Search Results (52)

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

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application. Full article

Background: Previous studies have demonstrated that involuntary smoking (e.g., secondhand smoke [SHS] and thirdhand smoke [THS]) is not only associated with an increased risk of several physical health problems, such as cardiovascular disease and cancer, but also impacts mental health, including depression and anxiety. However, the relationships between SHS and THS exposure and non-suicidal self-injury (NSSI), suicidal ideation (SI), and suicide attempts (SAs) remain unclear. Methods: Participants were recruited at a Chinese vocational college via voluntary online surveys conducted on campus. Self-reported SHS exposure was determined by the frequency of contact with smokers or detecting tobacco odors in living environments, while THS was assessed through regular contact with smoker-contaminated surfaces (e.g., clothing, furniture, textiles). Logistic regression analysis was performed to evaluate the associations of SHS and THS exposure with the prevalence of NSSI, SI, and SAs in never-smoking participants. Results: The study included 5716 participants (mean age = 19.3 years; females, 85.4%). The prevalence of SHS and THS exposure was 87.6% and 77.4%, with 8.8% reporting ≥15 min of SHS exposure on at least one day per week. After controlling for potential covariates, exposure to SHS (≥15 min on at least one day per week) was significantly associated with the odds of SAs (OR [95%CI] = 1.85 [1.17–2.91]). Additionally, daily THS exposure was significantly associated with increased past-year NSSI prevalence (2.35 [1.29–4.28]) compared to those without THS exposure, with similar associations observed for SI (2.11 [1.28–3.48]) and SAs (2.40 [1.23–4.69]). Conclusions: Exposure to SHS and THS was significantly associated with increased likelihood of NSSI, SI, and SAs among young adults at a Chinese vocational college. Further studies are needed to validate these associations across more diverse populations. Full article

Bikeshare has emerged as a sustainable mobility solution not only for addressing the first- and last-kilometer problem but facilitating short- and medium-distance travel. While existing research predominantly focuses on city-level Bikeshare Programs (BSPs), there is a paucity of studies examining university campus BSPs, particularly in terms of quantitative analysis of trip frequency and system operation sustainability. This paper presents a systematical framework to investigate university campus BSPs from two complementary perspectives: users’ travel characteristics and operational sustainability. To achieve this, two successive self-reported questionnaire surveys were conducted on the campus of South China University of Technology in 2017 and 2020, respectively. Subsequently, a multinomial logistic regression model was developed to identify the key factors influencing users’ travel frequency. Finally, a cost–benefit analysis was developed to assess the operational sustainability of the system. The findings reveal two significant insights: (1) the system was profitable under the 2017 fare policy, with the potential to maximize profits by strategically increasing fares while enhancing service quality; and (2) in 2020, when the fare is adjusted closer to the predicted optimal value, there is an increase in the proportion of high-frequency users, accompanied by improved user experience, reduced difficulty in bike access/return, and slightly lower pricing satisfaction. This study provides a valuable method that can be extended to the restricted service communities for effective planning and evaluation of bikeshare systems. Full article

In recent years, electric vehicles have become increasingly widespread, both in the logistics sector and in personal use. This increase, together with factors such as environmental concerns and government incentives, has brought energy consumption and range estimation issues to the forefront. In this study, the energy consumption of an electric cargo vehicle under different speed and load conditions is examined with an experimental and data-driven approach, and then used for range estimation. The raw data collected from the vehicle on the selected ~2 km route in Eskisehir Osmangazi University campus are combined into per-second samples with time synchronization and data cleaning. The route is divided into average of 150 m segments, and variables such as slope, energy consumption, and acceleration are calculated for each segment. Then, the data are used to train various machine learning models, such as Extra Trees, CatBoost, LightGBM, Voting Regressor, and XGBoost, and their performances regarding energy consumption-based range estimation are compared. The findings show that driving dynamics such as high speed and sudden acceleration, as well as road slope and load conditions, significantly shape the energy consumption and thus the remaining range. In particular, Extra Trees outperforms other machine learning models in terms of metrics such as R², RMSE and, MAE, with a reasonable computational time. The results provide applicable guidance in areas such as route optimization, smart battery management, and charging infrastructure to reduce range anxiety and increase the operational efficiency of electric vehicles. Full article

