Proposed Research Methodology Research Articles

Integrating Artificial Intelligence into Service Innovation, Business Development, and Legal Compliance: Insights from the Hainan Free Trade Port Era

This research aims to inspect the application of Artificial Intelligence (AI) in product and service innovation from the perspective of the Hainan Free Trade Port (HFTP) and its relationship with corporate transformation, legal compliance, and regulatory oversight. Being critical to the fourth industrial revolution, digital business and international cooperation, technology propels enterprises across various industries to transition from traditional models to intelligent and service-oriented ones. It also elucidates the theoretical foundations of AI products, the digital economy, and service innovation. It can be used to analyzes the challenges enterprises face in the HFTP while implementing AI technology, including funding, technology, management, operations, corporate culture, and innovative concepts. Based on the proposed research methodology, three hypotheses can be formulated. Hypothesis 1 states that the HFTP could facilitate enterprise transformation by applying supportive policies. Hypothesis 2 state that domestic laws and international agreements are urgently needed due to the legal risks arising from artificial intelligence. Hypothesis 3 state that HFTP enterprises comply with these laws while systemically assuring, in theory, and practice, the legal risks of artificial intelligence and its implications for legal regulation, which is a significant aspect of research, addressing legal risks related to data privacy, security, and algorithmic bias with many strategies being proposed. This shows how AI technology can change businesses in the HFTP, demonstrating the application of AI technology in the transformation of enterprises in the HFTP and the various risks they may encounter, providing valuable references for other enterprises regarding the practical significance of AI product and service innovation in the HFTP, and emphasizing the importance of international cooperation and legal instruction.

Application of Galvanostatic Non-Linear Impedance Spectroscopy to the Analysis of Metallic Material Degradation.

This study presents a novel application of Non-Linear Electrochemical Impedance Spectroscopy (NLEIS) in galvanostatic mode for the rapid, non-destructive assessment of metal degradation. By using galvanostatic mode instead of traditional potentiostatic methods, polarization-related challenges are mitigated, enabling more accurate and reliable analysis. The technique allows for the determination of corrosion rates (corrosion current) and material susceptibility to oxidation (Tafel coefficient) through a single measurement with a modulated AC perturbation signal. Theoretical assumptions of the method were validated through tests on both a non-linear model system and an experimental system. The proposed research methodology is highly effective for monitoring the condition of metallic materials in various environments, covering both anodic and cathodic processes.

Synthesizing a three Layer Architectural Framework for Adaptive Intelligent User Interfaces for the Disabled Community

Knowledge acquisition has been a bottleneck in the design for efficient User Interfaces for Disabled Community. The best suited interfaces have been oriented towards adaptability that is the User Interface needs to adapt and configure itself according to the needs and likes of the user. Such user interfaces can arouse interest in the mind of the disabled and motivate them to become an asset to the nation, and use their intellect to contribute to the society. This study pioneers an innovative approach, aiming to develop User Interfaces (UIs) for the Disabled Community. The research introduces a comprehensive three-layer architectural framework employing Learning, Trust, and Knowledge Models. Unlike conventional approaches, this Architectural Framework is designed to learn not only from the Disabled but also from their Assistants/Guides, employing principles of knowledge engineering. While the former offers static context, the latter contributes dynamic insights by observing behaviors, responses, and actions of the Disabled. This symbiotic relationship forms the foundational basis for developing intelligent adaptive UI’s that not only accommodate but actively engage and motivate the Disabled Community. The proposed research methodology involves a meticulous gathering of data through extensive user studies and interactions. By using the Human Computer Interface technology, the study aims to develop a robust framework capable of comprehensively understanding the preferences, needs, and nuances of disabled users. The expected outcomes include the development of a prototype adaptive UI, validated through rigorous testing and feedback from the disabled community. Additionally, the study anticipates contributing a comprehensive set of guidelines and best practices for designing intelligent adaptive UIs, ensuring comfort and ease for impaired users, boosting their confidence and encouraging greater involvement in socio-development activities.

