Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts. Full article

The participants in the Supplemental Nutrition Assistance Program (SNAP) consume greater amounts of sugar and sweetened beverages (SSBs) compared to non-eligible individuals, which could result in potential negative health outcomes. This can be attributed to the lack of restrictions on SSB purchases with SNAP benefits. In view of the increasing calls from advocates and policymakers to restrict the purchase of SSBs with SNAP benefits, we performed a systematic review to assess its impact towards SSB purchases and consumption. We searched articles from five databases—Cochrane, EBSCO, SCOPUS, Web of Science, and PubMed—and selected seven studies, four of which were randomized controlled trials (RCTs) and three were simulation modeling studies. All three simulation studies and one RCT reported outcomes in terms of consumption, while the other three RCTs reported outcomes in terms of purchases. All seven studies found that an SSB restriction led to a decrease in SSB consumption or purchases, with six studies reporting significant results. Nonetheless, limitations exist. These include limited studies on this subject, potential workarounds circumventing SSB restrictions, like making purchases using personal cash, potentially differed estimated effects when combined with incentives or other initiatives, and the limited geographical scope among the selected RCTs. Full article

Heracleum mantegazzianum is an invasive species in middle Europe. The mode of action of its invasiveness is still not known. Our study focuses on observation of potential allelopathic influence by the production and release of phytochemicals into its environment. Plant material was collected four times within one season (April, May, June, July 2019) at locality Lekárovce (eastern Slovakia) for comparison of differences in composition and potential allelopathy. Water extracts from collected samples were used for different biological assays. The total phenols and flavonoids were determined spectrophotometrically. The profile and content of phenolic components, including coumarins, were determined by two techniques of liquid chromatography along with in vitro evaluation of the free radical scavenging activity of extracts (DPPH, Hydroxyl, Superoxide, and FRAP). The changes in composition in extracts in different seasonal periods were evident as well as potential phytotoxic activity in some concentrations on specific model plants. The slight antioxidant activity was noted. The invasiveness of the current species could be supported by the excretion of its phytochemicals into its surroundings and by different modes of action influencing living organisms in its environment. Full article

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them. Adversarial attacks are attack samples produced by carefully manipulating the samples at the test time to violate the model integrity by causing detection mistakes. In this paper, we analyse the performance of five realistic target-based adversarial attacks, namely Extend, Full DOS, Shift, FGSM padding + slack and GAMMA, against two machine learning models, namely MalConv and LGBM, learned to recognise Windows Portable Executable (PE) malware files. Specifically, MalConv is a Convolutional Neural Network (CNN) model learned from the raw bytes of Windows PE files. LGBM is a Gradient-Boosted Decision Tree model that is learned from features extracted through the static analysis of Windows PE files. Notably, the attack methods and machine learning models considered in this study are state-of-the-art methods broadly used in the machine learning literature for Windows PE malware detection tasks. In addition, we explore the effect of accounting for adversarial attacks on securing machine learning models through the adversarial training strategy. Therefore, the main contributions of this article are as follows: (1) We extend existing machine learning studies that commonly consider small datasets to explore the evasion ability of state-of-the-art Windows PE attack methods by increasing the size of the evaluation dataset. (2) To the best of our knowledge, we are the first to carry out an exploratory study to explain how the considered adversarial attack methods change Windows PE malware to fool an effective decision model. (3) We explore the performance of the adversarial training strategy as a means to secure effective decision models against adversarial Windows PE malware files generated with the considered attack methods. Hence, the study explains how GAMMA can actually be considered the most effective evasion method for the performed comparative analysis. On the other hand, the study shows that the adversarial training strategy can actually help in recognising adversarial PE malware generated with GAMMA by also explaining how it changes model decisions. Full article

Photobiological treatment of reverse osmosis concentrate (ROC) using brackish diatoms is a green and sustainable technology that can enhance water recovery by removing dissolved silica from ROC while producing beneficial biomass. This study aimed to determine the optimum conditions for the photobiological treatment of ROC obtained from a full-scale advanced water purification facility using Gedaniella flavovirens Psetr3. While light color presented minor impacts on the silica uptake rate, the impact of color intensity was significant. The uptake rate improved from 28 ± 1 to 48 ± 7 mg/L/day by increasing photosynthetically active radiation (PAR) from 50 to 310 µmol m⁻² s⁻¹. Increasing the PAR further did not improve the performance. The optimum temperature was around 23–30 °C. While the silica uptake was slower at 10 °C, G. flavovirens Psetr3 was unable to survive at 40 °C. Experiments using sunlight as a light source verified the impact of temperature on the silica uptake and the detrimental effect of ultraviolet radiation on this diatom. The sunlight-based treatment effectively removed N-nitrosodimethylamine. The results of this study are being used in subsequent pilot-scale investigations and full-scale technoeconomic analysis and will contribute to the further development of this sustainable water technology. Full article

