Efficient IoT-Assisted Waste Collection for Urban Smart Cities.

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year.

Enhancing trash classification in smart cities using federated deep learning.

Efficient Waste management plays a crucial role to ensure clean and green environment in the smart cities. This study investigates the critical role of efficient trash classification in achieving sustainable solid waste management within smart city environments. We conduct a comparative analysis of various trash classification methods utilizing deep learning models built on convolutional neural networks (CNNs). Leveraging the PyTorch open-source framework and the TrashBox dataset, we perform experiments involving ten unique deep neural network models. Our approach aims to maximize training accuracy. Through extensive experimentation, we observe the consistent superiority of the ResNext-101 model compared to others, achieving exceptional training, validation, and test accuracies. These findings illuminate the potential of CNN-based techniques in significantly advancing trash classification for optimized solid waste management within smart city initiatives. Lastly, this study presents a distributed framework based on federated learning that can be used to optimize the performance of a combination of CNN models for trash detection.

The Effect of Physical Activity on Sleep Quality and Sleep Disorder: A Systematic Review.

Regular physical activity has several health benefits, including improved sleep quality and symptoms of sleep disorders. With the known benefits of moderate-intensity activities to sleep quality and a growing interest in using physical activity as a treatment approach for different sleep disorders, we conducted a systematic review to provide evidence-based data on the association between physical activity and sleep. A systematic search was carried out in PubMed, Embase, MEDLINE (Medical Literature Analysis and Retrieval System Online), Google Scholar, and Scopus, using predetermined search terms (Medical Subject Headings (MeSH) terms) and keywords. The included studies focused on exploring the effect of physical activity on sleep quality and sleep disorders or the association between physical activity and sleep outcomes. Relevant data were extracted, and the quality of the studies was evaluated using suitable methods. The collected findings were synthesized and discussed. The findings of this systematic review have potential implications for healthcare, public health policies, and health promotion.

Risk factors associated with postpartum depression in the Saudi population.

INTRODUCTION: Postpartum depression (PPD) is one of the major psychological disorders worldwide that affects both mother and child. The aim of this study was to correlate the risk of PPD with obstetric and demographic variables in Saudi females. MATERIALS AND METHODS: Data were collected by interviewing females 8-12 weeks postpartum. PPD symptoms were defined as present when subjects had an Edinburgh Postnatal Depression Scale score of 10 or higher. Variables included in this study were age, education, occupation, parity, baby's sex, pregnancy period, delivery type, hemoglobin level, anemia, and iron pills taken during pregnancy. RESULTS: Of the 352 postpartum females, the prevalence of PPD symptom risk was 117 (33.2%). Among the PPD symptomatic females, 66 (39.8%) had low hemoglobin levels, and 45 (40.5%) females were anemic during pregnancy (P≤0.05). These results suggest that early postpartum anemia, indicated by low hemoglobin level, is a significant risk factor for PPD (adjusted odds ratio 1.70, 95% confidence interval 1.05-2.74; P=0.03). Other variables, including age, parity, education, occupation, and delivery type, were not significantly correlated (P=0.15-0.95), but marginally indicative of the risk of depressive symptoms. CONCLUSION: Low hemoglobin level and anemia during pregnancy were risk factors for PPD in Saudi females. Many other factors may be considered risk factors, such as age, occupation, and parity. Anemic women need more attention and to be checked regarding their PPD, and treated if necessary.