Shortage Of Skilled Professionals Research Articles

AI-driven cyber threat detection for global logistics in United States

The integration of AI-driven cybersecurity systems is increasingly essential to the security and efficiency of global logistics operations in the United States. This thematic analysis investigates the major themes, challenges, and opportunities of AI in logistics cybersecurity, focusing on its effectiveness in threat detection and prevention, diverse applications, and implementation challenges. AI has been shown to significantly enhance threat detection, reducing response times by up to 65%, and proactively preventing cyber threats through predictive analytics. Key applications include AI-driven network monitoring, endpoint security, and supply chain risk management, each contributing to the overall resilience of logistics networks. However, several challenges hinder the widespread adoption of AI, including high initial costs, integration issues with legacy systems, and a shortage of skilled professionals. Additionally, regulatory compliance and ethical considerations, such as data privacy and transparency, must be addressed to ensure responsible deployment. Despite these barriers, empirical evidence highlights AI’s potential to revolutionize logistics cybersecurity, offering faster threat detection, reduced vulnerabilities, and more secure operations. Overcoming adoption challenges requires a concerted effort from industry stakeholders, with a focus on workforce development, regulatory adherence, and ethical AI practices. Future research should explore strategies to address these challenges, ensuring the full potential of AI in enhancing the security and efficiency of global logistics operations.

Business Intelligence and Artificial Intelligence for Sustainable Business Operations

In the modern business landscape, sustainability has become a fundamental goal for organizations, driven by growing environmental concerns, social responsibility, and the need for long-term profitability (Bocken et al., 2014). Companies are under increasing pressure to reduce their environmental footprint, optimize resources, and improve operational efficiency, all while maintaining competitiveness. Business Intelligence (BI) and Artificial Intelligence (AI) have emerged as key technologies in this transition, offering organizations the ability to make data-driven decisions that promote sustainability (Chen et al., 2020). BI encompasses tools and techniques that convert raw data into actionable insights, helping businesses optimize operations and minimize waste (Shollo & Galliers, 2016). On the other hand, AI, particularly machine learning and predictive analytics, enhances decision-making by forecasting trends, automating processes, and providing deeper insights into complex datasets (Jeble et al., 2020). This article explores the integration of BI and AI in driving sustainable business operations. It examines their individual contributions and the synergistic benefits they bring when combined. Key applications discussed include energy management, where BI helps track energy consumption patterns, and AI optimizes resource allocation to minimize waste (Kemp et al., 2021). In supply chain optimization, BI analyzes supplier performance and inventory levels, while AI forecasts demand and automates processes to reduce carbon footprints (Saghafian et al., 2020). Waste reduction efforts are enhanced through predictive analytics, which help anticipate production needs and reduce excess output (Karim et al., 2021). Environmental monitoring, powered by AI and IoT sensors, allows for real-time analysis of environmental conditions, ensuring compliance with sustainability standards (Khan et al., 2021). However, the implementation of these technologies also presents challenges. Data integration remains a significant barrier, as companies often face difficulties in harmonizing large datasets from disparate sources (Laudon & Laudon, 2019). The initial investment in BI and AI technologies can be high, making it difficult for small and medium-sized enterprises (SMEs) to adopt these solutions (Zhang et al., 2020). Additionally, a shortage of skilled professionals in data science and AI poses another challenge, limiting the effective use of these technologies (Brynjolfsson & McAfee, 2014). Despite these challenges, the potential of BI and AI to foster sustainable business operations is substantial, and overcoming these barriers will be key to unlocking their full potential. The article concludes by discussing strategies for successful implementation and the future outlook for BI and AI in sustainable business practices.

Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities

The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.

ATENÇÃO PRIMÁRIA À SAÚDE: IMPLICAÇÕES PARA A PROMOÇÃO DA EQUIDADE NO SUS

This research aimed to analyze the importance of Primary Health Care (PHC) in the promotion of equity in the Unified Health System (SUS), highlighting its contributions to the reduction of health inequalities and its role in universal access to care. To this end, a bibliographic research was carried out by surveying articles in the SciELO, PubMed and Google Scholar databases. The results indicated that PHC, through the Family Health Strategy (FHS), has been fundamental in promoting comprehensive and continuous care, especially in vulnerable areas, and is effective in disease prevention and health promotion. However, challenges such as underfunding, a shortage of skilled professionals, and a lack of infrastructure jeopardize its full implementation, especially in poorer regions. The research concludes that, although PHC has enormous potential to reduce health inequalities and improve access to services, greater investment and improved management is needed to ensure its effectiveness and universality, in addition to the importance of the qualification of professionals and the active participation of the community in health management.

