Scarcity Issues Research Articles

Spin-Electrochemistry of Transition Metal Oxides for Energy Storage: Concepts, Advances and Perspectives.

Developing high-capacity and cyclically stable transition metal (TM)-based electrode materials for energy storage devices, such as aqueous ion energy storage systems, is crucial for addressing the growing issue of energy scarcity. The spin state, or spin configuration of the d-electrons, plays a vital role in the electrochemical energy storage performance of these materials. However, there has been a lack of systematic descriptions regarding the role of spin configurations in electrochemical energy storage to date. This review aims to elucidate the advantages of controlling the spin states of metal centers to enhance energy storage performance and highlights recent progress in employing spin state regulation in electrochemical energy storage. Additionally, it covers the various characterization techniques used to determine spin states. Finally, we discuss the future prospects and challenges within this emerging field, with the aim of accelerating the development of spin-based electrochemical energy storage technologies. This review also seeks to provide clear and reliable directions for the design and preparation of novel energy storage materials.

Water desalination using atmospheric pressure plasma combined with thermal treatment

AbstractHerein, a novel method is presented for enhancing the thermal desalination process of saline water and seawater using atmospheric pressure plasma (APP). The effect of APP treatment combined with thermal heating (APP-TH) on the energy consumption, conductivity, and pH of seawater and saline water is investigated. Utilizing scanning electron microscopy and X-ray diffractometry, the evolution of the morphology, structure, and chemical composition of precipitated crystals is characterized. The APP-TH method reduces the energy consumption for desalination by 40.5% for saline water and by 52.82% for seawater when compared to the TH-only method. The pH value remains approximately unchanged, decreasing slightly for the saline water from 7.1 for untreated saline water to 7.05 after APP-TH treatment. However, after APP-TH treatment, the pH value of the seawater increased slightly, from 7 to 7.8. The total dissolved salts decreased after APP-TH treatment, lowering the conductivity of the saline water from 65,000 µS/cm to 160 µS/cm and the conductivity of the seawater from 58,200 µS/cm to 243 µS/cm. Moreover, the size of precipitated crystals from saline water is 31.47 nm after APP-TH treatment, compared to 55.59 nm after TH-only treatment. They also dropped from 41 nm to 39.5 nm for seawater. Compared with traditional approaches, this research proposes an optimistic solution to address global potable water scarcity issues.

Study on optimization of maize irrigation scheduling in Shaanxi Province

With the impact of climate change in recent years, the instability of precipitation and the increase of evaporation have made water resources in Shaanxi Province more and more stressed. How to maintain stable economic development while ensuring the sustainable development of agriculture within the limited water resources has become an important issue facing Shaanxi Province. This paper analyzes the temporal and spatial distribution characteristics of maize irrigation water requirement in Shaanxi Province based on meteorological data and obtains the ideal irrigation water requirement. However, considering the agricultural water scarcity issue in this region, this paper establishes different water scarcity scenarios, adopts the Jensen model to construct an objective function aimed at minimizing crop yield reduction, and solves it using a genetic algorithm to obtain the optimized maize irrigation scheduling under different water scarcity scenarios. The results analysis indicates the following: (1) The effective rainfall in Shaanxi Province from 1960 to 2019 shows a slight upward trend, while the maize water requirement and maize irrigation water requirement shows a downward trend. (2) The spatial distribution of maize irrigation water requirement in Shaanxi Province decreases gradually from north to south. High-value areas are mainly distributed in the northern regions of Yulin City and Yan’an City in Shanbei. Low-value areas are distributed in Ankang City in Shannan. (3) In the face of water scarcity, spring maize should ensure water use during the middle growth period, fast development period, and initial growth periods. Under the same water scarcity conditions, the yield reduction rate is the highest in Guanzhong, followed by the Shannan, and the lowest in Shanbei. This paper aims to provide a scientific and reasonable optimization plan for maize irrigation in Shaanxi Province by calculating and analyzing the maize irrigation water requirement in Shaanxi Province, combined with optimization tools such as genetic algorithms, to address the challenges brought by water resource constraints.