(1) Background: Although Candida albicans accounts for the majority of fungal infections, therapeutic options are limited and require alternative antifungal agents with new targets; (2) Methods: A biofilm formation assay with RPMI1640 medium was performed with Liriope muscari extract. A combination antifungal assay, dimorphic transition assay, and adhesion assay were performed under the biofilm formation condition to determine the anti-biofilm formation effect. qRT-PCR analysis was accomplished to confirm changes in gene expression; (3) Results: L. muscari extract significantly reduces biofilm formation by 51.65% at 1.56 μg/mL use and therefore increases susceptibility to miconazole. L. muscari extract also inhibited the dimorphic transition of Candida; nearly 50% of the transition was inhibited when 1.56 μg/mL of the extract was treated. The extract of L. muscari inhibited the expression of genes related to hyphal development and extracellular matrix of 34.4% and 36.0%, respectively, as well as genes within the Ras1-cAMP-PKA, Cph2-Tec1, and MAP kinase signaling pathways of 25.58%, 7.1% and 15.8%, respectively, at 1.56 μg/mL of L. muscari extract treatment; (4) Conclusions: L. muscari extract significantly reduced Candida biofilm formation, which lead to induced antifungal susceptibility to miconazole. It suggests that L. muscari extract is a promising anti-biofilm candidate of Candida albicans since the biofilm formation of Candida albicans is an excellent target for candidiasis regulation. Full article

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability. Full article

Southern Tibet and western Yunnan are areas with an intensive distribution of high-temperature geothermal systems in China, as an important part of the Himalayan Geothermal Belt (HGB). In recent decades, China has conducted systematic research on high-temperature geothermal fields such as Yangbajing, Gudui, and Rehai. However, a comprehensive understanding has not yet been formed. The objective of this study was to enhance comprehension of the high-temperature geothermal system in the HGB and to elucidate the hydrogeochemical characteristics of geothermal fluids. This will facilitate the subsequent sustainable development and exploitation of domestic high-temperature hydrothermal geothermal resources. To this end, this study analysed geothermal spring and borehole data from the Yangbajing, Gudui, and Rehai geothermal fields. Based on previous research results, the source, evolution, and reservoir temperature characteristics of geothermal fluids are compared and summarised. The main high-temperature geothermal water in the geothermal field is derived from the deep Cl-Na geothermal fluid. Yangbajing’s and Gudui’s geothermal waters are primarily recharged by snow-melt water, while Rehai’s geothermal water is mainly recharged by local meteoric water. The average mixing ratios of magmatic water in the Yangbajing, Gudui, and Rehai geothermal fields are 17%, 21%, and 22%, respectively. The Yangbajing and Gudui geothermal fields have a relatively closed geological environment, resulting in a stronger water–rock interaction compared to the Rehai geothermal field. As geothermal water rises, it mixes with shallow cold water infiltration. The mixing ratios of cold water in the Yangbajing, Gudui, and Rehai geothermal fields are 60–70%, 40–50%, and 20–40%, respectively. Based on the solute geothermometer calculations, the maximum geothermal reservoir temperatures for Yangbajing, Gudui, and Rehai are 237 °C, 266 °C, and 282 °C, respectively. This study summarises and compares the hydrogeochemical characteristics of three typical high-temperature geothermal fields. The findings provide an important theoretical basis for the development of high-temperature geothermal resources in the Himalayan Geothermal Belt. Full article

This study investigated the effect of verbal encouragement (VE) on static and dynamic balance in individuals with intellectual disabilities (IDs). A total of 13 mild IDs and 12 moderate IDs participants underwent static balance tests (bipedal stance on firm surface, under open eyes (OEs) and closed eyes (CEs), and foam surface, unipedal stance on firm surface) and dynamic balance assessments (Y Balance Test (YBT) and Expanded Timed Up-and-Go Test (ETUGT)) under VE and no VE (NO/VE) conditions. VE significantly reduced center of pressure mean velocity (CoP_Vm) values for mild IDs in firm bipedal CEs conditions. The mild IDs group exhibited improved YBT scores and enhanced ETUGT performances for both groups under VE. Incorporating VE as a motivational strategy in balance training interventions can positively impact static and dynamic balance in individuals with mild IDs, especially in challenging conditions like unipedal stances on firm surfaces. Full article