Artificial Intelligence (AI) and Health Communication Policy in Nigeria: Challenges and Prospects

Advances in Artificial Intelligence (AI) are reshaping health communication worldwide, a key aspect of enhancing public health. This study analyzed current health communication policies to pinpoint areas where AI-driven approaches are yet to be addressed. In Nigeria, integrating AI into health communication policies presents both opportunities and hurdles. The study examined how AI can boost health communication initiatives in Nigeria, particularly through AI-powered tools that enhance the spread of health messages, patient education, and public awareness campaigns. Additionally, the study highlighted challenges in implementing AI technologies, including limited infrastructure, inadequate data management, and a shortage of skilled professionals. The paper also presented the potential of Artificial Intelligence (AI) to transform health communication. It suggested policy guidelines to support its integration while reducing risks. Additionally, it recommended policy changes, training, and cooperation among stakeholders to leverage AI's power in health communication. In Nigeria, the outlook for AI in health communication is optimistic, assuming focused investments in technology, training, and regulatory reforms.

Using Data Science in Business: A Focus on Uzbekistan

This article examines the increasing role of Data Science in business, with a focus on Uzbekistan. It discusses the application of key Data Science techniques, such as machine learning, big data analytics, and data visualization, in sectors like banking, telecommunications, and retail. The benefits of Data Science, including market forecasting and risk management, are contrasted with the challenges faced by businesses in Uzbekistan, such as a shortage of skilled professionals and inadequate technological infrastructure. Additionally, successful case studies from both Uzbekistan and international companies are provided to illustrate the practical impact of Data Science. The article concludes by highlighting the importance of Data Science in driving Uzbekistan's digital transformation and improving its business competitiveness. Data Science consists of various methods and tools, including Machine Learning, Big Data Analytics and Data Visualization. These tools collectively enable businesses to extract insights and drive decision-making.

The State of Telepathology in Africa in the Age of Digital Pathology Advancements: A Bibliometric Analysis and Literature Review.

Telepathology emerges as a vital tool, offering significant promise for enhancing pathology services in Africa, a region historically challenged by healthcare access and resource limitations. This review explores the development, adoption, and impacts of telepathology in Africa through a comprehensive bibliometric analysis and literature review. A methodical search in PubMed for publications up to 2024 revealed 119 pertinent studies, out of which 47 met the inclusion criteria for a focused review on telepathology's role in African healthcare settings. This research has charted a clear trajectory of growing interest in telepathology, as evidenced by the annual increase in related publications and robust international collaboration. It underscores the expanding utility of telepathology in diagnostics, education, and research within Africa, particularly in domains like dermatopathology, neuropathology, and, notably, oncology. The integration of artificial intelligence into telepathology presents new frontiers for enhancing diagnostic accuracy and efficiency. However, the review also identifies persistent challenges such as infrastructural inadequacies, a shortage of skilled professionals, and regulatory hurdles. The study highlights the indispensable role of international partnerships in advancing telepathology in the region. This review proposes a strategic pivot toward "leapfrogging," an approach that allows Africa to skip traditional developmental hurdles by directly adopting cutting-edge technologies and practices.

Demand for and training of Chinese translators in the context of the �One Belt, One Road� initiative

