In this study, the effects of six different working fluids, hexane, petroleum ether, chloroform, acetone, methanol and ethanol on the energy and exergy performance are investigated in evacuated tube ...solar collectors with thermosyphon heat pipe under three different air velocities as 2, 3 and 4 ms−1. The six evacuated tube solar collectors with thermosyphon heat pipe with the same dimensions and properties are designated for the air heating and tested under the outdoor climatic conditions of Uşak, Turkey. The lowest energy and exergy efficiencies occur in the THPETC-Hexane under 2, 3 and 4 ms−1, the highest energy efficiency occurs in the THPETC-Acetone for air velocity of 2 and 3 ms−1 and in the THPETC-Chloroform for air velocity of 4 ms−1. The highest exergy efficiency occurs in the THPETC-Acetone for air velocity of 2 ms−1 and in the THPETC-Chloroform for air velocity of 3 and 4 ms−1.
•THPETC with different working fluids are investigated under different air velocities.•Hexane, petroleum ether, chloroform, acetone, methanol and ethanol are used.•Hexane has the lowest energy and exergy efficiency.•Acetone and chloroform show the best results for energy and exergy efficiency.
In-memory computing architectures present a promising solution to address the memoryand the power-wall challenges by mitigating the bottleneck between processing units and storage. Such architectures ...incorporate computing functionalities inside memory arrays to make better use of the large internal memory bandwidth, thereby, avoiding frequent data movements. In-DRAM computing architectures offer high throughput and energy improvements in accelerating modern data-intensive applications like machine learning etc. In this manuscript, we propose a vector addition methodology inside DRAM arrays through functional read enabled on local word-lines. The proposed primitive performs majority-based addition operations by storing data in transposed manner. Majority functions are achieved in DRAM cells by activating odd number of rows simultaneously. The proposed majority based bit-serial addition enables huge parallelism and high throughput. We validate the robustness of the proposed in-DRAM computing methodology under process variations to ascertain its reliability. Energy evaluation of the proposed scheme shows 21.7X improvement compared to normal data read operations in standard DDR3-1333 interface. Moreover, compared to state-of-the-art in-DRAM compute proposals, the proposed scheme provides one of the fastest addition mechanisms with low area overhead (<; 1% of DRAM chip area). Our system evaluation running the k-Nearest Neighbor (kNN) algorithm on the MNIST handwritten digit classification dataset shows 11.5X performance improvement compared to a conventional von-Neumann machine.
Health care activities can generate different kinds of hazardous wastes. Mismanagement of these wastes can result in environmental and occupational health risks. Developing countries are ...resource-constrained when it comes to safe management of hospital wastes. This study summarizes the main issues faced in hospital waste management in developing countries. A review of the existing literature suggests that regulations and legislations focusing on hospital waste management are recent accomplishments in many of these countries. Implementation of these rules varies from one hospital to another. Moreover, wide variations exist in waste generation rates within as well as across these countries. This is mainly attributable to a lack of an agreement on the definitions and the methodology among the researchers to measure such wastes. Furthermore, hospitals in these countries suffer from poor waste segregation, collection, storage, transportation and disposal practices, which can lead to occupational and environmental risks. Knowledge and awareness regarding proper waste management remain low in the absence of training for hospital staff. Moreover, hospital sanitary workers, and scavengers, operate without the provision of safety equipment or immunization. Unsegregated waste is illegally recycled, leading to further safety risks. Overall, hospital waste management in developing countries faces several challenges. Sustainable waste management practices can go a long way in reducing the harmful effects of hospital wastes.
Uncertainty and isolation have been linked to mental health problems. Uncertainty surrounding the COVID-19 pandemic has the potential to trigger mental health problems, which include anxiety, stress, ...and depression. This paper evaluates the prevalence, psychological responses, and associated correlates of depression, anxiety, and stress in a global population during the Coronavirus Disease (COVID-19) pandemic. A cross-sectional study design was adopted. 678 completed forms were collected during the COVID-19 quarantine/lockdown. An online questionnaire was designed and DASS-21 was used as the screening tool. A non-probability sampling technique strategy was applied. 50.9% of participants showed traits of anxiety, 57.4% showed signs of stress, and 58.6% exhibited depression. Stress, anxiety, and depression are overwhelmingly prevalent across the globe during this COVID-19 pandemic, and multiple factors can influence the rates of these mental health conditions. Our factorial analysis showed notable associations and manifestations of stress, anxiety, and depressive symptoms. People aged 18–24, females, and people in non-marital relationships experienced stress, anxiety, and depression. Separated individuals experienced stress and anxiety. Married people experienced anxiety. Single and divorced people experienced depression. Unemployed individuals experienced stress and depression. Students experienced anxiety and depression. Canada, the UK, and Pakistan are all countries that are experiencing stress and depression as a whole. An extended number of days in quarantine was associated with increased stress, anxiety, and depression. Family presence yielded lower levels of stress, anxiety, and depression. Lastly, lack of exercise was associated with increased stress, anxiety, and depression.
