Integration Methodology Research Articles

Drivers of Driving: A Review

As car ownership and usage expand globally, understanding the factors that influence the propensity to drive is crucial for promoting sustainable transportation. This literature review examined the factors influencing driving decisions through a systematic search of databases, rigorous screening of over 1000 articles, and analysis of 142 studies. The findings reveal that attributes of the built environment (e.g., density, diversity, accessibility), economic factors (e.g., income, costs of car ownership, policies), and psychological aspects (e.g., attitudes, social norms, perceptions) have significant impacts on driving behaviors. By employing an integrative methodology involving targeted searches, keyword analysis, and detailed evaluation, this review offers insights into the multifactorial nature of driving decisions. The synthesis of studies across multiple domains emphasized the need for a holistic approach to understanding and addressing the factors influencing the propensity to drive, laying a foundation for informed transportation policy and practice.

Methodology for Risk-Benefit Analysis in Research

Risk-benefit analysis in the context of research activities has recently become a mandatory exercise. It is an integral part of the more extensive exercise, the scientific and ethical review for research approval and subsequent monitoring and evaluation exercises. However, how to undertake the process remains a largely subjective process that results in somewhat 'incomplete' inventories of potential risks and benefits for many research activities. There is also the challenge of computing an overall study risk-benefit score when objective and subjective measures of risks, benefits, severity, and magnitude are involved. This study employed the integrative literature review methodology to summarize past empirical and theoretical literature to provide a more comprehensive understanding of a phenomenon. The result is a simple, concise, and practical guide for identifying research risks and benefits, the computation of risk scores, benefit scores, and the overall risk-benefit score in a research activity. These scores are subsequently utilized to compute the study's overall risk-benefit analysis score. This step-by-step process for the computation of a risk-benefit ratio will introduce clarity in the process of risk-benefit analysis to help ascertain if specific risk probabilities and their severity have been correctly assessed. Conversely, the approach is also helpful in ascertaining if study benefits and their magnitude have been adequately assessed and rated.

Solar Landscapes: A Methodology for the Adaptive Integration of Renewable Energy Production into Cultural Landscapes

The increasing use of solar energy is an integral step toward carbon neutrality. At the same time, outdoor solar farms are significantly altering existing cultural landscapes. This work examines the possibilities of integrating the use of solar energy into these landscapes in such a way that the unique, regional character of places is preserved and enhanced. The research project that was carried out developed a conceptual design approach that takes as its starting point landscape architectural and aesthetic analyses of existing sites in Styria, Austria, the spatial characteristics of the cultural landscapes in which they are embedded, and their suitability for generating solar energy. The comparison of a site’s characteristics with the technical possibilities evaluated from a literature review enables a responsive design practice using solar modules. The result is a method of landscape architectural design that integrates solar energy on the basis of an adaptive site-specific approach as well as a catalogue of sample cases that illustrate how designing with solar modules can honor and add value to existing places while enhancing their ecological, economic, and social functions.

الاقتراب من الزمن الحقيقي لتكوين الصور المتكاملة ثلاثية الابعاد

Generating a truly photo-realistic 3D real-time view capability in an ergonomic and cost effective manner is a crucial engineering challenge. In this paper, the advanced integral imaging methodology is proposed that is based on microlens arrays to generate 3D Holoscopic images by the intersection ray bundle that defines each image point in the associated scene at the correct spatial place. The core idea of the novel algorithm reported in this paper is to accelerate the computer generation of photo-realistic still 3D Holoscopic images based on multiprocessor ray-traced system. Consequently a significantly reduce in running time is successfully completed. Multiple subjective and objective images quality assessments are addressed and as a result there was not any observable distortion in images quality. The results obtained are very satisfactory and for the first time that the new hybrid algorithm has achieved the saving in terms of execution rendering time approximately (60 % - 78 %) that a near to real-time computer generation of 3D Holoscopic images. Keywords: Real-time Computer Generation content of 3D Holoscopic Images, Interpolation 3D integral images, Computer Graphics, Spatial Coherence, Ray Tracing, 3DTV.

