Search Results (41)

Carbon isotope of the kerogen (δ¹³C_org), steranes/hopanes (S/H), and C₂₈/C₂₉ sterane ratios in the source rocks from the SARK section at the Early Cambrian Yurtus Formation in the Fortunian Stage in the Tarim Basin of Northwest China reveal a positive excursion that is associated with biological diversity. The enrichment of vanadium/(vanadium + nickel) (V/(V + Ni)) ratios (0.64~0.99, averaging 0.87) for the Yurtus Formation of the Fortunian Stage provide evidence for predominant anoxic bottom water conditions. A sharply decreased V/(V + Ni) ratio in the middle Yurtus Formation suggests enhanced oxygen content of the water column in this interval. However, the total organic carbon (TOC) values in the sedimentary rocks show a marked increase in the middle Yurtus Formation, which is due to the enhanced productivity suggested by a positive δ¹³C_org increase of ~2.0‰ and enhanced S/H and C₂₈/C₂₉ sterane ratios. We suggest that the enhanced oxygen content may have contributed to the biological diversity during the Fortunian Stage in the Tarim Basin. The δ¹³C_org excursion first reported here associated with biological diversity can be correlated with that in South China and possibly elsewhere in this interval. Full article

The increasing adoption of renewable energy sources necessitates efficient energy storage solutions, with buildings emerging as critical nodes in residential energy systems. This review synthesizes state-of-the-art research on the role of batteries in residential settings, emphasizing their diverse applications, such as energy storage for photovoltaic systems, peak shaving, load shifting, demand response, and backup power. Distinct from prior review studies, our work provides a structured framework categorizing battery applications, spanning individual use, shared systems, and energy communities, and examines modeling techniques like State of Charge estimation and degradation analysis. Highlighting the integration of batteries with renewable infrastructures, we explore multi-objective optimization strategies and hierarchical decomposition methods for effective battery utilization. The findings underscore that advanced battery management systems and technological innovations are aimed at extending battery life and enhancing efficiency. Finally, we identify critical knowledge gaps and propose directions for future research, with a focus on scaling battery applications to meet operational, economic, and environmental objectives. By bridging theoretical insights with practical applications, this review contributes to advancing the understanding and optimization of residential energy storage systems within the energy transition. Full article

Developing crop varieties with combined salinity and waterlogging tolerance is essential for sustainable agriculture and food security in regions affected by these stresses. This process requires an efficient method to rapidly and accurately assess the tolerance of multiple genotypes to these stresses. Our study examined the use of a pot trial in combination with the assessment of multiple traits to assess the tolerance of 100 wheat (Triticum aestivum L.) genotypes sourced from around the world to these combined stresses. The stresses were imposed on the plants using 100 mM NaCl and by submerging the root systems of the plants in their bathing solutions. The data gathered were subjected to principal component analysis (PCA), and an integrated score (IS) for each genotype was calculated based on multiple morpho-physiological traits; the score was used to rank the genotypes with respect to tolerance or susceptibility. There were significant differences among the 100 wheat genotypes in terms of the relative reductions in their growth parameters and chlorophyll contents, suggesting a rich, genetic diversity. To assess the accuracy of this methodology and to gain insight into the causes of tolerance or susceptibility, the five most tolerant (Misr4 (W85), Corack (W41), Kzyl-Sark (W94), Hofed (W57), BAW-1157 (W14)), and two least tolerant (Livingstong (W60) and Sunvale (W73)) genotypes were selected based on their IS and PCA analysis. These genotypes were then grown hydroponically with and without salinity stress. The data from this second trial were again subjected to PCA, and their IS were calculated; there was reasonable agreement in the ranking of the genotypes between the two trials. The most tolerant genotype (W85; Misr4 from Egypt) and most susceptible genotype (W73; Sunvale from Australia) were then examined in further detail in a third trial. Plants of Misr4 (W85) had lower Na⁺/K⁺ ratios, higher superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase activities, and higher glutathione concentrations. As a result, plants of Misr4 (W85) had lower concentrations of reactive oxygen species (H₂O₂ and O₂^•−) and malondialdehyde than those of Sunvale (W73). This study offers an efficient methodology for the assessment of multiple sources of germplasm for stress tolerance. It has also identified germplasm that can be used for future breeding work and for further research on the mechanisms of tolerance and susceptibility to combined salinity and waterlogging stresses. Full article

As a result of the increase in the number of smart buildings and advances in technology, energy consumption in buildings has become increasingly important. The estimation of energy consumption in buildings is critical for energy efficiency. Accurate estimation of photovoltaic (PV) solar power plant efficiency is crucial for optimizing the performance of renewable energy applications. In this study, advanced machine learning regression models such as XGBoost, CatBoost, LightGBM, AdaBoost and Histogram-Based Gradient Boosting are used to predict PV efficiency based on ten internal features (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), Maximum Voltage (Vmpp), Maximum Current (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), and Module Temperature (Tm)) of PV module measurements from the Utrecht University Photovoltaic Outdoor Test Facility. As a result, CatBoost outperformed the others, achieving the lowest prediction error MSE of 0.002 and the highest R² value of 0.90. To interpret the model’s predictions, we applied Explainable Artificial Intelligence techniques, in particular SHAP and LIME, which identify key features affecting efficiency and increase model transparency. The integration of these methods provides valuable insights for PV solar power plant design and optimization. Full article

