Search Results (34,027)

The concentration of air pollutants could be affected by climate change in industrial park zones in Hidalgo state, Mexico (IPHSs). The goals of this work were: (a) to describe the aerosols’ behavior (PM₁₀ and PM_2.5) and air pollutants (SO₂, NO₂, O₃, and CO) in the IPHSs and (b) determine the climate variable behavior regarding the presence in IPHSs. The methodology consisted of structuring the time series of climate variables and air pollutants in six analysis regions. Afterwards, an annual average calculation, a count of days exceeding the allowed limits set by the official Mexican norms, an analysis of annual behavior by season, the Sen slope calculation, and correlation among variables were performed. Results demonstrated that Zone 2 is the most polluted, exceeding the allowed limits in the annual average (PM₁₀ > 36 μg/m³, PM_2.5 > 10 μg/m³, and NO₂ > 0.021 ppm) and having more than 1000, 96, and 11 days where the daily limit was exceeded in PM₁₀, PM_2.5, and SO₂, respectively. The minimum concentrations of the pollutants were observed during the summer–autumn seasons, coinciding with the highest precipitation. Regarding the correlations, the pollutants are negatively and statistically significantly correlated with precipitation (ranging from −0.81 to −0.43); meanwhile, the maximum temperature (ranging from +0.41 to +0.51) and evaporation (ranging from +0.39 to +0.54) are positively and statistically significantly correlated. In conclusion, the results could suggest that the presence of pollutants in the atmosphere may be influenced by the behavior of nearby regional climatic conditions in the IPHSs. Full article

This paper presents a cascaded control strategy for neutral-point-clamped three-level (NPC-3L) inverter-fed permanent magnet synchronous motors (PMSMs), integrating continuous-control-set model-predictive control (CCS-MPC) with mid-point voltage regulation and an online Lyapunov-stable neural-network (NN) disturbance observer. The outer CCS-MPC loop optimizes voltage vector application for accurate current tracking and harmonic suppression, while the inner loop balances mid-point voltage by adjusting the dwell times of P/N small-voltage vectors (VVs). The NN-based disturbance observer compensates parameter mismatches in real time, reducing steady-state dq-axis current errors. To validate the effectiveness of the proposed strategy, experiments are conducted using a three-phase PMSM fed by three-phase NPC-3L inverters. Experimental results demonstrate substantial improvements in mid-point voltage balance, current quality, and robustness against model uncertainties. Full article

Biological recolonization after cleaning remains a major challenge for the conservation of stone cultural heritage. As recolonization can start within months to a few years following intervention, developing rapid, field-deployable diagnostic approaches is crucial to better monitor microbial reappearance and to assess treatment performance in real time. Traditional evaluation methods lack the capacity to take into consideration non-cultivable microorganisms or assess functional traits relevant to recolonization. To bypass this gap, we applied on-site direct Whole-Genome Sequencing (Oxford Nanopore^® MinION™ sequencer) coupled with colorimetric analysis to understand the microbiome, resistome, and metabolic traits of subaerial biofilms present in untreated and treated (recolonized) areas of stone statues at the “Largo da Porta Férrea” (Coimbra’s UNESCO World Heritage site). Colorimetric analysis (ΔE of 32–40 in recolonized vs. 19–43 in untreated areas) and genomic data pointed out that the applied treatment provided only a short-term effect (roughly 4–5 years), with a marked decline in fungi (1–2% vs. 7–18%), coupled with an increased recolonization mainly by Cyanobacteriota (circa 35–45%) and several stress-resistant Bacteria (globally ~95% of reads vs. 73–79% in controls). Antimicrobial resistance profiles significantly differed between sites, with treated areas showing distinct and unique resistance genes, and plasmids containing the blaTEM-116 gene, which can indicate potential adaptive shifts in the resistomes profiles after intervention. Metabolic pathways analysis revealed that untreated areas retained more complete nitrogen and sulfur cycling gene sets, whereas treated areas showed reduced biogeochemical gene contents, consistent with earlier-stage recolonization steps. Given the current recolonization detection and the ongoing biofilm formation, routine monitoring efforts (e.g., every 6 months) are recommended. Overall, this study demonstrates the first on-site genomic characterization of recolonization events on heritage stone, providing a practical prompt-warning tool for conservation monitoring and future biofilm management strategies. Full article

