Integration Difficulties Research Articles

KEY TECHNOLOGIES FOR 1:10,000 RAPID MAPPING BASED ON GF-7 SATELLITE

Abstract. In order to solve the problems of low efficiency, high cost and long cycle of the traditional 1:10000 basic geographic information data production method (aerial photography combined with field mapping), this paper proposes a 1:10000 rapid mapping method based on GF-7 satellite data. It solves the key technical difficulties of automatic information recognition, extraction, integration and mapping of water body, building, road, vegetation, contour, and other elements. And an automatic software system of “image -> patch -> data -> map” is developed, which realizes the rapid production of 1:10000 basic geographic information data products and improves the ability of high-precision basic geographic information resource construction.

Quality of Life and Social Inclusion of Migrants and Refugees Attending an Elderly Care Training in Four Mediterranean Countries: Results from the HERO Project

The progressive population ageing observed in Western countries determines a growing need for long-term care for older people. At the same time, migrants and refugees often have integration difficulties in regards to the hosting country society and labour market, with one reason being the lack of EU recognition of the educational degrees obtained in their native country. Creating educational opportunities in elderly care for migrants may facilitate their social inclusion, increase their employability, and constitute a response to the growing labor demand. The HERO project moved in this direction by planning, designing, and carrying out an original training curriculum targeted to migrants and refugees from African and Middle Eastern countries, piloted in four Mediterranean countries (Cyprus, Greece, Italy, and Portugal). The impact of the training on migrants and refugees’ quality of life was assessed through the WHOQOL-BREF questionnaire, while social inclusion was assessed through semistructured interviews and participant observation carried out during the internship in elderly care facilities. Eighty-two migrants (70.7 percent women) were involved in the study. The results showed that despite the fact that the training did not have an impact on the trainees’ quality of life, it was associated with social inclusion. Four ideal types of migrant learners were drawn: “ex-professional trainees,” “fall-back trainees,” “care-oriented trainees,” and “nonprofessional care workers,” based on which as many possible educational pathways were drawn to optimise the trainees’ learning process. The study results shaped the formulation of suggestions on migrants’ education in elderly care.

Antagonistic Integration in the Private Sector-Driven Housing Developments in South Africa

Cities in apartheid South Africa did not exhibit inclusivity traits and city developments thereafter have not differed significantly. Neoliberal urbanism asserts that the city is envisaged as a playing field for the elites, and growing socio-economic inequalities are managed by creating privatised, customised, and networked spaces for consumption by the urban elites. This ideology seeks to enlarge the role of market forces in the housing sector, to increase the role of elites in shaping urban landscapes. This paper, anchored on the theory of neoliberal urbanism, showcases the difficulties of integration and inclusivity in a socio-economically mixed neighbourhoods in post-apartheid SA, using Shaka’s Head in the Ballito area of the KwaDukuza Municipality as the case study. The study engages literature on the production of space to demonstrate how the economically dominant urban classes have maintained hegemony over urban governance and suppressed the efforts of local governments to shape urban neighbourhoods and the interests of the previously disadvantaged groups. Data were obtained through in-depth interviews with role-players in physical planning and human settlements developments and supplemented with household interviews with residents of Shaka’s Head. Research results showed that the type of integration observed in the area can best be described as ‘antagonistic integration’. The paper recommends an increased role of the government in land ownership, physical development, and regulation of private developments. Keywords: Housing, Private developments, Urban communities, Integration, Antagonism

Digital Transformation for MSME Resilience in The Era of Society 5.0

The purpose of this paper is to discuss the effect of digital transformation on the resilience of MSMEs in the era of society 5.0 in Palembang City and determine the digital adoption rate among MSMEs, identify barriers to digital transformation, understand factors behind MSMEs' success or failure in the digital transition. The use of digital transformation certainly has an impact on both MSME players and consumers, who get convenience and are able to survive in the era of society 5.0. In this paper, researchers used quantitative analysis and linear regression analysis with the help of SPSS 22. The population in this study was 1,103 MSMEs in Palembang City, with a sample size of 110 MSMEs. The results of the research obtained using the t test and F test show that digital transformation has a positive and significant effect on the resilience of MSMEs. The magnitude of the relationship or correlation value obtained from the data is 60.2%, which can be explained by business actors carrying out data- based digital transformation, adopting a work culture, determining a simple roadmap, digital awareness, and an environment that supports the success of digital transformation, while the remaining 40.8% can be explained by the influence of other variables not examined by the author. Digital Transformation Challenges for MSMEs, such as Capital Limitations, Lack of Technology Knowledge, Lack of Trained Human Resources, Cyber Security Issues, Business Model Changes, Reliance on Third-Party Platforms, and Integration Difficulties with Legacy Systems, Cultural Adaptation Challenges, and Infrastructure In the future, this research can provide a comprehensive understanding of MSMEs' digital transformation landscape. Recommendations for MSMEs to harness digital opportunities and policy suggestions for governments to support MSMEs in their digital journey