The purpose of this study article is to provide a detailed examination of the performance of exergy electric panels, exergy efficiency panels and exergy solar panels under the climatic circumstances of the Utrecht region in the Netherlands. The study explores the performance of these solar panels in terms of both their energy efficiency and their exergy efficiency. Additionally, the study investigates critical factors such as solar radiation, module internal temperature, air temperature, maximum power, and solar energy efficiency. Environmental factors have a considerable impact on panel performance; temperature has a negative impact on efficiency, whereas an increase in solar radiation leads to an increase in energy and exergy output. These findings offer significant insights that can be used to increase the utilization of solar energy in locations that have a temperate oceanic climate, particularly in the context of the climatic conditions of the Utrecht region. The usefulness of the linear regression model in machine learning was validated by performance measures such as R², RMSE, MAE, and MAPE. Furthermore, an R² value of 0.94889 was found for the parameters that were utilized. Policy makers, researchers, and industry stakeholders who seek to successfully utilize solar energy in the face of changing climatic conditions may find this research to be an important reference. Full article

As global energy concerns escalate, there is a growing need for effective strategies to promote sustainable energy practices among individuals and communities. Gamification, the integration of game-design elements in non-game contexts, emerges as a promising tool to enhance user engagement and foster sustainable behaviour in energy management. In this review, we examine the theoretical aspects of gamification and its application in energy management in users’ households, highlighting its potential to transform repetitive or even monotonous tasks into engaging activities, focusing on studies that measure a long-term effect. We delve into various gamified elements adopted in long-term studies, such as feedback, social interactions, point systems, leader boards, narrative-driven challenges, etc., to understand their effect on user motivation and behavioural changes. From our set of studies, we found out that strong social game elements contribute the most to the long-term behaviour change of energy usage. One more condition of behaviour change is strong positive user satisfaction: the game should be engaging. We highlight the possible limitations of gamification in an energy management situation, a strong need for better practices of design and evaluation, and innovative approaches (such as DSM; Demand Side Management) in gamification for long-term engagement in household energy management. Full article

PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply and demand during the day. While the netting system in the Netherlands can currently negate the economic drawbacks of this discrepancy, grid congestion and imbalanced electricity prices show that improvements are highly desirable for the sustainability of electricity grids. This research analyzes the effectiveness of a Power-to-Domestic-Hot-Water (P2DHW) system at improving the utilization of excess PV electricity in Dutch households and compares it to similar technologies. The results show that the example P2DHW system, the WaterAccu, compares favorably as a low cost and flexible solution. In particular, for twelve different households differing in size (1–6 occupants), PV capacity (2.4–8 kWp), and size of hot water storage boiler (50–300 L), it is shown that the total economic benefits for the period 2024–2032 vary from −€13 to €3055, assuming the current net metering scheme is abolished in 2027. Only for large households with low PV capacity are the benefits a little negative. Based on a multi-criteria analysis, it is found that the WaterAccu is the cheapest option compared to other storage options, such as a home battery, a heat pump boiler, and a solar boiler. A sensitivity study demonstrated that these results are overall robust. Furthermore, the WaterAccu has a positive societal impact owing to its peak shaving potential. Further research should focus on the potential of the technology to decrease grid congestion when implemented on a neighborhood scale. Full article

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion. Full article

Objective: To determine if basal linear deposit (BLinD) is a specific lesion of age-related macular degeneration (AMD). Methods: The cohort was selected from a clinically and histopathologically validated archive (Sarks Archive) and consisted of 10 normal eyes (age 55–80 years) without any macular basal laminar deposit (BLamD) (Sarks Group I) and 16 normal aged eyes (age 57–88 years) with patchy BLamD (Sarks Group II). Only eyes with in vivo fundus assessment and corresponding high-resolution transmission electron microscopy (TEM) micrographs of the macula were included. Semithin sections and fellow-eye paraffin sections were additionally examined. BLinD was defined as a diffuse layer of electron-lucent vesicles external to the retinal pigment epithelium (RPE) basement membrane by TEM and was graded as follows: (i) Grade 0, absence of a continuous layer; (ii) Grade 1, a continuous layer up to three times the thickness of the RPE basement membrane (0.9 µm); (iii) Grade 2, a continuous layer greater than 0.9 µm. Bruch’s membrane (BrM) hyalinisation and RPE abnormalities were determined by light microscopic examination of corresponding semithin and paraffin sections. Results: BLinD was identified in both normal (30%) and normal aged (62.5%) eyes. BLinD was thicker in normal aged eyes (p = 0.045; 95% CI 0.04–3.4). BLinD thickness positively correlated with both the degree of BrM hyalinisation (p = 0.049; 95% CI 0.05–2.69) and increasing microscopic RPE abnormalities (p = 0.022; 95% CI 0.188–2.422). RPE abnormalities were more likely to be observed in eyes with increased BrM hyalinisation (p = 0.044; 95% CI 0.61–4.319). Conclusions: BLinD is most likely an age-related deposit rather than a specific lesion of AMD. Its accumulation is associated with increasing BrM hyalinisation and microscopic RPE abnormalities, suggesting a relationship with dysregulated RPE metabolism and/or transport. Full article