Wirelessly charging unmanned aerial vehicles (WCUAVs) can complete charging tasks without human intervention and may help us efficiently collect various types of geographically dispersed data in unmanned data collection systems (UDCSs). However, the limited number of wireless charging stations and longer wireless charging times also pose challenges to minimizing the Age of Information (AoI). Here, we provide a heuristic method to minimize AoI for WCUAVs. Firstly, the problem of minimizing AoI is modeled as a trajectory optimization problem with nonlinear constraints involving n sensor nodes, a data center, and a limited number of wireless charging stations. Secondly, to solve this NP-hard problem, an improved artificial plant community (APC) approach is proposed, including a single-WCUAV architecture and a multi-WCUAV architecture. Thirdly, a benchmark test set is designed, and benchmark experiments are conducted. When the number of WCUAVs increased from 1 to 2, the total flight distance increased by 12.011% and the average AoI decreased by 45.674%. When the number of WCUAVs increased from 1 to 10, the total flight distance increased by 87.667% and the average AoI decreased by 78.641%. The experimental results show that the proposed APC algorithm can effectively solve AoI minimization challenges of WCUAVs and is superior to other baseline algorithms with a maximum improvement of 9.791% in average AoI. Due to its simple calculation and efficient solution, it is promising to deploy the APC algorithm on the edge computing platform of WCUAVs. Full article

Sandflies spread the neglected vector-borne disease anthroponotic cutaneous leishmaniasis (ACL), which only affects humans. Despite decades of control, asymptomatic carriers, vector pesticide resistance, and low public awareness prevent eradication. This study proposes a fractional-order optimal control model that integrates biological and behavioral aspects of ACL transmission to better understand its complex dynamics and intervention responses. We model asymptomatic human illnesses, insecticide-resistant sandflies, and a dynamic awareness function under public health campaigns and collective behavioral memory. Four time-dependent control variables—symptomatic treatment, pesticide spraying, bed net use, and awareness promotion—are introduced under a shared budget constraint to reflect public health resource constraints. In addition, Caputo fractional derivatives incorporate memory-dependent processes and hereditary effects, allowing for epidemic and behavioral states to depend on prior infections and interventions; on the other hand, standard integer-order frameworks miss temporal smoothness, delayed responses, and persistence effects from this memory feature, which affect optimal control trajectories. Next, we determine the optimality conditions for fractional-order systems using a generalized Pontryagin’s maximum principle, then solve the state–adjoint equations numerically with an efficient forward–backward sweep approach. Simulations show that fractional (memory-based) dynamics capture behavioral inertia and cumulative public response, improving awareness and treatment efforts. Furthermore, sensitivity tests indicate that integer-order models do not predict the optimal allocation of limited resources, highlighting memory effects in epidemiological decision-making. Consequently, the proposed method provides a realistic and flexible mathematical basis for cost-effective and sustainable ACL control plans in endemic settings, revealing how memory-dependent dynamics may affect disease development and intervention efficiency. Full article