The traffic behavior characteristics within university campuses have received limited scholarly attention, despite their distinct differences from external road networks. These differences include the predominance of non-motorized vehicles and pedestrians in traffic flow composition, as well as traffic peaks primarily coinciding with class transition periods. To investigate the riding behavior of cyclists on university campuses, this study examines cyclist attention, proposes a novel method for constructing a rider attention recognition framework, utilizes a hierarchical ordered logistic model to analyze the factors influencing attention, and evaluates the model’s performance. The findings reveal that traffic density and riding style significantly influence cyclists’ eye-tracking characteristics, which serve as indicators of their attention levels. The covariates of lane gaze time and the coefficient of variation in pupil diameter exhibited significant effects, indicating that a hierarchical ordered logistic model incorporating these covariates can more effectively capture the impact of influencing factors on cyclist attention. Moreover, the hierarchical ordered logistic model achieved a 7.22% improvement in predictive performance compared to the standard ordered logistic model. Additionally, cyclists exhibiting a “conservative” riding style were found to be more attentive than those adopting a “aggressive” riding style. Similarly, cyclists navigating “sparse” traffic conditions were more likely to maintain attention compared to those in “dense” traffic scenarios. These findings provide valuable insights into the riding behavior of university campus cyclists and have significant implications for improving traffic safety within such environments. Full article

Background: Higher education institutions and public health agencies in the United States (US) have recognized that food insecurity is pervasive and interferes with student learning on multiple levels. However, less research has examined food insecurity among culturally diverse college students. A cross-sectional online survey was conducted to estimate the prevalence and predictors of food insecurity for US-born White, US-born Multicultural, and International students aged 18–34 at a Midwest university. The secondary aims were to describe dietary and meal characteristics, and the use of food assistance programs, including the on-campus food pantry. Methods: In April 2022, 853 students completed the 10-item US Adult Food Security Module, and demographic, dietary fat intake, food attitude, food access barriers, and nutrition assistance program usage questions using a socio ecological model (SEM) framework. Results: Food security prevalence was 73.3% (54.7% high, 18.5% marginal) and food insecurity prevalence was 26.7% (14.4% low, 12.3% very low). Significantly more International (26.8%) and Multicultural (35.6%) students were classified as food-insecure compared to White students (19.9%; p < 0.001). Binomial and multinomial logistic regression models indicated that predictors of food insecurity were intrapersonal factors of race/ethnicity, poor self-reported health, being an undergraduate, and the community barriers of high food costs and limited transportation. Conclusions: Dietary characteristics differed more by nativity–ethnicity groups than they did by food security levels. Food cost emerged as a strong influence on food choice for the food-insecure students. International students utilized more nutrition assistance programs, including the on-campus food pantry, than other groups. Full article

Background: Myocardial infarction (MI) is a common emergency with high rates of morbidity and mortality. Current risk stratification scores for non-ST-elevation MI (NSTEMI) use subjective or delayed information. Heart rate variability was shown to correlate with prognosis following MI. This study aimed to evaluate ultra-short heart rate variability (usHRV) as a prognostic factor in NSTEMI patients. Methods: A retrospective analysis was performed on 183 NSTEMI patients admitted to Rambam Health Care Campus in 2014. usHRV measures, including the standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD), were calculated. Logistic regression assessed whether clinical, laboratory, or usHRV parameters predicted severe in-hospital complications like heart failure (HF), atrial flutter/fibrillation (AFL/AF), ventricular tachycardia/fibrillation (VT/VF), and atrioventricular block (AVB). Both Cox and logistic regression were used for survival analysis. Results: Of 183 patients (71.6% male, mean age 67.1), 35 (19%) died within 2 years. In-hospital complications included 39 cases (21.3%) of HF, 3 cases (1.6%) of VT/VF, and 9 cases (4.9%) of AVB. Lower usHRV was significantly associated with higher mortality at 2 years and showed marginal significance at 90 days and 1 year. Increased usHRV was linked to a higher risk of in-hospital ventricular arrhythmia (VT/VF). Conclusions: Overall, this study is in agreement with previous research, showing a correlation between low usHRV and a higher mortality risk. However, the association between usHRV and the risk of VT/VF demands further investigation. More expansive prospective studies are needed to strengthen the observed associations. Full article

Food insecurity (FI) is associated with lower academic performance in university students. This research aimed to describe the relationship between a campus food pantry and academic performance, describe the characteristics of student pantry shoppers (PSs), and develop a model to predict academic success. Researchers obtained student pantry swipes and university data (2021–2022 academic year) to generate a dataset for grade point average (GPA) (N = 23,896) and a subset of PS sociodemographic data (N = 852). Variables (cumulative or term GPA) differed based on models. Explanatory variables were biological sex, age, frequency of pantry shopping, classification, Pell Grant eligibility, college, athlete status, citizenship, residency, ethnicity/race, honors, and first-generation status. The analysis included the two-sample t-test, logistic and multiple regression, and the least absolute shrinkage and selection operator (LASSO). There was no difference (t(921.8) = 0.518, p = 0.60) in the cumulative GPA between PSs (M = 3.001 [0.808]) and non-pantry shoppers (NPSs) (M = 3.016 [0.874]). In the fall term, PSs (M = 3.018 [1.012] earned a higher GPA (t(581.69) = −2.235, p = 0.03) than NPSs (M = 2.919 [1.123]). Pantry shoppers achieved academic success despite exhibiting risk factors for FI, including first-generation status, being of the female sex, and financial need. Targeted multicomponent campus programs are needed to provide food assistance to students at risk for FI. Full article

