Search Results (323)

Curcuma spp. is a popular ornamental crop valued for its vibrant appearance and suitability for both regular and off-season production. As global emphasis on freshwater conservation increases and with a demand for compact potted plants, reducing water use while maintaining high aesthetic quality presents a key challenge for horticulturists. Potassium silicate (PS) has been proposed as a foliar spray to alleviate plant water stress. This study aimed to evaluate the effects of PS on growth, ornamental traits, and photosynthetic parameters of off-season potted Curcuma cv. ‘Jasmine Pink’ under deficit irrigation (DI). Plants were subjected to three treatments in a completely randomized design: 100% crop evapotranspiration (ETc), 50% ETc, and 50% ETc with 1000 ppm PS (weekly sprayed on leaves for 11 weeks). Both DI treatments (50% ETc and 50% ETc + PS) reduced plant height by 7.39% and 9.17%, leaf number by 16.99% and 7.03%, and total biomass by 21.13% and 20.58%, respectively, compared to 100% ETc. Notably, under DI, PS-treated plants maintained several parameters equivalent to the 100% ETc treatment, including flower bud emergence, blooming period, green bract number, effective quantum yield of PSII (ΔF/Fm′), and electron transport rate (ETR). In addition, PS application increased leaf area by 8.11% and compactness index by 9.80% relative to untreated plants. Photosynthetic rate, ΔF/Fm′, and ETR increased by 31.52%, 13.63%, and 9.93%, while non-photochemical quenching decreased by 16.51% under water-limited conditions. These findings demonstrate that integrating deficit irrigation with PS foliar application can enhance water use efficiency and maintain ornamental quality in off-season potted Curcuma, promoting sustainable water management in horticulture. Full article

Agroecological transitions in collective urban gardens in Toulouse region were studied through the prism of global health (2011–2022). The specific meaning of “global health” in the context of urban gardens concerns the health of gardeners (well-being and physical health), plants, soil, and animals, as well as the interactions between humans and non-humans, which are crucial for gardeners. A sociotechnical research project was developed on four different collective gardening sites, consisting of the following: 1. surveys issued to 100 garden stakeholders to highlight issues and practices, participation in meetings with the social centers in charge of events, and focus groups; 2. participative agronomic and environmental measurements and field observations, including soil quality analyses; and 3. analysis of the available documentary corpus. In order to produce the results, these three research methods (surveys, agronomy, document analysis) were combined through a transdisciplinary approach, in that both the field experimentation outcomes and retrieved scientific publications and technical documents informed the discussions with gardeners. Consideration of the four different sites enabled the exploration of various contextual factors—such as soil or air quality—affecting the production of vegetables. A rise in the concerns of gardeners about the impacts of their activities on global health was observed, including aspects such as creating and enjoying landscapes, taking care of the soil and biodiversity, developing social connections through the transmission of practices, and regular outside physical activity and healthier eating. The increased consideration for global health issues by all stakeholders promotes the implementation of agroecological practices in gardens to improve biodiversity and adherence to circular economy principles. Four concepts emerged from the interviews: health, production of vegetables, living soil, and social interactions. Notably, nuances between the studied sites were observed, according to their history, environment, and organization. These collective gardens can thus be considered as accessible laboratories for social and agroecological experimentation, being areas that can strongly contribute to urban ecosystem services. Full article

Background: Severe psychiatric disorders are frequently associated with disruptions in health-related behaviors, including diet and lifestyle. This cross-sectional study aimed to assess and compare selected dietary and lifestyle behaviors among long-term psychiatric inpatients diagnosed with unspecified dementia (F03) or organic delusional disorder (F06.2) and a control group of mentally healthy individuals. Methods: A 50-item validated questionnaire was administered to 28 hospitalized patients and 10 control participants. Analyses included nutritional habits, physical activity, stimulant use, and hydration, using non-parametric tests and effect size indicators (Cramér’s V). Results: Significant differences were observed in meal regularity, frequency of meals, types of beverages consumed, and physical activity. Strong associations were found for meal types (V = 0.590) and stress-induced eating (V = 0.525). Conclusions: The observed behavioral differences may reflect disease-related effects, demographic variation, or a combination of both. Despite these limitations, the findings suggest key areas for further investigation and support the need for targeted dietary and lifestyle interventions in psychiatric settings. Full article

This study investigates the mechanical behavior and failure characteristics of chain slings under standard and non-standard fastening methods. Through dimensional inspections, fracture tests, and finite element analysis, we identified critical factors influencing chain failure. Chains exhibiting over 10% diameter reduction or increased pitch exceeded discard criteria and showed significant strength loss. Fracture loads in aged chains dropped by more than 35% compared to standards. Structural analysis revealed that standard fastening (using master links) ensures uniform stress distribution and higher load capacity, whereas non-standard fastening (direct wrapping on eyebolts) caused stress concentration, reduced tensile capacity by over 15%, and led to localized failure near contact areas. These results validate the structural soundness of international standards (DIN EN 818-4, ISO 3056) and highlight the risks of improper fastening. Practical recommendations include strict adherence to standard fastening methods, avoidance of direct wrapping, and implementation of regular inspections. The findings emphasize the need for design considerations regarding fastening geometry and suggest further research into fatigue life prediction and contact condition optimization. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

