Search Results (4,296)

Groundwater is an essential water resource utilized for agricultural, industrial, and home applications. Evaluating the variability of groundwater is essential for the conservation and management of this resource, as well as for mitigating the reduction in groundwater levels resulting from excessive extraction. This study aimed to define the groundwater potential zones (GWPZ) in Al-Madinah Al-Munawarah, Western Saudi Arabia, utilizing remote sensing and geographic information system (GIS) techniques, alongside meteorological data. Seven thematic maps were produced based on the regulatory characteristics of geology, drainage density, height, slope, precipitation, soil, and normalized difference vegetation index (NDVI). The influence of each theme and subunit/class on groundwater recharge was evaluated by weighted overlay analysis, including previous research and field data. The groundwater potential map was created via the weighted index overlay approach within a GIS. The groundwater potentials were classified into three categories: very poor, moderate, and good zones. The low groundwater potential regions encompass 805.81 km² (44.91%) of the research area, located in mountainous basement rocks, characterized by high drainage density and steep gradients. The moderate zones comprise 45.67% of the total area, covering 819.31 km², and are situated in low-lying regions at the base of mountainous mountains. Conversely, the favorable zones, comprising 9.42% of the total area, span 169.06 km² and are located within the alluvial deposits of the lowlands next to the Wadi Al-Hamd basin and agricultural farms. The results’ accuracy was confirmed by overlaying data from 26 wells onto the designated groundwater potential categories, revealing that all wells corresponded with regions of high groundwater potential. The generated map would contribute to the systematic and efficient management of groundwater resources in this area to meet the rising water demands of Al-Madinah. The groundwater potential map is one aspect of groundwater management. It is also very important to assess this potential further via groundwater temporal monitoring, groundwater balance, and modeling. Full article

Amid escalating climate change concerns, methane—a greenhouse gas with a global warming potential far exceeding that of carbon dioxide—demands urgent attention. The Canadian dairy industry significantly contributes to methane emissions through cattle enteric fermentation and manure management practices. Precise benchmarking of these emissions is critical for developing effective mitigation strategies. This study ingeniously integrates eight years of Sentinel-5P satellite data with advanced machine learning techniques to establish a methane concentration benchmark and predict future emission trends in the Canadian dairy sector. By meticulously analyzing weekly methane concentration data from 575 dairy farms and 384 dairy processors, we uncovered intriguing patterns: methane levels peak during autumn, and Ontario exhibits the highest concentrations among all provinces. The COVID-19 pandemic introduced unexpected shifts in methane emissions due to altered production methods and disrupted supply chains. Our Long Short-Term Memory (LSTM) neural network model adeptly captures methane concentration trends, providing a powerful tool for planning and reducing emissions from dairy operations. This pioneering approach not only demonstrates the untapped potential of combining satellite data with machine learning for environmental monitoring but also paves the way for informed emission reduction strategies in the dairy industry. Future endeavors will focus on enhancing satellite data accuracy, integrating more granular farm and processor variables, and refining machine learning models to bolster prediction precision. Full article

The reliability assesment of large power systems, particularly when considering both generation and transmission facilities, is a computationally demanding and complex problem. The sequential Monte Carlo simulation is arguably the most versatile approach for tackling this problem. However, assessing sampled states in the sequential Monte Carlo simulation is time-intensive, rendering its use less appealing, particularly if nonlinear network representation must be deployed. In this context, this paper introduces a tensor-based predictor–corrector approach to reduce the burden of state evaluations in power generation and transmission reliability assessments. The approach allows for searching for sequences of operation points which can be assigned as success states within the sequential Monte Carlo simulation. If required, failure states are evaluated using a cross-entropy optimization algorithm designed to minimize load curtailments taking into account discrete variables. Numerical results emphasize the applicability of the developed algorithms using a small test system and the IEEE-RTS79 test system. Full article

To achieve sustainable development, the transition from a fossil-based economy to a circular economy is essential. The use of renewable energy sources to make the overall carbon foot print more favorable is an important pre-requisite. In this context, it is crucial to valorize all renewable resources through an optimized local integration. One opportunity arises through the synergy between bioresources and green hydrogen. Through techno-economic assessments, this work analyzes four local case studies that integrate bio-based processes with green hydrogen produced via electrolysis using renewable energy sources. An analysis of the use of webGIS tools (i.e., Atlas of Biorefineries of IEA Bioenergy) to identify existing biorefineries that require hydrogen in relation to territories with a potential availability of green hydrogen, has never been conducted before. This paper provides an evaluation of the production costs of the target products as a function of the local green hydrogen supply costs. The results revealed that the impact of green hydrogen costs could vary widely, ranging from 1% to 95% of the total production costs, depending on the bio-based target product evaluated. Additionally, hydrogen demand in the target area could require an installed variable renewable energy capacity of 20 MW and 500 MW. On the whole, the local integration of biorefineries and green hydrogen could represent an optimal opportunity to make hydrogenated bio-based products 100% renewable. Full article

