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Financial capability is one of the primary drivers for buyers to make purchases. Therefore, sellers must set an optimum selling price and consider trade credit facilities to attract more demand. This paper proposes an inventory decision model in which customer demand depends on the price and number of credit installments to serve low-abled buyers. This study has developed a demand function with a positive impact on installment policies and the effect of the selling price. Two models have been formulated to optimize the selling price and positive stock time, m total profit, with and without installment policies. Then, numerical examples and sensitivity analysis illustrate the proposed model for different cases. The study has found that the selling price and positive stock time can be optimized. Profits can be higher in the case of an installment facility than in the case without an installment facility. It shows positive responses from the buyer to the installment policy.

The need to develop ecologically friendly sustainable building materials is made apparent by the worldwide construction industry's substantial contribution to global greenhouse gas emissions. The use of supplemental materials in concrete is one potential solution to lessen the environmental footprint. Thus, the purpose of this work is to use Machine Learning (ML) algorithms to forecast and create an empirical formula for the Compressive Strength (CS) of concrete with supplemental materials. Six distinct ML models--XGBoost, Linear Regression, Decision Tree, k-Nearest Neighbors, Bagging, and Adaptive Boosting--were trained and tested using a dataset that included 359 experimental data of varying mix proportions. The most significant factors used as input parameters are cement, aggregates, water, superplasticizer, silica fume, ambient curing, and supplemental material. Several statistical measures, such as Mean Absolute Error (MAE), coefficient of determination (R²), and Mean Square Error (MSE), were used to evaluate the models. XGBoost model outperformed the other models with R² values of 0.99 at the training stage. To ascertain how the input parameters affected the outcome, feature importance analysis using Shapely Additive exPlanations (SHAP) was conducted. It was demonstrated that curing age and cement type significantly affected the strength of concrete with high SHAP values. By eliminating experimental procedures, reducing the demand for labor and resources, increasing time efficiency, and offering insightful information for enhancing sustainable manufacturing of concrete, this research advances the low-cost production of concrete in the USA construction industry. [ABSTRACT FROM AUTHOR]

Cerebral blood volume (CBV) is an essential metric that indicates and evaluates various healthy and pathologic conditions. Most methods of CBV measurement are cumbersome and have a poor temporal resolution. Recently, it has been proposed that signals and derived metrics from cerebral near-infrared spectroscopy (NIRS), a non-invasive sensor, can be used to estimate CBV. However, this association remains vastly unexplored. As such, this scoping review aimed to examine the literature on the relationship between cerebral NIRS signals and CBV. A search of six databases was conducted conforming to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to assess the following search question: What are the associations between various NIRS cerebral signals and CBV? The database search yielded 3350 unique results. Seven of these articles were included in this review based on the inclusion and exclusion criteria. An additional study was identified and included while examining the articles' reference sections. Overall, the literature for this systematic scoping review shows extreme variation in the association between cerebral NIRS signals and CBV, with few sources objectively documenting a true statistical association between the two. This review highlights the current critical knowledge gap and emphasizes the need for further research in the area. [ABSTRACT FROM AUTHOR]

Performance in detection tasks can be improved by directing attention to task-relevant features. In this study, we evaluate the direction tuning of selective attention to motion features when observers detect point-light biological motion in noise. Feature-based attention strategy is assessed by capitalizing on the sensitivity of unattended steady-state visual-evoked potential (SSVEP) to the spreading of feature-based attention to unattended regions of space. Participants monitored for the presence of a point-light walker embedded in uniform dynamic noise in the center of the screen. We analyzed the phase-locked electroencephalogram response to a flickering random-dot kinematogram (RDK) in an unattended peripheral annulus for the 1 s prior to the onset of the target. We found the highest SSVEP power to originate from electrodes over posterior parietal cortex (PPC), with power modulated by the direction of motion in the unattended annulus. The SSVEP was strongest on trials in which the unattended motion was opposite the facing direction of the walker, consistent with the backstroke of the feet and with the global direction of perceived background motion from a translating walker. Coherence between electrodes over PPC and other brain regions successfully predicted individual participant's d-prime, with the highest regression coefficients at electrodes over ventrolateral prefrontal cortex (VLPFC). The findings are evidence that functional connectivity between frontal and parietal cortex promote perceptual feature-based attention, and subsequent perceptual sensitivity, when segregating point-light figures from masking surround.

