Technological Advances Research Articles

An Analysis of the Risks and Rewards of Merger and Acquisition Strategies in the Gaming Industry

Abstract: In the dynamic landscape of the gaming industry, companies strive for growth and competitiveness through strategic means, including mergers and acquisitions (M&A) for diversification. This paper examines the risks and rewards inherent in such strategies, focusing on their implementation within the gaming sector. Through case studies like Microsoft's acquisition of Mojang and Activision Blizzard's purchase of King Digital Entertainment, it explores the complexities of M&A diversification and its impact on long-term gains. Drawing from literature on M&A theories and diversification strategies, it delineates the nuances of M&A execution and integration, crucial for success in the fast-paced gaming environment. While successful diversification can lead to market expansion, technological advancement, and risk mitigation, failures like Activision Blizzard's struggle with King Digital Entertainment underscore the importance of strategic alignment, resource integration, and market foresight. By dissecting these cases, this paper offers insights for industry stakeholders to navigate the challenges and capitalize on the opportunities presented by M&A diversification in gaming.

Leadership Training in the Era of Digital Transformation: Case Study of Amazon

Abstract: Enterprises are entering the digital transformation era to adapt to rapidly changing market conditions and technological advancements. As organisations undergo digital transformation and reshape their business models and practices, this study explores how digital technology facilitates the development of new business models, changes corporate interaction channels, and the positive impact of digital transformation on leadership training. As a global leader in e-commerce and cloud computing, Amazon provides lessons and insights for other companies' leadership training in digital transformation through an in-depth study of its leadership training models and practices. This study explores in depth the impact of digital transformation on leadership training, analyses the effectiveness and challenges of leadership training in the context of digital transformation, and how to innovate leadership training models in the context of digital transformation. Analysing the case of Amazon enriches and improves the existing leadership training theories and provides new research perspectives and ideas for the academic community.

Fetal Face: Enhancing 3D Ultrasound Imaging by Postprocessing With AI Applications: Myth, Reality, orLegal Concerns?

The use of artificial intelligence (AI) platforms is revolutionizing the performance in managing metadata and big data. Medicine is another field where AI is spreading. However, this technological advancement is not amenable to errors or fraudulent misconducts. International organization and recently the European Union have released principles and recommendations for an appropriate use of AI in healthcare. In prenatal ultrasound diagnosis, the use of AI in daily practice is having a revolutionary impact. Notwithstanding, the diagnostic enhancement should be regulated, and AI applications should be developed to guarantee correct imaging acquisition and further postprocessing.

Impact and Prospects of Prepared Meals on the Catering Market

The increasing pace of modern life has catalyzed the rise of the prepared meals industry, reshaping consumer behaviors and market dynamics in the global catering sector. This study examines the impact of prepared meals on the catering market, focusing on consumer behavior, market dynamics, technological innovations, and sustainability issues. The prepared meals industry has seen exponential growth, driven by consumer demands for convenience, health, and environmentally friendly options. Technological advancements in food preservation, distribution, and packaging have played a pivotal role in improving product quality and safety while extending market reach. Financial analyses of key industry players like Tyson Foods, Inc., Nissin Foods Holdings Co., Ltd., and Nestl S.A. reveal varied strategies for capitalizing on this growth, highlighting significant contributions to their overall business performance. The sector faces challenges including environmental sustainability, health and nutritional scrutiny, and intense market competition. Strategic responses to these challenges, such as investing in sustainable practices, focusing on health and nutrition, leveraging new technologies, and enhancing consumer engagement, are essential for future success. This research provides critical insights into the evolution of the prepared meals market, offering a comprehensive overview that could guide stakeholders in navigating this dynamic landscape.

The Impact of the Development of Artificial Intelligence on Unemployment Rates

Abstract: The proliferation of artificial intelligence (AI) is a transformative force in global labor markets, presenting dual possibilities: the displacement of traditional jobs and the creation of new employment opportunities. This paper explores the dynamic relationship between AI development and unemployment rates, using empirical data and regression analysis from 2012 to 2022. It examines the impact of AI across various sectors, highlighting how technological advancements and public engagement with AI via platforms like Google Trends influence employment patterns. In addition, it also reveals that AI development has a generally negative correlation with unemployment rates, suggesting that increased AI adoption may be linked to lower unemployment levels. The findings also underscore the importance of adaptive policies and education systems to harness AIs potential for job creation, which contributes to the discourse on AIs economic implications by providing a nuanced analysis of its influence on employment, essential for policymakers and stakeholders in navigating the challenges and opportunities presented by AI technologies.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

From Algorithms to Market Dynamics: A Literature Review on High-Frequency Trading

Abstract: High-frequency trading (HFT) uses advanced technologies and sophisticated algorithms to make transactions at unprecedented speeds, which could significantly impact modern financial markets. This literature review examines the technological foundations, primary trading strategies, market impacts, regulatory environment, risk management practices, and future research directions in HFT. The findings reveal that HFT could increase market liquidity and price discovery but also increase volatility during periods of high market stress. Regulatory frameworks such as the SECs Market Access Rule and MiFID II aim to monitor and control HFT activities, at the same time effective risk management practices are crucial for maintaining market stability. Future research should focus on emerging technologies such as AI, ML, quantum computing, and blockchain, along with a better understanding of market structure and global regulatory coordination. This review provides valuable insights for market participants, regulators and researchers, contributing to a balanced perspective on HFTs role in contemporary financial markets.