Transition metal selenides have high theoretical capacities, making them attractive candidates for energy storage applications. Here, using the microwave-absorbing properties of the materials, we designed a simple and efficient microwave-assisted synthesis method to produce a composite made of nanospheres Ni_0.5Co_0.5Se₂ (NCSe) and highly conductive, stable Ti₃C₂T_x MXene. The Ni_0.5Co_0.5Se₂/Ti₃C₂T_x composites are characterized via scanning electron microscopy, X-ray diffraction, cyclic voltammetry, and electrochemical impedance spectroscopy. The findings indicate that 3D Ni_0.5Co_0.5Se₂ bimetallic selenide nanospheres were uniformly loaded within the few-layer Ti₃C₂T_x MXene wrapper in a short period. The optimal NCSe/Ti₃C₂T_x−2 electrode can demonstrate a specific capacitance of 752.4 F g^–1 at 1 A g^–1. Furthermore, the asymmetric supercapacitor combined with activated carbon maintains a capacitance retention of 110% even after 5000 cycles. The method of directly growing active substances on few-layer Ti₃C₂T_x MXene will provide inspiration for the manufacture of high-pseudocapacitance supercapacitors. Full article

Disentangling how climate oscillations and geographical events significantly influence plants’ genetic architecture and demographic history is a central topic in phytogeography. The deciduous ancient tree species Ulmus macrocarpa is primarily distributed throughout Northern China and has timber and horticultural value. In the current study, we studied the phylogenic architecture and demographical history of U. macrocarpa using chloroplast DNA with ecological niche modeling. The results indicated that the populations’ genetic differentiation coefficient (N_ST) value was significantly greater than the haplotype frequency (G_ST) (p < 0.05), suggesting that U. macrocarpa had a clear phylogeographical structure. Phylogenetic inference showed that the putative chloroplast haplotypes could be divided into three groups, in which the group Ⅰ was considered to be ancestral. Despite significant genetic differentiation among these groups, gene flow was detected. The common ancestor of all haplotypes was inferred to originate in the middle–late Miocene, followed by the haplotype overwhelming diversification that occurred in the Quaternary. Combined with demography pattern and ecological niche modeling, we speculated that the surrounding areas of Shanxi and Inner Mongolia were potential refugia for U. macrocarpa during the glacial period in Northern China. Our results illuminated the demography pattern of U. macrocarpa and provided clues and references for further population genetics investigations of precious tree species distributed in Northern China. Full article

Strigolactones (SLs) have potential to be used in sustainable agriculture to mitigate various stresses that plants have to deal with. The natural SLs, as well as the synthetic analogs, are difficult to obtain in sufficient amounts for practical applications. At the same time, fluorescent SLs would be useful for the mechanistic understanding of their effects based on bio-imaging or spectroscopic techniques. In this study, new fluorescent SL mimics containing a substituted 1,8-naphthalimide ring system connected through an ether link to a bioactive furan-2-one moiety were prepared. The structural, spectroscopic, and biological activity of the new SL mimics on phytopathogens were investigated and compared with previously synthetized fluorescent SL mimics. The chemical group at the C-6 position of the naphthalimide ring influences the fluorescence parameters. All SL mimics showed effects similar to GR24 on phytopathogens, indicating their suitability for practical applications. The pattern of the biological activity depended on the fungal species, SL mimic and concentration, and hyphal order. This dependence is probably related to the specificity of each fungal receptor–SL mimic interaction, which will have to be analyzed in-depth. Based on the biological properties and spectroscopic particularities, one SL mimic could be a good candidate for microscopic and spectroscopic investigations. Full article

