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The escalating demand for water, energy, and food, coupled with the imperative for sustainable development, necessitates innovative solutions to address the complex interdependencies within the water–energy–food nexus. In this context, agriculture and photovoltaics (Agri-PV or Agri–voltaics) systems have emerged as a promising approach to promoting sustainable agricultural practices while enhancing energy efficiency and food production. However, limited research, especially on the technical aspects of Agri-PV, has resulted in a knowledge gap regarding how to model and determine the suitability of Agri-PV for different crops based on local conditions. This study presents a novel approach to modeling and simulating Agri-PV systems for various major crops in developing countries, using Uzbekistan as a case study. It provides a blueprint for selecting suitable Agri-PV systems. The research investigates the technical feasibility of Agri-PV technology tailored to Uzbekistan’s agricultural landscape, with broader implications for Central Asia. Employing a systematic methodology, the study begins by selecting appropriate sites and crops for Agri-PV system testing, ensuring the relevance and applicability of the research findings to the local context. Using advanced software tools such as PVSyst, the study accurately calculates photosynthetically active radiation (PAR) values specific to selected crops, bridging a significant knowledge gap and providing empirical data essential for informed decision making. The methodology further incorporates an in-depth analysis of economic and technical considerations in selecting PV modules and inverters, enhancing the scientific accuracy of the study. By strategically modeling Agri-PV systems based on parameters like row density, module distance, and tilt angle, this research aims to optimize the integration of photovoltaic technology with agricultural practices in Uzbekistan. Moreover, this study helps to understand the impact of Agri-PV systems on the water–energy–food nexus, providing valuable insights into the potential benefits and challenges specific to the region. The study identifies the positive impact of Agri-PV on major crops and provides a suitable design and modeling approach for sustainable farming practices.

For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested.

Energy is a pivotal element for overall development. Therefore, affordable and clean energy for all (Sustainable Development Goal 7) is one of the important elements. Despite a suitable approach, Kyrgyzstan lags behind to achieve its Sustainable Development Goals by 2030. Improving access to energy can significantly impact the progress of many other sustainable development targets. However, access to reliable, sustainable, and adequate energy is a crucial task in rural Kyrgyzstan. Taking into consideration the energy situation and the importance of the availability of energy services, the overarching aim of the presented article is to map the potential of improving rural energy services to foster overall sustainable development goals. The article presents the most relevant information about rural energy usage behaviour based on three primary pillars: energy demand, energy carrier, and energy sources. In addition, the presented research article synthesises the key driving factors which have a direct or indirect impact on the energy services in rural areas. The summarised data displays a deep insight into a variety of complex and dynamic household energy consumption patterns. The article provides a potential solution nexus to foster improved energy services in rural Kyrgyzstan and therefore to foster the overall sustainable development in Kyrgyzstan. The findings of the article help to formulate a strategy to design rural energy services for policy makers and stockholders.

This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.

In rural Kyrgyzstan, the energy need is usually derived from multiple natural energy resources such as firewood, charcoal, agricultural residues, animal dung, and wood branches, which are considered common and predominant practices. Because of the non-sustainable resources and heavy reliance on the environment, Kyrgyzstan is one of the most vulnerable countries to climate change in Central Asia. On the contrary, the great renewable energy potential in Kyrgyzstan is untapped, which could be the most promising solution to ensuring sustainable energy supply in the country. However, because of the lack of scientific knowledge, current energy policies, and the lack of infrastructure, renewable resources are mainly untapped. To exploit the country’s renewable energy potential, there is a need for a systematic diagnosis to develop a strategy to explore renewables in Kyrgyzstan, which is currently missing in the existing literature. In that regard, the presented study aims to assess the current status of renewable energy sources by considering the local energy context from a potential point of view. Further to this, it provides a comparative overview through a matrix of strengths, weaknesses, opportunities, and threats. Such novel analysis would be the fundamental base for formulating policy advice and a national plan to enhance the utilization of renewable energy in Kyrgyzstan. The presented analysis was carried out based on the extensive literature review, the country’s national plan, and the existing energy policies of Kyrgyzstan. The article found out that there is huge potential available for the renewable energy market. As compared to other renewable energy sources, solar energy has great potential and can be considered one of the pioneer sustainable sources for integration into the country’s power generation framework.

More than half of the urban population of Sub-Saharan Africa lives in informal housing conditions. While urban areas are, in general, characterized by a high electrification rate, residents of informal settlements are still affected by energy poverty, the use of traditional energy sources and unreliable electricity supply. The aim of the study is to give an overview of different renewable-energy-based solutions which are able to improve local energy provision. These are Solar Home Systems, Mini-Grids, and Energy-Hubs. The technologies are compared to another option for improving energy supply, namely Grid Expansion. The analysis is based on 24 Key Performance Indicators, which can be classified into technical, economic, environmental, social, and political dimensions. The selection of indicators is based on the challenges prevalent in informal settlements that impede a comprehensive, sustainable energy supply. The literature-based indices are used to determine which of the four technologies is a suitable solution for minimizing the challenges prevailing in informal settlements. The resulting matrix provides a holistic comparison and serves as a decision aid in selecting the appropriate technology for future projects in informal settlements, depending on local conditions and the needs of the population. The results show that the Energy-Hub is a valid alternative for energy supply improvement in Informal Settlements.

