Search Results (2,849)

Background and Objectives: A correct diagnosis of beta-hemolytic group A streptococcus (GAS)-pharyngitis allows the prevention of complications and unnecessary use of antibiotics. The aim of this study was to assess the management of pediatric GAS-pharyngitis in Romanian general practitioners (GPs)’ practice. Material and Methods: a cross-sectional study was conducted using a questionnaire distributed to Romanian GPs. Results: In total, 56 GPs completed the questionnaire, mostly females (83.9%, n = 47) from an urban area (60.7%, n = 34). They treated 5–10 (35.7%) or more than 10 (32.1%) cases of GAS monthly and considered white exudate on tonsils (92.9%, n = 52) to be the most suggestive clinical sign. Of the GPs, 25% (n = 14) used the Centor Criteria, 10.7% (n = 6) performed a rapid antigen detection test, and 42.9% (n = 24) requested throat culture for diagnosis. The younger GPs used the Centor Criteria significantly more often (p = 0.027) than the older ones. Most GPs (69.6%, n = 39) preferred targeted antibiotic therapy. Amoxicillin-clavulanate was the most commonly used antibiotic (55.4%, n = 31). Most GPs preferred oral antibiotics (89%, n = 50) for 10 days (55.4%, n = 31). Conclusions: Antibiotic treatment was initiated mostly based on clinical symptoms and in a short-course therapy. GPs stated that they prefer targeted antibiotic therapy, but they did not use proper diagnostic tools. Full article

Variations in damper configuration strategies have a direct impact on the seismic damage patterns of long-span deck-type concrete-filled steel tube (CFST) arch bridges. This study developed an analysis and evaluation framework to identify the damage category, state, and progression sequence of structural components. The framework aims to investigate the influence of viscous dampers on the seismic response and damage patterns of long-span deck-type CFST arch bridges under near-fault pulse-like ground motions. The effects of different viscous damper configuration strategies and design parameters on seismic responses of long-span deck-type CFST arch bridges were systematically investigated, and the preferred configuration and parameter set were identified. The influence of preferred viscous damper configurations on seismic damage patterns of long-span deck-type CFST arch bridges was systematically analyzed through the established analysis and evaluation frameworks. The results indicate that a relatively optimal reduction in bridge response can be achieved when viscous dampers are simultaneously installed at both the abutments and the approach piers. Minimum seismic responses were attained at a damping exponent α = 0.2 and damping coefficient C = 6000 kN/(m/s), demonstrating stability in mitigating vibration effects on arch rings and bearings. In the absence of damper implementation, the lower chord arch foot section is most likely to experience in-plane bending failure. The piers, influenced by the coupling effect between the spandrel construction and the main arch ring, are more susceptible to damage as their height decreases. Additionally, the end bearings are more prone to failure compared to the central-span bearings. Implementation of the preferred damper configuration strategy maintains essentially consistent sequences in seismic-induced damage patterns of the bridge, but the peak ground motion intensity causing damage to the main arch and spandrel structure is significantly increased. This strategy enhances the damage-initiation peak ground acceleration (PGA) for critical sections of the main arch, while concurrently reducing transverse and longitudinal bending moments in pier column sections. The proposed integrated analysis and evaluation framework has been validated for its applicability in capturing the seismic damage patterns of long-span deck-type CFST arch bridges. Full article

This study explores entertainment consumption patterns in Romania, a transitional digital culture characterized by high digital connectivity but underdeveloped physical infrastructure. Employing a dual qualitative coding methodology, this research combines inductive analysis of consumer focus groups with deductive analysis of expert interviews, enabling a multi-layered interpretation of both overt behaviors and latent emotional drivers. Seven key thematic dimensions, motivational depth, perceived barriers, emotional needs, clarity of preferences, future behavioral intentions, social connection, and identity construction, were analyzed and compared using a Likert-based scoring framework, supported by a radar chart and comparison matrix. Findings reveal both convergence and divergence between consumer and expert perspectives. While consumers emphasize immediate experiences and logistical constraints, experts uncover deeper emotional motivators such as validation, mentorship, and identity formation. This behavioral–emotional gap suggests that, although digital entertainment dominates due to accessibility, it often lacks the emotional richness associated with physical formats, which are preferred but less accessible. This study underscores the importance of triangulated qualitative inquiry in revealing not only stated preferences but also unconscious psychological needs. It offers actionable insights for designing emotionally intelligent and culturally responsive entertainment strategies in digitally saturated yet infrastructure-limited environments. Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not direct attacks—to evaluate and compare explainable, interpretable, and opaque machine learning models. Through advanced preprocessing and feature engineering, we examine the trade-off between model performance and transparency in the early detection of suspicious connections. We evaluate explainable ML-based models such as k-nearest neighbours, fuzzy algorithms, decision trees, and random forests, alongside interpretable models like naïve Bayes, support vector machines, and non-interpretable algorithms such as neural networks. Results show that neural networks achieve the highest performance, with a macro F1-score of 0.8786, but explainable models like HFER offer strong performance (macro F1-score = 0.6106) with greater interpretability. The choice of algorithm depends on project-specific needs: neural networks excel in accuracy, while explainable algorithms are preferred for resource efficiency and transparency, as stated in this work. This work underscores the importance of aligning cybersecurity strategies with operational requirements, providing insights into balancing performance with interpretability. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article