NiTi-Cu Bimetallic Structure Fabrication through Wire Arc Additive Manufacturing.

This study endeavors to comprehensively explore and elucidate the seamless integration of NiTi shape memory alloys (SMAs) into multifaceted applications through the utilization of novel joining techniques. The primary focus lies in the utilization of wire arc additive manufacturing (WAAM) to deposit Nitinol (NiTi) onto Copper (Cu), thereby introducing a transformative approach for their integration into electro-mechanical systems and beyond. Through a detailed examination of the NiTi/Cu bimetallic junction, using advanced analytical techniques including SEM, XRD, and DSC analyses, this research aims to unravel the intricate complexities inherent within the interface. The SEM images and X-ray patterns obtained reveal a complex and nuanced interface characterized by a broad mixed zone comprising various constituents, including Ti(Ni,Cu)2, pure Cu, Ti2(Ni,Cu)3 precipitates, and Ni-rich NiTi precipitates. The DSC results, showcasing low-intensity broad peaks during thermal cycling, underscore the inherent challenges in demonstrating functional properties within the NiTi/Cu system. Recognizing the critical importance of an enhanced martensitic transformation, this study delves into the effects of heat treatment. Calorimetric curves post-annealing at 500 °C exhibit distinct transformation peaks, shedding light on the intricate influence of NiTi layer distribution within the junction. The optimal heat treatment parameters for NiTi/Cu junction restoration are meticulously explored and determined at 500 °C for a duration of 12 h. Furthermore, the study offers valuable insights into optimizing NiTi-Cu joints, with micro-hardness values reaching 485 HV and compressive strength scaling up to 650 MPa. These significant findings not only hold promise for diverse applications across various industries but also pave the way for further research directions and explorations into the realm of SMA integration and advanced joining methodologies.

Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study

In the context of the energy transition, the integration of land use considerations into energy planning can provide significant improvements. In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spatial resolution and a more detailed characterization of land, tailored to regional constraints and specificities. Additionally, an assessment of trade-offs with alternative land uses is necessary. Nevertheless, they are commonly neglected. This study addresses the challenge of incorporating land use aspects into ESOMs, with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. The findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

Efficacy of Acupuncture in Chronic Low Back Pain Treatment: A Literature Review

Aims: To evaluate the effects of the use of acupuncture as a treatment of chronic low back pain.
 Methodology: This study follows the integrative methodology of literature review, seeking articles published on PubMed, using keywords from Medical Subject Headings (MeSH) to form the search Formula: Acupuncture AND "low back pain". Inclusion criteria were randomized controlled trials published between 2018 and 2023, with free full text available in english or portuguese. The exclusion criteria was not answering this review’s orienting question. 
 Results: Several studies analyzed the efficacy of different acupuncture and electroacupuncture approaches in the treatment of chronic low back pain. One study highlighted invasive laser acupuncture with different wavelengths, revealing improvements in pain and disability scales after 4 weeks. Another research compared electroacupuncture at various frequencies, observing similar reductions in pain between the groups. Combined acupuncture (hands and ear) was more effective than usual medical treatment, showing greater reduction in pain and cure rate. Both electroacupuncture and manual acupuncture also demonstrated efficacy in reducing pain intensity and quality. Acupuncture with embedded wires stood out, showing positive and rapid results in chronic low back pain. Another study revealed that both electroacupuncture and a placebo had positive impacts on pain intensity.
 Conclusion: Even though there are multiple forms of acupuncture used throughout the studies, and even those that appear on multiple studies are done differently on those appearances, all are used as a way to reduce CLBP. In that sense, the positive effects from using traditional acupuncture or some different form of acupuncture as a treatment for CLBP is highlighted by the results from the studies that were a part of this review.