This study reports a successful selective waste collection initiative led by UFSC’s Araranguá campus in a municipality without a recycling system. The initiative, named “Recicla UFSC Ara”, was structured around three main components: (i) the installation of color-coded bins for recyclable waste (including paper, plastic, metals, and polystyrene) and non-recyclable waste in indoor and common areas; (ii) the establishment of a Voluntary Delivery Point (PEV) to gather specific recyclable materials, such as glass, electronics waste, plastic bottles, writing instruments, and bottle caps; and (iii) the execution of periodic educational community-focused campaigns aimed at encouraging participation from both the university and the broader local community. Recyclables were manually sorted and weighed during regular collection rounds, and contamination rates were calculated. Quantitative data collected from 2022 to 2025 were analyzed using descriptive statistics and one-way ANOVA to assess waste generation and contamination trends. Gathered recyclables were directed to appropriate partner institutions, including local “Ecoponto”, non-profit organizations, and corporate recycling programs. The study also conducted a literature review of similar university-led waste management programs to identify standard practices and regional specificities, providing a comparative analysis that highlights both shared elements and distinctive contributions of the UFSC model. Results demonstrate a significant volume of waste diverted from landfills and a gradual improvement in waste disposal practices among the university community. Targeted communication and operational changes mitigated key challenges, improper disposal, and logistical issues. This case underscores the role of universities as agents of environmental education and local sustainable development. Full article

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

This systematic review explores modern optimization methods for machine learning, distinguishing between gradient-based techniques using derivative information and population-based approaches employing stochastic search. Key innovations focus on enhanced regularization, adaptive control mechanisms, and biologically inspired strategies to address challenges like scaling to large models, navigating complex non-convex landscapes, and adapting to dynamic constraints. These methods underpin core ML tasks including model training, hyperparameter tuning, and feature selection. While significant progress is evident, limitations in scalability and theoretical guarantees persist, directing future work toward more robust and adaptive frameworks to advance AI applications in areas like autonomous systems and scientific discovery. Full article

Research on state corruption has flourished since the 1990s; however, studies focused on corruption within non-state organizations are still limited. In this study, we conducted a systematic review of 18,435 articles from the Web of Science database, covering the years 2002 to 2020. Using topic modeling Latent Dirichlet Allocation (LDA), we analyzed the field of corruption research. Our analysis identified four main dimensions: state corruption as the predominant field, private-to-public corruption, private-to-private corruption, and technological–biological corruption. Our findings indicate that state corruption has a well-established research tradition, whereas private corruption remains underexplored. We highlight key conceptual limitations in understanding the mechanisms of non-state corruption and propose the idea of operational deviation from regular procedures to address these issues. This article concludes that further empirical research is needed on non-state corruption to develop a conceptual framework specific to this area, which features distinct characteristics from state corruption. Finally, we suggest implications for policy and practice based on our findings. Full article

This article proposes a 3D mathematical model of the influence of electrical heterogeneity of the ion exchange membrane surface on the processes of salt ion transfer in membrane systems with axial symmetry; in particular, we investigate an annular membrane disk in the form of a coupled system of Nernst–Planck–Poisson and Navier–Stokes equations in a cylindrical coordinate system. A hybrid numerical–analytical method for solving the boundary value problem is proposed, and a comparison of the results for the annular disk model obtained by the hybrid method and the independent finite element method is carried out. The areas of applicability of each of these methods are determined. The proposed model of an annular disk takes into account electroconvection, which is understood as the movement of an electrolyte solution under the action of an external electric field on an extended region of space charge formed at the solution–membrane boundary under the action of the same electric field. The main regularities and features of the occurrence and development of electroconvection associated with the electrical heterogeneity of the surface of the membrane disk of the annular membrane disk are determined; namely, it is shown that electroconvective vortices arise at the junction of the conductivity and non-conductivity regions at a certain ratio of the potential jump and angular velocity and flow down in the radial direction to the edge of the annular membrane. At a fixed potential jump greater than the limiting one, the formed electroconvective vortices gradually decrease with an increase in the angular velocity of rotation until they disappear. Conversely, at a fixed value of the angular velocity of rotation, electroconvective vortices arise at a certain potential jump, and with its subsequent increase gradually increase in size. Full article

This paper analyzed the behavior of polymer composite materials reinforced with randomly oriented short natural fibers (hemp, flax, etc.) subjected to external stresses under quasistatic contact conditions with dry Coulomb friction. We presumed the composite body, a 2D flat rectangular plate, being in frictional contact with a rigid foundation for the quasistatic case. The manuscript proposes the finite element method approximation in space and the finite difference approximation in time. The problem of quasistatic frictional contact is described with a special finite element, which can analyze the state of the nodes in the contact area, and their modification, between open, sliding, and fixed contact states, in the analyzed time interval. This finite element also models the Coulomb friction law and controls the penetrability according to a power law. Moreover, the quasi-static case analyzed allows for the description of the load history using an incremental and iterative algorithm. The discrete problem will be a static and nonlinear one for each time increment, and in the case of sliding contact, the stiffness matrix becomes non-symmetric. The regularization of the non-differentiable term comes from the modulus of the normal contact stress, with a convex function and with the gradient in the sub-unit modulus. The non-penetration condition was achieved with the penalty method, and the linearization was conducted with the Newton–Raphson method. Full article