For paleoenvironmental reconstruction, paleontologists prefer large, well-preserved fossils. Yet, such specimens are rare, and countless small fragments, though abundant, often go unused. These fragments lack visible internal structure, thus requiring etching, a procedure not permitted on large, intact specimens. Our research introduces a three-step method to identify the nature of these small fragments. With their structures revealed, we can then analyze the chemical composition of identified tissues. The method was tested using samples of vertebrate fossils collected in Malawi. Even with a limited number of samples, multivariate analyses (Principal Component Analyses—PCA) of these chemical data effectively differentiate fossil and recent samples, as well as bone, dentin, and enamel. This approach successfully reveals the behavior of the mineralized tissues of fossil samples. Ultimately, by leveraging microstructural and chemical data, we can study previously unidentified fragments or rare fossils. This allows for the estimation of preservation state and helps to avoid biases in paleoenvironmental reconstructions. Full article

Background/Objectives: International students pursuing higher education in the United States face unique challenges that increase their risk of food insecurity, including limited financial resources, employment restrictions, and cultural barriers. While food insecurity among domestic students has been widely studied, limited research focuses on the lived experiences of international graduate students. This study explores the challenges, perceptions, and coping strategies related to food insecurity among international graduate students at a large public university in North Texas. Methods: This qualitative, ethnographic study involved 20 semi-structured interviews with international graduate students who were clients of the university’s food pantry. Participants were recruited using purposive convenience sampling. Interviews focused on students’ experiences with food access, financial constraints, campus resources, and cultural food preferences. Data were analyzed using thematic coding in MAXQDA. Two standardized food insecurity measures—the USDA and FAO scales—were also administered and analyzed using SPSS. Results: Findings revealed that 85% of participants experienced limited access to nutritious and culturally appropriate foods, with 70% reporting hunger due to financial constraints. Themes included lack of cooking skills, limited campus food options, difficulty accessing familiar groceries, and limited job opportunities. Students expressed that food insecurity significantly impacted their physical health, mental well-being, and social lives, though many continued to prioritize academics over personal nourishment. Conclusions: Food insecurity among international graduate students is multifaceted, shaped by financial, cultural, and institutional barriers. Addressing this issue requires culturally sensitive interventions, improved access to diverse food options, tailored student support services, and institutional efforts to better understand and meet the needs of international students. Full article

Moralization is the process by which preferences become moral values. We investigated a practice that is changing its moral status on college campuses in the United States: affirmative consent to sexual activity. We tested whether messages given to students just before they entered a party impacted their thinking about consent in moral terms—i.e., as a clear issue, with broad consensus, and an imperative to action. At two social clubs on a college campus in 2017, we randomly assigned moralistic vs. informational messages about consent, delivered at the party’s door. At the club that had pre-existing messaging about consent, the moralistic (vs. informational) message increased students’ thinking about consent in moral terms. By contrast, in the club without prior consent messaging, the informational (vs. moralistic) pledge increased students’ thinking about consent in moral terms. We then investigated and found weak evidence for a small reduction in administrative-level student conduct complaints compared to prior and subsequent years as a result of a one-night consent message treatment unique to each of the 12 clubs hosting a party. Theoretically, our findings make progress toward understanding processes of moralization. Pragmatically, they suggest the importance of locally tailored messages that reflect and shape the values of social groups. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

Span-wise homogeneous supersonic cavity flows display complicated structures due to shear layer breakdown, flow acoustic resonance, and even non-linear hydrodynamic-acoustic interactions. In practical applications, such as aircraft bays, the cavity is of finite width and has doors, both of which introduce distinctive phenomena that couple with the shear layer at the cavity lip, further modulating shear layer bifurcations and tonal mechanisms. In particular, asymmetric states manifest as ‘tornado’ vortices with significant practical consequences on the design and operation. Both inward- and outward-facing leading-wedge doors, resulting in leading edge shocks directed into and away from the cavity, are examined at select opening angles ranging from $22.5°$ to $90°$ (fully open) at Mach 1.6. The computational approach utilizes the Reynolds-Averaged Navier–Stokes equations with a one-equation model and is augmented by experimental observations of cavity floor pressure and surface oil-flow patterns. For the no-doors configuration, the asymmetric results are consistent with a long-time series DDES simulation, previously validated with two experimental databases. When fully open, outer wedge doors (OWD) yield an asymmetric flow, while inner wedge doors (IWD) display only mildly asymmetric behavior. At lower door angles (partially closed cavity), both types of doors display a successive bifurcation of the shear layer, ultimately resulting in a symmetric flow. IWD tend to promote symmetry for all angles observed, with the shear layer experiencing a pitchfork bifurcation at the ‘critical angle’ ($67.5°$). This is also true for the OWD at the ‘critical angle’ ($45°$), though an entirely different symmetric flow field is established. The first observation of pitchfork bifurcations (‘critical angle’) for the IWD is at $67.5°$ and for the OWD, $45°$, complementing experimental observations. The back wall signature of the bifurcated shear layer (impingement preference) was found to be indicative of the 3D cavity dynamics and may be used to establish a correspondence between 3D cavity dynamics and the shear layer. Below the critical angle, the symmetric flow field is comprised of counter-rotating vortex pairs at the front and back wall corners. The existence of a critical angle and the process of door opening versus closing indicate the possibility of hysteresis, a preliminary discussion of which is presented. Full article