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme.

Building construction in rural Kyrgyzstan is heavily dominated by earthen buildings. Old and inappropriate residential building structures contribute significantly to high domestic space heating energy consumption. Therefore, it is necessary to understand the relevant building construction techniques. However, the scant information on Kyrgyz building techniques, especially for high-altitude rural settlements, was the prime motivation to perform the presented study. The key objective of the study is to investigate residential building construction techniques in high-altitude rural Kyrgyzstan, and this was to be achieved by house visits during field trips, literature review, and pilot interviews with local people. The analysis enabled the detailed identification of individual building envelopes as well as predominant building materials to be recorded. Based on the assessment, a housing profile was created that represents the typical characteristics of traditional rural Kyrgyz houses. Furthermore, the study demonstrates the potential for energy savings in rural Kyrgyz houses of 50–70%. However, local conditions prevent people from making improvements to all domestic energy efficiency parameters simultaneously. Therefore, the study developed a ‘sequential roadmap’ to reduce domestic space heating demand in different phases based on simulation studies. Existing low-income rural Kyrgyz habitations can use the presented roadmap to reduce domestic space heating demand sequentially to overcome financial barriers and, therefore, contribute to establishing sustainable buildings in Kyrgyzstan. These results may be partially replicated in other Central Asian rural communities depending on their location and building characteristics.

Riparian forests are rare and valuable ecosystems in Central Asia, both due to their significance for biodiversity and to their provision of vital ecosystem services to local residents. However, the actual forest use behaviour is under-researched, official figures may not be trustworthy, and the question of over-use is up in the air. This paper sets out to shed light on riparian forest use behaviour by local residents using the example of Ak-Tal Village upon the Naryn River in Kyrgyzstan: Which economic use patterns do they practice (focusing on fuel wood and pasture)? Which other ecosystem services do they recognise? Is there forest over-exploitation? To answer these questions, this study builds on local knowledge, by applying the methods of focus group discussions and a household survey. Results show an extreme discrepancy between official wood consumption figures (50–60 m3 p.a.) and figures based on household wood consumption (310–404 m3 p.a.). The forest also serves as an important winter pasture over the seven months between October and April (stocking density 0.61 livestock units/ha), but payments for these ecosystem services are low, with annually 40 KGS/ha. Local residents are aware of additional material and nonmaterial ecosystem services of the riparian forest. Opinions diverge upon the question if there is forest over-exploitation, potentially because different stakeholders have different concepts of an optimal forest status. Consequently, optimal forest use behaviour can only be defined by the local users themselves, e.g., in a future stakeholder dialogue.

In both rural and urban areas, two-wheeler vehicles are the most common means of transportation, contributing to local air pollution and greenhouse gas emissions (GHG). Transitioning to electric two-wheeler vehicles can help reduce GHG emissions while also increasing the socioeconomic status of people in rural Kenya. Renewable energy systems can play a significant role in charging electric two-wheeled vehicles, resulting in lower carbon emissions and increased renewable energy penetration in rural Kenya. As a result, using the Conventional and Renewable Energy Optimization (CARNOT) Toolbox in the MATLAB/Simulink environment, this paper focuses on integrating and modeling electric two-wheeled vehicles (e-bikes) into an off-grid photovoltaic Water-Energy Hub located in the Lake Victoria Region of Western Kenya. Electricity demand data obtained from the Water-Energy Hub was investigated and analyzed. Potential solar energy surplus was identified and the surplus was used to incorporate the electric two-wheeler vehicles. The energy consumption of the electric two-wheeler vehicles was also measured in the field based on the rider’s driving behavior. The modeling results revealed an annual power consumption of 27,267 kWh, a photovoltaic (PV) electricity production of 37,785 kWh, and an electricity deficit of 370 kWh. The annual results show that PV generation exceeds power consumption, implying that there should be no electricity deficit. The results, however, do not represent the results in hourly resolution, ignoring the impact of weather fluctuation on PV production. As a result, in order to comprehend the electricity deficit, hourly resolution results are shown. A load optimization method was designed to efficiently integrate the electric 2-wheeler vehicle into the Water-Energy Hub in order to alleviate the electricity deficit. The yearly electricity deficit was decreased to 1 kWh and the annual electricity consumption was raised by 11% (i.e., 30,767 kWh), which is enough to charge four more electric two-wheeler batteries daily using the load optimization technique.

