Search Results (11,854)

C2H2-type zinc finger transcription factors play crucial roles in regulating plant growth and responses to abiotic stresses. Although pear is an economically important fruit crop with available genomic resources, the comprehensive analyses of its C2H2 gene family remain limited. In this study, we performed a genome-wide identification of C2H2 genes in pear, identifying 52 members that were unevenly distributed across 17 chromosomes. The predicted PyC2H2 proteins ranged from 343 to 1764 amino acids in length, with molecular weights between 12.4 and 62.7 kDa. Phylogenetic analysis classified these 52 PyC2H2 proteins into 10 classical clades, where genes with closer evolutionary relationships shared more similar conserved motifs. Collinearity analysis identified 37 collinear gene pairs between Arabidopsis thaliana and pear, suggesting evolutionary conservation. Promoter analysis revealed the presence of various cis-acting elements associated with hormone and stress responses, including Box 4, G-box, and GT1-motif. Additionally, the expression patterns of PyC2H2 genes differed among pear organs. These results provide valuable insights into the potential roles of PyC2H2 genes in drought stress responses and offer a foundation for future molecular breeding of drought-tolerant pear cultivars. Full article

Hazard risk monitoring of groundwater depletion and land subsidence due to excessive groundwater extraction is crucial for groundwater resource development, especially in densely populated, small-island developing sites. The island of Bali, Indonesia, represents such an urban environment at risk of land subsidence arising from groundwater depletion. The total percentage of groundwater depletion was calculated and interpolated spatially using measurements of groundwater level from 2008 to 2017 at 18 monitoring well sites available in the area. Furthermore, time-series synthetic-aperture radar (SAR) interferometry processing was applied to estimate the temporal change in land displacement using the Phased Array type L-band SAR (PALSAR) data from 2007 to 2010. The result of downward displacement, signifying subsidence, corresponded with the Global Navigation Satellite System (GNSS) data measurements at stations distributed in the observed subsided areas, i.e., CDNP and CPBI. The displacement varied consistently with changes in groundwater level. In regard to maintaining groundwater utilization, the hazard–risk relation of the groundwater depletion, i.e., low (<10%), moderate (10–25%), and high (>25%), and the presence/absence of subsidence were utilized to classify groundwater conservation into safe, vulnerable, critical, and damaged zones. This application can be considered effective in providing spatial information for sustainable groundwater management. Full article

Effective network screening methods play a significant role in highway safety management programs and contribute to sustainable mobility by facilitating the reduction in all crashes, including fatalities and injuries across the transportation system. This study presents a comprehensive analysis comparing the effectiveness of three new network screening techniques for pinpointing safety improvement locations on rural roads. The proposed methods are the Global Risk Scoring (GRS), the Crash Risk Index (CRI), and the Predicted Empirical Bayes (P-EB) methods. The analysis utilized 10 years of roadway geometry, traffic, and crash data from state-owned rural highways in Oregon, with the first five years (2011–2015) used for model development and the subsequent five years (2016–2020) for validation. Comparative tests assessed consistency with historical crash rankings and temporal stability across observation periods. The analysis revealed distinct strengths among the screening methods. The GRS method demonstrated a high level of consistency with historical crash data, while the P-EB method exhibited superior consistency across different time periods, suggesting its value for long-term safety planning. The CRI method demonstrated reasonable consistency in performance, irrespective of the test carried out. While no single method outperforms the others in all scenarios, each has unique advantages and data requirements that can better suit the agency’s needs, given available resources. This research provides actionable insights for improving safety management strategies and advancing sustainable mobility. Full article