This study investigates the feasibility of using a composite material comprising slate reinforced with cork sheets for architectural purposes like facades and wall coverings. The research involves the comprehensive characterisation of both slate and cork materials along with the evaluation of the silicone adhesive used in their bonding process, specifically Sikasil^® HT from SIKA^®. It was found that both slate and cork exhibited low wettability, which was enhanced through cold plasma treatment. Subsequently, a composite sandwich structure was fabricated and subjected to impact testing in a drop tower, along with fire resistance evaluations. The fire tests revealed that when subjected to a flame of 900 °C for 15 min, the slate alone heated rapidly, reaching 500 °C within 3 min on the side opposite to the flame. However, the sandwich structure reached 260 °C on the cork side (opposite to the flame) at 7.5 min, maintaining this temperature until the deterioration or detachment of the cork between 11 and 12 min. This provided insulation and delayed ignition. The sandwich structure maintained its fire resistance due to the insulating properties of cork and the superior thermal resistance of silicone compared to other adhesives up to 260 °C. Overall, the results suggest the potential suitability of this sandwich structure for architectural applications. Its favourable adhesion properties and acceptable fire resistance indicate that it could serve as a viable alternative for construction materials in architectural contexts. Full article

The objective of this study was to identify and classify the spectrum of mutations found in the BRCA1 and BRCA2 genes associated with breast and ovarian cancer in female patients in Romania. Germline BRCA1 and BRCA2 mutations were investigated in a cohort of 616 female patients using NGS and/or MLPA methods followed by software-based data analysis and classification according to international guidelines. Out of the 616 female patients included in this study, we found that 482 patients (78.2%) did not have any mutation present in the two genes investigated; 69 patients (11.2%) had a BRCA1 mutation, 34 (5.5%) had a BRCA2 mutation, and 31 (5%) presented different type of mutations with uncertain clinical significance, moderate risk or a large mutation in the BRCA1 gene. Our investigation indicates the most common mutations in the BRCA1 and BRCA2 genes, associated with breast and ovarian cancer in the Romanian population. Our results also bring more data in support of the frequency of the c.5266 mutation in the BRCA1 gene, acknowledged in the literature as a founder mutation in Eastern Europe. We consider that the results of our study will provide necessary data regarding BRCA1 and BRCA2 mutations that would help to create a genetic database for the Romanian population. Full article

To investigate the hydrodynamic noise characterization of hydraulic turbines with runner blade defects, this article establishes the intact machine model and three kinds of models with runner blade defects. Using the Computational Fluid Dynamic (CFD) and Computational Acoustic (CA) hybrid simulation computational methods, the hydrodynamic noise field of the hydraulic turbine is numerically simulated, and the results of the acoustic near field and acoustic far field are shown. 1. The double-row leaf grille and the runner are the primary sound source areas of the hydraulic turbine, and the intensity of sound radiation from these areas is positively correlated with the degree of runner blade defects. 2. As the runner blade defects develop, the sound power level (SWL) increases more significantly in the guide vanes near the nose of the spiral case in the double-row leaf grille. The most pronounced increase in the SWL is observed at the defective craters on the runner blades. 3. The frequency of the defective noise signal is primarily concentrated in the low-frequency band. The dominant frequency amplitude associated with runner blade defects increases and rises after the occurrence of defects. Secondary frequency changes are also observed, and the location of these changes varies at different receiving points. Full article

Pulmonary manifestations of vasculitis are associated with significant morbidity and mortality in affected individuals. They result from a complex interplay between immune dysregulation, which leads to vascular inflammation and tissue damage. This review explored the underlying pathogenesis of pulmonary involvement in vasculitis, encompassing various forms such as granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), eosinophilic granulomatosis with polyangiitis (EGPA), and anti-GBM disease. Mechanisms involving ANCA and anti-GBM autoantibodies, neutrophil activation, and neutrophil extracellular trap (NETs) formation are discussed, along with the role of the complement system in inducing pulmonary injury. Furthermore, the impact of genetic predisposition and environmental factors on disease susceptibility and severity was considered, and the current treatment options were presented. Understanding the mechanisms involved in the pathogenesis of pulmonary vasculitis is crucial for developing targeted therapies and improving clinical outcomes in affected individuals. Full article

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results. Full article

In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak generalization ability of the trained models when transferred to real-world applications. To address this challenge, we present an innovative maximum entropy Deep Q-Network (ME-DQN), which leverages an attention mechanism. The framework solves complex and sparse reward tasks through probabilistic reasoning while eliminating the trouble of adjusting hyper-parameters. This approach aims to merge the robust feature extraction capabilities of Fully Convolutional Networks (FCNs) with the efficient feature selection of the attention mechanism across diverse task scenarios. By integrating an advantage function with the reasoning and decision-making of deep reinforcement learning, ME-DQN propels the frontier of robotic grasping and expands the boundaries of intelligent perception and grasping decision-making in unstructured environments. Our simulations demonstrate a remarkable grasping success rate of 91.6%, while maintaining excellent generalization performance in the real world. Full article

