Global journal of Business and Integral Security

Challenges of Labor Migration: Perception of Domestic/Foreign Workers and Management on Occupational Health and Safety in Croatia

2024-12-02T07:41:54-08:00

Publicly available data shows that a very high percentage of foreign workers are employed in the construction sector in the Republic of Croatia. Labor migration brings challenges and opportunities for economic and social development, but also challenges in the implementation of health and safety measures in the workplace. In order to develop effective safety measures and preventive strategies, it is important to understand how the different groups, e.g. local and foreign workers and management, perceive safety. There are legal frameworks and guidelines for the implementation of health and safety measures to ensure the highest possible level of safety. Within this framework, no distinction is made between foreign workers, local workers and management. Since the perception of safety is a fundamental aspect of workplace safety and plays a key role in protecting the well-being of workers, it is necessary to further investigate how occupational safety and health is perceived by foreign and domestic workers and management in the Republic of Croatia and whether there are differences between these groups. The paper provides an overview and conclusions from the relevant recent research as well as an announcement of empirical research in Croatia.

Impact of Fine-Tuning on Model Training: A Comprehensive Study Using the SQuAD Benchmark

2024-12-02T07:38:57-08:00

Purpose - This study investigates the effect of fine-tuning on model training, and the impact of model training on the environment in terms of carbon footprint emissions. As language modeling evolves, we are obtaining complex and large models with a large number of trained parameters. However, only large enterprises are able to train these large models using large datasets. It is not possible for small and mid-size enterprises due to their computation resources capability. This study proposes a fine-tuning model training pipeline for such enterprises to get the same model capability as large enterprises with less number of model parameters training using SQuAD benchmark. The pre-trained BERT base model is incorporated in combination with fine-tuning methods in this study.
Findings - The results explain us that fine-tuned models need less computation time and less number of trainable parameters without losing too much model capabilities compared to the fully trained model, which indirectly helps us to protect the environment as it emits less carbon footprints.

Onboarding in the Humanitarian Sector Using Digital Platforms: The IFRC Global Onboarding Portal Case Study

2024-12-02T07:35:24-08:00

The humanitarian sector faces unique challenges, including the need for rapid mobilisation, cultural diversity and limited resources. Digital transformation, particularly in onboarding processes, offers strategic solutions by streamlining operations, improving efficiency, communication, collaboration and staff engagement, while ensuring alignment with the organisation's mission. This article explores the role of digital platforms, specifically the International Federation of Red Cross and Red Crescent Societies (IFRC) Global Onboarding Portal, in standardising onboarding processes across global teams. In addition, the research highlights the importance of agile project management strategies and human-centred design in the development of the onboarding programme. Future research is recommended to assess the long-term impact of the onboarding programme through longitudinal studies and to explore the potential of artificial intelligence to optimise onboarding processes.

Identify Consumer Brand Affinity During Point of Purchase Using SEM to Aid Business Growth

2024-12-02T07:29:44-08:00

This research is being executed to understand how the consumer perspective of brand equity based on the success achieved through top-of-mind presence resulting in business profitability. The important question is: What are the key drivers to a particular Brand’s success and failure and how it can be course corrected to achieve the desired outcome by tweaking a particular under-performing driver without any bias. The method involved in identifying the key drivers to enhance Brand equity is called Structural Equation Modeling (SEM).

Generating Recommendation Insights as Part of the Software Upgrade and Decommissions Life Cycle in the Information Technology Industry

2024-12-02T07:25:39-08:00

As part of Digital Transformation needs, the Organizations are investing more and more in the
Technology and Infrastructure like software upgrades, software renewals, software replacements,
Cloud migrations etc., apart from investment in Business, People, and Processes. In this context, it is
not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with
another software. There is no unified approach or solution available today which integrates key
system assets data such as Software Versions, Platform Compatibility, Dependent Software versions,
Investment and Operational Costs, Open defects and fixes, Software Performance Metrics and Service
level objectives. Due to this, the so-called decision making is a tedious process that takes time and
effort. This research paper proposes Software Upgrade and Decommissions Life Cycle and outlines
the requirements, design and build approach for generating recommendation insights. This also
proposes using Data Mining and Machine Learning models on the input data sets that are needed to
take a decision on software upgrade or software decommissioning.

Standardizing Emotional Bias to Address Unethical Billing Practices

2024-12-02T07:21:49-08:00

The proliferation of algorithm-driven billing systems, such as Google Ads and modern electricity meter billing systems, has opened the lid to significant ethical challenges. It will focus on the analysis of billing algorithms in Google Ads and electricity meter systems. The study will analyze the billing algorithm of Google Ads, a system accused of opacity in pricing and the likelihood of overpricing as a result of hidden charges, to find improvements that will increase transparency and standardization. It does apply to electricity meter billing systems, usually prone to estimation errors and some unexpected high bills, in developing measures to ensure accurate and fair billing. The standardized step of this research in carrying out these measures underlines the reduction of bias and enhancement of transparency toward the goal of a fairer environment of billing for consumers.