Background: Missed nursing care, defined as essential patient care that is omitted or delayed, is a growing source of concern due to its effects on healthcare quality and patient safety. Our aims in this study were twofold: first, we examined the extent and types of missed nursing care, and second, we analyzed the relationship between the care missed by hospital nurses and the staffing and resource adequacy in an academic medical center. Methods: A descriptive cross-sectional study was conducted during the period between November 2022 and July 2023. Data were collected using a self-administered questionnaire that comprised items on socio-demographic and work-related characteristics, items on staffing and resource availability, and items from the ‘MISSCARE’ Survey. Results: The most frequently missed nursing care involved pressure-relieving interventions (Mean = 2.39) and ambulation/mobilization (Mean = 2.27), while medication administration (Mean = 1.60) and glucose monitoring (Mean = 1.56) were missed the least. Labor resource inadequacy (β = 0.315, p < 0.001) and communication and teamwork deficits (β = 0.285, p < 0.001) were positively associated with missed nursing care, whereas staffing and resource adequacy showed an inverse association (β = −0.164, p = 0.006). The model explained 49.8% of the variance in missed nursing care (R² = 0.498). Conclusions: These findings highlight that missed nursing care is a system-level issue primarily associated with staffing and resource constraints rather than individual characteristics. Improving staffing adequacy, resource availability, and interprofessional collaboration may reduce care omissions and enhance patient safety in Saudi Arabian academic medical centers. Full article

The rapid accumulation of plastic waste and the depletion of fossil resources have intensified global efforts to develop biodegradable polymeric materials derived from renewable feedstocks. In this context, starch-based films have emerged as one of the most promising alternatives to conventional petroleum-based plastics, owing to their wide availability, low cost, biodegradability, and ability to form continuous films using simple and scalable processing techniques. Starch is a naturally occurring polysaccharide composed primarily of amylose and amylopectin, whose molecular structure is rich in hydroxyl (–OH) groups. These functional groups promote extensive intermolecular hydrogen bonding, enabling starch gelatinization and film formation in aqueous systems. However, the same hydroxyl-rich structure confers a pronounced hydrophilic character, resulting in high moisture sensitivity, poor water vapor barrier properties, and limited dimensional stability under humid. Consequently, improving the hydrophobicity of starch-based films remains one of the most critical challenges for their practical application in food packaging. This review aims to summarize and critically discuss the main strategies reported for improving the hydrophobicity of starch-based films. The review focuses on composition and processing approaches, including (i) chemical modification of starch, (ii) incorporation of hydrophobic additives, (iii) reinforcement with natural fibers and nanocellulosic materials, (iv) polymer blending and multilayer/gradient architectures, and (v) processing strategies, including film homogenization, shear treatment and aging conditions. Emphasis is placed on the mechanisms governing hydrophobicity enhancement, comparative performance indicators, and current limitations. Full article

Wind represents a pervasive yet mechanistically distinct environmental factor that reshapes species interactions primarily through habitat compression—reducing effective habitat area via behavioral avoidance, rather than altering resource availability as seen in temperature- or rainfall-driven models. This study introduces a a novel wind-modified Lotka–Volterra competition model that advances existing disturbance-dependent frameworks through two key innovations: (1) a wind-speed-dependent carrying capacity, formally expressed as the initial carrying capacity divided by a linear function of wind speed and species-specific wind sensitivity, which explicitly quantifies wind-induced habitat contraction as a nonlinear function of wind exposure; and (2) a species-specific wind sensitivity coefficient that can be experimentally calibrated. Through a rigorous stability analysis and numerical simulations, we demonstrate how wind speed modulates competitive outcomes by altering equilibrium densities and stability. Our results reveal: (a) wind can reverse competitive dominance, disproportionately excluding species with higher wind sensitivity coefficients; (b) critical wind speed thresholds exist, beyond which populations collapse due to mechanisms akin to Allee effects and demographic stochasticity; and (c) wind nonlinearly regulates coexistence, with moderate speeds sometimes stabilizing it and extreme speeds driving effective extinction. This framework provides a theoretical foundation for setting conservation thresholds and assessing the ecological impacts of wind energy projects. Full article

