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Strategic, Legal, Financial, and Operational Risks for Businesses During COVID-19 Pandemic
Get PDFThe economic effect of the COVID-19 pandemic is as significant as the threat it poses to public health. Companies are negotiating a new world dealing with new risks. Organizations seek confidence in their risk management and control frameworks and reaction strategies for the short, medium, and long term as COVID-19 grows to threaten commercial life. As a result, it is critical for firms to be proactive in analyzing their risk and susceptibility from many aspects. This exploratory research seeks to discuss some major risks faced by many organizations during the pandemic, namely, the strategic, compliance, regulatory, financial, and operational risks. To thrive in this new business climate, organizations must take immediate action to limit risks and prepare for both fast and gradual recovery. Businesses that properly manage risk not only endure but also strengthen their endurance and equip themselves to capitalize on new possibilities.  
International Segmentation of Countries Using Unsupervised Machine Learning Algorithms
Get PDFAs markets grow more global, global market segmentation becomes increasingly important for creating, marketing, and selling items globally. Operating a business in many nations introduces unique obstacles. International markets are more diverse than domestic markets due to differences in historical, institutional, cultural, and political environments among nations. Clustering algorithms enable multinational companies to give better-personalized products and customer services to their foreign customers. By gaining a deeper understanding of the nation’s characteristics, companies may identify the appropriate products or services to distribute, choose the best marketing channels for the target consumers, discover new and important insights, and launch new business strategies. This study used unsupervised machine learning algorithms such as Affinity Propagation, DBSCAN, and Hierarchical clustering to segment 150 countries. The segments of countries were established based on their brand awareness, gross national income, and trade liberalization, which are considered to be some of the most relevant qualities to employ when determining the macro segmentation of countries in the context of international business. This research emphasizes and recommends that various machine learning technologies be used to construct segmented and countrywide business strategies and marketing tactics in order to advance the global market expansion
The Impacts of AI on Manufacturing, Trade and Labor Market
Get PDFArtificial intelligence is becoming increasingly significant in our lives and economy, and it is already affecting our environment in a variety of ways. The race to enjoy its benefits is hot around the world, and global leaders — the United States and Asia – have emerged. Many people consider AI as a source of increased productivity and economic progress. By analyzing enormous amounts of data, it can improve the efficiency with which things are done and dramatically improve the decision-making process. It can also result in the development of new products, services, markets, and industries, increasing customer demand and providing new revenue streams. However, AI has the potential to be extremely disruptive to the economy and society. Some fear that it would lead to the formation of super corporations — wealth and knowledge centres – which will harm the economy as a whole. It may also exacerbate the gap between developed and developing countries, as well as increase the demand for individuals with specific talents while displacing others; the latter tendency could have far-reaching implications for the labor market. Experts also warn that it has the potential to widen inequality, lower wages, and reduce the tax base. While these worries are legitimate, there is no agreement on whether or not the associated hazards will materialize. They aren\u27t certain, and a well-crafted policy might encourage AI research while limiting its negative consequences
The Role of Graphene in Advancing Quantum Computing Technologies
Get PDFThe advent of quantum computing heralds a transformative shift in computational capabilities, leveraging the principles of quantum mechanics to solve problems intractable for classical computers. Among the myriad of materials investigated for quantum computing, graphene stands out due to its exceptional electronic properties. This paper presents a comprehensive review of the current landscape in graphene-based quantum computing, highlighting the pivotal role of graphene’s ballistic transport, high carrier mobility, and unique band structure in the development of quantum bits (qubits). Focusing on recent advancements, including the integration of graphene into Josephson junctions and circuit quantum electrodynamics (cQED) systems, we analyze the progress and challenges in fabricating graphene-based quantum devices. Through a detailed examination of experimental milestones, particularly the seminal work by Kroll et al. (2018), we assess the potential of graphene to enhance qubit resilience and functionality. We identify key challenges facing the field, such as scalability and qubit coherence, and discuss potential solutions that could pave the way for next-generation quantum computing devices. Concluding with future research directions, this review underscores the necessity for interdisciplinary collaboration to harness graphene’s full potential in quantum computing, offering insights into unsolved problems and emerging technologies