Short-time and long-time dynamics of the zeolite films in the light of alternative diffusion models

Analysis of the dynamics of diffusion of two mixtures of hydrogen and heavier components through a flat zeolite film was carried out. The concepts of short-time and long-time dynamics were introduced. They derive from the observation of temporal trajectories obtained by applying abrupt perturbations of concentrations on one side of the membrane.An alternative description of the diffusion dynamics was proposed compared to the commonly accepted model that employs a matrix of thermodynamic factors Γ. In this way, the difficulty of analytical determination of this matrix for more complex adsorption equilibria was eliminated.The model equations were expressed in a matrix form, which enables its easy extension to any number of components. The proposed research methodology was illustrated using an example of diffusion of two selected mixtures, i.e. {H2, n-C4H10} and {H2, CO2} in silicalite-1. These gaseous mixtures have applications in specific technological processes.The two models were used to perform quantitative benchmarking of short-time and long-time dynamics of flat zeolite membranes. The assumed boundary conditions correspond to a semi-batch Wicke-Kallenbach diffusion cell. It was demonstrated that the phenomena occurring in short-time and long-time diffusion represent two different dynamic features important from a scientific and practical point of view.A quantitative measure for degree of confinement was proposed based on dynamic adsorption-desorption hysteresis.

A Novel Brillouin and Langevin Functions Dynamic Model for Two Conflicting Social Groups: Study of R&D Processes

We consider a two-group social conflict under the corporates’ research and development (R&D) business processes. Conflict participants are divided into two groups depending on their attitude to new ideas, technologies, and behavioral style for R&D creative problems—innovators and adapters. We reveal the contradiction that arises between the need to include both types of employees in one project team and their objectively antagonistic positions regarding the methods and approaches to R&D processes. The proposed research methodology is based on a modern post-non-classical paradigm formed on the principles of coherence, interdisciplinarity, openness, and nonlinearity, as well as a sociophysical approach to the social conflicts modeling. We use the general theories of magnetism, paramagnetism, and functions of P. Langevin and L. Brillouin to describe the dynamics of group participants’ preferences regarding the style of conflict behavior. The analogy of paramagnetism, consisting in the orienting effect of the magnetic field, is used to describe social groups interactions that have not only their own interests, but are also influenced by the opinions of opposite social groups. A two-dimensional, four-parameter map represents the dynamics of group conflict. Modeling results show that regardless of the initial states and with certain parameters of intra-group and intergroup interactions, the trajectories eventually converge to an attractor (limit cycle) in a two-dimensional space. No non-periodic or chaotic modes are identified in the two-group conflict, which determines the controllability of the described conflict. The results of the simulation experiments are used as decision support and contradictions resolution aimed at forming the required modes of the corporates’ research and development business processes and ensuring the group participants’ cohesion and depolarization. The results of testing the model at an industrial enterprise are presented.

Effectiveness of ESG Performance Evaluation in Improving Corporate Sustainability: A Mixed Method Approach

The growing importance of global competition force companies not merely to sustain productivity and financial growth and plan for long-term competitiveness. Therefore, Environmental, Social, and Governance (ESG) principles, which guide companies in incorporating sustainability into their business practices, play a pivotal role in navigating the complexities of sustainable operations. Effective ESG performance evaluation is crucial in resolving business concerns. Accordingly, this research aims to analyze the most suitable approach to materializing and addressing ESG issues in corporate strategies. The proposed research methodology combines qualitative and quantitative analyses, including interviews, thematic analysis, and the analytical hierarchy process (AHP). The findings reveal that the Integrated Approach is the most effective strategy, with a weight of 24.1%, comprehensively balancing environmental, social, and governance priorities. The Stakeholder-Based Approach is also considered important, with a weight of 23.8%, emphasizing stakeholder engagement and collaboration. Meanwhile, the Compliance-Based Approach and the Opportunity-Based Approach exhibit lower effectiveness. This research contributes to a profound understanding of ESG performance evaluation, implementation strategies, and the role of sustainability in driving long-term business success while positively contributing to the environment and society. The limitations of the research and directions for future research are also discussed.