Background/Objectives: COVID-19 has made a tremendous impact, causing a massive number of deaths worldwide. The inadequacy of health facilities resulted in shortage of resources and exhaustion of frontline workers who had to manage in a short time many patients with no tools to prioritize those at high risk. This study intended to disclose the architecture of such complex disease and enhance the management of hospitalized patients, preventing severe outcomes. Methods: We performed a retrospective multicenter study aimed at refining the best predictive model for COVID-19 mortality, integrating 19 genetic and 13 clinical features. We trained three machine learning (ML) models (GBM, XGB and RF) on a dataset of 532 COVID-19 hospitalized Italian patients, among the 605 recruited during the first wave of the pandemic, when vaccines were not available. Results: All the models achieved great values for accuracy, AUROC, f1, f2 and PR-AUC metrics. XGB’s f1 optimization resulted in better performance providing fewer false positives (N_f1 = 26 versus N_f2 = 27, N_PR-AUC = 29), and mostly false negatives (N_f1 = 63 versus N_f2 = 69, N_PR-AUC = 69), being the main goal to answer. We next delved into the feature importance to understand which features contribute to the model’s decision: age was the main driver of mortality prediction, followed by ventilation. The remainder was equally distributed between genetic (HLA-DRA rs3135363, PPARGC1A rs192678, CRP rs2808635, ABO rs657152) and other clinical features, demonstrating that genetic data did not confound, but rather implemented, the power of the model. Conclusions: Our results suggest that integrating genetic and clinical data into ML models is crucial for identifying high-risk cases within the vast disease heterogeneity, enabling the P4-medicine approach to improve patient outcomes and support the healthcare system. Full article

Despite having approximately 24 million native speakers, Romanian remains a low-resource language for automatic speech recognition (ASR), with few accurate and publicly available systems. To address this gap, this study explores the challenges of adapting modern speech recognition models, such as wav2vec 2.0 and Conformer, to Romanian. Our investigation is a comprehensive analysis of the two models, their capabilities to adapt to Romanian data, and the performance of the trained models. The research also focuses on unique attributes of the Romanian language, data collection techniques, including weakly supervised learning, and processing methodologies. Building on the previously introduced Echo dataset of 378 h, we release CRoWL (Crawled Romanian Weakly Labeled), a weakly supervised dataset of 9000 h created via automatic transcription. We obtain strong results that, to the best of our knowledge, are competitive with or exceed publicly reported results for Romanian under comparable open evaluation settings, with Conformer attaining 3.01% WER on Echo + CRoWL and wav2vec 2.0 reaching 4.04% (Echo) and 4.17% (Echo + CRoWL). In addition to the datasets, we also release our most capable models as open source, along with their training plans, thereby providing a solid foundation for researchers interested in languages with limited representation. Full article

Conventional production methods of aragonite production utilize chemical inducers to promote the evolution of the calcite crystalline phase to the aragonite phase of calcium carbonate. The chemical inducers used require a considerable amount of magnesium chloride (MgCl₂) to induce crystallization, which is a major operational cost. Application of such materials in large amounts can be a deterrent to achieving a sustainable and economically feasible end-product derived from carbon dioxide (CO₂) molecules. A number of previous research works focused mainly on optimizing the usage of MgCl₂ or introducing alternative chemical inducers for aragonite production. In this work, we are proposing the usage of natural seawater as it is a naturally available and abundant resource to induce the synthesis and continuous production of aragonite compounds. Due to inconsistent quality and salinity of the natural seawater sampled, harvested, and dried, Red Sea Salt is utilized, blended at 33 g/L throughout the laboratory experiments for better statistical control, and is referred to as blended or artificial seawater. A methodology of utilizing seawater, which has a considerable concentration of MgCl₂ compound, can be utilized as a sustainable, natural, and economically feasible natural inducer to synthesize aragonite has been developed by utilizing artificial seawater for laboratory proof of concept. The main effects identified for the optimization of aragonite synthesis are lime (CaO) feedstock concentration in seawater, reaction temperature, and reaction duration. The experiment results indicated that only by increasing temperature and reaction duration, or both, can the aragonite yield be increased. It is suggested that the range of operation to obtain > 80% aragonite purity has been identified with the reaction temperature at 90 °C, reaction duration of 10 min, and CaO concentration in seawater at 1 g/L. The quality of the aragonite synthesized via seawater is characterized using XRD, ICP, FESEM, and TGA, and compared with aragonite particles synthesized using MgCl₂ inducers. In comparison, seawater aragonite has lower residual alkalinity compared to both calcite and aragonite via MgCl₂ and has a mixture of predominantly needle-shaped crystalline structure and remnants of cubic-shaped particles, presumably calcite, suitable for application in food, beverages, and pharmaceuticals (calcium antacids, nutritional supplements, chewable, lozenges). Full article