How to create an art therapy paradigm for Generation Z in the post-epidemic era within contemporary media systems—— Introduction to the design and iterative process of the Cyber Empath - Cyber Confessional project

The main objective of this report is to explore and design an art therapy paradigm within the contemporary media system that is conducive to the mental health development of the Generation Z community in the context of the post-epidemic era, and that is truly applicable to the community and in harmony with its zeitgeist. The project is guided by the integrative methodology of art therapy and Carl Gustav Jung's theory of the psychology of religion. It draws on the religious ritual of "confession to a priest" in form; critically deconstructs the religious power of the ritual in content; injects new connotations that resonate with the zeitgeist of Generation Z in spirit; combines immersive experience design with contemporary media systems in technology; and attempts to create a therapeutic cyberspace guided by the digital avatar for Generation Z. Additionally, the report documents the intervention events and new findings so far during the construction of the project.

Strategies for Master Data Management Integration and Their Benefits

This study examines the efficacy of various methodologies employed for the integration of Master Data Management (MDM) and assesses the corresponding advantages in modern data-centric organizations. The main goal is to improve the processes of managing organizational data, tackle existing issues, and maximize the overall effectiveness of decision-making based on data. The research employs a comprehensive methodology, undertaking a meticulous examination of the current body of literature pertaining to Master Data Management (MDM) and integration methodologies. Both qualitative and quantitative research methodologies are utilized to assess the efficacy of various Master Data Management (MDM) integration solutions. The research design incorporates meticulous data gathering methodologies and sophisticated analytic approaches to guarantee the production of reliable and resilient findings. The study investigates various solutions for integrating Master Data Management (MDM), including consolidation, federation, and coexistence. Comprehensive elucidations of each method are furnished, bolstered by empirical illustrations and empirical analyses, proffering pragmatic perspectives. This paper provides a systematic analysis of the benefits associated with the integration of Master Data Management (MDM), with a particular focus on enhancements in data quality, decision-making processes, and operational efficiency. The importance of MDM integration in modern business environments is substantiated by empirical evidence and illustrative examples. In summary, this study highlights the crucial significance of integrating Master Data Management (MDM) in achieving organizational success. Through the examination of obstacles, the provision of pragmatic approaches, and the presentation of empirical illustrations, this research adds to the current scholarly understanding of efficient data management. It is recommended that further research endeavors be undertaken to delve deeper .....

Review of research on performance indicators for water utilities

Abstract Water utilities are affected by various social, environmental, and technological factors and are increasingly required to enhance their infrastructure and long-term efficiency. Performance indicators are useful tools for assessing the operational, financial, environmental, and social aspects of water systems. Given this background, the author reviewed the literature on performance indicators for the water sector and summarized the research trends as follows. As a perspective, there are a lot of good mathematical and theoretical studies on distribution pipes and leakage management. Future research should address the problems of water utilities, by using multiple levels of performance indicators including social and environmental context in the long term. Asset management and utility management studies address diverse and current problems faced by water utilities. However, there is still room for improvement in standardizing the methodology for data collection, processing, and integration. In addition, it is recommended for future research, to include carbon neutrality aspect, to include pipeline materials and soil information in leakage management, to extend asset management studies to treatment plants, with including additional indicators about human and financial resources.

Health Care Professional Education on Cancer Screening of SGM Individuals: An Integrative Review.

Sexual and gender minority (SGM) individuals are disproportionately affected by cancer. Health care professional (HCP) and health sciences education rarely includes content on cancer screening in this population. This article aims to synthesize literature on educational programs for HCPs and health sciences students related to cancer screening for SGM individuals. An integrative review methodology guided a systematic search of five databases: CINAHL, PubMed, Embase, PsycInfo, and ERIC. Articles were included if they were empirically-based and described educational programs targeted at HCPs and health sciences students with content of cancer screening for SGM people. Eleven studies met the inclusion criteria. Of these, three were pilot studies and the majority of the articles (n = 9) used pre- and post-test designs. All of the interventions showed efficacy in increasing knowledge, attitudes, skills, and behavior. However, the vastly different programs and the fact that most of the cancer screening content was embedded in programs with broad SGM issues content limit the ability to make firm recommendations for any one particular program. In-person didactic content delivered multiple times incorporating modalities such as standardized patient encounters, case studies, and guest speakers increased knowledge, attitude, and skills of participants. Future studies should incorporate behavioral theories, develop more consistent aspects of educational programs, report demographic and pertinent data on participants, and utilize established instruments to measure outcomes when conducting educational interventions on HCPs and students in this area.