Industrial production of asymmetric wettability patterned fabric for efficient atmospheric water harvesting

While harvesting water from the atmosphere is considered as a promising technology to address the increasing issue of water scarcity, its wide applications are still limited by the difficulty in mass production of water harvesting materials. Here, an industrial-producible asymmetric wettability patterned fabric (AWPF) with high water harvesting capacity is developed using a continuous and low-cost weaving technology. Four textile patterns are developed for high-efficiency atmospheric water harvesting. The alternating patterned surface in AWPF is constructed with hydrophilic bumps and hydrophobic grooves using one set of green fluorine-free superhydrophobic polyester warp yarns and two sets of superhydrophobic polyester/hydrophilic viscose weft yarns. We optimized the water collection performance of the single-sided AWPF by spraying co-MBAA-DVB superhydrophobic material on the back of the AWPF to construct a wetting gradient, that is, double-sided AWPF. The double-sided AWPF shows a strong water harvesting capacity of 2840.2 mg·6h−1·cm−2, which increased the water harvesting efficiency by 43.2 % compared with the unoptimized single-sided AWPF. To reduce the pressure drop, 6 mm wide the double-sided AWPF was designed, resulting in a water harvesting capacity that is 1.23 times higher than that of without cutting. This advanced industrialized weaving technology with an asymmetric fabric structure design holds great potential for substantial water harvesting.

Global spatial and temporal dynamics of the green water coefficient and analysis of factor from 1992 to 2020

In recent years, the impacts of climate change have variably affected the global ecological environment, exacerbating water scarcity and related issues. Meantime, as part of soil evapotranspiration and plant transpiration in water resources, green water is of great significance in stabilizing the global water cycle and promoting the sustainable development of agriculture. Therefore, the green water coefficient in this study calculates the conversion rate of precipitation to green water, which is important for the balance between blue and green water and the stability of the ecological environment. Based on trend analysis methods and Geodetector, this study analyses the spatial and temporal characteristics and driving mechanisms of the green water coefficient on the globe and on each land-use type from 1992 to 2020. The findings revealed that: (1) The global green water coefficient is clearly spatially differentiated, showing certain latitudinal distribution differences, with the lowest near the equator. The areas with higher green water coefficients are mainly in northern and southern Africa, central Asia, Oceania and northern and western North America, while the areas with lower green water coefficients are concentrated in northern South America and Southeast Asia. (2) The global average value of green water coefficient decreases before increasing. Except for water, the green water coefficient of other land use types (crop land, forest land, grassland, urban land, unused land) changes significantly and there are fewer abrupt change points. Areas where the green water coefficient is too low and changes drastically are not suitable for rainfed agriculture. (3) Different influential factors act on the green water coefficients of different land use types, and there is a two-factor enhancement and non-linear enhancement relationship between them, with a combination of factors affecting the green water coefficient. By studying the characteristics of the green water coefficient globally and across different land use types, we quantified the influence of climate factors such as precipitation, temperature, wind speed, evapotranspiration, radiation, etc., on the green water coefficient, thereby providing some information for future global rain-fed agriculture and water resources management.

Efficient leaching of valuable metals from spent lithium-ion batteries using green deep eutectic solvents: Process optimization, mechanistic analysis, and environmental impact assessment