Inpainting noisy remote-sensing images can reduce the cost of acquiring remote-sensing images (RSIs). Since RSIs contain complex land structure features and concentrated obscured areas, existing inpainting methods often produce color inconsistency and structural smoothing when applied to RSIs with a high missing ratio. To address these problems, inspired by tensor recovery, a lightweight image Inpainting Generative Adversarial Network (GAN) method combining low-rankness and local-smoothness (IGLL) is proposed. IGLL utilizes the low-rankness and local-smoothness characteristics of RSIs to guide the deep-learning inpainting. Based on the strong low rankness characteristic of the RSIs, IGLL fully utilizes the background information for foreground inpainting and constrains the consistency of the key ranks. Based on the low smoothness characteristic of the RSIs, learnable edges and structure priors are designed to enhance the non-smoothness of the results. Specifically, the generator of IGLL consists of a pixel-level reconstruction net (PIRN) and a perception-level reconstruction net (PERN). In PIRN, the proposed global attention module (GAM) establishes long-range pixel dependencies. GAM performs precise normalization and avoids overfitting. In PERN, the proposed flexible feature similarity module (FFSM) computes the similarity between background and foreground features and selects a reasonable feature for recovery. Compared with existing works, FFSM improves the fineness of feature matching. To avoid the problem of local-smoothness in the results, both the generator and discriminator utilize the structure priors and learnable edges to regularize large concentrated missing regions. Additionally, IGLL incorporates mathematical constraints into deep-learning models. A singular value decomposition (SVD) loss item is proposed to model the low-rankness characteristic, and it constrains feature consistency. Extensive experiments demonstrate that the proposed IGLL performs favorably against state-of-the-art methods in terms of the reconstruction quality and computation costs, especially on RSIs with high mask ratios. Moreover, our ablation studies reveal the effectiveness of GAM, FFSM, and SVD loss. Source code is publicly available on GitHub. Full article

Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions. Full article

Springs located in urban historic areas are important for groundwater management, the protection of green spaces, and the preservation of park functions and urban structure. This article presents the results of a study of selected Warsaw springs in the city center under conservation protection, focusing on their hydrogeological characteristics, hydrogeochemical analysis, and pressures associated with urban development. Field and laboratory analyses, as well as hydrodynamic modeling, made it possible to assess the quantity and quality of water from the springs. Hydrodynamic studies showed that the area of the spring recharge zone of 13.77 ha is characterized by an average time of water exchange of approx. 26 years and a low infiltration recharge, an average of 18 mm/year. Hydrogeochemical analyses showed that spring water has a complex, multi-ion hydrogeochemical type: Cl-SO₄-HCO₃-Ca-Na, Cl-HCO₃-SO₄-Ca-Na, Cl-HCO₃-Na-Ca, and NO₃-Cl-HCO₃-Ca-Na, including the occurrence of hazardous substances such as PAH and BTEX, PCBs, non-ionic detergents, and heavy metals. The results indicate that urbanization significantly affects groundwater levels and spring recharge areas, which can limit the availability of water in green and recreational areas. The results of the study indicate the need for action to increase groundwater resources through managed aquifer recharge for rainwater management in densely built-up areas. In terms of water quality measures, due to the unsatisfactory chemical water status, the use of spring water for irrigation of urban vegetation or its incorporation into the active recreational infrastructure of the park currently appears to be fraught with considerable risk, hence the need to take protective action in the spring recharge zone through the regular monitoring of groundwater quality, the legal designation of protection zones, and the implementation of policies that support urban water retention. It is necessary to implement pre-treatment solutions (aeration, desalination) or introduce appropriately resistant vegetation. Any type of activity that allows the use of water after treatment will certainly contribute to making the park more attractive as a place of recreation and leisure for residents. Findings from the research can support decisions on protecting green spaces and adapting cities to climate change. Full article

The demand for maritime–coastal tourism has been intensifying, but its offerings are sometimes limited to a few activities. Some of these activities do not require specific skills or certifications, while others do. This study aimed to investigate what type of activities are carried out by tourism and recreational vessels in the coastal area of the central Algarve (Portugal). To this end, data from the automatic identification system (AIS) of recreational vessels was used to monitor and categorise these activities in a non-intrusive manner. A model (TORMA) was defined to facilitate the analysis of AIS data and relate them to five independent variables (distance from the coast, boat speed, bathymetry, seabed type, and number of pings). The results of the analysis of more than 11 thousand hourly AIS records for passenger, sailing, and charter vessels showed that the 14 most regular ones had strong seasonal patterns, greater intensity in summer, and spatial patterns with more records near some coastal cliffs. This study provides valuable information on the management of motorised nautical activities near the coast and at sea, contributing to more informed and effective tourism regulation and planning. Full article