Search Results (14,160)

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article

In arid regions, the challenges posed by rainfall data availability, missing data, and limited historical records significantly affect hydrological modeling studies and climate change assessments. For various hydrology applications, it is essential to implement advanced techniques in order to obtain a complete dataset series. This study explores the implementation of multiple machine learning techniques to address the complexity of filling daily rainfall data for 88 rainfall stations in the Al-Batinah region of Oman, covering the period from 1993 to 2024. The machine learning models applied in this study include Multiple Linear Regression (MLR), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Gradient-Boosting Trees (GBT). A non-clustering approach is used as well as a clustering approach as part of the methodology. In the first method, rainfall stations are not clustered, while in the second method, optimal cluster numbers are calculated using K-means clustering. The target station utilizes the nearby rainfall station data located within a 50 km radius with the highest correlation coefficients. A novel Ensemble Fusion Model has been applied to improve the efficacy of multiple predictive models, including the RF Fusion Model (RF) and Multi-Model Super Ensemble Fusion Model (MMSE). The estimation approaches are further enhanced and evaluated by Bayesian optimization of hyperparameters, dataset imputation utilizing Multiple Imputation by Chained Equations (MICE), and Leave-One-Year-Out (LOYO) cross-validation. Based on the results, it can be concluded that the GBT model performs the best in both cluster and non-cluster approaches. A further benefit of applying Ensemble Fusion Models to rainfall gap-filling methods is that the coefficient of determination (R²) for clustering and non-clustering approaches increases to 22.5% and 22.2%, respectively. Full article

The system made by a charged particle interacting with a single electrostatic wave which propagates perpendicularly to the magnetic field, at a frequency larger than the cyclotron one, has been extensively studied in the literature due to its implications for ion heating in magnetized plasmas. It is known that a threshold in the electrostatic potential must be exceeded in order for stochastic particle motion and heating to occur. Regardless of its amplitude, however, the electrostatic wave induces a periodic oscillation in the particle motion. We show, by analytical and numerical arguments, that this dynamic is non-adiabatic, meaning that the particle does not land back in its initial state when the wave is slowly turned off. This way, particle energization (although not rigorous heating) occurs even under sub-threshold conditions. Full article

This work develops and analyzes a novel fractional-order chemostat system (FOCS) with a Caputo fractional derivative (CFD) featuring a sliding memory window and periodic boundary conditions (PBCs), designed to model microbial pollutant degradation in sustainable water treatment. By incorporating the Caputo fractional derivative with sliding memory (CFDS), the model captures time-dependent behaviors and memory effects in biological systems more realistically than classical integer-order formulations. We reduce the two-dimensional fractional differential equations (FDEs) governing substrate and biomass concentrations to a one-dimensional FDE by utilizing the PBCs. The existence and uniqueness of non-trivial, periodic solutions are established using the Carathéodory framework and fixed-point theorems, ensuring the system’s well-posedness. We prove the positivity and boundedness of solutions, demonstrating that substrate concentrations remain within physically meaningful bounds and biomass concentrations stay strictly positive, with solution trajectories confined to a biologically feasible invariant set. Additionally, we analyze non-trivial equilibria under constant dilution rates and derive their stability properties. The rigorous mathematical results confirm the viability of FOCS models for representing memory-driven, periodic bioprocesses, offering a foundation for advanced water treatment strategies that align with Sustainable Development Goal 6 (Clean Water and Sanitation). This work establishes a comprehensive mathematical framework that bridges fractional calculus with sustainable water treatment applications, providing both theoretical foundations and practical implications for optimizing bioreactor performance in environmental biotechnology. Full article