Cognitive Robotics: Integrating Artificial Intelligence and Embodied Intelligence for Advanced Problem Solving

Artificial intelligence (AI) and embodied intelligence are combined in cognitive robotics, which marks a paradigm change in highly developed problem-solving skills. This study offers a comprehensive review of the theoretical underpinnings, practical difficulties, practical applications, and moral implications of cognitive robots. The study explores machine learning methods, natural language processing tools, and computer vision technologies after introducing the fundamental concepts. It clarifies embodied intelligence further by emphasizing the important roles that sensory data fusion, robotic actuation, and adaptive behaviours play in the cognitive capabilities of robots. The study then moves on to discuss cognitive robotics' integration difficulties, including issues in sensory data fusion, cognitive dissonance in AI-driven robotics, and human-robot interaction. Applications in autonomous cars, healthcare, manufacturing, and assistive technologies demonstrate how cognitive robotics is transforming many different industries. The ethical obligations involved in the creation and use of cognitive robotic systems are highlighted, along with ethical issues relating to employment, privacy, and ethical AI practises. In-depth analysis is done of the present and foreseeable difficulties in cognitive robotics. Along with cognitive difficulties like contextual comprehension and moral conundrums, technical obstacles linked to sensor limits and processing restrictions are also studied. The study highlights cutting-edge research projects and multidisciplinary partnerships that provide answers to these problems, paving the path for the development of cognitive robots in the future. The article envisions a future where cognitive robots would play a part in space exploration, disaster response, and personalised robotics. It also surveys integration possibilities with cutting-edge technologies like augmented reality and the Internet of Things. The study summarises the key findings and presents the implications for future research, and emphasises the important part cognitive robotics plays in resolving difficult issues. To ensure that cognitive robots continue to have an impact on a future that is smarter, more connected, and understanding, ethical issues, multidisciplinary collaboration, and responsible development practises are necessary.

Special Education for Students with Special Needs (Mobility Disabilities and Deafness): Mitigation of Future Integration Difficulties in Society and in the Labour Market

Special Education for Students with Special Needs (Mobility Disabilities and Deafness): Mitigation of Future Integration Difficulties in Society and in the Labour Market

Adapting Corpus Linguistics for Primary EFL Education in Vietnam: Perspectives from Linguistics Scholars

This qualitative study investigates the perspectives of Vietnamese linguistics scholars on the integration of corpus linguistics into primary English as a Foreign Language (EFL) education. Through semi-structured interviews with nine scholars from two leading Vietnamese institutions, the research explores the applicability, challenges, and strategies of implementing corpus linguistics for young learners in Vietnam. Participants, categorized into novice, mid-career, and near-end career stages, offer diverse viewpoints on this innovative teaching approach. The underpinning theoretical frameworks are sociocultural theory and constructivist learning theory. The thematic analysis reveals four key themes: tailoring corpus linguistics to young learners’ needs and abilities, the challenges in implementing corpus linguistics in primary EFL settings, the gap between theoretical knowledge and practical application, and the role of cultural context in corpus linguistics application. The study finds a consensus on the need for simplifying and contextualizing corpus materials to suit young learners’ cognitive and cultural backgrounds. Challenges identified include adapting corpus materials, teacher training inadequacies, and curriculum integration difficulties. The gap between theory and practice highlights the need for hands-on teacher training and comprehensive resource development. Furthermore, the importance of culturally relevant corpus materials is emphasized, balancing local and global cultural elements. This study contributes to the field of EFL education by highlighting the specific challenges and strategies for implementing corpus linguistics in the Vietnamese primary education context. It underscores the importance of culturally and pedagogically sensitive approaches in language teaching innovations.