This paper analyzes a finite-capacity $GI/M/2/N$ queue with two heterogeneous servers operating under a multiple working-vacation policy, Bernoulli feedback, and customer impatience. Using the supplementary-variable technique in tandem with a tailored recursive scheme, we derive the stationary distributions of the system size as observed at pre-arrival instants and at arbitrary epochs. From these, we obtain explicit expressions for key performance metrics, including blocking probability, average reneging rate, mean queue length, mean sojourn time, throughput, and server utilizations. We then embed these metrics in an economic cost function and determine service-rate settings that minimize the total expected cost via the Bat Algorithm. Numerical experiments implemented in R validate the analysis and quantify the managerial impact of the vacation, feedback, and impatience parameters through sensitivity studies. The framework accommodates general renewal arrivals ($GI$), thereby extending classical ($M/M/2/N$) results to more realistic input processes while preserving computational tractability. Beyond methodological interest, the results yield actionable design guidance: (i) they separate Palm and time-stationary viewpoints cleanly under non-Poisson input, (ii) they retain heterogeneity throughout all formulas, and (iii) they provide a cost–optimization pipeline that can be deployed with routine numerical effort. Methodologically, we (i) characterize the generator of the augmented piecewise–deterministic Markov process and prove the existence/uniqueness of the stationary law on the finite state space, (ii) derive an explicit Palm–time conversion formula valid for non-Poisson input, (iii) show that the boundary-value recursion for the Laplace–Stieltjes transforms runs in linear time $\mathcal{O}\left(N\right)$ and is numerically stable, and (iv) provide influence-function (IPA) sensitivities of performance metrics with respect to $({\mu }_1,{\mu }_2,\nu ,\alpha ,\varphi ,\beta )$. Full article

This study examines the evolution of the Yellow River Basin’s urban corporate network from 2003 to 2023, aiming to understand how intercity connectivity and decision-making authority have developed. Using headquarters–subsidiary linkages of listed firms, we measure connectivity and control of cities within the urban system and employ spatial error models to identify their main determinants. The results show that the network has become denser and more geographically inclusive, especially in the middle and lower reaches. However, a clear hierarchy remains, and upstream integration stays limited. Community structures are anchored by capitals, and multi-core patterns strengthen over time. Coastal hubs in Shandong handle the most significant volumes of ties, while interior capitals such as Zhengzhou, Lanzhou, Xi’an, and Taiyuan concentrate authority—a contrast that has intensified since 2013. Connectivity and control often diverge, and disparities in both have increased. Administrative rank remains the strongest predictor of a city’s position, although its influence has decreased as factors such as openness, development, producer services, and innovation have gained importance. Transportation accessibility and human capital consistently support both connectivity and control, while government intervention initially restricts network roles but becomes less influential over time. These findings suggest that intercity corporate linkages have expanded, yet decision-making authority has not dispersed and remains concentrated in a small set of capitals. Governance that coordinates across provinces is necessary to ensure that increasing linkages translate into shared economic opportunities while protecting the basin’s fragile ecological environment. Full article

This work presents a time-domain approach for characterizing the Ground Reaction Forces (GRFs) exerted by a pedestrian during running. It is focused on the vertical component, but the methodology is adaptable to other components or activities. The approach is developed from a statistical perspective. It relies on experimentally measured force-time series obtained from a healthy male pedestrian at eight step frequencies ranging from 130 to 200 steps/min. These data are subsequently used to build a stochastic data-driven model. The model is composed of multivariate normal distributions which represent the step patterns of each foot independently, capturing potential disparities between them. Additional univariate normal distributions represent the step scaling and the aerial phase, the latter with both feet off the ground. A dimensionality reduction procedure is also implemented to retain the essential geometric features of the steps using a sufficient set of random variables. This approach accounts for the intrinsic variability of running gait by assuming normality in the variables, validated through state-of-the-art statistical tests (Henze-Zirkler and Shapiro-Wilk) and the Box-Cox transformation. It enables the generation of virtual GRFs using pseudo-random numbers from the normal distributions. Results demonstrate strong agreement between virtual and experimental data. The virtual time signals reproduce the stochastic behavior, and their frequency content is also captured with deviations below 4.5%, most of them below 2%. This confirms that the method effectively models the inherent stochastic nature of running human gait. Full article

