Designing the home healthcare supply chain during a health crisis

HCSRL: hyperledger composer system for reducing logistics losses in the pharmaceutical product supply chain using a blockchain-based approach

Blockchain technology uses a secure and decentralised framework for transaction management and data sharing within supply chains. This is particularly crucial in the pharmaceutical industry, where product authenticity and traceability are paramount. Blockchain plays a pivotal role in preventing product loss and counterfeiting, while simultaneously enhancing transparency and efficiency throughout the supply chain. The research introduces a step-by-step approach to implementing a proof-of-concept (PoC) for Supply Chain Risk Management (SCRM) through blockchain technology. This PoC involves simulating a supply chain process to assess feasibility and measure key performance indicators. Engaging stakeholders and gathering feedback is integral to refining the blockchain-based SCRM system. The study rigorously evaluates the performance of the SCRM blockchain across various test scenarios, featuring differing numbers of organizations and clients. Multiple fabric networks are employed to assess the system's scalability and performance under diverse conditions. The results of these comprehensive tests inform practical deployment decisions and highlight areas for potential optimization and further development. So this research provides valuable insights into the application of blockchain in pharmaceutical supply chains, offering a roadmap for implementation and improving supply chain security, efficiency, and transparency.

What shapes food waste behaviors? New insights from a comprehensive action determination model

The sharp increase in food waste poses a serious threat to food security and environmental sustainability. However, most existing studies have investigated the determinants of food waste behaviors in a separate behavioral process without considering the potential impacts of different factors in an integrated process. To narrow this gap, a comprehensive action determination model (CADM), which integrates network embeddedness and incentive measures, was constructed to explore the impact of various determinants in different processes on food waste behaviors, using data collected from 913 residents in eastern China via an online survey. The empirical results showed that environmental concern was the largest positive factor in predicting personal norms (β = 0.80, p < 0.001), followed by network embeddedness. With the habitual process considered, residents with ingrained waste habits were more likely to waste food (β = 0.38, p < 0.001). Moreover, the normative process alleviates behavioral decisions via intentions to reduce food waste. This study confirmed the differences in the situational process and suggested that menu tips increase food waste behaviors, while incentive measures reinforce the influence of intentions on behavior. We therefore address the insufficient ingredients on the effects of different processes on behavior and provide a new perspective for formulating behavioral intervention policies.

Integration of Pakistan's stock market with the stock markets of top ten developed economies

This study examines the integration of Pakistan's Stock Market with the stock markets of the top ten largest economies in the world-USA, China, Japan, Germany, the UK, India, France, Italy, Brazil, and Canada-from January 2015 to October 2020. To examine long- and short run integration, this study employed Johansen and Juselius co-integration and pair-wise Granger causality tests. In the long run, the results indicated that Pakistan's Stock Market is not integrated with these markets. This implies that the market is more attractive in portfolio diversification for international investors, and vice versa. In the short run, the results revealed that, except for China, Pakistan's stock market integrates with the remaining nine markets. However, Pakistan's stock market exhibits a bidirectional relationship with the USA, Japan, Germany, the UK, and France in the lead-lag relationship. However, its relationship with India, Italy, Brazil, and Canada is unidirectional, with Pakistan's stock market leading, while these markets are following. For Pakistani investors, China is the optimal market, and vice versa. Importantly, our findings help policymakers to comprehend Pakistan's dynamic relationship with its trading partners. To the best of our knowledge, no prior study has employed advanced techniques to address the time-varying correlation among the selected markets. By determining Pakistan's stock market integration with its trading partners, this study aimed to fill this empirical literature gap.

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

Natural resource usage has produced various environmental challenges. Green process innovation has been considered a viable option that can help both industry and society. This study investigates the impact of green process innovation and green product innovation on corporate financial performance. We based our findings on a sample of 280 listed non-financial firms operating in South Asia. Information was gathered from firms' annual and CSR reports from 2012 to 2022. This study's data was analyzed using a two-step dynamic panel system GMM, correlation analysis, multicollinearity diagnostic tests, and descriptive statistics. Corporate financial performance is measured with ROA, ROE and Tobin's Q. Overall findings of the study show that green innovation has a significant positive impact on all measures of financial performance. Investing in the innovation of green products and green process can assist businesses in avoiding environmental concerns and regulatory penalties, while also assisting them in establishing new market prospects and achieving new levels of success with their green products. In addition, developing products that are friendly to the environment is tightly connected to expanding green competencies, promoting a company's green image, and improving the company's financial performance. Particularly useful for policymakers in developing countries, the study's findings can be used to introduce paradigm-shifting legislation and penalties that speed up business adoption of green process innovation.