Introduction: Toxic Epidermal Necrolysis (TEN) and Steven–Johnson Syndrome (SJS) are rare yet dangerous dermatological emergencies presenting as necrosis of the skin and mucous membranes due to an immune reaction which may be associated with the use of pharmaceuticals—predominantly non-steroidal anti-inflammatory drugs (NSAIDs), antibiotics, and antiretroviral drugs. During the postpartum period, women are administered numerous pharmaceuticals, including NSAIDs, analgesics, and antibiotics, due to pain and their susceptibility to infections, exposing them to potential adverse effects including allergies and immune reactions. Case Report and Review: The case reported here is a rare description of a patient in the early postpartum phase who presented with epidermal necrolysis whilst remaining hospitalized after a cesarean delivery. The multidisciplinary approach, early diagnosis, and treatment ensured the patient’s full recovery. Intravenous immunoglobulin treatment resulted in a rapid therapeutic effect. This literature review offers an insight into the epidemiology, diagnostic process, and treatment of this infrequent dermatological syndrome during the postpartum period. Results: Antibiotic treatment is a common culprit of TEN in this population; hence, clinicians should remain vigilant during antibiotic administration. Differential diagnosis with toxic shock syndrome is crucial, as TEN and SJS symptoms may mimic sepsis, which is a more common diagnosis in the postpartum period. Conclusions: The condition of the skin during the postpartum period should be closely monitored, as some systemic diseases may manifest abruptly as profound, postpartum hormonal changes affect the immunological response. Upon the discovery of suspicious skin lesions concomitant with systemic symptoms, an immediate multidisciplinary approach involving obstetricians and dermatologists is key to a rapid diagnosis and treatment to avoid maternal mortality. Full article

This study investigates the horizontal positioning accuracy of a low-cost, multi-frequency GNSS receiver operating in static mode using a newly released PPP-RTK correction service delivering localized corrections. To the authors’ knowledge, this represents one of the first performance evaluations of this service, which optimizes correction data based on the approximate receiver location. The results are compared against those from the previous version of the service, which provided non-localized corrections. Analyses were conducted in both the time and frequency domains, employing robust statistical tools to characterize error behavior. The localized service achieved a mean horizontal error of approximately 0.020 m and a 95% Circular Error Probable (CEP95) of 0.046 m, in line with its declared performance. By contrast, the earlier non-localized service yielded a mean horizontal error of approximately 0.074 m and a CEP95 of 0.124 m under comparable static conditions, confirming the significant improvement achieved by localized corrections. Spectral and wavelet analyses revealed a dominant 33 mHz harmonic in the positioning error, corresponding to the 30 s update period of atmospheric corrections, indicating a periodic influence arising from the correction stream. Continuous wavelet analysis further identified intervals in which this harmonic was absent, during which positioning accuracy improved markedly (CEP95 reduced to 0.019 m). To properly address the non-Gaussian nature of the error distribution, bias-corrected and accelerated (BCa) bootstrap methods were applied to estimate confidence intervals. Overall, the results demonstrate the benefits of localized corrections, while emphasizing the importance of accounting for the temporal structure of correction data in PPP-RTK performance assessments. Future developments will focus on kinematic scenarios and adaptive filtering strategies to mitigate periodic errors induced by correction updates. Full article

Background/Objectives: Excessive sugar, fat, and salt intake heighten susceptibility to metabolic syndrome and other chronic metabolic conditions. Biofortification (i.e., enhancing the nutritional content of crops) emerges as a sustainable new approach to address dietary deficiencies. Methods: This study evaluated the impact of an acute nutritional intervention in a controlled, randomized, single-blind trial involving healthy adults aged 50–79 years, in late middle age and early older adulthood utilizing biofortified vegetables enriched with iodine and molybdenum, aimed to explore short-term biochemical responses to the consumption of iodine- and molybdenum-biofortified lettuce. The study was designed as a controlled dietary intervention including both a biofortified and a non-biofortified lettuce group, matched for handling and composition. It was powered to detect short-term biochemical responses, providing initial insights into the physiological impact of micronutrient biofortification. Dietary intake was carefully monitored throughout the 12-day period to control for confounding dietary effects. Results: The intervention was associated with decreased plasma levels of triglycerides, AST, and ALT, and increased plasma levels of HDL-cholesterol and the satiety hormone PYY, suggesting enhanced metabolic regulation. Conclusions: These biochemical markers reflect early metabolic adaptations that may inform future research on the metabolic impact of micronutrient biofortification. This study also highlights the potential of crop biofortification as a sustainable, strategy to enhance the nutrient density of vegetables within controlled dietary patterns. Full article