A paradigm shift in crisis management: The nexus of AGI‐driven intelligence fusion networks and blockchain trustworthiness

AbstractIn an era characterized by vast data streams and complex socioeconomic dynamics, the fusion and precise analysis of multi‐sourced intelligence has emerged as a pivotal challenge. To address this, the study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology. Drawing from diverse fields including informatics, computer science, data analytics, and network security, the research adopts an integrative methodology comprising both a comprehensive literature review and systems analysis. Key findings highlight the prowess of AGI‐driven IFNs in enhancing governmental early warning systems for crisis management. These networks underscore a paradigm shift from reactive postevent measures to proactive pre‐event forecasting, thus bolstering the efficacy of governmental responses. Moreover, the decentralized nature of blockchain technology ensures data integrity, fostering trust in interdepartmental data sharing—an essential for efficient crisis management in hierarchical administrative structures. This study accentuates the need for redefining crisis management strategies, emphasizing data‐driven decision‐making and seamless intelligence sharing to ensure optimal outcomes.

Statistical study of the influence of the data sampling interval on the estimation of wind turbine energy

Main deficiency for wind power is variability. It is really difficult to predetermine the wind’s potential since wind velocity cannot be controlled or predicted with pinpoint accuracy. In this paper, two different methods are applied in order to estimate the monthly wind energy for different sites; those estimations are done using Weibull distribution and calculating energy with direct integration methodology. In additional, the influence of the data sampling interval is studied. Results show that the computational method based on the integration of the power is more accurate. Also, it is shown that the hourly time resolution provides satisfactory accuracy in wind resource estimation, in comparison with one minute’s resolution. Results allow better design of wind or hybrid systems. Moreover, wind speed acquisitions are minimized. A shorter time of treatment and less expensive measurement equipments are required.

Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis

The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.

#picturesofchange: Physical self-representations in social media as a sign of change in sports- and movement culture

The mediatization of communicative action is a process that centrally moderates social change. Changes in communication (e.g., through new kinds of media) thus have momentous consequences for social forms and processes. As essential cultural signs, social bodies, their medial representation, and areas of society in which bodies play a role are interrelated with changes in communication. Currently, physical self-representations in digital media play a key role and hold strong possibilities of influence on the culture of sports and movement. The aim of this study was to identify the effects of visual self-representations in social media on this culture. Following an integrative review methodology, we systematically organised the current state of empirical evidence and identified the implications that digital self-representations in social media (esp., Instagram, Facebook) have on sports and movement culture and therefore possibly on physical education (PE). Six electronic databases, reference lists, and citations of full-text articles were searched for English and German language peer-reviewed articles. The search string combined different terms relating to social media and sports culture. Two independent reviewers screened all identified studies for eligibility and assessed the methodological quality of the included studies. The results point to direct changes in some sports and movement cultures such as the prosumtion (consumption and production) of videos in skateboarding or the perpetuation of body ideals in the fitness culture. Other sports and movement cultures are changed indirectly via different new use cases for the athletes, as they can use social media to either build communities or gain visibility. As the practice of prosuming bodily self-representations is a growing part of the lifeworld of adolescents, such practices could become content within PE. Therewith, students could acquire competencies that allow them to intellectually relate to these practices, which could help them in understanding, changing or transcending their existing realities.

Advanced hierarchical planning and design for low-carbon landscape architecture leveraging virtual reality technology

Abstract The incorporation of sophisticated artificial intelligence technologies and data mining methodologies is critical for the planning and design of low-carbon landscape architectural environments. To enhance the three-dimensional reconstruction capabilities and visual esthetics of hierarchical planning and design within low-carbon landscape architectural environments, this study introduces a novel methodology predicated on virtual reality (VR) technology. This approach harnesses the diverse pictorial structure feature distribution inherent to low-carbon landscape architectural environments, amalgamated with VR's feature structure reorganization and enhancement techniques. Furthermore, we propose methodologies for joint measurement and multi-level integration to refine the hierarchical planning and design process within these environments. Simulation outcomes demonstrate the efficacy of this method in achieving superior three-dimensional reconstruction and visual representation, thereby augmenting the planning and design quality within the domain of low-carbon landscape architecture.

Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis.

As Artificial Intelligence (AI) technologies such as Generative AI (GenAI) have become more common in academic settings, it is necessary to examine how these tools interact with issues of authorship, academic integrity, and research methodologies. The current landscape lacks cohesive policies and guidelines for regulating AI's role in academic research which has prompted discussions among publishers, authors, and institutions. This study employs inductive thematic analysis to explore publisher policies regarding AI-assisted authorship and academic work. Our methods involved a two-fold analysis using both AI-assisted and traditional unassisted techniques to examine the available policies from leading academic publishers and other publishing or academic entities. The framework was designed to offer multiple perspectives, harnessing the strengths of AI for pattern recognition while leveraging human expertise for nuanced interpretation. The results of these two analyses are combined to form the final themes. Our findings indicate six overall themes, three of which were independently identified in both the AI-assisted and unassisted, manual analysis using common software tools. A broad consensus appears among publishers that human authorship remains paramount and that the use of GenAI tools is permissible but must be disclosed. However, GenAI tools are increasingly acknowledged for their supportive roles, including text generation and data analysis. The study also discusses the inherent limitations and biases of AI-assisted analysis, necessitating rigorous scrutiny by authors, reviewers, and editors. There is a growing recognition of AI's role as a valuable auxiliary tool in academic research, but one that comes with caveats pertaining to integrity, accountability, and interpretive limitations. This study used a novel analysis supported by GenAI tools to identify themes emerging in the policy landscape, underscoring the need for an informed, flexible approach to policy formulation that can adapt to the rapidly evolving landscape of AI technologies.

Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity.

Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.

Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the “teaching” process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

Modelling the impact land use change on flood risk: Umia (Spain) and Voglajna (Slovenia) case studies

Accurate quantification and assessment flood hazards is critical for mitigation and prevention. This study proposes a methodological framework for analysing this hazard and its relationship to agricultural and forestry land use. The objective is to obtain an integrative methodology based on the HEC-HMS model and to apply different simulations, with land use changes as indicator in two different study areas, taking into account the different characteristics of each basin and the different regulations of each area. This was done in two river basins, the Umia Basin (north-western Spain) and the Voglajna Basin (eastern Slovenia). The hydrological models obtained showed a very high performance in the calibration and validation periods. In the Umia River Basin agricultural use has priority over forestry, with food security taking precedence over water security. Reforestation only upstream has been shown to be almost as beneficial as reforestation throughout the basin. However, the use of abandoned land currently limits the reduction of peak discharge. Nevertheless, for an event designed with a 100-years return period, a reduction of about 12% was achieved. The increase in agricultural use promoted by the administration could increase this peak by about 6%, thus increasing the flood hazard, but it has been shown that this use on less permeable soils and upstream would not significantly increase this peak (<1% for the event studied). On the other hand, the Voglajna Basin has a smaller catchment area and fragmented land use, and a mosaic landscape. For the 100-years return period the reduction in peak discharge is only few percent compared to the baseline scenario even if 30% of the agricultural land (about 7% of the total area) is changed to forest land use. The information provided by the simulations is a useful indicator that can be incorporated into management plans to ensure appropriate decision making by the administration. Not only for the application of nature-based solutions (NBS) and providing evidence-base for the NBS and the reduction of flood risk, but such information is key when the use changes from forestry to agriculture and vice versa, as it provides tools to ensure food and water security. In both case studies it was demonstrated how stakeholders need to undertake optimal and strategic planning and management in order to reduce the risk of flooding. In turn, the use of this modelling, as well as the calculation of scenarios from a perspective that not only evaluates different land use changes, but also incorporates different regulations, is presented as an innovative and realistic analysis.