Background/Objectives: Identification of tools to minimize opioid-related harms is critical in the U.S. The purpose of this study was to better understand community responder and healthcare provider perceptions and preferences regarding the design and function of a potential new opioid sensor device (OSD). Methods: Adults aged ≥ 18 years employed as community responders or healthcare providers in Alabama were recruited via email to participate in an anonymous online cross-sectional survey informed by the Unified Theory of Acceptance and Use of Technology (UTAUT). Primary outcomes were assessed via multiple-choice and 7-point Likert-type scales (1 = strongly disagree, 7 = strongly agree) and included the following topics: (1) past OSD utilization (4 items); (2) perceived importance of OSD design elements (15 items); (3) OSD function and cost preferences (3 items); and (4) UTAUT measures including perceived usefulness of OSDs (3 items), ease of use (4 items), social factors (4 items), resources (4 items), concerns (3 items), and intentions (3 items). Differences in UTAUT measures across professions were assessed via Mann–Whitney U tests, and predictors of OSD utilization intention were analyzed via multiple linear regression. Results: Respondents (N = 145) included pharmacists (40.0%), nurses (23.4%), physicians (14.5%), behavioral health (4.8%), social work (4.8%), and law enforcement (0.7%). Availability in hospital emergency departments was rated as the most important device element (mean [SD] score: 6.66 [0.80]), followed by sensitivity and specificity of the test (6.42 [0.98]), rapid detection time (6.42 [0.88]), ability to detect opioids in a broad range of substance (6.42 [0.93]), and availability in law enforcement offices (6.33 [1.08]). A 2–5 min detection time was rated as reasonable by 32.6% of respondents, with 53.0% preferring to pay <USD 15 per test. There were no statistically significant differences in UTAUT scale scores across professions. Perceived usefulness (β = 0.493; p < 0.001), social acceptance (β = 0.281; p = 0.023), and resource availability (β = 0.708; p = 0.002) were positive predictors and perceived ease of use was a negative predictor (β = −0.472; p = 0.007) of intention to use an OSD. Conclusions: Newly developed OSDs should consider prioritizing accessibility in hospital emergency departments and law enforcement offices, ability to detect a broad range of opioids, detection time between 2 and 5 min, and cost less than USD 15 per test. Future research may explore perspectives from a more diverse sample across multiple states and different professional roles. Full article

This study focuses on the development of wood-based particleboard that address resource efficiency, environmental sustainability, and health-related concerns. The conventional particleboard industry relies on synthetic, predominantly formaldehyde-based adhesives, which pose environmental, health, and end-use risks. Rising raw material prices, regulatory restrictions, and increasing competition in the wood-processing sector have further highlighted the importance of alternative biomass resources for particleboard production. In response to these challenges, this study investigates the suitability of available sawdust resources derived from the production residues of cellular wood materials and recycled particleboards, combined with natural suberinic acids mixture obtained from birch outer bark as a binder. The effects of furnish structure, binder content (15–21%), pressing temperature (190–220 °C), pressing rate (0.9–1.7 min/mm), and board density (650–850 kg/m³) on the resulting particleboard properties were evaluated. The results demonstrate that it is possible to meet the requirement values for thickness swelling (≤17%) and internal bonding strength (≥0.40 N/mm²) specified for interior fitment boards, including furniture applications according to EN 312, Type P2. The bending properties of the best-performing particleboards are very close to the requirement values (MOE ≥ 1800 N/mm², MOR ≥ 11 N/mm²), indicating the potential for further improvement at the target density range. Furnish structure, board thickness, density, and pressing temperature were identified as the most influential factors affecting the final board properties. Full article