Brachyspira hyodysenteriae is the primary cause of swine dysentery, characterized by bloody to mucoid diarrhea due to mucohaemorhagic colitis in pigs. The disease primarily affects pigs during the growth and finishing stage. The control and prevention of B. hyodysenteriae consists of the administration of antimicrobial drugs, in addition to management and adapted feeding strategies. A worldwide re-emergence of the disease has recently been reported with an increasing number of isolates demonstrating decreased susceptibility to several crucially important antimicrobials in the control of swine dysentery. This compromises the possibilities to eradicate B. hyodysenteriae from infected pig farms. A novel non-antibiotic zinc chelate has been reported to demonstrate positive effects on fecal quality and consistency, general clinical signs, average daily weight gain, and B. hyodysenteriae excretion during and after a 6-day oral treatment. The objective of the present study was to evaluate the zinc chelate (IntraDysovinol^® 499 mg/mL; IntraCare) within an eradication schedule with naturally occurring swine dysentery due to B. hyodysenteriae resistant to pleuromutilins under field conditions in Belgium. We evaluated a 14-day treatment schedule combined with alternative management measures (including partial depopulation of post-weaning facilities and improved external and internal biosecurity measures) and thorough cleaning and disinfection (including 2% NaOH) of the buildings and the sows from day 7 of treatment onwards. This alternative approach for B. hyodysenteriae eradication was evaluated on 18 pig farms over a 5-year period. All enrolled eradication programs were evaluated as successful at least 6–9 months after the finalization of the protocol. In conclusion, the zinc chelate product has an excellent potential for application within an eradication protocol of B. hyodysenteriae that are diagnosed as resistant to pleuromutilins. The combined approach of zinc chelate treatment and alternative management measures is demonstrated to be successful in the eradication of farrow-to-wean, farrow-to-finish, and finishing pig farms under field conditions in Belgium. Full article

Background and Objective: Evaluating vaccine effectiveness (VE) is essential to implementing prevention strategies, and our objective was to estimate the VE of SARS-CoV-2 messenger RNA (mRNA) vaccines in preventing SARS-CoV-2 infection. Materials and Methods: We carried out a population-based, prospective cohort study on the Borriana COVID-19 cohort (Valencia Community, Spain) during the 2021–2023 period, considering all SARS-CoV-2 cases that occurred after the SARS-CoV-2 vaccine campaign started in January 2021 (first approach), as well as only symptomatic cases (second approach). Multivariable robust Poisson regression models were employed. Results: In this cohort with 301 participants, 285 were vaccinated, among whom 228 received only SARS-CoV-2 mRNA vaccines, and 57 received mRNA vaccines and other vaccines. In the first approach, there were 226 cases and 75 non-cases. The adjusted VE for three doses of vaccine was 37% (95% confidence interval [CI]: 22–49%) to prevent infection. In the second approach, with 153 symptomatic cases after excluding 73 asymptomatic cases, the adjusted VE for three doses of vaccine was 50% (95% CI 33–63%) to prevent symptomatic infection. Three doses of vaccine exhibited modest but significant protection against infection and symptomatic infection. Conclusions: This study recommends surveilling SARS-CoV-2 infections and variants, vaccinating at-risk populations, and developing new vaccines. Full article

The principal problem with the analysis of nonlinear dynamical systems is that it is repetitive and inefficient to simulate every initial condition and parameter configuration individually. This not only raises the cost of computation but also constrains scalability in the exploration of a large parameter space. To solve this, we restructured and extended the computational framework so that variation in the parameters and initial conditions can be automatically explored in a unified structure. This strategy is implemented in the brain-inspired nonlinear dynamical model that has three parameters and multiple coupling strengths. The framework enables detailed categorization of the system responses through statistical analysis and through eigenvalue-based assessment of the stability by considering multiple initial states of the system. These results reveal clear differences between periodic, divergent, and non-divergent behavior and show the extent to which the strength of the coupling $k_{ij}$ can drive transitions to stable periodic behavior under all conditions examined. This method makes the analysis process easier, less redundant, and provides a scalable tool to study nonlinear dynamics. In addition to its computational benefits, the framework provides a general method that can be generalized to models with more parameters or more complicated network structures. Full article