Relevance. The need to investigate the specifics of Chinese translator training within the framework of the global programme �One Belt, One Road� is primarily conditioned by the constant changes in various aspects, particularly in structural, semantic, and artistic aspects. Purpose. The purpose of this study was to investigate the global initiative �One Belt, One Road� in the context of training professional human resources to perform Chinese language translations. Methodology. This study analysed materials available on the websites of several higher education institutions. The analysis focused on specific parameters such as the distinct features of training students in Chinese studies, the education of translators working with Chinese, and the principles, methods, and techniques used in teaching the Chinese language. Results. Analyses of translator training have identified key skills for students: parallel language and culture study, adherence to translation norms like business etiquette, and mastery of technical translation tools. In Kyrgyz universities, methods include verbal (lectures, discussions), visual (audio and video materials), and practical (creative works, exhibitions, quizzes, conferences). For Chinese translators under the �One Belt, One Road� initiative, training spans multiple linguistic levels: phonetic (phrases, accents), grammatical (categories, meanings), lexical (vocabulary related to the initiative and various fields), syntactic (word order, links), and stylistic (business etiquette, unique vocabulary). Conclusions. The study found that the �One Belt, One Road� initiative's language policy focuses on planning Chinese language status, foreign languages, language structure, educational language use, and language services. The key challenges are the shortage of skilled professionals, intergovernmental cooperation, infrastructure standards, financial integration, and cultural exchange. In the future, this study can be used to develop technical improvements in Kyrgyz-Chinese translation, to improve the training of Chinese specialists within the framework of the global initiative �One Belt, One Road�. Keywords: lingua franca; linguistic levels; linguistic policy; linguistic parameters; terminological units

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review.

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases-fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation-the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes- crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs.

Compliance of Pharmaceutical Manufacturing Companies to Good Manufacturing Practices in Heating, Ventilation, and Air‐Conditioning Systems: The Case of Local Ethiopian Firms

Background: Good manufacturing practice (GMP) is a part of quality management that maintains product quality and manages it according to the criteria of fitness for use. The heating, ventilation, and air‐conditioning (HVAC) system comprises the equipment, technology, and procedure that ensure product quality through maintaining heat, ventilation, and coolness of pharmaceutical manufacturing firms. The aim of the study was to evaluate the GMP compliance of HVAC systems and assess the opportunities and challenges of improving these systems in pharmaceutical manufacturing companies in Ethiopia.Methods: The study was conducted in eight local pharmaceutical manufacturing companies in Ethiopia from April 20 to August 30, 2021, by using a concurrent mixed‐method approach to evaluate the implementation of GMP in HVAC systems. The pharmaceutical firms were directly observed by using a structured and standard observational checklist that was adopted from the WHO minimum GMP requirements. In order to understand the challenges and opportunities, face‐to‐face interviews with key informants using a purposive sampling technique were conducted.Results: The study findings revealed that the local pharmaceutical companies applied for 67.1% of the GMP requirements of the HVAC systems. The GMP implementation status in the basic quality features of HVAC systems of local pharmaceutical companies was 75% on premises, 70.67% in HVAC system design and maintenance practices, 56.25% in product protection, 61.25% in environmental protection, and 75% in cross‐contamination prevention. The primary obstacles to implementing GMP in the HVAC systems were a shortage of skilled professionals, HVAC system spare parts, and foreign currency besides poor practice of HVAC system calibration.Conclusion: The study revealed that the local pharmaceutical manufacturing companies did not adhere to GMP standards in HVAC systems.

Convolutional neural network-based analysis of pediatric chest X-ray images for pneumonia detection

Detection of pneumonia is generally done using chest X-ray images, traditionally assessed manually by radiologists. However, this method has limitations, including the potential for human error, variability in interpretation, and a shortage of skilled professionals, particularly in resource-limited settings. Prompted by these challenges and the increasing potential of machine learning in medical diagnostics, we investigated the efficacy of advanced computational models in distinguishing between normal and pneumonia-affected lung images. We hypothesized that an adapted version of the VGG16 model, a convolutional neural network (CNN), would outperform the standard VGG16 and simpler Multilayer Perceptrons (MLPs) in terms of accuracy and reliability. Utilizing a dataset from the Guangzhou Women and Children’s Medical Center, we evaluated the performance of these three models on pediatric chest X-ray images. The MLP showed moderate effectiveness with 78.4% accuracy but struggled with complex image data. The standard VGG16 achieved better results with 90.9% accuracy but displayed overfitting tendencies. The adapted VGG16 model, with reduced filter sizes and dropout layers, demonstrated the highest accuracy at 95.6%, indicating superior performance and stability. These findings suggest that tailored deep learning models like the adapted VGG16 can significantly enhance pneumonia diagnosis from chest X-ray images, offering a balance of accuracy, efficiency, and generalizability. This advancement holds substantial implications for improving diagnostic processes in pediatric healthcare, particularly in settings with limited resources.