Background: Healthcare systems worldwide face growing challenges in anticipating and managing patient surges, particularly in times of public health crises, natural disasters, or seasonal peaks. The ability of healthcare organisations to forecast and respond to such demand fluctuations—referred to as organisational readiness for patient capacity surge—has become a critical determinant of service continuity and patient outcomes. Despite the urgency, there remains a lack of consolidated evidence on how healthcare authorities measure, evaluate, and operationalise this readiness. This systematic review aims to identify and synthesise existing literature that presents case studies, methodologies, and strategic frameworks used to evaluate organisational preparedness for patient surge capacity. It also explores resource allocation mechanisms, hospital capacity planning algorithms, and temporary facility strategies documented in healthcare settings. Methods: The review was conducted across two major scientific repositories, i.e., PubMed and Web of Science (WoS). A set of four structured search queries were formulated to capture the breadth of the topic, focusing on demand forecasting, hospital capacity planning, workforce models, and resource management within the context of healthcare surge demand. The search was limited to publications from the last 10 years (2014–2024) to ensure the inclusion of contemporary practices and technologies. Resutls: A total of 142 articles were selected for detailed analysis. The articles were categorised into six thematic groups: (i) empirical case studies on healthcare surge management; (ii) hospital resources and capacity scaling; (iii) ethical frameworks guiding surge response; (iv) IT-driven algorithms and forecasting tools; (v) policy evaluations and actionable lessons learned; and (vi) existing systematic reviews in related domains. Notably, several articles provided evidence-based frameworks and simulation models supporting predictive planning, while others highlighted real-world implementation of temporary care facilities and staff redeployment protocols. Conclusions: The review underscores the fragmented yet growing body of literature addressing the multidimensional nature of surge preparedness in healthcare. While algorithmic forecasting and capacity modelling are advancing, gaps remain in standardising metrics for organisational readiness and incorporating ethical considerations in surge planning. Limitations of this review include potential selection bias and the subjective categorisation of articles. Future research should aim to develop integrative frameworks that couple technical, operational, and ethical readiness for patient surge scenarios. Full article

Background: Previous studies have indicated an association between endoplasmic reticulum stress (ERS) and the formation of kidney stones. To further investigate this mechanism, this research sought to identify key genes linked to ERS in calcium oxalate (CaOx) kidney stones. Methods: Key cells with the highest ERS-related gene (ERSRG) scores were identified through single-cell analysis. These key cells were then categorized into high- and low-score groups based on their average ERSRG scores. To identify key genes, we analyzed the intersection of key ERSRGs and differentially expressed genes (DEGs) within key cells, focusing on genes demonstrating significant expression differences between control and CaOx kidney stone samples. A nomogram was constructed using these key genes to predict the risk of CaOx kidney stones. Gene set enrichment analysis (GSEA) was further performed to explore the functions of these key genes in the disease. Additionally, secondary clustering analysis was conducted on key cells to identify subtypes and evaluate the expression of key genes within these subtypes. Finally, the identified key genes were validated using quantitative real-time PCR (qRT-PCR) and Western blot analysis on cultured HK-2 cells, which were exposed with 2 mM CaOx for 24 h at 37 °C with 5% CO₂ or incubated with regular culture medium. Results: Endothelial cells were identified as key cells, and nine key genes were pinpointed in CaOx kidney stones: ACSL4, PTK2, DUSP4, MMP7, PHLDB2, TGM2, PPT1, SPARCL1, and LTF. The nomogram developed from these key genes demonstrated robust predictive ability for CaOx kidney stones risk. Additionally, GSEA revealed that olfactory transduction was enriched by key genes except PTK2. Secondary clustering analysis identified four key cell subtypes within endothelial cells, with LTF, MMP7, and SPARCL1 showing significantly differential expression between control and CaOx kidney stones groups across all key cell subtypes. qRT-PCR and Western blot analyses revealed that, compared to the control group, CaOx-exposed HK-2 cells exhibited significantly increased expression of ACSL4, MMP7, TGM2, PPT1, and LTF (p < 0.05), while showing significantly decreased expression of PTK2, DUSP4, SPARCL1, and PHLDB2 (p < 0.05). Conclusions: This study identified key genes associated with ERS in CaOx kidney stones through single-cell and transcriptomic analysis. The discovery of these genes provides new insights into the treatment of CaOx kidney stones and offers valuable references for subsequent research. Future research should focus on elucidating the precise roles of these candidate genes in CaOx stone pathogenesis to assess their potential for therapeutic intervention. Full article