THE USE OF REAL-TIME FEEDBACK TECHNOLOGIES IN 3X3 WOMEN'S BASKETBALL TRAINING

Basketball 3x3, considered an urban team sport, relies on quick and efficient decision-making during gameplay. Developing decision-making behavior is crucial due to the complexity of team dynamics. This aspect can be further investigated and improved with the help of neuroscience and digital technologies. Research hypotheses include integrating virtual reality as well as utilizing feedback technologies in training. The proposed research methodology involves analyzing data from multiple sources, pedagogical experiments, and sociological research. The aim is to identify patterns and trends in decision-making behavior, and furthermore to evaluate the effectiveness of training methods. The estimated results target enhancing knowledge about decision-making behavior in 3x3 basketball, with an impact on the training and performance of senior athletes.

A map of long-term care for people with dementia at the level of local communities

Long-term care for people with dementia has been studied using the Rapid Assessment of Needs and Services method, which has been used to map long-term care needs in order to stimulate the development of care that effectively meets the needs of people with dementia. In this paper, we present the results of three focus groups with key stakeholders in long-term care for people with dementia in three selected communities, involving 29 people from a variety of organisations: social work centres, homes for the older people, health centres, general and psychiatric hospitals, municipalities and non-governmental organizations. Qualitative analysis of the empirical material showed that the following problems are prevalent in the communities: (i) acute and unmet needs of informal caregivers, (ii) a multi-tiered system of long-term care that puts people in an unequal position, (iii) a lack of community-based long-term care, and inadequate responses to the needs of people with dementia. Even in communities where there are various forms of support, both institutional and community-based, available across all service sectors, long-term care for people with dementia is based on informal care. We propose the development of a national strategy for family caregivers, with special attention to informal caregivers of people with dementia. The support offered by existing facilities is inadequate, poorly accessible, or nonexistent. Homes for older people are overcrowded, and people with dementia wait months or years to be admitted to a home. Home-based assistance is not evenly distributed across communities, and its scale (20 hours per week) in particular is insufficient to meet the needs of people with dementia. There is a lack of alternative forms of care that enable people with dementia to remain in their own home environment for as long as possible, for example in the form of personal assistance. We suggest that the development of long-term care for people with dementia should be based on the proposed research methodology.

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Understanding the Importance of Balanced Nutrition: Steps to Optimal Health

This article underscores the importance of balanced nutrition as the foundation of optimal health in an era of modern lifestyles and varied diets. Proper nutrition contributes to physical and mental health, and prevents chronic diseases such as obesity, diabetes and heart disease. Understanding the essence of balanced nutrition is considered the first step towards optimal health. The proposed research methodology includes literature analysis, population surveys, and interviews with nutritionists to understand the understanding and implementation of balanced nutrition. The results are expected to generate practical guidelines to promote a balanced diet. Balanced nutrition is considered an important foundation for physical and mental health, supporting organ function, healthy growth, a strong immune system, and preventing chronic diseases. It is also important during the growing years of children and adolescents, and plays a role in mental health by reducing the risk of depression and anxiety. Overall, understanding and implementing balanced nutrition is expected to help individuals and communities build healthy and sustainable lifestyles, improving physical, mental health and productivity.

Investigating the Influence of Decisive Variables on Employee Attrition in Special Needs Schools

Human Resources are backbone for any organisation. Special educators play a substantial role in empowering the differently-abled. Acquiring and retaining the qualified workforce in the field of special education is a question of concern. Attrition across-the-board threatens not only their quality of education, but also leads to moral and psychological and emotional disturbance of differently abled. Special educators who are effectively trained and are conscious of the challenges are the need-of-the-hour. More of the prevailing papers in this context are the outcome of multifaceted investigations performed all over the world other than India. To fill the gap, the current study aims to investigate the contributing factors for the high attrition of educators engaged in mentoring and nurturing special children. The proposed research methodology is twofold - descriptive research methodology followed by empirical study. For determining the factors influencing the employee turnover, the researcher has used excel and Python. The results explored the relationship between selected factors and employee attrition viz. Work-life balance, Organizational culture, Remuneration, Superior- Subordinate relationship. It’s not only a grueling state for parents but also proves to be a curse for innocent gifted students to get adapted. If this question is not suitably addressed and explored right now, the lives of exceptional learners will be at stake. Key Words: Employee churn, Special Education, Differently abled, HR Analytics.