Impact of cloud deployment on operational expenses of healthcare centers
Get PDFHealthcare is among the most advanced industries when it comes to embracing and adopting moderntechnology in some way. Cloud computing approaches, in conjunction with the Internet of Things, areadvantageous to extract information from healthcare records. In many cases, cloud computing combinedwith IoT and AI will pave the way for new avenues of medical innovation and insight. Cloud computing\u27sgrowing acceptance in healthcare extends much beyond simply storing data on cloud infrastructure.Healthcare providers are already embracing this technology to increase efficiency, optimize processes,reduce healthcare costs. The objective of this research was to investigate whether the deployment of cloudcomputing can assist in reducing operational costs in healthcare centers. We used panel data ranging from2008 to 2019 for 156 healthcare centers. The Fixed Effect (FE) model and Random Effect (RE) modelhave been employed. The results suggest that the deployment of cloud computing significantly assists inreducing the operational costs in healthcare centers
Patient-Centric Ethical Frameworks for Privacy, Transparency, and Bias Awareness in Deep Learning-Based Medical Systems
Get PDFThe rapid advancement and deployment of deep learning-enabled medical systems have necessitated the development of robust ethical frameworks to address potential challenges and pitfalls. Based on the foundational principles of medical ethics—non-maleficence, beneficence, respect for patient autonomy, and justice—three ethical frameworks are proposed in this study for the deployment and oversight of deep learning systems in healthcare. This study presents these three distinct yet interconnected ethical frameworks focusing on patient privacy, transparency, and bias mitigation. The patient privacy framework argues for the importance of patient autonomy. It advocates for informed consent, emphasizing the need for patients to be apprised of the system\u27s workings, benefits, potential risks, and alternatives. Consent should be voluntary, devoid of implicit coercion, and patients must retain the right to revoke it without repercussions. The framework also included the principles of transparency, beneficence, privacy, continual consent, accessibility, and accountability. It champions the idea that consent is dynamic, necessitating regular updates, especially when significant system changes occur. Our ethical framework for transparency accentuates the need for full disclosure. Stakeholders should be provided with a general overview of the system\u27s operations, its inputs, and decision-making processes. Performance metrics, including accuracy, sensitivity, and specificity, should be transparently communicated. Openness, through open-source initiatives and third-party audits, is promoted. The principles of accountability, data transparency, continuous improvement, inclusivity, and external validation are also made integral to this framework, ensuring that stakeholders are consistently informed and engaged. The bias minimization framework highlights the imperative of awareness. Stakeholders should be educated about potential biases and their ramifications. The system should be regularly evaluated for inherent biases, both overt and subtle. Representation is crucial; training data must reflect diverse populations, considering various demographic factors. This framework also promotes fairness, ensuring equitable system performance across different patient groups. Transparency in bias reporting, accountability in bias correction, continuous monitoring, inclusivity in stakeholder engagement, and collaboration with interdisciplinary teams are also included and discussed
Proactive Fault Tolerance Through Cloud Failure Prediction Using Machine Learning
Get PDFOne of the crucial aspects of cloud infrastructure is fault tolerance, and its primary responsibility is to address the situations that arise when different architectural parts fail. A sizeable cloud data center must deliver high service dependability and availability while minimizing failure incidence. However, modern large cloud data centers continue to have significant failure rates owing to a variety of factors, including hardware and software faults, which often lead to task and job failures. To reduce unexpected loss, it is critical to forecast task or job failures with high accuracy before they occur. This research examines the performance of four machine learning (ML) algorithms for forecasting failure in a real-time cloud environment to increase system availability using real-time data gathered from the Google Cluster Workload Traces 2019. We applied four distinct supervised machine learning algorithms are logistic regression, KNN, SVM, decision tree, and logistic regression classifiers. Confusion matrices as well as ROC curves were used to assess the reliability and robustness of each algorithm. This study will assist cloud service providers developing a robust fault tolerance design by optimizing device selection, consequently boosting system availability and eliminating unexpected system downtime