Diet influences brain health through many connected metabolic and molecular pathways, and these effects are stronger in obesity. This review links diet quality with cognitive decline and dementia risk. Ultra-processed, high-fat, high-sugar diets drive weight gain, insulin resistance, and chronic inflammation. These changes trigger brain oxidative stress, reduce DNA repair, deplete NAD⁺, disturb sirtuin/PARP balance, and alter epigenetic marks. Gut dysbiosis and leaky gut add inflammatory signals, weaken the blood–brain barrier, and disrupt microglia. Mediterranean and MIND diets, rich in plants, fiber, polyphenols, and omega-3 fats, slow cognitive decline and lower dementia risk. Trials show extra benefit when diet improves alongside exercise and vascular risk control. Specific nutrients can help in certain settings. DHA and EPA support brain health in people with low omega-3 status or early disease. B-vitamins slow brain shrinkage in mild cognitive impairment when homocysteine is high. Vitamin D correction is beneficial when levels are low. A practical plan emphasizes healthy eating and good metabolic control. It includes screening for deficiencies and supporting the microbiome with fiber and fermented foods. Mechanism-based add-ons, such as NAD⁺ boosters, deserve testing in lifestyle-focused trials. Together, these measures may reduce diet-related brain risk across the life span. At the same time, artificial intelligence can integrate diet exposures, adiposity, metabolic markers, multi-omics, neuroimaging, and digital phenotyping. This can identify high-risk phenotypes, refine causal links along the diet–obesity–brain axis, and personalize nutrition-plus-lifestyle interventions. It can also highlight safety, equity, and privacy considerations. Translationally, a pattern-first strategy can support early screening and personalized risk reduction by integrating diet quality, adiposity, vascular risk, micronutrient status, and microbiome-responsive behaviors. AI can aid measurement and risk stratification when developed with privacy, equity, and interpretability safeguards, but clinical decisions should remain mechanism-aligned and trial-anchored. Full article

This paper analyzes a finite-capacity Markovian queueing system with two customer types, each assigned to a separate buffer, and a single alternating server whose service priority is dynamically controlled by a queue-length-based threshold policy. The arrivals of both customer types follow independent Poisson processes, and the service times are generally distributed. The server alternates between the two buffers, granting service priority to buffer 1 when its queue length exceeds a specified threshold immediately after service completion; otherwise, buffer 2 receives priority. Once buffer 1 gains priority, it retains it until it becomes empty, with all priority transitions occurring non-preemptively. We develop an embedded Markov chain model to derive the joint queue length distribution at departure epochs and employ supplementary variable techniques to analyze the system performance at arbitrary times. This study provides explicit expressions for key performance measures, including blocking probabilities and average queue lengths, and demonstrates the effectiveness of threshold-based control in balancing service quality between customer classes. Numerical examples illustrate the impact of buffer capacities and threshold settings on system performance and offer practical insights into the design of adaptive scheduling policies in telecommunications, cloud computing, and healthcare systems. Full article

Objectives: Gestational diabetes mellitus (GDM), particularly when combined with overweight or obesity, is associated with adverse neonatal outcomes such as high birth weight and increased adiposity. We determined the effect of a preconception lifestyle intervention initiated before and continued throughout pregnancy on neonatal, birth-related, and body composition outcomes at birth and 6–8 weeks of age in children of participants in the BEFORE THE BEGINNING randomized controlled trial. Methods: People (N = 167) at increased risk of GDM and planning pregnancy were randomly allocated 1:1 to intervention or control. The intervention included time-restricted eating and exercise training. Time-restricted eating involved consuming all energy within ≤10 h/day, ≥5 days per week, and the amount of exercise was set using a heart rate-based physical activity metric (Personal Activity Intelligence, PAI), with the goal of ≥100 weekly PAI points. The main outcome of interest in this report was the proportion of infants with birth weight > 4.0 kg. Results: Among 106 live births, 21% (11/53) of infants in the intervention group and 28% (15/53) in the control group had birth weight > 4 kg (p = 0.367). Mean birth weight did not differ significantly between groups (mean difference −159.3 g, 95% confidence interval −375.7 to 57.2, p = 0.148). No significant between-group differences were found for additional neonatal, birth-related, or early postnatal body composition outcomes. Conclusions: In this secondary analysis, we found no evidence of effects of a preconception lifestyle intervention on the risk of macrosomia or neonatal body composition. Full article