Ricotta cheese whey (RCW) is a by-product with nutritional potential, but its use in the human diet is limited due to its high salinity. Chlorella vulgaris can use RCW as a substrate to enhance biomass productivity. The aim of this work was to evaluate different conditions for C. vulgaris growth in RCW, during scaling-up analysis. After preliminary assays to select growth conditions, two systems were prepared as follows: 500 mL Erlenmeyer flasks (control-system) and a 3 L Bioreactor. Microfiltrated RCW was used as a substrate for C. vulgaris LPMA39 production. Biomass was measured and productivity at 96 h, cell growth kinetics behaviour, biomass biochemical characterisation, and the efficiency of nutrient removal were determined. Both systems presented the same biomass concentration at 96 h (2.2–2.8 g·L⁻¹) and productivity (0.021–0.027 g·L⁻¹·h⁻¹). Nevertheless, 11 h lag-period for cell adaptation to the 3 L Bioreactor was required; thereafter, cells grew faster (µ_max: 0.32 ± 0.08 h⁻¹) than control-system. Finally, slight but significantly lower C_max: 2.14 ± 0.08 was obtained when comparing it to control-system. Lipids, proteins, and pigment contents decreased by the scaling-up; meanwhile, higher reduction in chemical oxygen demand (COD), total phosphorus, and total nitrogen were recorded in the 3 L Bioreactor. Identifying the operating conditions that improve C. vulgaris performance in non-diluted RCW remains a challenge from a sustainability standpoint. Full article

Background/Objectives: The delayed effects of organophosphate poisoning may manifest years after exposure, often masked by age-related diseases. The aim of this retrospective cohort study was to identify the biochemical “trace” that could remain in patients decades after poisoning. We determined a wide range of biochemical parameters, along with the spectrum of esterified and non-esterified fatty acids (EFAs and NEFAs, respectively), in the blood plasma of a cohort of elderly patients diagnosed with occupational pathology (OP) due to (sub)chronic exposure to organophosphates in the 1980s. Methods: Elderly patients with and without a history of exposure to organophosphates were retrospectively divided into two groups: controls (n = 59, aged 73 ± 4, men 29% and women 71%) and those with OP (n = 84, aged 74 ± 4, men 29% and women 71%). The period of neurological examination and blood sampling for subsequent analysis was from mid-2022 to the end of 2023. Determination of the content of biomarkers of metabolic syndrome, NEFAs, and EFAs in blood plasma was performed by HPLC-MS/MS and GC-MS. Results: The medical histories of the examined elderly individuals with OP and the aged control group included common age-related diseases. However, patients with OP more often had hepatitis, gastrointestinal diseases, polyneuropathy, and an increased BMI. Analysis of metabolic biomarkers revealed, in the OP group, a decrease in the concentrations of 3-hydroxybutyrate (p < 0.05), 2-hydroxybutyrate (p < 0.0001), and acetyl-L-carnitine (p < 0.001) and the activity of butyrylcholinesterase (BChE) (p < 0.05), but an increase in the esterase activity of albumin (p < 0.05). Correlation analysis revealed significant relationships between albumin esterase activity and arachidonic acid concentrations in the OP group (0.64, p < 0.0001). A study of a wide range of fatty acids in patients with OP revealed reciprocal relationships between EFAs and NEFAs. A statistically significant decrease in concentration was shown for esters of margaric, stearic, eicosadienoic, eicosatrienoic, arachidonic, eicosapentaenoic, and docosahexaenoic fatty acids. A statistically significant increase in concentration was shown for non-esterified heptadecenoic, eicosapentaenoic, eicosatrienoic, docosahexaenoic, γ-linolenic, myristic, eicosenoic, arachidonic, eicosadienoic, oleic, linoleic, palmitic, linoelaidic, stearic, palmitoleic, pentadecanoic, and margaric acids. Decreases in the ratios of omega-3 to other unsaturated fatty acids were observed only for the esterified forms. Conclusions: The data obtained allow us to consider an increased level of NEFAs as one of the main cytotoxic factors for the vascular endothelium. Modification of albumin properties and decreased bioavailability of docosahexaenoic acid could be molecular links that cause specific manifestations of OP-induced pathology at late stages after exposure. Full article

