gaps, synthesizing behavioral evidence, and offering a roadmap for future research and design innovation. By better aligning packaging functionalities with real-world behaviors, this work supports the development of consumer-informed solutions to mitigate HFW and promote sustainable food systems. Full article

The photovoltaic (PV) energy storage (ES) inverter is an effective way to solve the problems of energy shortage and environment pollution. However, when considering the constraints such as economic benefits and power supply reliability, the energy optimization and dispatching of this PV–ES system poses great challenges. This paper proposes an optimization method based on the combination of the particle swarm algorithm and non-linear penalty function to dispatch the energy of household PV–ES inverter. Based on the established optimization model of the PV–ES inverter system, compared with the static penalty function, the penalty factor can be automatically adjusted according to the range beyond the constraint by using the proposed non-linear penalty function. Furthermore, the particle swarm algorithm is used as the optimization engine, and the energy scheduling scheme is obtained by the combination of the particle swarm algorithm and the proposed non-linear penalty function. Finally, the simulation and hardware-in-the-loop results verify the correctness of the proposed algorithm, compared with the static penalty function, the user electricity expenses can be effectively reduced, and economic requirements can be met. Full article

Background: Nowadays, the microbial degradation of cellulose represents a new perspective for reducing cellulose waste from industry and households and at the same time obtaining energy sources. Methods: We isolated and enriched two aerobic (at 37 °C and 50 °C) and one anaerobic microbial consortium from an anaerobic bioreactor for biogas production by continuous subculturing on peptone cellulose solution (PCS) medium supplemented with 0.3% treated or untreated Whatman filter paper under static conditions. Samples were taken every 7 days until day 21 to determine the percentage of cellulose biodegradation. We determined the antimicrobial resistance of aerobic and anaerobic consortia and some single colonies by disc diffusion method, against 42 clinically applied antibiotics. PCR analyses were performed to search for the presence of eight genes for cellulolytic activity and nine genes for antibiotic resistance. By metagenomics analysis, the bacterial and fungal genus distributions in the studied populations were determined. Results: Aerobes cultured at 50 °C degraded cellulose to the greatest extent (47%), followed by anaerobes (24–38%) and aerobes (8%) cultured at 37 °C. The bacterial sequence analysis showed that the dominant phyla are Bacillota and Bacteroidetes and genera—Paraclostridium, Defluvitalea, Anaerobacillus, Acetivibrio, Lysinibacillus, Paenibacillus, Romboutsia, Terrisporobacter, Clostridium, Sporanaerobacter, Lentimicrobium, etc. in a different ratio depending on the cultivation conditions and the stage of the process. Some of these representatives are cellulolytic and hemicellulolytic microorganisms. We performed lyophilization and proved that it is suitable for long-term storage of the most active consortium, which degrades even after the 10th re-inoculation for a period of one year. We proved the presence of ssrA, ssrA BS and blaTEM genes. Conclusions: Our findings demonstrated the potential utility of the microbial consortium of anaerobes in the degradation of waste lignocellulose biomass. Full article

The article designs a home photovoltaic installation equipped with energy storage using PVSyst software 7.4. The aim of the research was to design and select an energy storage for a household that uses an average of 396.7 kWh per month. The designed PV installation system was characterised by a significant share of stored energy—at the level of 32%, which allows the household to reduce energy consumption from the power grid. The results of the analysis showed that the use of energy storage increases leads to a reduction in energy losses and improves the energy self-sufficiency of the facility. The article also compared, using the IPCC 2013 GWP 100a and IMPACT World+ methods, three variants of households with different energy sources. It was shown that a household using the national energy mix generates a significant carbon footprint, higher compared to variants powered by renewable energy. The study showed that obtaining energy from renewable sources reduces the potential negative impact of energy consumption on the environment. Full article

Households with photovoltaic installations contribute to reducing greenhouse gas emissions and mitigating global climate change. To fully utilize the benefits of clean solar energy, it is essential to ensure its efficient use, which can be achieved by consuming all generated energy locally, within the household or a microgrid community, eliminating wastage during the grid transportation and storage. In this work, we propose a method to enhance self-consumption by eliminating simultaneous bidirectional energy flow in the phase lines of a three-phase grid-tied household system, particularly in cases of significant load asymmetry. We developed an adaptive power flow management (APFM) algorithm which distributes solar-generated energy across the household grid’s phase lines based on their respective loads and solar power generation level. A simulation based on real-world data demonstrated that the APFM algorithm both eliminates simultaneous active power import and export flows and ensures that all power exported from the household to the DSO grid remains symmetrical across all phase lines. As observed from the simulation results, applying the APFM algorithm reduced the daily imported and exported energy between the household and the DSO grid by an average of 27.5% during the spring–autumn period for a specific household. Additionally, reducing energy flow led to a 5% average increase in self-consumption within the household grid, with peak improvements reaching 16.5%. Full article

Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system’s performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system’s effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system’s ability to adapt to different operational environments. Full article