The accumulation of over 11 million tons of spent lithium-ion batteries (LIBs) by 2030 highlights a critical environmental challenge posed by their large-scale retirement. The efficient recycling valuable metals from spent LIBs can both reduces environmental impact and mitigates the pressing issue of metal resource scarcity. In this context, deep eutectic solvents (DESs) have become a promising option as an eco-friendly solvent, exhibiting great potential for recycling spent LIBs. Therefore, this work proposed an innovative green DES system consisting of choline chloride (ChCl), DL-malic acid (MAL), and glycerol for the efficient leaching of valuable metals from spent LIBs. Comprehensive experiments were conducted to determine the optimum leaching conditions (130 °C, 60 g/L, MChCl:MAL:Glycerol of 1:1:3, 3 h), achieving high leaching efficiencies of 94.6% for Ni, 96.8% for Co, 93.8% for Mn, and 96.4% for Li. Through characterization techniques, kinetics studies, and density functional theory (DFT) calculations, the leaching process was predominantly governed by surface chemical reaction (1-(1-x)1/3 = kt) within the shrinking core model, exhibited activation energies of 49.89 kJ/mol, 47.66 kJ/mol, 50.51 kJ/mol, and 22.24 kJ/mol for Ni, Co, Mn, and Li, respectively. The propensity for DES to leach metal ions followed the order: Li > Co > Ni > Mn, determined by binding energy and energy gaps. Cl− and −COOH within the DES were capable of forming stable complexes with reduced transition metal ions, revealing an efficient coordination leaching mechanism. Additionally, a life cycle assessment (LCA) was conducted on the environmental impacts of the DES leaching process, confirming it as an effective and environmentally friendly method for recycling spent LIBs. This work avoided the employment of corrosive acids and alleviated the generally harsh conditions associated with DESs leaching, providing a viable solution for recovering spent LIBs.

Optimization Study of the Structural Parameters of an Artificial Percolation Intake Riverbed Based on a Numerical Orthogonal Test

Research on water abstraction techniques in sandy rivers is crucial for addressing water scarcity issues in northern China. This study introduces an artificial percolation water intake riverbed structural model designed to prevent sand intake and facilitate the extraction of clear water from sandy rivers, the main function of which is to artificially construct a helical flow field to realize the separation of water and sand and, at the same time, percolate the water. In this paper, we use a numerical orthogonal test method to study the influence of structural parameters and their combinations on water extraction by percolation, and the preferred combination of structural parameters. The main conclusions of this paper are as follows: Under the condition of certain structural parameters of the artificial riverbed, the single-width flow rate has an important effect on the mean circumferential flow rate of the vortex tube and the vortex tube seepage volume. The effect of different structural parameters on the mean circumferential flow rate of the vortex tube and the vortex tube seepage varies in magnitude. The longitudinal slope of artificial riverbed i had the greatest and most significant effect on the mean circumferential flow rate of the vortex tube, and each structural parameter had some effect on the vortex tube seepage Q but not to a significant level. When the sand content of the water flow is 15 kg/m3 and the maximum sand grain size of the suspended mass is 2 mm, the optimal structural parameter combinations of the artificial riverbed under different working conditions are as follows: Combination 20 is the optimal structural parameter combination for q = 0.3 m2/s; Combination 24 is the optimal structural parameter combination for q = 0.6 m2/s; and Combination 24 is the optimal structural parameter combination for q = 0.9 m2/s. The present study is of great significance for understanding the influence of each structural parameter and its combination on the effect of the water intake and sand discharge of an artificial percolation intake riverbed, and for optimizing the combination of the structural parameters of an artificial percolation intake riverbed.

Accessible Infrastructure Development for the Visually Impaired

This paper focuses on daily struggles of visually impaired individuals with public infrastructure and services. Although it is now a cliche on making progress globally in disability rights, the reality on the ground is different. Infrastructure supporting independent mobility and social inclusion for the blind are often far from providing such aids and considerations, something that makes them navigate through a world whose design does not reflect the needs of the visually impaired people. The study brings out the critical issues of scarcity of tactile paving, inadequacy of braille signage, and complete absence of audible traffic signals, each of which is a grim reminder of how systematic these individuals are left out from being part of society. These are the physical barriers, but much more is beyond this. This research proposes practical solutions that can develop more inclusive standards, integrate innovative technologies, and overcome social and financial hurdles. By raising these issues, the paper seeks to contribute important ideas for policymakers, urban planners, and advocacy groups to truly motivate the collective action needed to make public spaces fully and widely accessible. The findings underscore the need for shared responsibility in infrastructure improvement, which will significantly enhance the quality of life for the blind and help reach society’s goals of being fairer and more inclusive.

