Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Cervical cancer is defined as a cancer arising in the cells of cervix that causes unusual vaginal bleeding, discharges, pain in the pelvic region, or pain during sexual activity. Cervical cancer is currently reported to be the fourth most prevalent malignancy among women globally. Surgery includes pelvic lymphadenectomy as well as radical hysterectomy, radiotherapy, as well as chemotherapy are the most common therapies for treating cervical cancer. Another approach includes targeted medication which affects the epidermal growth factor receptor (EGFR) and cyclooxygenase-2 (COX-2) for the curing cervical cancer. However, these therapies have the potential for risks and complications: surgery can result in bleeding and may cause organ damage surrounding the surgery, and clots may also start to form in the deep veins of the legs; radiotherapy can result in menopause, infertility, discomfort, or pain during intercourse; and chemotherapy can actually impact rapidly dividing cells along with cancer cells in the human body system. In this review, we will discuss about the use of several Randomised controlled trials (RCTs) for treating malnutrition in various oncology patients. In this review, we will discuss about the various therapeutic effects of natural products isolated from different microorganisms in treating cervical cancer.

The UK fully legalised open market share repurchases in 1981, and to our knowledge no study has investigated the business cycle’s influence on repurchase decision-making. We address this aspect and investigate the period 1985-2014. This is relevant as the business cycle factors impact the firm-specific variables such as cash flow, profitability, dividends and capital structure, and these factors traditionally influence repurchase decisions. This forms the paper’s theoretical intuition, and the empirical objectives test the business cycle’s influence on the decision to undertake a repurchase, and also its influence on repurchases values. The results find that the business cycle influences both the decision of undertaking repurchases and repurchases’ values, and this influence has aggregately remained positively associated with economic prosperity. Thus, the frequency of repurchase announcements by British firms is more probable during prosperous economic circumstances. The results also reveal that the repurchase-business cycle relationship witnessed a structural break in 1996:Q2, and the real difference following this break is the increase in the business cycle’s influence on the decision regarding repurchase values. The paper thus contributes to existing literature by directly testing the UK’s repurchase-business cycle relationship, and providing detailed empirical evidences that business cycle conditions strongly impact the repurchase decision-making.

The business world is a competitive place, and enterprises must cultivate sustainable relationships with their clients to gain customer loyalty. Concepts of relationship marketing (RM) are becoming vital in the service industry. Different RM strategies are used to attract customers, but some of these strategies do not achieve customer loyalty as easily as predicted due to customers’ changing behaviour patterns. This paper investigates the impact of RM strategies on customer loyalty through relationship qualities (customer satisfaction and trust) by concentrating on the high-end hairdressing sector in Auckland. Additionally, this research investigates the expectations of customers of high-end hair salons formed by RM strategies, which influence their satisfaction, trust, and customer loyalty to their service provider. The results indicate that brand image shows a significant relevance to trust, satisfaction, and customer loyalty claiming that brand image is an essential element of RM. Additionally, this investigations revels that, hair salons in Auckland are looking forward to ascertaining a sustainable relationship with their clients through a spectrum of strategies such as positive brand image, good service quality, reasonable price, and good value deals to earn optimistic relationship quality, which will ultimately escalate to customer loyalty. Findings of this research will be useful in supporting hair salons to gain a greater understanding of their customers by adopting effective RM strategies.

Economy has various stages which have been described as economic cycle and due to that the circulatory movement observed, some have faster cycle some have longer cycle which depends on numbers of factors and from that inflation is an important segment. Various terms have been described for inflation, in simple meaning when price of different commodities have been raised compared to previous stage due to depreciation of currency having lesser purchasing power. Perception of people towards inflation and its impact is the main crux of the study, because how the people think about technical terms is vital matter. The present study is based on primary data collected from 277 respondents through questionnaire from the Gujarat state India. The perception has been analysed from four statement have been asked in Lickert’s five scale form. The one-way ANOVA has been used to test the hypotheses and it has been found from that in first statement regarding price of commodity once increase then it will not decrease due to inflation has significant difference with place, education and age of the respondents and in rest three statement no significant differences have been observed.

Keeping in view the concept of agglomeration economies and the New Economic Geography (NEG) angle, this paper makes an attempt to examine the rural to urban population movement at the district level in India. The findings do not favour a strong positive association between levels of urbanization and migration rates.  However, there exists a cluster of districts which are able to attract migrants on a large scale in spite of being already urbanized. The work participation rate, share of services and construction work, and literacy rate all form parts of this positive nexus, indicating that opportunities exist with increased levels of urbanization which in turn prompt people to migrate. The positive spill-over effects of higher levels of urbanization are not limited to the urban spaces only as the adjoining rural areas also seem to have undergone   a significant transformation. The land use pattern and activities are changing and some of the developmental impact is evident. However, the regional variations in this respect are evident. There are sharp differences in the relationship between urbanization level and migration rates across regions. The nature of urbanization and its determinants and the outcome variables of urbanization and migration also unravel regional variations. Besides, there are many districts with higher levels of urbanization; yet, they are not able to attract migrants at a rapid pace. New investment opportunities can be created in these spaces to reduce the cost of growth and make employment generation process more effective, facilitating the rural population to take the benefits of agglomeration economies.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.