A Sustainable Value Chain Strategy for Steel Industry Decarbonization Featuring Danieli as a Case Study on Green Steel Technologies

2024-12-02T06:59:56-08:00

The steel industry, responsible for approximately 7-9% of global CO2 emissions and a significant source of industrial greenhouse gases, faces an urgent need for comprehensive decarbonization efforts to align with international climate targets, as outlined in the Paris Agreement and the International Energy Agency’s (IEA) Net Zero by 2050 report, to achieve long-term sustainability and global emission reduction goal. This paper discusses the solutions offered by DANIELI throughout the steel production process from beginning to end with a focus, on innovations from the upstream, to the downstream process. Key technologies include the hydrogen-based ENERGIRON Direct Reduction process, the Digimelter® EAF system, and the ECOGRAVEL® slag recycling solution, each significantly reducing emissions and energy consumption. The midstream stage features automation tools like Q-Melt for process optimization, while downstream efforts focus on emissions control with advanced filtration systems. Global collaborations, such as partnerships with Liberty Steel and Algoma Steel, illustrate Danieli’s commitment to transitioning steel production towards low-carbon, efficient, and circular processes. Danieli’s holistic approach sets a new standard for a sustainable, net-zero steel industry.

The Role of Transformative Technologies in Enhancing the Quality of Nursing Care During the COVID-19 Pandemic

2024-12-02T06:57:20-08:00

The COVID-19 pandemic has imposed unprecedented challenges on healthcare systems, and nursing care quality has become a focal point of concern. This paper aims to evaluate the pivotal role of transformative technologies in enhancing the quality of nursing care during the ongoing pandemic. The results provide insights into nurses’ adoption and utilization of digital tools and platforms to mitigate the unique challenges posed by COVID-19. Telemedicine, a cornerstone of these technologies, has enabled healthcare professionals to provide remote consultations and monitor patients, reducing exposure and improving access to care. Additionally, electronic health records have facilitated seamless information sharing among healthcare providers, enhancing care coordination and reducing errors. Telemonitoring technologies have empowered nurses to remotely observe patient vital signs and symptoms, allowing for early intervention and personalized care plans. Furthermore, wearable devices and mobile applications have promoted patient engagement and self-management, reinforcing the patient-nurse partnership.
In conclusion, transformative technologies have revolutionized nursing care quality during the COVID-19 pandemic, which expanded the nursing role in healthcare delivery and strengthened the ability to adapt to dynamic challenges. These technologies remain essential for keeping nursing care patient-centered, efficient, and safe throughout the pandemic.

Global AI Readiness Clusters: Patterns and Disparities in Governance and Technological Infrastructure

2024-12-02T06:54:50-08:00

Artificial intelligence (AI) increasingly stands out as an important driver of innovation and economic growth. However, in terms of preparedness to harness the full potential of AI, not all countries are equally well-placed. Such a wide gap mainly arises from government policies, technological infrastructure, and, above all, the mindset for innovative technologies. This study highlights the global urgency of AI preparedness, which may impact the future development of countries in areas such as agriculture, healthcare as well as education. It was based on several indicators, including measurements of government effectiveness and technology infrastructure, coupled with methods of data analysis such as clustering techniques as well as PCA that help classify countries in order regarding their preparation for AI. The results grouped countries in three cluster based on AI readiness, governance as well as information and communication technology infrastructure, which should be helpful for policymakers making decisions to further develop the AI landscape in various countries.

Combining Blockchain and AI for Fraud Detection: Building Secure, Transparent, and Sustainable Financial Ecosystems

2024-12-02T06:52:32-08:00

In today's rapidly evolving financial landscape, fraudulent activities within live transactional
systems present significant risks to economic stability and institutional trust. This paper explores
integrating Blockchain and Artificial Intelligence (AI) as a comprehensive strategy for detecting
and preventing fraud in real-time financial transactions. Blockchain provides a decentralized,
tamper-resistant ledger, ensuring secure, transparent data, while AI excels at identifying patterns
and anomalies indicative of fraud. By leveraging both technologies, this research proposes a
framework to improve fraud detection efficiency and reduce vulnerabilities in financial systems.
Smart contracts further enhance this by automating fraud detection. The paper also discusses the
challenges of implementing this integrated system, including Blockchain scalability and AI’s
computational demands. Ultimately, this combination has the potential to transform fraud
prevention strategies, contributing to more secure, transparent, and sustainable financial
ecosystems.