Mathematical model for assessing the influence of vibrations for multicopter elements

The article discusses the problem of studying the probabilistic characteristics of vibrations of the propellermotor group of an unmanned aerial vehicle such as a multicopter. The results of the research are presented in the form of quantitative values of the vibration parameters of the propeller-motor group of the agrodrone at given values of the input parameters of the electric drive control system, taking into account the influence of external factors. It is shown that the values of the parameters characterizing the vibrations of the propeller-motor group of the agrodrone can be different depending on the operating modes of the engines and changes in external conditions.Based on the experimental studies carried out, sets of measured random vibration parameters were obtained, which made it possible, based on the application of mathematical statistics methods, to calculate the mathematical expectations and dispersions of the amplitudes and velocities of vibration processes. The obtained probabilistic characteristics make it possible to study emissions (crossing a given level) of processes, to evaluate the intensity and probabilistic characteristics of the process going beyond the given boundaries.A mathematical model of the vibration process is substantiated, which allows, on the basis of the obtained experimental data, to assess the influence of vibrations on the elements of equipment installed on a multicopter, to assess its performance and reliability, and to determine tolerances on equipment parameters. The computer modeling of the probabilistic characteristics of vibration processes clearly demonstrated the efficiency of the proposed research methodology.

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

A Novel Technique for Substrate Toughening in Wood Single Lap Joints Using a Zero-Thickness Bio-Adhesive.

In contemporary engineering practices, the utilization of sustainable materials and eco-friendly techniques has gained significant importance. Wooden joints, particularly those created with polyurethan-based bio-adhesives, have garnered significant attention owing to their intrinsic environmental advantages and desirable mechanical properties. In comparison to conventional joining methods, adhesive joints offer distinct benefits such as an enhanced load distribution, reduced stress concentration, and improved aesthetic appeal. In this study, reference and toughened single-lap joint samples were investigated experimentally and numerically under quasi-static loading conditions. The proposed research methodology involves the infusion of a bio-adhesive into the wooden substrate, reinforcing the matrix of its surfaces. This innovative approach was developed to explore a synergetic effect of both wood and bio-adhesive. The experimentally validated results showcase a significant enhancement in joint strength, demonstrating an 85% increase when compared to joints with regular pine substrates. Moreover, the increased delamination thickness observed in toughened joints was found to increase the energy absorption of the joint.

Understanding the failure process of ventures: a perspective of the behavioral strategy

PurposeThis study aims to endeavor to shed light on the underlying causal mechanisms behind the failure of startups by examining the failure process in such organizations. To achieve this goal, the study conducted a comprehensive review of the literature on the definition of failure and its various dimensions, resulting in the compilation of a comprehensive list of causes of startup failure. Subsequently, the failure process was analyzed using a behavioral strategy approach that encompasses rationality, plasticity and shaping, as well as the growth approach of startups based on dialectic, teleology and evolution theories.Design/methodology/approachThe proposed research methodology was a case study using process tracing, with the sample being a failed platform in the ride-hailing technology sector. The causal mechanism was further explicated through the combined application of the behavioral strategy approach and interpretive structural modeling analysis.FindingsThe findings of the study suggest that the failure of startups is a result of interlinked causes and effects, and growth in these organizations is driven by dialectic, teleology and evolution theories.Originality/valueThe outcomes of the research can assist startups in formulating an effective strategy to deliver the right value proposition to the market, thereby reducing the chances of failure.

<b>Systematic Review of Semantic Analysis Methods</b>

NLP (natural language processing) is a very broad subcategory of computer science that focuses on human language processing in computers. Each different NLP processing technique focuses on different parts of linguistics, with semantics being the main focus of this manuscript. This manuscript aims to utilize a systematic review of several published papers ranging from 1998 to the current day to review and summarize critical analysis regarding the three main models that this paper focuses on: Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Neural Network based models. This manuscript compares these models against each other, weighs their advantages and disadvantages, and provides their uses. The selected studies in this review were analyzed and examined to ensure that they meet the quality and standards of the proposed research methodology. The results show that Neural network-based solutions are the most popular Semantic Analysis model in Academia (doubling the number of results of ESA and LSA combined), and they are usually the best in most tasks. However, there are specific scenarios and circumstances in which relying on the older LSA and ESA models could be beneficial.