Background: Clinical pharmacy services (CPS) have been shown to confer significant advantages in patient care. It remains to be clarified how CPS resources are allocated across routine care settings. It remains to be clarified which recommendations are made to resolve the drug-related problems (DRP) identified by CPS and which adverse drug reactions (ADR) actually arise from the identified DRP. Methods: Following positive ethical approval, patient chart analyses, evaluation of pharmacy documentation on CPS and pharmacist interviews were performed to characterize CPS at all medical departments of the Bundeswehr Hospital Hamburg. We developed and pre-tested instruments for standardization: A Standard Operating Procedure (SOP) for the practical exercise and documentation of CPS by the pharmacists performing them, a standardized form (checklist) for retrospective data collection as part of this study, and a standardized questionnaire for conducting the pharmacist interviews including a risk assessment according to the NCC-MERP score. Results: In total, 1000 CPS were documented in 504 patients (mean age: 69.95 years; 229 female) on 16,705 treatment days. A total of 66.87% CPS was initiated when pharmacists participated in ward rounds. In all CPS, “Indications” was the topic addressed most frequently (37.70%). “Agents for obstructive respiratory diseases” was the most frequently involved drug class (11.32%). The most frequent processing time per CPS was 16–30 min (48.61%). The number of CPS ranged from 0.36/100 treatment days in dermatology to 12.47 in oncology. Severity of 358 DRP was classified “very severe” (5.03%), “severe” (42.74%), “moderate” (34.36%), “low” (15.08%), “very low” (1.40%), or “without impact” (1.40%). The probability of DRP occurrence was classified as “high” in 13.13% and “very high” in 3.35%. In 15.36% of the DRP, an ADR actually occurred. In 504 patients, 932 specific recommendations were forwarded to solve the DRP identified during CPS. Of those, 53.97% were implemented. Conclusions: In almost all CPS, a considerable number of DRP with serious clinical consequences were identified. Half of the forwarded recommendations were implemented. Full article

Neutralizing antibodies (nAbs) are key indicators of protection against SARS-CoV-2, and their measurement remains essential for monitoring vaccine responses and population immunity. While the plaque reduction neutralization test (PRNT) is the gold standard, it relies on replicative viruses and is not suited for high-throughput applications. Here, both an in-house and a commercial pseudovirus-based neutralization (PBN) assay were standardized and compared with PRNT to assess performance and concordance. The in-house PBN employed a VSV-ΔG pseudovirus encoding NanoLuc and displaying the SARS-CoV-2 Spike from the Wuhan or Omicron BA.1 variants in HEK293T-hACE2 cells, whereas the commercial assay (Integral Molecular, Philadelphia, PA, USA) used a lentiviral backbone with Renilla or GFP reporters and Wuhan or Omicron XBB.1.5/XBB.1.9 Spikes in Vero E6-ACE2-TMPRSS2 cells. Both assays showed strong correlations with PRNT, the commercial assay; moreover, they offered superior reproducibility and scalability, while the in-house version provided a cost-effective alternative suitable for BSL-2 settings. A total of 600 serum samples from vaccinated individuals were analyzed by commercial PBN at collection time points, from pre-vaccination to twelve months post–second dose, enabling large-scale screening, revealing marked differences in neutralization between Wuhan and Omicron XBB.1.5/1.9, and allowing unbiased classification of low, medium, and high responders using k-means clustering. The geometric mean titers (log₁₀ GMT) highlighted a ~1.5 log₁₀ (eightfold) reduction in neutralizing activity against Omicron, reflecting antibody waning and antigenic drift. Altogether, this study integrates assay standardization, PRNT comparison, and large-scale immune profiling, establishing a robust framework for harmonized pseudovirus-based neutralization testing. Full article