Game analysis of enterprise data sharing from a supply chain perspective

[Research Purpose] In the era of the digital economy, there is an urgent need to explore solutions to various problems faced by enterprises in their digital transformation, such as the lack of data resources, data silos, and information asymmetry within supply chains. [Method/Contribution] Leveraging evolutionary game theory and adopting a supply chain perspective, this study integrates the government and upstream/downstream enterprises into a unified analysis framework. In this study, a three-party evolutionary game model under government coordination aimed at fostering data openness and sharing among supply chain enterprises is constructed. Simulation analyses are conducted on decision-making strategies concerning data sharing between the government and supply chain enterprises across different scenarios. [Research Conclusion] It is observed that the high level of benefits and low costs associated with data sharing incentivize supply chain enterprises to actively open and share their data. Notably, government incentives significantly encourage data openness among these enterprises by subsidizing the cost of data sharing, "especially evident when the incentive coefficient exceeds 0.6," thereby guiding them toward collaborative data-sharing initiatives. Finally, it is also found that data sharing further promotes the digital transformation of the supply chain, optimizing decision-making processes, resource allocation, and operational efficiency. Through data sharing, better forecasting, inventory management, and risk mitigation strategies can be implemented. Moreover, data sharing fosters collaboration among supply chain partners enhances transparency and trust, and makes the supply chain more synchronized and responsive, which leads to value cocreation within the supply chain, with downstream enterprises being more incentivized than upstream enterprises by this value cocreation.

Replacement of failed items in a two commodity retrial queueing-inventory system with multi-component demand and vacation interruption

This study investigates a crucial aspect of inventory management, which is the process of replacing failed items. In dynamic commercial environments, it is essential to efficiently and strategically replace failed items to maintain operational efficiency and ensure profitability. We consider a two-commodity retrial queueing-inventory system with vacation interruption. Upon purchasing the first commodity, the second commodity is provided as a complimentary item. In contrast, no item is given as a complimentary for the purchase of the second item. Only the first commodity is stored in a dedicated pooled storage for replacement when it fails. The ( s , Q ) policy governs replenishing the first commodity while the second is replenished through instantaneous ordering. The model considers the multi-component demand rate for customer arrivals. Server vacations are initiated during customer absence in waiting hall or when the first commodity is unavailable. We formulate a level-dependent quasi-birth-and-death process, and its steady-state probability vector is computed using Neuts and Rao's truncation method. The stability condition for the system is derived, and various system performance measures, including expected total cost, number of replaceable items, and customers in the waiting hall and orbit, are established. The comparative analysis between the system with replacement is done with the regular model without replacement, which revealed the efficiency of replacement. The analysis of multi-component demand towards homogeneous arrival highlights the impact of multi-component demand on boosting customer arrival. Also, parametric sensitivity analysis has been conducted numerically over total cost, mean number of failed items for replacement, and mean number of customers in the waiting hall and orbit.

Impacts of external factors on Ethiopia's economic growth: Insights on foreign direct investment, remittances, exchange rates, and imports

This study conducts a rigorous examination of relationships that are often assumed but rarely tested within the context of Ethiopia's journey towards sustainable development. An autoregressive distributed lag model is employed using annual time series data from 1982 to 2021 to investigate the short- and long-term impacts of foreign direct investment (FDI), remittances, real exchange rates, and imports on Ethiopia's economic growth. By incorporating additional control variables, this study contributes nuanced insights that were previously lacking in the literature. The findings reveal intriguing patterns that both align with and deviate from existing frameworks. Contrary to some prior studies, foreign investment consistently emerges as a significant driver of economic growth over time. However, remittances demonstrate only transient significance, highlighting the need for cautious policy considerations. The influence of exchange rates on economic growth proves to be unexpectedly complex and nonlinear, challenging conventional assumptions. The empirical validation of these multifaceted realities underscores the importance of this analysis. Furthermore, robustness tests conducted in this study confirm the reliability of the findings while shedding light on additional intricacies. For instance, the relationship between imports and growth is context-dependent and exhibits ambiguities that call for careful consideration. The theoretical and practical implications derived from this research offer valuable insights for researchers and policymakers alike. The recommendations put forth emphasize the promotion of sustained prosperity through evidence-based strategies that prioritize community development.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

A paradigmatic approach to the molecular descriptor computation for some antiviral drugs

In theoretical chemistry, topological indices are commonly employed to model the physico-chemical properties of chemical compounds. Mathematicians frequently use Zagreb indices to calculate a chemical compound's strain energy, melting point, boiling temperature, distortion, and stability. The current global pandemic caused by the new SARS-CoV-2, also known as COVID-19, is a significant public health concern. Various therapy modalities are advised. The issue has become worse since there hasn't been enough counseling. Researchers are looking at compounds that might be used as SARS and MERS therapies based on earlier studies. In several quantitative structure-property-activity relationships (QSPR and QSAR) studies, a variety of physiochemical properties are successfully represented by topological indices, a sort of molecular descriptor that just specifies numerical values connected to a substance's molecular structure. This study investigates several irregularity-based topological indices for various antiviral medicines, depending on the degree of irregularity. In order to evaluate the effectiveness of the generated topological indices, a QSPR was also carried out using the indicated pharmaceuticals, the various topological indices, and the various physiochemical features of these antiviral medicines. The acquired results show a substantial association between the topological indices being studied by the curve-fitting approach and the physiochemical properties of possible antiviral medicines.

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

Introduction

Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.

Purpose

The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study.

Method

Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images.

Result

The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset.

Conclusion

The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.