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection. Full article

Modern agriculture requires optimizing available resources to maximize production while minimizing environmental impact without increasing economic costs. Hydroponic agriculture replaces soil with inert media that provide physical support for plants but do not supply nutrients. In this type of agricultural production, fertilization with nutrient solutions is essential, as they supply the 15 elements necessary for proper plant development. These solutions consist of mixtures of different amounts of fertilizers dissolved in water. In this context, a method based on a simulated annealing algorithm is proposed, a metaheuristic that optimizes fertilizer quantities in grams to achieve target concentrations in parts per million for six macronutrients and nine micronutrients. The algorithm addresses a multi-objective optimization problem, balancing two competing goals: first, maximizing the accuracy of the fertilizer balance to achieve the required nutritional levels, and second, minimizing the total cost of the fertilizer mixture. The algorithm’s fitness function weights the total cost of the fertilizers used and the total relative error between the concentrations obtained and those desired, allowing the relative importance of cost and accuracy in the nutrient solution to be adjusted. The results of three experiments with varying nutrient levels are presented for a 1000-L water tank. The first experiment consisted of three macronutrients and two micronutrients. The second configuration added three macronutrients and two micronutrients, for a total of ten nutrients. Finally, five micronutrients were added to complete the 15 essential nutrients for plants. It is important to note that there are several methods for calculating micronutrients that contribute to precision agriculture, increasing the complexity of finding a solution that meets established nutritional requirements. The nutrient concentrations in parts per million required for tomato cultivation during the vegetative development stage. To balance nutrient accuracy and solution cost, we applied weighting factors of $0.65,0.75,0.85,0.90,0.95,$ and $1.0$ for accuracy. The corresponding weights for cost were calculated as the complement of these values (totaling 1). By favoring nutrient accuracy with a weighting of 1, accuracies of 0.00500, 0.02618, and 0.03077 parts per million were achieved in each experiment, respectively. Meanwhile, the lowest cost is $2.06$, $2.72$, and $2.70$ USD for the aforementioned experiments. Full article

Meteorological drought is a major natural hazard in semi-arid regions, where high climate variability and strong dependence on precipitation intensify pressure on water resources and socio-economic systems. This study examined the spatiotemporal characteristics of meteorological drought in the Wadi Sly basin (northwestern Algeria) over the period 1967–2022, using long-term monthly precipitation records from seven meteorological stations. The Standardized Precipitation Index (SPI) was calculated at multiple time scales (1-, 3-, 6-, 9-, and 12-month) to characterize drought onset, severity, persistence, and temporal variability. In addition, drought severity probability and frequency analyses were conducted to evaluate the likelihood and recurrence of different drought classes. The results indicate pronounced inter-annual and decadal variability in drought conditions, with severe and prolonged drought episodes occurring during the mid-1980s, early-to-mid-1990s, and late 2010s. During these periods, SPI values frequently fell below −2.0, signifying extreme drought conditions. Spatial analysis reveals strong basin-wide synchronicity of drought events, suggesting the influence of large-scale atmospheric drivers, although localized variations in drought intensity remain evident. Overall, near-normal conditions dominate the record (accounting for approximately 60–70% of observations), while moderately dry conditions occur more frequently than moderately wet conditions at several stations. Drought characteristics exhibit strong scale dependence, with short-term droughts prevailing at shorter SPI time scales, while longer time scales emphasize drought persistence and accumulation. Overall, the findings indicate an increasing prominence of long-duration drought conditions in recent decades, as evidenced by recurrent low SPI values at longer aggregation scales. Such conditions may pose heightened risks to groundwater recharge processes and long-term water resource availability. Despite the limitations inherent in precipitation-based indices, this study provides a robust statistical framework for drought characterization and contributes valuable insights for improved drought monitoring, early warning systems, and climate-resilient water resource management in semi-arid basins. Full article

Biopolymers can be derived from biological sources, including protein blends with plasticizers, starch, enzymatic synthesis, microorganisms, and algae. They are classified into polynucleotides, polysaccharides, and polypeptides, including polyhydroxyalkanoates, polylactic acid, and thermoplastic starch. Blending polymers with plasticizers and nanoparticles enhances their mechanical, thermal, and barrier properties. Biopolymers have various applications, such as in packaging, textiles, medical devices, cosmetics, agriculture, food products, emulsifiers, construction additives, bioplastics, and biofuels. Some of the advantages of biopolymers include their biodegradability, use of renewable resources, and reduced environmental impact. Nevertheless, certain disadvantages persist, such as high production costs, inadequate waste management systems, material quality loss during recycling, and the limited availability of raw materials. In this context, castor oil has emerged as a promising raw material for biopolymer production, with notable applications in coatings and sealants, and, consequently, bioplastics have become a sustainable and feasible alternative to conventional plastics that aligns with the principles of the circular economy. Moreover, new biopolymers are constantly being developed, and innovative applications are increasingly being explored across industries. The aim of the present review is to analyze the potential of biopolymers as sustainable alternatives to conventional plastics by evaluating their sources, production methods, advantages, limitations, and applications. Full article