The northern part of the globe is dominated by industrialisation and is well-developed. For many years, the southern part of the world (South Asia, Africa etc.) has been a target of research concentrating on access to energy (mainly electricity) in rural regions. However, the Central Asian region has not been a focus of energy research compared to South East Asia and Africa. Despite plentiful domestically available energy resources, the energy supply in Central Asia is very unevenly distributed between urban and rural areas. Almost half of the total population of Central Asia lives in rural areas and there is a lack of access to modern energy services to meet primary needs. To analyse the energy situation (i.e., electricity, heating, hot water consumption, cooking, etc.) in rural Central Asia, this paper reviews residential energy consumption trends in rural Central Asian regions as compared to urban areas. Furthermore, the paper illustrates the potential of renewable energies in Central Asia. To perform the study, a qualitative comparative analysis was conducted based on a literature review, data, and statistical information. In summary, the presented article discusses the rural energy situation analytically and provides in-depth insights of Central Asian energy infrastructure.

Circulating asymmetrical dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthesis, has been proposed as a biomarker for clinical outcome. Dimethylarginine dimethylaminohydrolase (DDAH) is the main enzyme responsible for ADMA metabolism and elimination. Adipose tissue ADMA concentrations and DDAH activity and their role in diabetes and obesity have not yet been investigated. In this study, we evaluated clinical microdialysis in combination with a sensitive analytical method (GC-MS/MS) to measure ADMA concentrations in extracellular fluid. Adipose tissue ADMA concentrations were assessed before and during an oral glucose tolerance test in lean healthy subjects and subjects with diabetes (n = 4 each), and in morbidly obese subjects before and after weight loss of 30 kg (n = 7). DDAH activity was determined in subcutaneous and visceral adipose tissue obtained during laparoscopic surgery (n = 5 paired samples). Mean interstitial ADMA concentrations did not differ between study populations (healthy 0.17 ± 0.03 µM; diabetic 0.21 ± 0.03 µM; morbidly obese 0.16 ± 0.01 and 0.17 ± 0.01 µM before and after weight loss, respectively). We did not observe any response of interstitial ADMA concentrations to the oral glucose challenge. Adipose tissue DDAH activity was negligible compared to liver tissue. Thus, adipose tissue ADMA plays a minor role in NO-dependent regulation of adipose tissue blood flow and metabolism.

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from the European Space Agency (ESA) Sentinel-1 and 2 missions, Advanced Land Orbiting Satellite (ALOS) PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest. A five-fold cross-validation (repeated 100 times) of ground data showed that the highest classification accuracy of 80.5% is achieved for bands 2, 3, 4, 8, 11, and 12 from Sentinel-2, the ratio of bands VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) from Sentinel-1, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on optical bands alone was 72.7% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.4%. The classification accuracy is sufficient for many management applications for indigenous forest, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management.

Two new main group element carboranylamidinates were synthesized using a bottom-up approach starting from o-carborane, ortho-C2B10H12 (1, = 1,2-dicarba-closo-dodecaborane). The first divalent germanium carboranylamidinate, GeCl[HLCy] (3, [HLCy]− = [o-C2B10H10C(NCy)(NHCy)]−, Cy = cyclohexyl), was synthesized by treatment of GeCl2(dioxane) with 1 equiv. of in situ-prepared Li[HLCy] (2a) in THF and isolated in 47% yield. In a similar manner, the first antimony(III) carboranylamidinate, SbCl2[HLiPr] (4, [HLiPr]− = [o-C2B10H10C(NiPr)(NHiPr)]−), was obtained from a reaction of SbCl3 with 1 equiv. of Li[HLiPr] in THF (56% yield). The title compounds were fully characterized by analytical and spectroscopic methods as well as single-crystal X-ray diffraction. Both compounds 3 and 4 are monomeric species in the solid state, and the molecular geometries are governed by a stereo-active lone pair at the metal centers.

Indigenous forests cover 23.9% of New Zealand’s land area and provide highly valued ecosystem services, including climate regulation, habitat for native biota, regulation of soil erosion and recreation. Despite their importance, information on the number of tall trees and the tree height distribution across different forest classes is scarce. We present the first region-wide spatial inventory of tall trees (>30 m) based on airborne LiDAR (Light Detection and Ranging) measurements in New Zealand—covering the Greater Wellington region. This region has 159,000 ha of indigenous forest, primarily on steep mountainous land. We implement a high-performance tree mapping algorithm that uses local maxima in a canopy height model (CHM) as initial tree locations and accurately identifies the tree top positions by combining a raster-based tree crown delineation approach with information from the digital surface and terrain models. Our algorithm includes a check and correction for over-estimated heights of trees on very steep terrain such as on cliff edges. The number of tall trees (>30 m) occurring in indigenous forest in the Wellington Region is estimated to be 286,041 (±1%) and the number of giant trees (>40 m tall) is estimated to be 7340 (±1%). Stereo-analysis of aerial photographs was used to determine the accuracy of the automated tree mapping. The giant trees are mainly in the beech-broadleaved-podocarp and broadleaved-podocarp forests, with density being 0.04 and 0.12 (trees per hectare) respectively. The inventory of tall trees in the Wellington Region established here improves the characterization of indigenous forests for management and provides a useful baseline for long-term monitoring of forest conditions. Our tree top detection scheme provides a simple and fast method to accurately map overstory trees in flat as well as mountainous areas and can be directly applied to improve existing and build new tree inventories in regions where LiDAR data is available.