To thoroughly understand the profile of phenolic phytochemicals in kidney bean seeds cultivated in a cold region, the extractions, contents, antioxidant activities, compositions of free and bound phenols in the seed coat and cotyledon, and also relevant color attributes, were investigated. The results indicated that ultrasound-assisted extraction was an efficient method for free phenols. The bound phenols in seed coat and cotyledon were released more efficiently by alkali-acid and acid-alkali sequential hydrolysis, respectively. Under the optimized extractions, total phenols (TPC), flavonoids (TFC), and anthocyanins (TAC) ranged in 7.81–32.89 mg GAE/g dw, 3.23–15.65 mg RE/g dw, and 0–0.21 mg CE/g dw in the whole seeds of the five common kidney beans. There was a big difference in phenolic distribution between red and white seeds. From whole seed, the phenols in the four red cultivars mainly existed in free state (78.84%) and seed coat (71.56%), while the phenols in the white ‘Sark’ divided equally between free (51.18%) and bound (48.82%) states and consisted chiefly in cotyledon (81.58%). The correlation analyses showed that the antioxidant activities were significantly and positively correlated with TPC and TFC. The phenolic attributes were closely associated with the color of the seed coat. Red seeds had higher total contents of phenols than white seeds. TAC had a positively significant correlation with redness. Brightness and yellowness showed a negatively significant correlation with TPC, TFC, and antioxidant capacities, which were necessarily linked with redness degree and spot in red seeds. The spotted red ‘Yikeshu’ with the most outstanding performance on phenolic attributes was selected to analyze phenolic compounds with UHPLC-QE-MS. Among the 85 identified phenolics, 2 phenolic acids and 10 flavonoids were dominant. The characteristic phenolics in free and bound states were screened in both seed coat and cotyledon, respectively. The available information on the phenolic profile may expand the utilization of kidney beans as a nutritional ingredient in the food industry. Full article

Solar photovoltaics (PV) continues to grow rapidly across the world and now accounts for a very considerable proportion of all non-fossil-fuel electricity. With the continuing urgency of greenhouse gas abatement, the growth of solar PV is inevitable. Competition with other land uses and the desire to optimize the efficiency of the panels by making use of water cooling are compelling arguments for offshore floating PV (OFPV), a trend that could also benefit from the existing infrastructure recently built for offshore wind farms. Building on our earlier work, we present a larger dataset (n = 82) located around the globe to assess global yield (dis)advantages while also accounting for a modified form of water cooling of the offshore panels. Using our results regarding the Köppen–Geiger (KG) classification system and using a statistical learning method, we demonstrate that the KG climate classification system has limited validity in predicting the likely gains from OFPV. Finally, we also explore a small subset of sites to demonstrate that economics, alongside geography and technology, impacts the feasibility of locating PV panels offshore. Full article

Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape. Full article

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet. Full article

In recent years, serious energy games (SEGs) garnered increasing attention as an innovative and effective approach to tackling energy-related challenges. This review delves into the multifaceted landscape of SEG, specifically focusing on their wide-ranging applications in various contexts. The study investigates potential enhancements in user engagement achieved through integrating social connections, personalization, and data integration. Among the main challenges identified, previous studies overlooked the full potential of serious games in addressing emerging needs in energy systems, opting for oversimplified approaches. Further, these studies exhibit limited scalability and constrained generalizability, which poses challenges in applying their findings to larger energy systems and diverse scenarios. By incorporating lessons learned from prior experiences, this review aims to propel the development of SEG toward more innovative and impactful directions. It is firmly believed that positive behavior changes among individuals can be effectively encouraged by using SEG. Full article

A lack of aggregate analysis concerning energy communities exists in the academic literature. The authors utilized a combination of literature reviews and desk research to fill this gap. The existing debate on the classification of energy communities was summarized and aligned. Discovered classifications were used to analyze the status quo of the sector. The authors found nearly 4000 energy communities with 900,000 members in the European Union. On average, there are 844 members per one energy community. Germany, the Netherlands, Denmark, and the United Kingdom are at the forefront of the movement. Different countries have different primary sources of renewable energy utilized by energy communities, and membership structures vary based on the energy source and corporate purpose of the energy community together with the sector’s maturity in a certain country. Predominantly, hydro and biomass are used by energy communities in Alpine countries, solar energy is used in Germany, Spain, and France, wind in the Netherlands and Denmark, and different renewables in the United Kingdom. More members have joined the hydro, biomass, and wind communities than solar communities. Each country has national and regional associations of energy communities. In addition, intermediary actors, researchers, and consultancy agencies have shown a growing interest in the deployment of the movement. Achieving a conformity of business models Europe-wide would probably be impossible and pointless. Distinct geographical, institutional, and policy context-specific conditions stimulate diversity rather than conformity. Full article