A Case Study on the Possibility of Extending the Service Life of the Demining Machine Belt

The operational practice of the design of the Bozena 5 demining machine has shown that its belts are the critical component that fundamentally affects the functionality of the entire machine. This article is a practical continuation and extension of the previous research results from the point of view of materials (research of the uniaxial fatigue life in bending and torsion), calculation (creation of the necessary mathematical, analytical and numerical models for the research) and construction (i.e., patented design of the belt tensioning of this machine). All these actions are aimed at a single objective—to achieve a condition that guarantees a sufficient service life without malfunctions, since repairing these machines in the field is often impossible. Therefore, this study examined the fatigue life of welded joints (uniaxial bending and torsion) of S960 QL and S500MC steels welded by MAG technology. Subsequently, the data were compared with previous results (electron and laser welds) and the influence of each type of weld on the fatigue life relative to the base material was discussed. It was found that conventional MAG technology had a more significant negative impact on the fatigue life of the base material than non-conventional technologies. This trend was particularly true for the bending stress. At the same time, the bending stress was identified by the FEM analysis as the dominant load on the belt. The maximum stress in the belt link under the considered boundary conditions was approximately 240 MPa (in bending). This stress corresponded to the continuous fatigue life (more than 107 cycles) for both base materials tested (S960QL, S500MC). In the whole studied spectrum of controlled deformation amplitudes (Manson–Coffin), the life of MAG welds was lower in comparison with the base material and with welds made by unconventional technologies. All the activities carried out so far (research on microstructure, hardness, strength, residual stresses, tribological properties and fatigue life) have shown that the original belt design (S500MC) using MAG technology has significant deficiencies in the state of optimal life. It is expected that the proposed material change (use of S960QL instead of S500MC) and work with advanced technologies will bring this state significantly closer.

The Rise of Digital Banking in the UK Financial Sector and Its Impact on Commercial Banks

In the contemporary financial domain, the emergence of digital banks has revolutionized operational models in traditional commercial banking. This article delves into how digital banks, through advanced technology and customer-centric services, compel commercial banks to revaluate their operational and regulatory frameworks. Specifically, it focuses on the digital banking market in the UK, analyzing how digitalization and technological progress are driving the evolution of competitive dynamics within the financial sector. As one of the worlds foremost financial hubs, the UKs trends in financial market development have profound implications for the global financial system. The British government and regulatory bodies have proactively adapted to these shifts, framing policies that facilitate innovation while ensuring security and compliance within digital banking. This backdrop sets the stage for this report to explore the transformative impact of digital banks on traditional banking models in the UK, aiming to understand the regulatory adaptations and the overall influence of digital entities on the conventional banking landscape.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

Research on Vocational Education Reform and Innovation Based on Information Technology Practice

Based on the perspective of information technology reform, higher requirements have been put forward for student management and course teaching in vocational colleges. Therefore, teachers should actively explore suitable opportunities and practical paths to introduce information technology and rely on advanced technology, equipment, and software to promote the reform and development of virtualization, datafication, and intelligence in course teaching. Due to the limitations and influences of various practical factors, teachers face many problems in the application of information technology, which to some extent hinder classroom teaching reform and innovation. Given this, this article discusses the application of information technology, which plays an important role in classroom teaching reform and student ability improvement. This study will briefly explain the concept and characteristics of information technology, analyze the existing problems in current information technology teaching, and finally propose specific reform and innovation practice paths.

Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment.

Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.
Approach. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.
Main Results. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.
Significance. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.

Simulating unsteady flows on a superconducting quantum processor

Recent advancements of quantum technologies have triggered tremendous interest in exploring practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a potential direction. Here, we report an experiment on the digital simulation of unsteady flows with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. Note that the former case is an inviscid potential flow, and the latter one is an artificial vortical flow with an external body force. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications.

Spatially resolved metabolomics: From metabolite mapping to function visualising.

Mass spectrometry imaging (MSI)-based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography-tandem mass spectrometry (LC-MS)-based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI-driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation. HIGHLIGHTS: MSI-driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.