Epidemiology and Etiology of Schizophrenia
Get PDFThe present landscape of therapy options for schizophrenia includes a large number of well-established atypical antipsychotic drugs. These medicines may assist in the management of positive symptoms of the condition, such as hallucination, delusion, and disordered speech. On the other hand, this is only successful in treating a subset of individuals. In addition, the influence of antipsychotic medicine that is currently on the market has a little effect on the cognitive plus negative schizophrenia symptoms. As a consequence of this, the market for schizophrenia requires treatments that use innovative mechanisms of action. At the present, every atypical antipsychotic medicine target dopaminergic transmission, and as a result, they all have a comparable effectiveness profile when it comes to lessening the intensity of psychotic actions and thoughts. The protection profiles of these two options vary from one another in only very minor ways. Unfortunately, not all patients respond well to atypical antipsychotic medication
Selecting Optimal Overseas Warehouse Location in Global Supply Chain: An Application of Binary Integer Programming
Get PDFOverseas warehouses are crucial nodes in today\u27s worldwide supply chain. They offer timely delivery of products to remote customers at a low cost and with few shipping issues. A foreign warehouse is a cross-border solution that may be used to solve cross-border B2B and B2C transactions. Strategically positioned warehouses allow organizations to effortlessly cross borders across multiple countries, regardless of where company headquarters and production or assembly units are situated. We argued that selecting an overseas warehouse site has always come down to determining where the majority of a company\u27s foreign clients are located such that when a customer places an order, it is fulfilled by the distribution warehouse closest to that client. In this study, a warehouse site optimization framework was developed using Binary Integer Programming to help decision-makers choose the best region, or sites, to build an overseas warehouse facility to meet expected customer demands. The proposed model is focused on determining the appropriate placement of the warehouse from a set of potential locations in order to decrease the travel distance between warehouse facilities and overseas customers. The overseas customers are assumed to be served by the warehouse that is closest to them geographically. When the number of to be overseas customers served is large, they might be organized into clusters. This pre-processing is based on the assumption that the warehouse charged with servicing the overseas customers of a certain cluster would care for all of them in that cluster. We presented four distinct case situations with varying characteristics. The k-means approach is used within the context of Binary Integer Programming to divide C overseas customers into G distinct and non-overlapping subgroups. Warehouse site influences transportation costs, and when new supply routes are introduced or a current supply chain is re-engineered, a thorough warehouse placement analysis is required. The proposed model would hope to help choose the best location for warehouses as well as other immovable infrastructure in the global supply chain
Web Traffic Prediction Using Autoregressive, LSTM, and XGBoost Time Series Models
Get PDFWeb traffic is vital to the success of any online company or website in the current era of digital technology. Insightful marketing, web development, and resource allocation choices may be made with the support of reliable online traffic forecasts. In this study, we investigate the effectiveness of the Autoregressive (AR), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost) time series modeling strategies for forecasting website traffic. We evaluate the accuracy of these models in forecasting future online traffic by comparing their results on a real-world dataset. The performance of four different models for predicting a target variable was evaluated based on the provided information. The AR model had the highest test error, indicating poor performance, while the ARIMA model had a lower test error than the AR model, but its high SMAPE value on the training dataset suggested overfitting. The LSTM model had the lowest test error, but its high SMAPE value on the training dataset indicated that it may not have captured underlying patterns in the data well. The XGBoost model had a relatively low test error, suggesting good performance, and performed slightly better on the testing dataset than the ARIMA model. The study did not consider external factors that may impact website traffic, such as changes in search engine algorithms or other external shocks. These external factors can significantly impact website traffic, and not considering them may limit the generalizability of our study\u27s findings