Institutional factors such as data standardization, data interoperability, and the ability to include other stakeholders (e.g., civil society) in ecosystems are now understood as crucial considerations in Open Government Data (OGD) implementation. However, most of the current understanding of institutional factors linked to OGD growth has evolved around highly digitalized and developed countries. This study aims to address this gap by investigating open data implementation in Indonesia, a developing country that has been an early advocate of OGD in the "Asia Pacific". In 2014, Indonesia implemented Satu Data Indonesia (One Data Indonesia) as a national initiative to promote OGD objectives. We employ institutional dimensions as the central theoretical lens to unpack the successes and impediments to Satu Data Indonesia's development. This research dataset is built on 16 expert interviews from government (n = 14) and private (n = 2) organizations that directly contribute to the growth of Satu Data Indonesia, qualitatively triangulated with documentary analysis of key policy and regulatory documents. These new data provide important insights into the complexity and challenges of nationwide OGD implementation and data-sharing. Our findings show that despite its being an early advocate, Indonesia's OGD initiative is still in the early stages of development, impeded by several policy and administrative bottlenecks. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) into agriculture is a pivotal solution to address the pressing challenges posed by rapid population growth and escalating food demand. Traditional farming methods, unable to cope with this surge, often resort to harmful pesticides, deteriorating soil health. However, the advent of AI promises a transformative shift toward sustainable agricultural practices. In the context of the United States, AI's historical trajectory within the agricultural sector showcases a remarkable evolution from rudimentary applications to sophisticated systems focused on optimizing production and quality. The future of American agriculture lies in AI-driven innovations, spanning various facets such as image sensing for yield mapping, labor management, yield optimization, and decision support for farmers. Despite its numerous advantages, the deployment of AI in agriculture does not come without challenges. This paper delved into both the benefits and drawbacks of AI adoption in the agricultural domain, examining its impact on the agro-industry and the environment. It scrutinized the emergence of robot farmers and AI's role in reshaping farming practices while acknowledging the inherent problems associated with AI implementation, including accessibility, data privacy, and potential job displacement. Moreover, the study explored how AI tools can catalyze the development of agribusiness, offering insights into overcoming existing challenges through innovative solutions. By comprehensively understanding the opportunities and obstacles entailed in AI integration, stakeholders can navigate the agricultural landscape adeptly, fostering a more sustainable and resilient food system for future generations.4th [ABSTRACT FROM AUTHOR]

The availability of safe water, sanitation, and hygiene (WASH) facilities in schools is essential for a healthy learning environment and achieving sustainable development goals (SDGs) 4 and 6. Despite its importance, comprehensive studies on drinking water quality, sanitation, and hygiene in schools are scarce. This study explicitly assessed the WASH services and gaps in 43 educational institutions, located in Tongi, Bangladesh, through field and laboratory investigations. Thirteen physicochemical and bacteriological parameters were analyzed, and water quality was classified using an Integrated Water Quality Index (IWQI). Hygiene and sanitation were evaluated through observations and data from school administrators on water sources, toilets, handwashing facilities, and solid waste management. Results showed that WASH services exceeded the national average, but all schools had dangerously high Escherichia coli levels (mean: 43.95 CFU/100 mL) in drinking water, posing health risks. Additionally, 89.72% of samples showed elevated manganese levels, 35% had high iron, and 41.86% had increased conductivity. About 35% of water was unsuitable for drinking based on IWQI. Schools lacked the capacity to monitor WASH quality, especially drinking water. A strategic framework for safe WASH facilities is recommended. The findings can lead the policymakers to prioritize the improvements in WASH facilities for attaining SDG 6. [ABSTRACT FROM AUTHOR]

Objectives: Lead (Pb), a toxic heavy metal, is a serious concern for poultry that negatively affects their productivity and health. To combat those issues efficiently, it is necessary to include feed supplements that have rich antioxidant properties for satisfactory health and productivity. Spirulina platensis (Sp), a microalgae, is a compound that provides several health benefits for humans and animals. This study explores that supplementation of Sp in diet as well as in water reduces the burden of Pb in different tissues, improves hematology, and improves the productive performance of advanced-age laying hens. Materials and Methods: Forty birds were separated into four groups: the control (C), Spirulina (Sp), Pb, and (Pb + Sp) groups. The Pb group was given Pb acetate at a dose of 2 gm/l in water ad libitum for 4 weeks. Sp group was fed Sp at a dose of 4 gm/kg feed. The Pb + Sp group was given Pb and Sp as in the previous groups. Results: Productive performance and hematology such as hemoglobin (Hb), packed cell volume, red blood cell, mean corpuscular volume, mean corpuscular Hb (MCH) concentration, and MCH were significantly (p < 0.05) decreased in Pb-treated groups compared to controls. The distribution of Pb concentration was highest in the bones and lowest in the gizzard. However, Sp treatment significantly (p < 0.05) increased the productive performance and the hematological parameters. Moreover, Pb concentration in different organs significantly decreased in the group treated with Sp. Conclusion: This study indicates that Sp can possibly be used as a natural and powerful dietary additive to mitigate heavy metal intoxication in chickens, thereby being efficient and effective for production. [ABSTRACT FROM AUTHOR]