Generation of Circularly Polarized Strong-Field Terahertz Waves

Abstract Terahertz (THz) circular dichroism (TCD) spectroscopy has been widely used to study the chiral characteristics of biological macromolecules and various other materials. With the rapid development of strong-field THz generation and field-modulated techniques, the advancement of tunable strong-field TCD spectroscopy technology is of great significance. In this work, we built an integrated strong-field TCD spectroscopy system. We employed the tilted-pulse-front technique to generate linearly polarized strong-field THz radiation and a reflective metasurface for linear-to-circular conversion of the strong-field THz radiation. We obtained circularly polarized THz radiation with an ellipticity of over 0.9 in the frequency range from 0.38 THz to 0.61 THz, with a LTC conversion efficiency of over 90%. The peak electric field strength of the obtained strong-field circularly polarized THz radiation exceeded 100 kV/cm. This system is expected to be used to study the chiral characteristics of materials under strong-field conditions, as well as changes in the chiral characteristics under different field conditions.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Transforming Assessment in Education: A Critical Reflection

Abstract: This article systematically explores the evolution of educational assessment, particularly the transition from traditional summative assessments to student-centered formative assessments. Traditional summative assessments, such as final exams and standardized tests, focus on evaluating students' learning outcomes at the end of an instructional period, emphasizing final scores. In contrast, formative assessments focus on continuous feedback throughout the learning process, helping students adjust their learning methods and improve self-regulation skills. The article highlights that formative assessments not only foster the development of critical thinking and problem-solving abilities but also encourage students' self-directed learning and reflection. Additionally, with the advancement of modern technology, educational assessments have become more flexible and personalized, utilizing data analysis and online tools to enable teachers to monitor students' learning in real-time and make dynamic adjustments. The introduction of culturally responsive assessments ensures fairness by considering students' diverse backgrounds, reducing potential biases. The article emphasizes that educational assessments should continuously adapt to changing educational needs, providing practical guidance for educators to design effective strategies, which play a significant role in improving student learning outcomes and teaching quality.

Predictive modeling of compressive strength in silica fume‐modified self‐compacted concrete: A soft computing approach

AbstractSelf‐compacting concrete (SCC) is a specialized type of concrete that features excellent fresh properties, enabling it to flow uniformly and compact under its weight without vibration. SCC has been one of the most significant advancements in concrete technology over the past two decades. In efforts to reduce the environmental impact of cement production, a major source of CO2 emissions, silica fume (SF) is often used as a partial replacement for cement. SF‐modified SCC has become a common choice in construction. This study explores the effectiveness of soft computing models in predicting the compressive strength (CS) of SCC modified with varying amounts of silica fume. To achieve this, a comprehensive database was compiled from previous experimental studies, containing 240 data points related to CS. The compressive strength values in the database range from 21.1 to 106.6 MPa. The database includes seven independent variables: cement content (359.0–600.0 kg/m3), water‐to‐binder ratio (0.22–0.51), silica fume content (0.0–150.0 kg/m3), fine aggregate content (680.0–1166.0 kg/m3), coarse aggregate content (595.0–1000.0 kg/m3), superplasticizer content (1.5–15.0 kg/m3), and curing time (1–180 days). Four predictive models were developed based on this database: linear regression (LR), multi‐linear regression (MLR), full‐quadratic (FQ), and M5P‐tree models. The data were split, with two‐thirds used for training (160 data points) and one‐third for testing (80 data points). The performance of each model was evaluated using various statistical metrics, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), objective value (OBJ), scatter index (SI), and a‐20 index. The results revealed that the M5P‐tree model was the most accurate and reliable in predicting the compressive strength of SF‐based SCC across a wide range of strength values. Additionally, sensitivity analysis indicated that curing time had the most significant impact on the mixture's properties.

Product recommendation for online sales systems based on transaction sessions

The advancement of information technology has influenced and accelerated the growth of many economic fields and industries, including e-commerce. This is one area of information technology that has rapidly developed and gained widespread popularity due to the significant benefits it offers to the community. Today, the need for online shopping is increasing because of its convenience and timesaving, especially for busy people. Historical session data plays an important role in helping sales systems recommend orders to customers that suit their personal preferences. This study proposes models for recommending products on online sales systems based on transaction sessions using memory-based collaborative filtering methods, including user-based and item-based, and model-based collaborative filtering methods including SVD and KNN. The experimental results show that the SVD model has a better rating prediction performance than other techniques. Therefore, it is probably proposed for product recommendations on online sales systems.

The Current Situation and Optimization Path of Enterprise Supply Chain Management Construction in the Context of Digitalization

Abstract: The advancement of information technology has resulted in a rapid rise of a new phase of technical and industrial revolution, propelling the global shift from an industrial economy to a digital economy at an accelerated rate. Digital transformation has become an irreversible and pervasive tendency in contemporary society. Despite the significant growth of China's economy, the industrial supply chain is not operating at its highest efficiency. To achieve sustainable cost reduction and efficiency, enterprises need to promote digital transformation and supply chain upgrading. By doing so, enterprises can optimize resource allocation among different links in the supply chain and enhance the quality of their development. This study conducted an investigation of the value benefits, problems, and difficulties of enterprise supply chain digital transformation, using Jingdong as an example. The following article aims to provide a credible reference for organizations by discussing the successful paths and measures of digitizing the supply chain, using Jingdong's experience and activities in supply chain digital transformation as a reference.