We address the effect of local financial conditions on the demand for non-life insurance. We consider the spread between the interest rates faced by the insured on the local credit market and the return rates earned by the insurer on national or international financial markets, sketching how it influences the present value of an insurance policy; we then use the local invariance of the insurer’s returns to identify the effect on demand. Drawing on a panel of Italian provinces with ample variability in insurance density as well as borrowing conditions, we show that the demand for non-life insurance decreases with the borrowing rate. We separate between different non-life insurance lines, finding a stronger effect for the lines prevailing in advanced economic systems. Credit conditions turn out to be an important factor of non-life insurance development, and they help to explain the underdevelopment of insurance markets in Southern Italy. Full article

This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R² values, indicating stronger predictive accuracy. The results suggest that gait’s nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies. Full article

In this study, the fatigue crack initiation and growth behaviors of an additively manufactured (AM) Ti-6AI-4V alloy were investigated, and its prospect for fatigue applications was evaluated. The AM specimens were first fabricated by selective laser melting (SLM) and then underwent a cycle of annealing at 800 °C for 2 h and hot isostatic pressing (HIP) treatment at 920 °C/150 MPa/3 h followed by surface machining. Prefabricated spherical defects with different diameters (1.0 mm and 2.0 mm) were introduced to examine the efficacy of HIP treatment for eliminating the built defects. Both fracture morphology and microstructure were characterized to reveal the failure mechanism of these tested specimens. The results suggest that both the fatigue lives and fatigue crack growth resistances of most SLM+HIP-processed specimens are much higher than those of traditional wrought material, thus highlighting that the AM Ti-6AI-4V alloy can be a better candidate for future fatigue applications. However, due to the large variability in fatigue performance, the current SLM+HIP-processed Ti-6Al-4V alloy still cannot meet the demand for high safety and reliability. Full article

Triticale is recognized worldwide because of its high protein and lysine contents, high production capacity, and adaptability to biotic and abiotic stress conditions, these qualities being taken over from wheat and rye. Triticale is widely used in various fields, such as animal feed in various forms, medicine, baking, beer and alcohol brewing, cellulose, bioethanol industry, and many others. Thus, the demand for triticale grain is increasing, and this has led to the research and improvement in culture technology to obtain superior products, both quantitatively and qualitatively. The purpose of this study was to identify the best varieties of triticale cultivated in the northwest of Romania, sown in different plots, and fertilized on the ground. Additionally, this study was carried out over a period of two years at the Livada Agricultural Research and Development Station, Satu Mare County. This study was located on acidic soil with a pH between 5.19 and 6.65 and a humus content of 2.82%. The climatic conditions in the reference period were extremely variable; in the first year, a deficit of more than 90 mm of precipitation was registered, and in the second year of this study, an increase of more than 34 mm. The effects of additional fertilization were influenced by the level of precipitation. In 2023, additional fertilization with foliar fertilizer brought production increases of 884 kg/ha, compared with 2022, where foliar fertilization in drought conditions led to a decrease in production. The Utrifun variety proved to be the most productive; this foliar fertilized and with biostimulator, sown at 550 seeds/m², recorded an increase in production of over 4500 kg/ha compared with the Negoiu control and sown at 650 seeds/m² and fertilized with foliar fertilizer had an increase of over 4370 kg/ha compared with the Negoiu control. It recorded a production of 9700 kg/ha sown at 550 seeds/m² and fertilized only on the soil, and sown at 450 seeds/m² and additionally fertilized with foliar fertilizer recorded a production of 10.900 kg/ha. Utrifun was followed by Zvelt which, sown at 450 seed/m² and fertilized on the soil, recorded 9500 kg/ha, ensuring an increase of 1800 kg/ha compared with the Negoiu control. The lowest production was achieved by the Tulnic variety, which is 2022, sown at 650 seed/m² and fertilized on the ground, recorded 5874 kg/ha, 1621 kg/ha less than the control variant. The increases in production obtained by these varieties will be confirmed in a subsequent study under different climatic conditions. Full article

Oscillating grease-lubricated slewing bearings are used in several applications. One of the most demanding and challenging is the rotor blade bearings of wind turbines. They allow the rotor blades to be turned to control the rotational speed and loads of the complete turbine. The operating conditions of blade bearings can lead to lubricant starvation of the contacts between rolling elements and raceways, which can result in wear damages like false brinelling. Variable oscillating amplitudes, load distributions, and the grease properties influence the likelihood of wear occurrence. Currently, there are no methods for rating this risk based on existing standards. This work develops an empirical methodology for assessing and quantifying the risk of wear damage. Experimental results of small-scale blade bearings show that the proposed methodology performs well in predicting wear damage and its progression on the raceways. Ultimately, the methods proposed here can be used to incorporate on-demand lubrication runs of pitch bearings, which would make turbine operation more reliable and cost-efficient. Full article

The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400–2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes’ activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H₂O₂] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H₂O₂, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products. Full article