Over the past decade, China’s industrialization and urbanization have accelerated rapidly, leading to the extensive consumption of fossil fuels and the accumulation of atmospheric pollutants, which pose significant health risks to the population. This study analyses the spatiotemporal evolution patterns of major air pollutants over the past decade based on data from meteorological- and environmental-factor monitoring from various observation stations in the Yangtze River Delta region of China from 2018 to 2024, as well as air pollution monitoring and statistical data such as mortality rates of weather-sensitive diseases and socioeconomic attributes of patients. Based on mathematical models, a quantitative ‘dose–response’ relationship is established among meteorological factors, air pollution factors and mortality rates of sensitive diseases within the region. (1) PM_2.5 and ozone are the primary air pollutants in the Yangtze River Delta region, with significant self-correlation characteristics in pollutants observed in coastal areas and regions around provincial capitals. (2) The synergistic effects of temperature + NO₂ and relative humidity + SO₂ significantly impact mortality from sensitive diseases, while the cumulative lag effect of relative humidity on respiratory diseases exhibits a V-shaped temporal variation. (3) Pollutant cumulative lag effects are pronounced, with a 10 μg/m³ increase in PM_2.5 leading to a 0.93% and 0.71% rise in the mortality risks of non-accidental and circulatory system diseases over the lag period of 15 days, compared to a single-day lag, showing an additional 0.06% and 0.04% increase, respectively. Full article

Background: Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare vasculitis, and contemporary population data from Central and Eastern Europe are limited. Aim: To describe hospital-based incidence, patient characteristics and comorbidities among EGPA hospitalizations in Poland (2014–2023), including differences by age, sex and place of residence. Methods: This retrospective, population-based study used nationwide hospital discharge records with an EGPA diagnosis. First EGPA-coded hospitalizations were used to estimate annual incidence per 1,000,000 inhabitants. Demographics, duration of stay and accompanying comorbidities (cardiovascular disease, pulmonary disease, and asthma) were analyzed for all EGPA hospitalizations. Place of residence was classified as urban or rural. Group differences and temporal trends were assessed using appropriate parametric and non-parametric tests and regression models, with a two-sided p value < 0.05 considered statistically significant. Results: Between 2014 and 2023, 911 patients had a first EGPA-coded hospitalisation in Poland, corresponding to a mean annual hospital-based incidence of 2.38 per 1,000,000 inhabitants (range 1.28–3.38); incidence declined significantly from 2014 to 2019 (p < 0.001) and was disrupted during the COVID-19 period. Overall, 3524 EGPA hospitalisations were recorded, and women were more frequently hospitalised than men (54.5% vs. 45.2%; p < 0.001). Mean age at hospitalisation increased over time, with patients in 2023 being about 5–6 years older than in 2014 (p ≤ 0.009). Median length of stay was 8 days for first admissions and 5 days for all EGPA stays and shortened significantly over the study period (p < 0.001). Cardiovascular disease, pulmonary disease and asthma were present in 23.6%, 35.3% and 32.3% of patients, respectively. Cardiovascular disease was more common in men and in rural residents (both p < 0.001) and was associated with older age (p < 0.001), whereas pulmonary disease was associated with younger age (p < 0.001). Among women, the proportions with pulmonary disease and asthma decreased over time (p = 0.009 and p = 0.025). Conclusions: EGPA in Poland is rare, with hospital-based incidence comparable to other European and Asian populations. The hospitalized EGPA population is aging and cardiovascular comorbidity is increasingly prominent, especially in older and rural patients, while recorded pulmonary disease and asthma in women are decreasing. Full article

Bovine ketosis represents a critical metabolic challenge in modern dairy production, primarily affecting high-yielding cows during the periparturient period. The disorder arises from severe negative energy balance (NEB), where energy demands exceed supply, triggering substantial mobilization of adipose tissue and hepatic accumulation of non-esterified fatty acids (NEFAs). This stress overwhelms hepatic oxidative capacity, leading to incomplete fatty acid oxidation and excessive ketone body production—primarily β-hydroxybutyrate (BHB), acetoacetate, and acetone. While genomics enables the identification of candidate genes and genetic variations influencing ketosis susceptibility, metabolomics provides comprehensive, multi-matrix metabolic signatures across blood, milk, urine, and tissues, often revealing predictive biomarkers in the dry period and pre-clinical stage. This review provides a comprehensive overview of ketosis etiology, emphasizing the roles of NEB, fat mobilization, hormonal dysregulation, and oxidative stress. We systematically examine candidate genes, signaling pathways, and the diverse metabolomic profiles underlying disease pathogenesis, and discuss current prevention strategies focusing on periparturient nutritional management and therapeutic interventions. Understanding these integrated molecular and metabolic mechanisms offers valuable insights for developing targeted breeding programs and precision management approaches to enhance metabolic health and economic efficiency in dairy cattle. Full article