Retail location preferences: a comparative analysis

There is widespread criticism that gambling retailers are concentrated in deprived communities and that the concentration is deliberately targeted. However, this study opines that before deliberate targeting can be supported, a comparative analysis of gambling locations with a more conventional retail group is necessary. Hence this study examined the location preferences of gambling and food and grocery retailers (FGRs) to evaluate the notion of deliberate concentration of gambling retailers in deprived communities. Comparative analyses assessed relationships between FGRs floorspaces and overall gambling locations compared to socio-economic deprivation. Results showed similarities and disparities in retail locations, but gambling provisioning were more concentrated in deprived areas compared to food provisioning. Implications for policy and practice are presented

Retail concentration - deliberate or not?

This study investigates the notion of deliberate concentration and targeting of deprived neighbourhoods by alleged antisocial retailers (AASRs) in England at neighbourhood geography. To measure deprivation, this paper adopts the principles of geodemographics to build an area classification using variables extracted from the 2011 UK Census Data. It thereafter compares the location of both AASRs and food and grocery retailers (FGRs) across the different classification to understand the similarities and differences in their location preferences in order to investigate the notion of targeting ascribed to these retailers. Results reveal complex patterns and emphasise the need for micro-scale analysis

Securing mobile edge computing using hybrid deep learning method

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques

Identification of genomic regions associated with differences in fleece type in Huacaya and Suri alpacas (Vicugna pacos).

The difference in fleece type is the distinguishing trait between the two types of alpacas (Vicugna pacos), Huacaya and Suri. The Suri fleece type has been found to be inherited dominantly over the Huacaya type, resulting in offspring with the Suri phenotype. The aim of our study was to map genomic regions associated with the two different fleece types. In this study, 91 alpacas (54 Huacayas and 37 Suris) from Germany and Switzerland were genotyped using the 76k alpaca SNP array. Only 59k chromosome-localised markers map to the alpaca reference assembly VicPac3.1, and after quality control 49 866 SNPs, were retained for population structure assessment and to conduct a genome-wide association study. Both principal component and neighbour-joining tree analysis showed that the two fleece-type cohorts overlapped rather than forming two distinct clusters. Genome-wide significantly associated markers were observed in the scaffold region of chromosome 16 (NW_021964192.1), which contains a cluster of keratin genes. A haplotype predominantly found in Suri alpacas has been identified which supports dominant inheritance. Variant filtering of nine whole-genome sequenced alpacas from both fleece types in the critical interval of 0.4 Mb did not reveal perfect segregation of either fleece type for specific variants. To our knowledge, this is the first study to use the recently developed species-specific SNP array to identify genomic regions associated with differences in fleece type in alpacas. There are still some limitations, such as the preliminary status of the reference assembly and the incomplete annotation of the alpaca genome

Neural and behavioural changes in male periadolescent mice after prolonged nicotine-MDMA treatment

The interaction between MDMA and Nicotine affects multiple brain centres and neurotransmitter systems (serotonin, dopamine and glutamate) involved in motor coordination and cognition. In this study, we have elucidated the effect of prolonged (10 days) MDMA, Nicotine and a combined Nicotine-MDMA treatment on motor-cognitive neural functions. In addition, we have shown the correlation between the observed behavioural change and neural structural changes induced by these treatments in BALB/c mice.We observed that MDMA (2 mg/Kg body weight; subcutaneous) induced a decline in motor function, while Nicotine (2 mg/Kg body weight; subcutaneous) improved motor function in male periadolescent mice. In combined treatment, Nicotine reduced the motor function decline observed in MDMA treatment, thus no significant change in motor function for the combined treatment versus the control. Nicotine or MDMA treatment reduced memory function and altered hippocampal structure. Similarly, a combined Nicotine-MDMA treatment reduced memory function when compared with the control. Ultimately, the metabolic and structural changes in these neural systems were seen to vary for the various forms of treatment. It is noteworthy to mention that a combined treatment increased the rate of lipid peroxidation in brain tissue

Assessment of Machine Learning Classifiers for Heart Diseases Discovery

Heart disease (HD) is one of the utmost serious illnesses that afflict humanity. The ability to anticipate cardiac illness permits physicians to deliver better knowledgeable choices about their patient’s wellbeing. Utilizing machine learning (ML) to minimize and realize the symptoms of cardiac illness is a worthwhile decision. Therefore, this study aims to analyze the effectiveness of some supervised ML procedures for detecting heart disease in respect to their accuracy, precision, f1-score, sensitivity, specificity, and false-positive rate (FPR). The outcomes, which were obtained using python programming language were compared. The data employed in this investigation came from an open database of the National Health Service (NHS) heart disease which originated in 2013. Through the machine learning (ML) technique, a dimensionality reduction technique and five classifiers were employed and a performance evaluation between the three classifiers- principal component analysis (PCA), decision tree (DT), random forest (RF), and support vector machine (SVM). The NHS database contains 299 observations. The system was evaluated using confusion matrix measures like accuracy, precision, f1-score, sensitivity (TPR), specificity, and FPR. It is concluded that ML techniques reinforce the true positive rate (TPR) of traditional regression approaches with a TPR of 98.71% and f-measure value of 68.12%. The true positives rate which is the same as the sensitivity was used to evaluate the accuracy of the classifiers and it was deduced that the PCA + DT outperformed that of the other two with a sensitivity of 98.71% and since the value is on the high side, this implies that the classifier will be able to accurately detect a patient with HD in his or her body

Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance

Human Resources’ purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees’ perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. Human Resource employees no longer need to manually handle vast amounts of data with the advent of data mining. Data mining’s primary goal is to uncover information hidden in data patterns and trends in order to produce results that are close to ideal. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using Principal Component Analysis. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best in the prediction of employee performance

Assessment of Non-Timber Forest Products Utilization and its Contribution to Household Income among Farmers in Quan’ Pan Local Government Area of Plateau State, Nigeria

This study assessed non-timber forest products utilization and its contribution to household income among farmers in Quan’ Pan Local Government Area of Plateau State, Nigeria. Multistage sampling technique was used in the selection of 150 respondents for the study. Descriptive statistics and multiple regression analysis were used to achieve the objectives of the study. Findings from the study revealed the mean age of the farmers to be 40years.  About 71% of the respondents were males with greater number (89%) of them married.  Majority (58%) of the respondents had primary education as their highest level of education. Findings from the study also revealed that 82% of the respondents had farming as their major occupation having average farm sizes of 1.6hectares. All (100%) interviewed farmers agreed that they collect variety of NTFPs throughout the year for their daily subsistence and income generation. Income from NTFPs contributed the second largest share of household income with a share of 27.0% after income from agriculture (51.0%). Firewood (100%), charcoal (39.0%), fruit nuts (37.0%), bush meat (27.0%), bamboo (26%), medicinal herbs (22%), honey (11%), fodder (67%), mushroom (7%) etc. were the major NTFPs extracted and utilized in the study area. The result of multiple regression revealed that gender (0.944), household size (0.432), educational status (-0.385), farmland size (-0.581) and non-farm income (-0.024) were significant determinant of income from NTFPs. The study recommended that, in recognition of substantial contribution of income from NTFPs to household income, stakeholders and policymakers should consider the NTFP sector in forest conservation measures that could meet the needs of forest-dependent communities

An Analyses of Onion Marketing in Toro District of Toro Local Government Area in Bauchi State, Nigeria

The broad objective of the study was to determine the profitability and marketing efficiency of onion marketing in Toro District of Toro local government area in Bauchi State. Questionaire was the main instrument of data collection from a sample of fourty (40) onion traders in the study area. Analytical tools used were; descriptive statistics, gross margin, marketing margin and market efficiency. Majority (67.5%) of the respondents were males. Also, majority (55%) of onion Traders falls within the age bracket of 41 - 50 years of age, the result indicates high proportions (85.00%) of the respondents were educated. The years of Onion marketing experience of the respondents indicates that half of the respondents (50%) had 11 - 20 years of marketing experience in onion trading and the study also indicates that majority (52.50%) went into onions marketing with money between ₦21,000:00 - ₦30,000:00. Greater proportion (87.50%) of the respondents belongs to cooperative associations. Major problems of onion marketing identified were those of transportation, storage facilities, starting capital, market information and low price. Result of the costs and returns analysis revealed that onion marketing was profitable to the turn of N1,315.00 per bag for wholesale and N2,020.00 per bag for retail traders of onion marketers respectively. This finding also indicated that marketing of onion is efficient with total efficiency of 141.28% for wholesalers and 150.80% for retailers. Proper functional programmes and policies should be made to improve onion production and marketing in the study area

Spectroscopic characterization, DFT calculations, in vitro pharmacological potentials, and molecular docking studies of N, N, O-Schiff base and its trivalent metal complexes

In this study, trivalent metal complexes of the category: [M(L)(H2O)nCly] obtained from the interaction of metal3+ ion salts with organic N, N, O-Schiff base (HL) (where: HL = 4-{(Z)-((2-{(E)-((2-hydroxyphenyl)methylidene)amino}ethyl)imino)methyl}-2-methoxyphenol; n, y = 1 or 2 and M = Ti(III), Fe(III), Ru(III), Cr(III) and Al(III)) were synthesized and characterized viz molar conductance, FT-IR, and UV–Vis spectroscopies, elemental analyses, thermal analyses (TGA and DTA), and UV–Vis spectroscopy, theoretical calculations. A distorted octahedral structure around the metal ions was proposed based on the obtained experimental and calculated data. Thermal examination of the complexes signposts the step-by-step disintegration to give the final decomposition product as metal oxides. Moreover, DFT calculations were executed utilizing the B3LYP/LANL2DZ theory level, which revealed that the synthesized metal (III) complexes were more stable than the free ligand (HL). The value of ΔE for HL is 4.60 eV while the related values for the complexes of Cr(III) (C1), Ru(III) (C2), Fe(III) (C3), Al(III) (C4), and Ti(III) (C5) are respectively 2.59, 3.68, 3.15, 1.64, and 2.75 eV. Scavenging abilities of DPPH and ABTS radicals by the test compounds revealed promising antioxidant behavior. It was observed that the compounds are proficient DPPH radical scavengers in a dose-dependent configuration. Ru(III); IC50 = 1.69 ± 2.68 µM for DPPH and Ti(III); IC50 = 8.70 ± 2.78 µM for ABTS performed best. Similarly, the complexes demonstrated higher antimicrobial activities compared to HL against the designated strains, while ciprofloxacin acted as a standard antibiotic. Furthermore, the ligand and its most effective complexes C2 and C5 were docked against the targets S. aureus DNA gyrase (2XCT), S. pneumoniae DNA gyrase (5BOD), and E. coli DNA gyrase (5L3J). The binding sites were evaluated and the docking results showed that the studied molecules bind to the targets through classical O—H…O and/or N—H…O hydrogen bonds, as well as via hydrophobic contacts