Exploring social media use for healthcare professionals

This concise paper introduces the importance of Healthcare Professionals (HCP) voice on Social Media Platforms (SMP) to combat widespread mis/disinformation around health issues that is now prevalent on these platforms. While HCPs actions have been acknowledged as effective in dispelling myths surrounding health topics, the specific strategies employed by HCPs on different SMPs to address these issues remain largely unexplored. Employing a technology affordance perspective, this paper outlines a proposed research methodology aimed at investigating the strategies employed by HCPs for correcting misinformation across various SMPs and offers guidance in this area.

Deep Learning-based Precision Diagnosis of Lung Diseases on the Internet of Medical Things (IoMT)

Lung disease is one of the common and severe pathological conditions that affect the respiratory system, causing respiratory illness and potential mortality. In recent times, deep learning paradigm based on the Internet of Medical Things (IoMT) platform has been adopted as a viable solution to address the challenges encountered in detection of lung diseases which are characterized by their diverse nature and the complexities associated with their diagnosis. In this work, we have proposed an approach that aims to achieve accurate prediction and analysis of lung diseases. The proposed research methodology presents a Deep Learning-based Accurate Lung Disease Prediction (DL-ALDP) model based on deep learning algorithms to enhance its predictive capabilities. The DL-ALDP framework integrates several preprocessing techniques, including Wiener filtering, optimized region growing method (ORGM)-based feature extraction, and Contrast limited AHE (CLAHE)-based segmentation. The accurate prediction of lung diseases is achieved by utilizing a Deep Neural Network (DNN) for classification purposes. The DL-ALDP technique, as suggested, attained a precision of 86.77%, sensitivity of 82.47%, specificity of 92.87%, accuracy of 92.08%, and F1 score of 89.42%. The findings of this research underscore the prospective utility of deep learning techniques in forecasting and analyzing lung ailments within the context of the IoMT platform. Through IoMT capabilities, healthcare practitioners can avail themselves of enhanced prognostic accuracy and timeliness, resulting in superior patient care and outcomes.

Analysis of Flow-Induced Vibration Control in a Pontoon Carrier Based on a Pendulum-Tuned Mass Damper

The pendulum-tuned mass damper (PTMD) is a widely used vibration-damping device capable of transferring and dissipating structural vibration energy, resulting in reduced structural amplitude, and offering both structural and performance advantages. Given the susceptibility of the submerged expendable conductivity, temperature, and depth profiler (SSXCTD) buoyancy platform to flow-induced vibrations during the upwelling process, the PTMD effectively suppresses the main structure’s amplitude under flow field effects. To this end, we investigated the application and design of the PTMD in the SSXCTD buoyancy platform and analyzed its vibration reduction performance. Moreover, we conducted finite volume simulations of the structure using ANSYS FLUENT fluid simulation software, providing insights into its motion under flow field effects and validating the PTMD’s effectiveness in mitigating the buoyancy platform’s flow-induced vibrations. Our research results demonstrate that the PTMD effectively alleviates the impact of seawater flow on the buoyancy platform, leading to a significant improvement in its operational stability. The proposed research methodology and findings serve as valuable references for the design and optimization of other expendable marine detection equipment.

Profitability Analysis of Selected Low Impact Development Methods for Decentralised Rainwater Management: A Case Study from Lublin Region, Poland

Water scarcity affects at least 11% of the population and 17% of the territory of the European Union. Simultaneously in cities there is a problem of urban floods caused by violent and intense rainfalls. Decentralized drainage systems are designed to capture rainwater runoff at the place of precipitation and improve the hydrological conditions through the use of surface and subsurface infiltration, retention, and evapotranspiration techniques. The purpose of this paper was to evaluate the financial profitability of selected Low Impact Development (LID) methods for decentralized management of rainwater disposed from the roofs of two different buildings. In the presented conditions, the use of dual installations and infiltration tunnels turned out to be cost-effective solutions, whereas infiltration boxes were unprofitable for both buildings. The most unprofitable solution would become profitable if the investment costs were reduced by as much as 67% or if it was possible to generate 2.28 times more benefits. Although the research was carried out for specific conditions, the obtained results may be helpful in the implementation of similar investments. They are also a kind of contribution to the assessment of the cost-effectiveness of LID on a global scale due to the universality of the proposed research methodology.