Emerging Nanomaterials for Drinking Water Purification: A New Era of Water Treatment Technology.

The applications of nanotechnology in the field of water treatment are rapidly expanding and have harvested significant attention from researchers, governments, and industries across the globe. This great interest stems from the numerous benefits, properties, and capabilities that nanotechnology offers in addressing the ever-growing challenges related to water quality, availability, and sustainability. This review paper extensively studies the applications of several nanomaterials including: graphene and its derivative-based adsorbents, CNTs, TiO2 NPs, ZnO NPs, Ag NPs, Fe NPs, and membrane-based nanomaterials in the purification of drinking water. This, it is hoped, will provide the water treatment sector with efficient materials that can be applied successfully in the water purification process to help in addressing the worldwide water scarcity issue.

Dual-branch network with hypergraph feature augmentation and adaptive logits adjustment for long-tailed visual recognition

The long-tailed distribution presents issues of data scarcity and significant class imbalance, which causes the model to increase the prediction tendency for the head classes and reduce performance on tail classes. The decoupling learning strategy separates the learning process into representation learning and classifier learning to enhance model performance. However, this approach does not delve into the complex semantic relationships within images and overlooks the bias of the model confidence. To address this pervasive issue, we propose a novel algorithm called Hypergraph Feature Augmentation and Adaptive Logits Adjustment for Long-tailed Visual Recognition (HALR) based on a decoupling learning framework. For the representation learning task, we extract hypergraph features from the mixed input samples to capture the global spatial contextual semantic information of the images. For the classifier learning task, we propose an adaptive logits adjustment function that automatically corrects prediction score biases, thereby yielding robust decision boundaries. Extensive experiments on widely-used benchmark datasets, including CIFAR10/100-LT, ImageNet-LT, and iNaturalist 2018, validate the efficacy of HALR. For example, the test accuracy of HALR on CIFAR10-LT with an imbalance factor of 100 is 88.25 %, which is a 17.85 % improvement over the baseline model accuracy and 0.68 % higher than the state-of-the-art model. The results demonstrate that HALR effectively optimizes representation and classifier learning tasks for long-tailed learning, mitigating the negative impact of class imbalance and data scarcity.

Diffusion models-based motor imagery EEG sample augmentation via mixup strategy

Deep representation learning has been widely explored for decoding motor imagery electroencephalogram (MI-EEG) to build EEG-tailored brain-computer interfaces. Due to the labor-intensive and time-consuming recording to MI-EEG, recently BCIs were suffered from sample scarcity, especially for the limitation of sample diversity. To solve this issue, we proposed a novel sample augmentation method, Diffusion models-based EEG Sample Augmentation method via Mixup strategy (DESAM), to solve sample scarcity and improve sample diversity for both multivariate time-series and time–frequency images forms. To reduce the characteristics conflicts brought by the augmented samples, a mixup strategy with data weighting was proposed for both forms. To validate the efficacy of sample augmentation by DESAM, we conducted comparative experiments on three publicly available MI-EEG datasets, and reported the average decoding accuracies and Kappa values on augmented samples by various deep learning models. For the BCI Competition IV datasets 2a, 2b, and 1, the two forms of DESAM sample augmentations achieved improvements for both accuracies and Kappa values. Ablation studies have demonstrated the necessity and significance of our DESAM method, and it makes a possible manner to solve sample scarcity issue of MI-EEG.