This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic disturbances. Moreover, it shows promising results in providing a reasonably accurate disturbance model despite structural disparities between the actual disturbance and the model. By improving the precision of process models under deterministic disturbances, the proposed method paves the way for developing refined and reliable control strategies, aligning with the evolving demands of modern industries and laying solid groundwork for future research aimed at broadening application across diverse industrial practices. Full article

In this study, the tribological properties of nanocomposites based on ultra-high molecular weight polyethylene (UHMWPE) filled with nano-CuO and 2-mercaptobenzothiazole (CuO/MBT) in mass ratios of 1:1 and 2:1 were investigated. In the supramolecular structure of UHMWPE nanocomposites, spherulites of several hundred micrometers in size are formed. The density of UHMWPE nanocomposites slightly increases relative to the pure polymer, reaching a maximum at 2 wt.% CuO/MBT in both ratios. The Shore D hardness and compressive stress of the UHMWPE nanocomposites showed an improvement of 5–6% and 23–35%, respectively. The wear resistance and coefficient of friction of UHMWPE nanocomposites were tested using a pin-on-disk configuration under dry friction conditions on #45 steel and on P320 sandpaper. It was shown that the wear rate of UHMWPE nanocomposites filled with 2 wt.% CuO/MBT decreased by ~3.2 times compared to the pure polymer, and the coefficient of friction remained at the level of the polymer matrix. Abrasive wear showed an improvement in UHMWPE nanocomposites filled with 1 wt.% CuO/MBT compared to the polymer matrix and other samples. The worn surfaces of the polymer composites after dry friction were examined by scanning electron microscopy and IR spectroscopy. The formation of secondary structures in the form of tribofilms that protect the material from wear was demonstrated. Due to this, the wear mechanism of UHMWPE nanocomposites is transformed from adhesive to fatigue wear. The developed materials, due to improved mechanical and tribological properties, can be used as parts in friction units of machines and equipment. Full article

The estimated ultimate recovery (EUR) of a single well must be predicted to achieve scale-effective shale gas extraction. Accurately forecasting EUR is difficult due to the impact of various geological, engineering, and production factors. Based on data from 200 wells in the Weiyuan block, this paper used Pearson correlation and mutual information to eliminate the factors with a high correlation among the 31 EUR influencing factors. The RF-RFE algorithm was then used to identify the six most important factors controlling the EUR of shale gas wells. XGBoost, RF, SVM, and MLR models were built and trained with the six dominating factors screened as features and EUR as labels. In this process, the model parameters were optimized, and finally the prediction accuracies of the models were compared. The results showed that the thickness of a high-quality reservoir was the dominating factor in geology; the high-quality reservoir length drilled, the fracturing fluid volume, the proppant volume, and the fluid volume per length were the dominating factors in engineering; and the 360−day flowback rate was the dominating factor in production. Compared to the SVM and MLR models, the XG Boost and the RF models based on integration better predicted EUR. The XGBoost model had a correlation coefficient of 0.9 between predicted and observed values, and its standard deviation was closest to the observed values’ standard deviation, making it the best model for EUR prediction among the four types of models. Identifying the dominating factors of shale gas single-well EUR can provide significant guidance for development practice, and using the optimized XGBoost model to forecast the shale gas single-well EUR provides a novel idea for predicting shale gas well production. Full article

In recent years the world has seen an increase in the popularity of farmer’s markets, short food supply chains and local food systems. This growth can be attributed to the public’s growing consciousness of the impracticality of the global food system, globalization’s waste of fossil fuels, the fear of food chemicals, and small farmers’ desire to directly sell their products, among other things. Although there are a wealth of farmer’s market surveys and research on this topic that has been conducted over the past decades around the world, scant data have been collected about farmer’s markets in the south of Spain. This study focuses on the organic farmer’s market in Granada (Spain) and consists of five surveys developed in 8 years which are analyzed to help better understand this market that was first established in the Spring of 2013. It will also consider research on farmer’s markets in Europe and beyond in order to compare the situation of the Granada market as well as bringing in some new ideas of how it can be improved. Full article