Over the past decades, the amount of waste has dramatically increased worldwide due to rapid population growth and urbanization. Inefficient waste collection and transportation, known as the waste collection vehicle routing problem (WCVRP), negatively impacts economic, environmental, and social dimensions. This issue has drawn considerable attention from local and national governments. There is an urgent need for sustainable practices in waste collection and transportation. This paper conducts an exhaustive literature review on the WCVRP. The review covers various aspects, including waste types, common model characteristics, objective functions, solution methods, datasets and case studies. The analysis indicates a need for further research on underrepresented waste types, such as medical waste (MW). It also stresses the importance of incorporating more model characteristics to better capture the complexities of real-world scenarios. Moreover, there is a lack of multiple objectives optimization models that concurrently address economic, environmental, and social dimensions, in line with sustainable development goals. Additionally, there is insufficient research on hybrid algorithms, especially regarding their application to uncertainty management and advanced techniques. Finally, the use of hybrid testing is restricted, highlighting the need for diverse tests to validate solution methods under various real-world conditions. This study outlines a roadmap for decision-makers in the WCVRP domain, offering opportunities for the evolution of more efficient, adaptable, and sustainable waste collection and transportation systems.Implications: The discussion of WCVRP is an urgent global concern in waste management that requires immediate attention. Through a multi-dimensional evaluation of the research papers, this review paper provides recommendations for future research and practice in WCVRP. Initially, while urban solid waste has received significant attention, other categories remain insufficiently examined. Future research should focus on efficient collection and transportation strategies for these types. Then, although common characteristics are well-explored, this review emphasizes the need for further investigation into lesser-studied characteristics and vehicle types in WCVRP models. Next, current models predominantly prioritize cost and public health exposure risk minimization. There is a necessity for more holistic approaches that incorporate multiple objectives, particularly those crucial for achieving sustainable development goals. Moreover, hybrid algorithms have emerged as efficient solutions, yet advanced technologies coupled with uncertainty management strategies remain underutilized, presenting significant potential to address the evolving complexities of WCVRP. Finally, the study highlights the importance of datasets and case studies in validating WCVRP models. Hybrid tests enable researchers to comprehensively evaluate WCVRP solutions, providing insight into their performance under various conditions. In conclusion, these implications offer a roadmap for advancing WCVRP research and guiding practical strategies to contribute to the development of more efficient, adaptable, and sustainable waste collection and transportation systems.
A comprehensive study was conducted to identify the salinization origins and the major hydrogeochemical processes controlling the salinization and deterioration of the Gaza coastal aquifer system through a combination approaches of statistical and geostatistical techniques, and detailed hydrogeochemical assessments. These analyses were applied on ten physicochemical variables for 219 wells using STATA/SE12 and Surfer softwares. Geostatistical analysis of the groundwater salinity showed that seawater intrusion along the coastline, and saltwater up-coning inland highly influenced the groundwater salinity of the study area. The hierarchical cluster analysis (HCA) technique yielded seven distinct hydrogeochemical signature clusters; (C1&C2: Eocene brackish water invasion, C3 saltwater up-coning, C4 human inputs, C5 seawater intrusion, C6 & C7 rainfall and mixing inputs). Box plot shows a wide variation of most of the ions while Chadha's plot elucidates the predominance of Na-Cl (71.6%) and Ca/Mg-Cl (25%) water types. It is found that, the highest and the lowest levels of salinization and the highest level of nitrate pollution were recorded in the northern area. This result reflects the sensitivity of this area to the human activities and/or natural actions. Around 90.4% of the wells are nitrate polluted. The main source of nitrate pollution is the sewage inputs while the farming inputs are very limited and restricted mostly in the sensitive northern area. Among the hydrogeochemical processes, ion exchange process was the most effective process all over the study area. Carbonate dissolution was common in the study area with the highest level in clusters 6, 7, 4 and 2 in the north while Gypsum dissolution was significant only in cluster 1 in the south and limited in the other clusters. This integrated multi-techniques research should be of benefit for effective utilization and management of the Gaza coastal aquifer system as well as for future work in other similar aquifers systems.
The Coronavirus disease 2019, or COVID-19, has shifted the medical paradigm from face-to-face to telehealth. Telehealth has become a vital resource to contain the virus spread and ensure the continued care of patients. In terms of preventing cardiovascular diseases, automating electrocardiogram (ECG) classification is a promising telehealth intervention. The healthcare service ensures that patient care is appropriate, comfortable, and accessible. Convolutional neural networks (CNNs) have demonstrated promising results in ECG categorization, which require high accuracy and short training time to ensure healthcare quality. This study proposes a one-dimensional-CNN (1D-CNN) arrhythmia classification based on the differential evolution (DE) algorithm to optimize the accuracy of ECG classification and training time. The performance of 1D-CNNs of different activation functions are optimized based on the standard DE algorithm. Finally, based on MIT-BIH and SCDH arrhythmia databases, the performances of optimized and unoptimized 1D-CNN are compared and analysed. Results show that the 1D-CNN optimized by the DE has higher accuracy in heartbeats classification. The optimized 1D-CNN improves from 97.6% to 99.5% on MIT-BIH and from 80.2% to 88.5% on SCDH. Therefore, the optimized 1D-CNN shows improvements of 1.9% and 8.3% in the two datasets, respectively. In addition, compared with the unoptimized 1D-CNN based on the same parameter settings, the optimized 1D-CNN has less training time. Under the conditions of ReLU function and 10 epochs, the training takes 9.22 s on MIT-BIH and 10.35 s on SCDH, reducing training time by 67.2% and 64.2%, respectively.
Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Viruses with small circular ssDNA genomes encoding a replication initiator protein can infect a wide range of eukaryotic organisms ranging from mammals to fungi. The genomes of two such viruses, a cyclovirus (CyCV-SL) and gemycircularvirus (GemyCV-SL) were detected by deep sequencing of the cerebrospinal fluids of Sri Lankan patients with unexplained encephalitis. One and three out of 201 CSF samples (1.5%) from unexplained encephalitis patients tested by PCR were CyCV-SL and GemyCV-SL DNA positive respectively. Nucleotide similarity searches of pre-existing metagenomics datasets revealed closely related genomes in feces from unexplained cases of diarrhea from Nicaragua and Brazil and in untreated sewage from Nepal. Whether the tropism of the cyclovirus and gemycircularvirus reported here include humans or other cellular sources in or on the human body remains to be determined.