Advancements in synergistic fermentation of probiotics and enzymes for non‐grain feed raw materials

AbstractTo address the escalating challenge of food scarcity and the associated conflicts between human and animal consumption, it is imperative to seek alternative resources that can substitute for traditional feed. Non‐grain feed (NGF) raw materials represent a category of biomass resources that are distinct from grains in their composition. These materials are characterized by their high nutritional content, cost‐effectiveness, ample availability, and consistent supply, which contribute to their significant economic potential. Nonetheless, the extensive application of NGF is currently hindered by several limitations, including a high concentration of antinutritional factors, suboptimal palatability, and an offensive odor, among other shortcomings. The synergistic fermentation of probiotics and enzymes (SFPE) is an innovative approach that integrates the use of a diverse array of enzymes during the feed fermentation process, as well as various strains of probiotics throughout the feed digestion process. This method aims to enhance the nutritional value of the feed, diminish the presence of antinutritional factors, and improve the overall palatability, thereby facilitating the optimal utilization of NGF. This strategy holds the promise of not only replacing conventional feed options but also mitigating the pressing issue of grain scarcity. This paper delves into the practical applications of NGF and presents an overview of the latest research advancements in SFPE fermentation techniques, which can provide cutting‐edge and valuable reference for researchers who devote themselves to research in this field in the future.

Synthesis of tin selenide nanoparticles using Polygonum avicular extract decorated on graphitic carbon nitride for enhancing photodegradation of amoxicillin trihydrate and photoproduction of hydrogen peroxide

In this research, tin selenide nanoparticles (SnSe-NPs) were synthesized using Polygonum avicular extract via the hydrothermal method and decorated on graphitic carbon nitride (g-C3N4) by the simple sonicated method to produce SnSe/g-C3N4 composites. The characterization and properties of the SnSe/g-C3N4 composites, as compared to pristine SnSe and g-C3N4, were determined by Fourier-transform infrared spectroscopy, X-ray powder diffraction, scanning electron microscope, transmission electron microscopy, field emission scanning electron microscopy, X-ray photoelectron spectroscopy, ultraviolet–visible diffuse reflectance spectroscopy, Mott-Schottky, and electrochemical impedance spectroscopy. As a result, the SnSe-NPs possessed the rod-like shape and after the decoration onto the g-C3N4 sheet surface, it reduced the bandgap energy of the g-C3N4 from 2.58 downward 1.43 eV. Besides, the photoactivities of the 10-SnSe/g-C3N4 catalyst were investigated via both sides of amoxicillin trihydrate (AMXT) photodegradation and hydrogen peroxide (H2O2) photoproduction, in which the results were achieved 87.84 % AMXT and 85.48 μM H2O2, respectively, under visible light. Furthermore, the photodegradation was consistent with the pseudo-first-order kinetics and it performed the highest photodegradation at pH ∼5, while the importance of isopropyl alcohol as a trapping agent and •O2− radicals was affirmed in the H2O2 photoproduction. Moreover, the photoproduction of H2O2 was remained after 4 cycles achieved at 62.70 % in the final cycle, which demonstrated the stability and reusability of the 10-SnSe/g-C3N4. These aforementioned results demonstrate that the SnSe/g-C3N4 material is a promising candidate for addressing environmental pollution and energy scarcity issues.

Deep learning-powered efficient characterization and quantification of microplastics

Characterizing and quantifying microplastics (MPs) are time-consuming and labor-intensive tasks traditionally. This paper presents an artificial intelligence (AI) framework aiming to automate these tasks by integrating computer vision and deep learning techniques. The approach leverages Fourier Transform Infrared (FTIR) spectra and visual images. Primary novelties of this research involve the development of: (1) an AI framework integrating efforts of data processing, analytics, visualization, and human-computer interaction; (2) a method for transforming FTIR data into contour images; (3) data augmentation strategies for resolving data scarcity and imbalance issues; (4) deep learning models for identifying MPs; (5) computer vision algorithms for quantifying MPs; and (6) an engineer-friendly graphic user interface (GUI) for enhancing data accessibility. The AI framework has been applied to polyethylene, polypropylene, polystyrene, polyamide, ethylene-vinyl acetate, and cellulose acetate. Results confirmed the efficacy of the framework, exhibiting high accuracy scores in classification (98 %), segmentation (99 %), and quantification (96 %) tasks. This research advances the capability of automatic assessment of MPs.