Background: Previous studies showed that the fourth SARS-CoV-2 vaccine dose has a protective effect against infection, as well as against severe disease and death. This study aimed to examine whether knowledge of a high-level antibody after the third dose may reduce compliance to the fourth booster dose among healthcare workers (HCWs). Methods: We conducted a prospective cohort study among HCWs vaccinated with the first three doses at Rambam Healthcare Campus, a tertiary hospital in northern Israel. Participants underwent a serological test before the fourth booster vaccine was offered to all of them, with results provided to participants. The population was divided into two groups, namely those with antibodies below 955 AU/mL and those with 955 AU/mL and higher, a cutoff found protective in a previous study. Multiple logistic regression was carried out to compare the compliance to the fourth booster between the two groups, adjusted for demographic and clinical variables. Results: After adjusting for the confounding variables, the compliance was higher in those with antibody levels below 955 AU/mL (OR = 1.41, p = 0.05, 95% CI 1.10–1.96). In addition, male sex and age of 60 years and above were also associated with higher vaccination rates (OR = 2.28, p < 0.001, 95% CI 1.64–3.17), (OR = 1.14, p = 0.043, 95% CI 1.06–1.75), respectively. Conclusions: Knowledge of the antibody status may affect compliance with the booster dose. Considering waning immunity over time, reduced compliance may affect the protection of HCWs who declined the fourth dose. Full article

This paper introduces a drone-based surrogate project aimed at serving as a preliminary educational platform for undergraduate students in the Electrical and Computer Engineering (ECE) fields. Utilizing small Unmanned Aerial Vehicles (sUAVs), this project serves as a surrogate for the inspection of wind turbines using scaled-down pedestal fans to replace actual turbines. This approach significantly reduces the costs, risks, and logistical complexities, enabling feasible and safe on-campus experiments. Through this project, students engage in hands-on applications of Python programming, computer vision, and machine learning algorithms to detect and classify simulated defects in pedestal fan blade (PFB) images. The primary educational objectives are to equip students with foundational skills in autonomous systems and data analysis, critical for their progression to larger scale projects involving professional drones and actual wind turbines in wind farm settings. This surrogate setup not only provides practical experience in a controlled learning environment, but also prepares students for real-world challenges in renewable energy technologies, emphasizing the transition from theoretical knowledge to practical skills. Full article

In recent years, many students have faced economic hardship and experienced food insecurity, even as universities strive to create more equitable pathways to college. There is a need for a more holistic perspective that addresses the complexity of food insecurity amongst college students. To this end, we examined the relationship between the social determinants of health, including college food insecurity (CoFI) and childhood food insecurity (ChFI), and their relationship with well-being measures. The study sample was a convenience sample that included 372 students at a public university who responded to an online survey in fall 2021. Students were asked to report their food security status in the previous 30 days. We used the following analytical strategies: chi-square tests to determine differences between food secure (FS) and food insecure (FI) students; binary logistic regression of CoFI on student demographics and ChFI; and ordinal or binary logistic regression for well-being measures. Black students, off-campus students, first-generation students, in-state students, and humanities/behavioral/social/health sciences majors were more likely to report CoFI. FI students were more likely to have experienced ChFI and to have lower scores on all well-being measures. ChFI was associated with four well-being measures and its effects were mediated by CoFI. College student health initiatives would benefit from accounting for SDOH, including ChFI experiences and its subsequent cumulative disadvantages experienced during college. Full article

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout ($T_{initial}$) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model $T_{initial}$ and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller–switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time. Full article

Food insecurity (FI) is a pressing concern among university students in the United States, and the COVID-19 pandemic has exacerbated this issue. Providing food assistance for university students has become more challenging due to pandemic-related consequences and interventions. This study aims to (1) analyze social inequalities in FI among university students in a large public university during the pandemic, (2) investigate the association of their utilization of campus, community, and federal food assistance programs (FAPs) and FI, and (3) understand the barriers students face in accessing FAPs. Survey questionnaires were distributed to students to gather their socio-demographics, FI, and usage of FAPs. Logistic regression was utilized to assess the relationship between students’ FI and their use of FAPs. Among the surveyed students (n = 282), 33.7% reported experiencing FI. Higher FI rates were observed among socially vulnerable student groups, for example, non-Hispanic Black (62.5%) and Hispanic students (38.7%), compared with non-Hispanic White students (32.1%). FAPs had a limited influence on students’ FI due to low utilization. The primary barriers to FAPs were insufficient information, ineligibility, and social stigma. The findings suggest it is crucial to reduce barriers to using FAPs and develop targeted interventions for marginalized students to address inequalities in FI. Full article