In this study, the analysis of the new finance chaotic model was expanded to Atangana–Baleanu–Caputo fractional derivative. The existence solution was investigated using the fixed model theorem of ...the new model. Then, the uniqueness solution of model was examined by using the Sumudu transformation.
Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use ...of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.
Semi-supervised machine learning can be used for obtaining subsets of unlabeled or partially labeled dataset based on the applicable metrics of dissimilarity. At later stage, the data is completely ...assigned the labels as per the observed differentiation. This paper provides a clustering based approach to distinguish the data representing flows of network traffic which include both normal and Distributed Denial of Service (DDoS) traffic. The features are taken for victim-end identification of attacks and the work is demonstrated with three features which can be monitored at the target machine. The clustering methods include agglomerative and K-means with feature extraction under Principal Component Analysis (PCA). A voting method is also proposed to label the data and obtain classes to distinguish attacks from normal traffic. After labeling, supervised machine learning algorithms of k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and Random Forest (RF) are applied to obtain the trained models for future classification. The kNN, SVM and RF models in experimental results provide 95%, 92% and 96.66% accuracy scores respectively under optimized parameter tuning within given sets of values. In the end, the scheme is also validated using a subset of benchmark dataset with new vectors of attack.
BackgroundDepressive disorders are among the common mental health conditions in the general outpatient setting and affect patients’ load and treatment outcomes. People who suffer from depression ...frequently consult general practitioners and prefer to attribute their symptoms to physical illness rather than mental illness. Little is known about the magnitude and associated factors of depression among patients attending general outpatient services in Somalia. The study aimed at determining the prevalence and associated factors of depression among them.MethodsThis is an institution-based cross-sectional study among randomly selected 422 patients who attended general outpatient services of two hospitals in Mogadishu. We applied three standardized instruments, such as the Somali version of the Patient Health Questionnaire (PHQ-9), the Oslo Social Support Scale (OSSS-3), and the Perceived Stress Scale-10 (PSS-10). We analyzed data using the statistical software SPSS version 29. We calculated prevalence and its 95% Confidence Interval (CI) and identified associated factors by bivariate and Multivariate analysis. We considered the association significant when p value is < 0.05.ResultsThe prevalence of depression symptoms was found to be 55% (95% CI 50–60%). The result also showed that 55.0% were females, 50.7% were aged between 26 and 44 years, 44.3% were single, 29.9% achieved tertiary education, and 44.3% were unemployed. Multivariate analysis established that age of between 26 and 44 years (aOR = 2.86, 95%CI:1.30–6.29, p = 0.009), being separated/divorced (aOR = 2.37, 95%CI: 1.16–4.82, p = 0.018), income level of ≤$100 (aOR = 3.71, 95% CI:1.36–10.09, p = 0.010), and high stress levels (aOR = 20.06, 95%CI:7.33–54.94, p < 0.001) were independent factors that significantly associated with depressive symptoms.ConclusionThis study found high levels of depression among patients attending outpatient clinics, with age, marital status, education level, income level, family history of psychiatry disorder, and stress level being key predictors. Regular screening among patients in outpatient clinics and proper referral are crucial in ensuring that those at high risk of depression are managed effectively.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A fractional order mathematical model of the Caputo-Fabrizio type is presented for an alcoholism model. The existence and the uniqueness of the alcoholism model were investigated by using a ...fixed-point theorem. Numerical solutions for the model were obtained by using special parameter values.
This study investigated the influence of digital payment systems on banks’ stability by exploring their effect on the Z-score of the Jordanian banking sector during the period from 2004 until 2022. ...It specifically focused on liquidity risks generated from e-payment transactions and how sufficient capital adequacy ratios enhance banking sector stability over both short-term and long-term periods by standing against sudden volatilities yielded from large amounts of transactions executed through digital payment systems. To achieve this objective, the study utilizes time series dual regression analyses of vector autoregression and vector error correction models on E-views 12 to cover the time variation influences of digital payment on the banking sector Z-score. The regression results indicate varied effects between the benefits and risks of digital payment systems on a bank’s Z-score that influence the immediate sector’s stability, indicating that while digital payment systems can initially hold liquidity risks, leading to short-term instability; the strategic implementation of robust capital adequacy ratio stands as a protective buffer by fostering long-term banking sector resilience. The results also suggest future predictions and insights for financial sector legislators and regulators emphasizing the need for monitoring strategies that stimulate continuous innovations in the digital payment infrastructure while constantly ensuring the stability and resilience of the banking sector. Thus, prudent liquidity management and the reinforcement of capital buffers are encouraged to pilot the dual challenges and opportunities that appeared at the stages of the digital payment process, ultimately guiding the sector toward continuous growth and sustainability. AcknowledgmentThe author is grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research.
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