The Impact of Inter-Basin Water Transfer between Johor and Singapore on Johor’s Water Supply System, 1927 to 1941

Abstract Inter-basin water transfer between Johor and Singapore from 1927 to 1941 had a significant impact on Johor’s water supply system. This study aims to assess the consequences of this transfer and provide a contemporary perspective on its implications. Existing research on inter-basin transfers and their impact on water systems between Singapore and Johor is abundant, but there is a notable lack of research specifically addressing Johor’s water system during this time. By examining the historical context and socio-economic dynamics that shaped the inter-basin transfer, this study aims to contribute a unique perspective to the existing body of knowledge. The findings will enhance our understanding of the impacts of inter-basin water transfers and provide valuable insights for water resource management in the region. It also contributes to a comprehensive understanding of the impact of such transfers on contemporary water scarcity issues and provides practical insights for sustainable water resource management globally.

Diffusion probabilistic priors for zero-shot low-dose CT image denoising.

Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for training, which are challenging to obtain in clinical settings. Existing unsupervised deep learning-based methods often require training with a large number of low-dose CT images or rely on specially designed data acquisition processes to obtain training data. To address these limitations, we propose a novel unsupervised method that only utilizes normal-dose CT images during training, enabling zero-shot denoising of low-dose CT images. Our method leverages the diffusion model, a powerful generative model. We begin by training a cascaded unconditional diffusion model capable of generating high-quality normal-dose CT images from low-resolution to high-resolution. The cascaded architecture makes the training of high-resolution diffusion models more feasible. Subsequently, we introduce low-dose CT images into the reverse process of the diffusion model as likelihood, combined with the priors provided by the diffusion model and iteratively solve multiple maximum a posteriori (MAP) problems to achieve denoising. Additionally, we propose methods to adaptively adjust the coefficients that balance the likelihood and prior in MAP estimations, allowing for adaptation to different noise levels in low-dose CT images. We test our method on low-dose CT datasets of different regions with varying dose levels. The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods. Our method achieves PSNR of 45.02 and 35.35dB on the abdomen CT dataset and the chest CT dataset, respectively, surpassing the best unsupervised algorithm Noise2Sim in the comparative methods by 0.39 and 0.85dB, respectively. We propose a novel low-dose CT image denoising method based on diffusion model. Our proposed method only requires normal-dose CT images as training data, greatly alleviating the data scarcity issue faced by most deep learning-based methods. At the same time, as an unsupervised algorithm, our method achieves very good qualitative and quantitative results. The Codes are available in https://github.com/DeepXuan/Dn-Dp.

Techno-economic process design of multi-stage flash- reverse osmosis for water and power production using solar-wind resources

This paper introduces a newly developed hybrid power plant design that combines renewable energy sources to produce both electricity and freshwater. The design offers a higher gain output ratio (GOR) and improved efficiency in power and freshwater production, all at a lower cost compared to existing research papers. To generate power, the power plant incorporates a range of technologies, such as a horizontal-axis wind turbine (WT), photovoltaic panels (PV), parabolic trough collectors (PTC), a steam Rankine cycle, a multi-stage flash (MSF) desalination unit that cleverly harnesses excess heat from the Steam Rankine cycle, and an Reverse osmosis (RO) unit. With the assistance of ASPEN Plus, ASPEN Economic, and HOMER PRO software, the process parameters undergo significant improvement. According to the results, the power plant has the capacity to generate 57.63 MW of electricity and produce 2505 m3h of freshwater. Additionally, it has a GOR of 9.09 and utilizes 7285 m3h of seawater, which has a renewability factor of 97.6 %. In addition, the efficiency of the steam Rankine cycle stands at 32 %, providing notable energy savings. The production costs for electricity and freshwater are 0.037 $kW and 1.38 $m3, respectively, indicating their affordability. The economic analysis reveals promising results for the project, with an anticipated annual profit of 41 % and a relatively short capital payback period of 2.4 years. By implementing the proposed hybrid cogeneration power plant, the region can tackle its water and energy scarcity issues with a sustainable and long-lasting solution.