Evolution of Tourism through Travel Agencies: A Case Study on Bangladesh

The aim of this research paper is to examine the role of travel agencies in the advancement of tourism in Bangladesh. Travel agents are currently assuming a significant role in facilitating this relationship. The aforementioned entities engage in the production of various package tours, distribution of these packaged products, and promotion of tourism in Bangladesh to potential tourist clientele. However, the travel agents in Bangladesh are limited in their ability to fulfil their job to its fullest degree. This is mostly due to the small size of their organizations, insufficient money, a shortage of skilled professionals, a lack of government policy support, and several other restraints that they encounter. It is imperative to address these issues and promptly implement appropriate measures to enhance the performance of travel agencies and facilitate the desired growth of the tourism sector in Bangladesh.

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.

Maternal health outcomes in the context of fragility: a retrospective study from Lebanon

Background and aimsThe Lebanese healthcare system faces multiple challenges including limited capacities, shortage of skilled professionals, and inadequate supplies, in addition to hosting a significant number of refugees. While subsidized services are available for pregnant women, representing the majority of the refugee population in Lebanon, suboptimal access to antenatal care (ANC) and increased maternal mortality rates are still observed, especially among socioeconomically disadvantaged populations. This study aimed to review the maternal health outcomes of disadvantaged Lebanese and refugee pregnant women seeking ANC services at primary healthcare centers (PHCs) in Lebanon.MethodsA retrospective chart review was conducted at twenty PHCs in Lebanon, including Ministry of Public Health (MOPH) and United Nations Relief and Works Agency for Palestine refugees (UNRWA) facilities. Data was collected from medical charts of pregnant women who visited the centers between August 2018 and August 2020. Statistical analysis was performed to explore outcomes such as the number of ANC visits, delivery type, and onset of delivery, using bivariate and multivariable logistic regression models.ResultsIn the study, 3977 medical charts were analyzed. A multivariate logistic regression analysis, revealed that suboptimal ANC visits were more common in the Beqaa region and among women with current abortion or C-section. Syrians had reduced odds of C-sections, and Beqaa, Mount Lebanon, and South Lebanon regions had reduced odds of abortion. Suboptimal ANC visits and history of C-section increased the odds of C-section and abortion in the current pregnancy. As for preterm onset, the study showed an increased likelihood for it to occur when being Palestinian, having current C-section delivery, experiencing previous preterm onset, and enduring complications at the time of delivery.ConclusionThis study suggests the need for low-cost interventions aiming at enhancing access to ANC services, especially among pregnant women in fragile settings.

Integrating Block chain Technology to Harness Big Data: Methods, Obstacles, and Future Outlook

The distributed and secure nature of blockchain technology makes it an effective tool for combating corruption, increasing transparency, and streamlining processes across fields like healthcare, government, and finance. This research demonstrates that blockchain technology can revolutionize government transparency, financial management, and corporate structures. In nations with high corruption indices, combining blockchain with forensic accounting significantly enhances the openness and accountability of anti-corruption efforts. System integration challenges, legal constraints, a shortage of skilled professionals, and scalability issues hinder blockchain adoption from reaching its potential. Overcoming these obstacles requires further research, legal frameworks, and cross-sector collaboration. To enhance decision-making in a data-driven economy and maximize blockchain's potential, a governance model that integrates big data analytics with blockchain could enable secure, transparent, and scalable adoption

Ethical issues in long-term care settings: Care workers' lived experiences.

Professional care workers face ethical issues in long-term care settings (LTCS) for older adults. They need to be independent and responsible, despite limited resources, a shortage of skilled professionals, global and societal changes, and the negative reputation of LTCS work. Our aim was to describe the care workers' lived experiences of ethical issues. The findings can be used to gain new perspectives and to guide decision-making to improve the quality of care, occupational well-being and nursing education. Focus group interviews were analyzed using a hermeneutic-phenomenological method. The analysis comprised three steps: naïve reading, structural analysis, and comprehensive understanding. We randomly sampled LTCS service providers in Finland and 53 care workers with different educational backgrounds from seven organizations participated in focus group interviews in 2021. This was a sensitive study, which was connected to the participants' individual views of the world, professional ethics and social and health care legislation. The participants' provided informed consent and their anonymity was guaranteed. Care workers spoke about their lived experiences of ethical issues in an emotional way, using practical examples. They talked about how they were experts at caring and advocating for residents, balanced the responsibilities of their different roles, and defended their work to the wider society. The care workers said that ethical aspects of their work were too difficult to solve on their own. There were elements of their working environment and practices that caused unnecessary strain and they needed the commitment of managers, organizations, and society to solve ethical issues in LTCS. Ethical issues were related to the well-being of both residents and care workers and reflected both internal and external pressures. Some issues could not be resolved by individuals and needed input from managers, organizations, and society.

