This research examines the synergistic influence of inventory competition, artificial intelligence (AI) adoption, and digital marketing intensity on quality improvement decisions within contemporary supply chains. With a focus on enhancing product and service quality, we investigate the intricate relationships among these variables. A quantitative approach involving 380 supply chain professionals reveals that heightened inventory competition, increased AI adoption, and robust digital marketing significantly contribute to quality enhancement initiatives. The study builds upon prior research by empirically validating these connections and offers practical insights for supply chain practitioners. The findings underscore the strategic imperative of organizations to strategically balance these factors to optimize their quality management strategies, fostering customer satisfaction and competitiveness. While offering valuable contributions, the study acknowledges limitations in terms of self-reported data and a specific respondent group. Future research could extend this investigation to diverse industries and geographical contexts. In the end, this study sheds light on the complex interplay that exists between inventory competition, the use of AI, digital marketing, and judgments about quality improvement. As a result, a road map has been provided for efficient quality management of supply chains in the digital age.

In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.

In Indonesia, natural disasters cases have significantly increased from time to time and have the largest impact on economic losses. To avoid losses in the future due to natural disasters, the insurance company needs to estimate the risk and determine the rate of premium that would be charged to the policyholder. In conjunction with the present issue, this study seeks to determine the premium rate and estimate the size claim of insurance by considering the mitigation fund reserve decisions using The Collective Risk Model (CRM). The data was analyzed using the Poisson process with Weibull distribution to determine the natural disaster frequency and losses. The distribution of losses is estimated using Maximum Likelihood Estimation (MLE), and the magnitude of losses was estimated using the CRM. Also, the mean and variance estimators of the aggregate risk were used to estimate the premium charged. The results indicated that expectation and variance of the frequency of incident claims have the same value, i.e., 2562. Also, the loss claims follow the Weibull distribution with the expected value and variance of 5.81309×10¹⁰ and 2.5301×10²², respectively. The mean and variance of the aggregate (collective) claims are 148,931,365,800,000 and 7.35×10²⁵, respectively. In conclusion, this study has successfully determined the efficient pure premium model through the Standard Deviation Principle (SDP). SDP provides a much cheaper premium than the Expected Value Principle with the same loading factor. In addition, SDP considers the standard deviation of the collective risk of natural disasters. The implications of the results of the premium determination are expected to be the basis for decision-making for insurance companies and the government in determining insurance policies for natural disaster mitigation.

Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.

In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.

Polyester fibers are the most used in the manufacture of ropes for mooring systems and offshore operation, thus being constantly subjected to different situations. Such requests are implicated in a variety of load conditions, and their effects must be studied. This work presents data referring to an experimental study on the behavior of the quasi-static and dynamic stiffness of polyester yarns considering different mechanical levels of degradation and use. The study is performed with five different types of multifilament samples, these were extracted from a virgin spool and sub-ropes tested for tension and fatigue. The experimental procedure is carried out through an initial characterization where the linear density, the Yarn Break Load - YBL and the linear tenacity of the samples are determined. Continuing with the experimental tests, a procedure standardized by ISO 18962-2 is then carried out, consisting of three quasi-static stages and three dynamic stages, where the data acquired in the tests allow the determination of a dimensionless stiffness value. The results showed an increase in the quasi-static stiffness, tending to a plateau, and a linear increase in the dynamic stiffness, but with somewhat similar behavior between the samples. The results related to the total quasi-static stiffness also show that the specimens extracted from sub-ropes that underwent fatigue present greater total non-dimensionalized stiffness, this is indicative of the mechanical fatigue procedure as an improvement of the specimens, giving them greater stiffness, and consequently greater stresses rupture, a behavior that should be explored in future studies.

COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full.

The article analyzes the stress-strain state in the zone of plastic deformation during special caliber rolling. The principle of caliber rolling technology is described, which makes it possible to combine shear and compression deformations in the cross-section of metals, alloys, and metal-matrix composites. The analysis results of stresses and strains during shear rolling in a diamond pass, which included compressive strains that had not been considered in previous studies, revealed that localization or point inversion of stresses and strains is observed in the plastic deformation zone. Stress and strain are localized along the minor and major diagonals of the diamond pass.

A series of isoxazole derivatives, 3-aryl-5-[5-(4-nitrophenyl)-2-furyl]-2,3-dihydroisoxazoles (5a-j) were synthesized by cyclocondensation reaction between 1-aryl-3-[5-(4-nitrophenyl)-2-furyl]prop-2-en-1-ones (4a-j) and hydroxylamine hydrochloride in presence of sodium acetate in glacial acetic acid at reflux temperature. Formerly, compounds (4a-j) were synthesized by the condensation of 5-(4-nitrophenyl) furan-2-carbaldehyde (3) with various aromatic ketones by using alkali as catalyst. The structures of the synthesized various isoxazole have been characterized by using elemental analysis, Infrared,¹H-NMR spectroscopy and further supported by Mass spectroscopy. All the products have been screened for their in-vitro biological assay like antibacterial activity towards Gram-positive and Gram-negative bacterial strains and antifungal activity towards Aspergillus niger at a concentration of 40 µg/ml. It was exposed that the compounds 5a, 5c, 5e, 5f, and 5i showed inspiring antibacterial and antifungal activity compared to the used reference standard.

Additive manufacturing has been one of the most used techniques in the recent years because of its capabilities to fabricate complex structures as required by customer and industrial need from a 3D computer-aided design model without the usage of any tooling, dies and heavy machinery makes it a step ahead in the present manufacturing techniques. In the current study the author’s focus on the welding or joining of additive manufactured Polylactic acid (PLA) parts made by Fused Deposition Modeling (FDM). There are several techniques for welding these additive manufactured parts. This study mainly focuses on the joining of 3D printed PLA parts using a 3D pen and investigations on its mechanical properties experimentally. It is a very cheap and effective technique when compared to the other welding methods. This could overcome the drawback of small bed size in most 3D printers by joining smaller parts and it can also be used for repairing the defects caused during the 3D printing. Moreover the experimental testing of the mechanical properties also confirmed that the tensile, flexural and impact strength of 3D pen welded specimens retrieved above 70% of the strength to the original PLA specimen proving it to be a very effective method.