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The study investigates the determinants of tax revenue in Zimbabwe using the ARDL approach for the period 1980 to 2022. This study aims to offer a thorough summary of the different factors that influence tax revenue within the framework of economic and social factors. The variables included in the analysis are GDP growth, the share of agriculture in GDP, private consumption expenditure, inflation, foreign direct investment, real interest rates, trade openness, shadow economy and population growth. The results indicate that private consumption expenditure and share of agriculture in GDP negatively and significantly impact tax revenue in the long run. GDP growth, inflation, foreign direct investment and real interest rates exhibit a positive but insignificant impact on tax revenue. Trade openness, shadow economy and population growth are negatively and insignificantly related to tax revenue. The short-run analysis indicates that lagged tax revenue, GDP growth, private consumption expenditure, inflation, and trade openness significantly impact tax revenue, while the share of agriculture in GDP and the shadow economy significantly hinder tax revenue. Real interest rates and population growth have positive but insignificant impacts on tax revenue. The study’s findings provide valuable guidance to policymakers in formulating policies and strategies that enhance tax revenue collection and support the government’s domestic resources mobilisation agenda by uncovering the relationships between tax revenue and its determinants.

Photoelectrochemical (PEC) sensing is an emerging technological innovation for monitoring small substances/molecules in biological or non–biological systems. In particular, there has been a surge of interest in developing PEC devices for determining molecules of clinical significance. This is especially the case for molecules that are markers for serious and deadly medical conditions. The increased interest in PEC sensors to monitor such biomarkers can be attributed to the many apparent advantages of the PEC system, including an enhanced measurable signal, high potential for miniaturization, rapid testing, and low cost, amongst others. The growing number of published research reports on the subject calls for a comprehensive review of the various findings. This article is a review of studies on electrochemical (EC) and PEC sensors for ovarian cancer biomarkers in the last seven years (2016–2022). EC sensors were included because PEC is an improved EC; and a comparison of both systems has, expectedly, been carried out in many studies. Specific attention was given to the different markers of ovarian cancer and the EC/PEC sensing platforms developed for their detection/quantification. Relevant articles were sourced from the following databases: Scopus, PubMed Central, Web of Science, Science Direct, Academic Search Complete, EBSCO, CORE, Directory of open Access Journals (DOAJ), Public Library of Science (PLOS), BioMed Central (BMC), Semantic Scholar, Research Gate, SciELO, Wiley Online Library, Elsevier and SpringerLink.

Clay soil obtained from the Gwagwalada area of Abuja in the Northern part of Nigeria was obtained and used for this study to determine the suitability of water pH on soil strength for waste containment facilities such as landfills and surface impoundments. Water pH influences the strength and durability of soils employed in these facilities and the potential for chemical reactions that can damage containment barriers. An unconfined compressive strength test was conducted using British Standard Light (BSL) and British Standard Heavy (BSH) compaction at varying water pH (i.e., Acidic, Neutral, and Alkaline water) to see the effect it can have on the life span of the clay as a liner material. High-quality liner materials must meet the minimum unconfined compressive strength requirement of 200 kN/m². According to the test outcomes, when the amount of lime in the soil increased, especially at 4% content, the maximum dry density (MDD) increased to 1.81, 1.78, and 1.71 Mg/m³, and the optimum moisture content (OMC) decreased to 15.0,14.0, 16.0% for BSL compaction similar trend to BSH. The MDD is 1.93,1.83, 1.90 Mg/m³, and OMC is 13.2, 14.0, and 12.4% for BSH compaction for acidic, alkaline, and neutral water, respectively. The unconfined compressive strength (UCS) decreases as moulding water increases. The UCS values meet the 200 kN/m² minimum requirement for soils compacted with acidic and neutral pH water for all compaction efforts. When treated with neutral water, the recommendation of 4% lime addition compacted at BSH for liner material construction at -2%, 0, +2% OMC. It is also recommended that alkaline water is unsuitable for constructing liners because it reduces the strength of treated soil. [ABSTRACT FROM AUTHOR]

Roots of crop species produce exudates with biologically active chemicals which are known to affect the growth of crops and weed species. An experiment was conducted at the Uganda National Crop Resources Research Institute, Namulonge during 2016 to identify compounds released in root exudates of potted Cymbopogon nardus, Desmodium uncinatum, upland rice (NERICA 1), Mucuna pruriens and Zea mays (LONGE 6H) at forty-five days after planting. This marked near the average stationary phase for test crop growth when secondary metabolite levels were high. Organic compounds in soils were extracted using solid-phase micro-extraction (SPME) and by solvent extraction. Samples were subjected to analysis using a 7890A Gas Chromatography system. Data files were transferred into a distinct folder and data was uploaded onto the XCMS online platform for pairwise comparison and other related statistical analyses in the National Institute of Science and Technology Library. The blank soil produced 15 terpenoids, two alcohols and one each of trihalomethanes, ethers, phenols, ketones, furans, alkanes and aldehydes. Cymbopogon exuded five terpenoids, one phenol and an alkane. Desmodium plant roots released three terpenoids, one alkane and a phenol. The rice crop produced eight terpenoids, two alkanes and a furan. Five terpenoids, one phenol and an alkane were released by the mucuna crop, while six terpenoids were found in maize soil. The profiled compounds from cymbopogon, desmodium, rice, mucuna and maize could be responsible for allelopathic properties expressed by the study crops in natural and agricultural ecosystems and could be used in synthesis and development of herbicides.

In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most dangerous cancer. Because of the high melanoma death rate, skin cancer is divided into non-melanoma and melanoma. The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions. Sometimes, pathology and biopsy examinations are required for cancer diagnosis. Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images. With recent advancements in hardware and software technologies, deep learning (DL) has developed as a potential technique for feature learning. Therefore, this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification (SCSODTL-SCC) technique. The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images. Primarily, Dull razor approach-related hair removal and median filtering-based noise elimination are performed. Moreover, the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images. Furthermore, the NASNetLarge-based feature extractor with a hybrid deep belief network (DBN) model is used for classification. Finally, the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process, showing the novelty of the work. The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset. The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures. [ABSTRACT FROM AUTHOR]

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance, medicine, science, engineering, and so on. Statistical data mining (SDM) is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data. It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves. Thus, this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning (SDMIRPSMODL) model. In the presented SDMIRP-SMODL model, the feature subset selection process is performed by the SMO algorithm, which in turn minimizes the computation complexity. For rainfall prediction. Convolution neural network with long short-term memory (CNN-LSTM) technique is exploited. At last, this study involves the pelican optimization algorithm (POA) as a hyperparameter optimizer. The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class. The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques. [ABSTRACT FROM AUTHOR]

There is a growing concern for exploring the animal-human interface through surveillance of peri-urban animals. Wildlife at this interface has been reported to pick up and redistribute antimicrobial-resistant bacteria and other infectious diseases. Samples from two stray Owl fledglings found at the National Veterinary Research Institute, Nigeria were used for this study. Corynebacterium amycolatum, Mammalicoccus siuri, and Escherichia coli were isolated and identified from cloacal and oropharyngeal swabs collected from the birds. Almost all (4/5) of the isolates had multidrug resistance. Also, three species of Leptospira (L. enterohaemorrhagica. L grippotyphosa, and L. mini) were identified by the microscopic agglutination technique (MAT). Our findings emphasised the growing One Health concerns for antimicrobial resistance (AMR) spread in the environment and the importance of Wildlife in periurban centres as sentinels for potential zoonotic transmission. [ABSTRACT FROM AUTHOR]