An Artificial Intelligence Cycle Model Against the Shortage of Skilled Professionals - An AI-based Holistic Solution Approach for Human Resources

In order to counter the impending shortage of skilled professionals in the aging societies of our time in many western countries such as Germany, solutions for business and society are urgently needed. Here, artificial intelligence (AI) can play an important role in mitigating the problem with the help of diverse applications. At the same time, it is important to consider both the needs of the respective employee and the company to ensure that the use of AI has a positive impact on the organization and finds social acceptance. In this article, we describe the newly developed OSQE model (Optimize, Secure, Qualify, Expand) shown in Figure 1 from Annex, which for the first time outlines an AI cycle against the shortage of skilled professionals in a holistic approach that focuses equally on people and companies. This can serve organizations as a guide for strategy development, decision-making for and implementation of AI-supported measures in an entire cycle of an employee's affiliation with a company. The model takes three driving forces into account: companies, professionals, and AI applications. In the model, the measures to be implemented are prioritized with ascending numbering based on what would be most urgent for a company to implement. All measures relate to areas of action that place people at the center and can be assigned to the classic cycle of belonging of an employee in the company. In this regard, the opportunities that AI offers to professionals and companies are highlighted.

Artificial Intelligence And Cyber Security

The rise of cyber threats has made it necessary for organizations to adopt more advanced and effective security solutions. AI-powered cybersecurity tools can help organizations detect and respond to potential cyber threats faster, more efficiently, and more accurately. These tools use machine learning algorithms to analyze vast amounts of data, including network traffic, user behavior, and security-related events, and can identify patterns and anomalies that could indicate potential threats. However, the use of AI in cybersecurity comes with challenges, including the potential for bias and errors in AI-based systems and a shortage of skilled professionals who can develop, deploy, and maintain these systems. Organizations must weigh the benefits and challenges of using AI in cybersecurity and develop a comprehensive strategy that incorporates the right mix of people, processes, and technology to ensure optimal cybersecurity posture

The perfect cyber storm – security budgets, skills shortages and automated attacks

A combination of economic pressures, sophisticated assaults that maximise returns and a shortage of skilled professionals now mean that businesses can expect to become increasingly vulnerable to attack as they enter 2023.

Assessing the psychosocial work environment of migrant and non-migrant workers in inpatient mental health centres: A feasibility study.

The number of migrant workers in Germany has increased over the last decades and will probably further increase in the context of a growing cultural diversity of the population and shortage of skilled professionals. Since migrant workers face different challenges, they may experience poorer psychosocial work environments than non-migrants. A negative psychosocial work environment can increase burnout and depression symptoms. To this date no study has investigated differences in the perceived psychosocial work environment in the mental health field. The aim of this study is to evaluate the feasibility of a cross sectional study comparing the perceived psychosocial work environment of migrants and non-migrant workers in inpatient mental health centres in Germany. The study was conducted in four inpatient mental health centres in Germany using the Copenhagen Psychosocial Questionnaire. All staff members (N = 659) categorized in seven professional groups were invited to participate in the study. The feasibility of the study was determined by four criteria (1) Implementation of the study in inpatient mental health centres (2) Representativity of the sample (3) Reliability and usability of the questionnaire and (4) Variability of collected data. Three of four feasibility criteria were achieved. The study was successfully implemented in four mental health centres, the usability of the used questionnaire was confirmed as well as the variability of the data. The targeted response rate was partially met, and the total number of migrant workers could not be provided, which limits the representativity of the sample. In conclusion, a main study is feasible, but an effort must be put in an effective recruitment strategy to obtain valid results.