The COVID-19 pandemic has negatively impacted the world economy and global society. However, small- and medium-sized enterprises are among the most vulnerable and affected groups of businesses, and in some cases, life-saving interventions have resulted in serious existential implications. The difficulties of insufficient demand, non-negligible fixed costs, and inadequate financing are unsustainable for many firms. Thus, the main aim of this study is to evaluate the variables influencing business activities, apply macroeconomic variables to compare the business environments in fifteen European countries, and utilize appropriate statistical techniques to confirm the results. Significant differences exist in the business climate across selected European countries, as identified by the TOPSIS method, CPI, and GCI. Low levels of corruption, strong economic stability, and high competitiveness make countries like Germany and Austria attractive for business environments. Estonia is also a leader in technological innovation and low corruption. Conversely, Bulgaria and Romania are struggling with higher levels of corruption and reduced competitiveness, potentially impeding business endeavours. The Visegrad Group countries are in the middle of the spectrum, scoring average to good but with opportunities for improvement in corruption and innovation. Overall, the business climate in these countries is diverse, reflecting their unique economic, political, and social circumstances. Full article

This paper presents PLEXOS modelling of the Nordic and Baltic low-carbon electricity market until 2030, using a total of 35 different weather years’ (1982–2016) ERAA profiles as inputs for the modelling and focusing on the occurrence of severe electricity scarcity situations in Finland, analyzing their duration and depth. The expected development of generation and demand is modelled based on available authoritative sources, such as ENTSO-E TYNDP and national projections. The present amount of nuclear power in Finland and growing amounts of wind and solar generation across the Nordic electricity system are modelled. This study analyzes scarcity situations by calculating residual loads and the expected electricity spot market prices assuming the different weather years with the generation fleet and demand in 2024 and 2030 scenarios. This study finds that, despite the very significantly growing amount of variable renewable generation (42.5 TWh/a increase in wind generation from 2024 to 2030 in Finland only), the frequency and severity of scarcity situations will increase from 2024 to 2030. The main reasons are the retirement of Combined Heat and Power plants and the transition to more electrified district heating in Finland and the expected demand growth. The findings indicate that without further measures Finland is not sufficiently prepared for cold winter periods with high heating and electricity demand and events of serious scarcity can occur. Full article

Anaerobic digestion followed by microalgal cultivation is considered a promising renewable alternative for the production of biomethane with reduced effluent generation, thus lowering the environmental impact. In this arrangement, in addition to generating energy, the microalgae act by potentiating the refinement of the effluents generated via anaerobic digestion (digestates). In this study, the microalga Tetradesmus obliquus was cultivated in photobioreactors with the final digestate resulting from the co-digestion of Arundo donax L. plant biomass and cattle wastewater. The biotechnological route used was efficient, and the biogas production ranged from 50.20 to 94.69 mL gVS⁻¹. The first-order kinetic model with variable dependence (FOMT) provided the best fit for the biogas production data. In the microalgal post-treatment, the removal values ranged from 81.5 to 93.8% for the chemical oxygen demand, 92.0 to 95.3% for NH₄⁺-N, and 41.7 to 83.3% for PO₄³⁻ after 26 days. The macromolecular composition of the algal biomass reached lipid contents ranging from 33.4 to 42.7%. Thus, the proposed process mediated by microalgae can be considered promising for the bioremediation and recovery of effluents produced by agriculture through the use of microalgal biomass for bioproduct production. Full article

Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions. Full article

Over the past 30 years, researchers have developed X-ray-focusing telescopes by employing the principle of total reflection in thin metal films. The Wolter-I focusing mirror with variable-curvature surfaces demands high precision. However, there has been limited investigation into the removal mechanisms for variable-curvature X-ray mandrels, which are crucial for achieving the desired surface roughness and form accuracy, especially in reducing mid-spatial frequency (MSF) errors. It is essential to incorporate flexible control in deterministic small-tool polishing to improve the tool’s adaptability to curvature variations and achieve stable, Gaussian-like tool influence functions (TIFs). In this paper, we introduce a curvature-adaptive prediction model for compliance figuring, based on the Preston hypothesis, using a compliant shaping tool with high slurry absorption and retention capabilities. This model predicts the compliance figuring process of variable-curvature symmetrical mandrels for X-ray grazing incidence mirrors by utilizing planar tool influence functions. Initially, a variable-curvature pressure model was developed to account for the parabolic and hyperbolic optical surfaces’ curvature characteristics. By introducing time-varying removal functions for material removal, the model establishes a variable-curvature factor function, which correlates actual downward pressure with parameters such as contact radius and contact angle, thus linking the variable-curvature surface with a planar reference. Subsequently, through analysis of the residence time distribution across different TIF models, hierarchical filtering, and PSD distribution, real-time correction of the TIFs was achieved to enable customized variable-curvature polishing. Furthermore, by applying a time-varying deconvolution algorithm, multiple rounds of flexible polishing iterations were conducted on the mandrels of a rotationally symmetric variable-curvature optical component, and the experimental results demonstrate a significant improvement in form accuracy, surface quality, and the optical performance of the mirror. Full article