Water scarcity and public health concerns in rural India: a case study of Nongpok Sekmai and surrounding villages

This study examines the critical issue of water scarcity and its impact on public health in Nongpok Sekmai and surrounding villages in India. Despite government initiatives to provide universal tap water access by 2024, these villages lack safe drinking water. Traditional sources like open wells are contaminated, and the primary river is drying up due to illegal sand mining and gravel extraction activities upstream. This situation results in a significant public health burden, with villagers facing financial strain due to waterborne diseases and the need to purchase water for basic needs. The study also examines the negative link between on-premise access to safe water and mothers' time spent on water collection. The study highlights the need for a multi-pronged approach. Renewed government efforts to implement the tap water initiative or provide alternative clean water sources like filtration plants or community wells are crucial. Additionally, stricter enforcement is needed to curb illegal mining activities and ensure river sustainability. Furthermore, exploring community-based solutions like rainwater harvesting and raising awareness about waterborne diseases and sanitation practices can empower the population. Addressing this water crisis demands a collaborative effort from the government, local communities, and environmental agencies. This study employed a mixed-methods approach, utilizing visual photographic analysis and community engagement to investigate water contamination in Nongpok Sekmai river, Manipur. The findings highlight key contamination indicators such as turbidity, algae blooms, and floating debris, while also addressing the health and social impacts perceived by the local population.

Distributed photovoltaic power forecasting based on personalized federated adversarial learning

Existing distributed photovoltaic (PV) power forecasting methods fail to address the impact of sample scarcity and heterogeneity in PV power data. Moreover, training a single prediction model proves challenging to meet the personalized forecasting needs of different PV stations in distributed environments. This paper proposes a personalized federated generative adversarial network (PFedGAN)-based DPV power forecasting method. Leveraging the federated learning (FL) framework, it achieves collaborative training of prediction models among DPV stations while preserving data privacy. y introducing generative adversarial networks (GAN) and personalized strategy optimization into the FL training process, it alleviates data scarcity issues and reduces the impact of data heterogeneity. A TimesNet-DeepAR (TNE-DeepAR) power prediction model is designed, where the TimesNet module extracts correlations between PV power data from different time periods, and the DeepAR module facilitates PV power prediction, mitigating the effects of meteorological factors' multi-periodic variations on PV power. Experimental results show that the proposed hybrid prediction model reduces the average mean absolute percentage error (MAPE) by 30–40 % compared to single models. The proposed approach reduces the MAPE by 9.79 % compared to traditional methods and by 49.62 % for PV stations with scarce data.

The Performance of Solar Still in Continental Climates: A Case Study in Poland

This paper addresses the global issue of water scarcity and the need for sustainable development by exploring eco-friendly desalination technology – solar still. Although solar still has lower productivity compared to conventional fossil-fueled desalination technologies, much research is being conducted to enhance its productivity. Most studies focus solely on optimizing water mass in solar still, neglecting other correlated factors. Hence, this paper presents mathematical modeling and experimental studies on the impact of water mass, initial temperature, and solar insolation on solar still performance in Polish weather conditions. The findings reveal that solar still productivity ranges from 215 mL/m2/day to 5079 mL/m2/day between June and October in Poland. It is observed that solar still productivity decreases with increasing water mass beyond the critical threshold of solar insolation on the glass cover plane, which is 2.676 kWh/m2/day. However, below that threshold, the effect of water mass on the solar still productivity strongly depends on its initial temperature.