Agricultural workers—individuals employed for labor in agriculture—are at high risk of various negative health outcomes, with many impacted by both the existence of health disparities and stress. While the issue of farm stress and associated psychosocial health outcomes has been studied in the general agricultural population, research investigating these issues specifically within the agricultural worker population is sparse. This study presents data from the United States Western Region Agricultural Worker Stress Survey (N = 354), which gauged workers’ perceived stress levels, sources of stress, desired stress management topics, and preferred methods of receiving information and support services. Long working hours, working in extreme temperatures, and a lack of time emerged as the top three stressors. On average, workers across the Western region of the U.S. are experiencing a moderate level of stress, with younger workers reporting greater stressor pileup than their older counterparts. Retirement planning was cited as the most preferred stress management topic, regardless of demographic. Lastly, workers chose in-person counseling as the support modality that they would most likely utilize. This research provides a variety of stress management recommendations such as working with farm owners to increase the safety of their operation, investing in face-to-face counseling services, and utilizing community health workers as sources of support. Full article

Studying consumers’ willingness to pay (WTP) for sustainable certification labels and preferences in consumption is a relevant issue for policymakers. Several studies have revealed a positive WTP a premium price for many certified products. The aim of this paper is to assess an overview of Italian consumers’ WTP for eight different sustainable certification labels and to collect information about their consumption preferences and perceptions in consumption. Participants were selected by stratified simple random sampling, using regional distribution, gender, and age as stratification criteria, to obtain a representative sample of n = 3600. Eight ordered logit models were estimated to understand how consumer sociodemographic characteristics influence the price premium. The results show important differences in WTP among different certification labels; a higher WTP emerged for ethical certifications than for environmentally focused labels. Younger individuals; women; and those with higher education, income and life satisfaction, as well as consumers in southern regions, were significantly more willing to pay premiums for certified products. However, a key finding for policymakers is that the stated price premium consumers are willing to pay falls significantly short of the actual higher costs of these products in supermarkets. Furthermore, insights into consumer perceptions and preferences revealed that quality and origin are perceived as key price drivers, while method of production holds less importance. It also emerged that consumers primarily seek a balance between quality and price, with only a small segment prioritizing certified products. Full article

Background: Endoscopic therapies for Crohn’s disease (CD) strictures, including endoscopic balloon dilation (EBD) and endoscopic stricturotomy (ESt), are less invasive interventions compared to surgery. ESt is advantageous for strictures that are longer, more fibrotic, or adjacent to anatomic structures requiring precision, and it has shown a high rate of surgery-free survival. Methods: We designed a microsimulation state-transition model comparing ESt to surgical resection for CD strictures. We calculated quality-adjusted life years (QALYs) over a 10-year time horizon; secondary outcomes included costs (in 2022 USD) and incremental cost-effectiveness ratios (ICERs). We used a societal perspective to compare our strategies at a willingness-to-pay (WTP) threshold of 100,000 USD/QALY. Sensitivity analyses, both deterministic and probabilistic, were performed. Results: The surgery strategy cost more than 2.5 times the ESt strategy, but resulted in nine more QALYs per 100 persons. The ICER for the surgery strategy was 308,787 USD/QALY; thus, the ESt strategy was determined more cost-effective. One-way sensitivity analyses showed that quality of life after ESt as compared to that after surgery, the likelihood of repeat intervention, and surgical mortality and cost were the most influential parameters shifting cost-effectiveness. Probabilistic sensitivity analyses favored ESt in most (65.5%) iterations. Conclusions: Our study finds endoscopic stricturotomy to be a cost-effective strategy to manage primary or anastomotic Crohn’s disease strictures. Post-intervention quality of life and probabilities of requiring repeated interventions exert most influence on cost-effectiveness. The decision between ESt and surgery should be made considering patient and stricture characteristics, preferences, and cost-effectiveness. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article