A Simple Nonlinear Classifier Using a Multimode Optical Chip

Neural network accelerator based on photonic‐integrated circuits is emerging as a promising technology for fast, power‐efficient, and parallel computing. Under such technology, optics is mainly used for linear transformations, e.g., through an array of cascaded switching networks. Nonlinear activation is implemented either electronically requiring extra optical–electrical conversion or via nonlinear optical materials that often suffer from high loss, large power consumption, and difficulty in integration. Herein, an optical neural chip with only one multimode waveguide, fabricated using low‐cost linear optical materials, plus seven heater electrodes to control the multimode interference, is proposed. The nonlinear networks are intrinsically integrated in the electrical‐to‐optical signal conversion through the waveguide. The linear computation, in the electronic domain, is included in the mandatory step to convert the input matrix to the intermediate current values on the seven electrodes. Though extremely simple, the proposed system can classify nonlinear datasets and images by optical readout with high accuracy and without calibration. Prospects for future development are given at the end. In this work, an alternative route is offered to exploiting the classic multimode interference for advanced optical computing applications.

Towards the Augmentation of Digital Twin Performance.

Digital Twin (DT) aims to provide industrial companies with an interface to visualize, analyze, and simulate the production process, improving overall performance. This paper proposes to extend existing DT by adding a complementary methodology to make it suitable for process supervision. To implement our methodology, we introduce a novel framework that identifies, collects, and analyses data from the production system, enhancing DT functionalities. In our case study, we implemented Key Performance Indicators (KPIs) in the immersive environment to monitor physical processes through cyber representation. First, a review of the Digital Twin (DT) allows us to understand the status of the existing methodologies as well as the problem of data contextualization in recent years. Based on this review, performance data in Cyber-Physical Systems (CPS) are identified, localized, and processed to generate indicators for monitoring machine and production line performance through DT. Finally, a discussion reveals the difficulties of integration and the possibilities to respond to other major industrial challenges, like predictive maintenance.

Data-driven integration framework for four-dimensional building information modeling simulation in modular construction: a case study approach

Abstract Modular construction is becoming more popular because of its efficiency, cost-saving, and environmental benefits, but its successful implementation necessitates detailed planning, scheduling, and coordination. Building information modeling (BIM) and four-dimensional (4D) simulation techniques have emerged as invaluable tools for visualizing and analyzing the construction process in order to meet these requirements. However, integrating distinctive data sources and developing comprehensive 4D BIM simulations tailored to modular construction projects present significant challenges. Case studies are used in this paper to define precise data needs and to design a robust data integration framework for improving 4D BIM simulations in modular construction. The validation of the framework in a real-world project demonstrates its efficacy in integrating data, promoting cooperation, detecting risks, and supporting informed decision-making, ultimately enhancing modular building results through more realistic simulations. By solving data integration difficulties, this research provides useful insights for industry practitioners and researchers, enabling informed decision-making and optimization of modular building projects.

Data Fusion for Smart Civil Infrastructure Management: A Conceptual Digital Twin Framework

Effective civil infrastructure management necessitates the utilization of timely data across the entire asset lifecycle for condition assessment and predictive maintenance. A notable gap in current predictive maintenance practices is the reliance on single-source data instead of heterogeneous data, decreasing data accuracy, reliability, adaptability, and further effectiveness of engineering decision-making. Data fusion is thus demanded to transform low-dimensional decisions from individual sensors into high-dimensional ones for decision optimization. In this context, digital twin (DT) technology is set to revolutionize the civil infrastructure industry by facilitating real-time data processing and informed decision-making. However, data-driven smart civil infrastructure management using DT is not yet achieved, especially in terms of data fusion. This paper aims to establish a conceptual framework for harnessing DT technology with data fusion to ensure the efficiency of civil infrastructures throughout their lifecycle. To achieve this objective, a systematic review of 105 papers was conducted to thematically analyze data fusion approaches and DT frameworks for civil infrastructure management, including their applications, core DT technologies, and challenges. Several gaps are identified, such as the difficulty in data integration due to data heterogeneity, seamless interoperability, difficulties associated with data quality, maintaining the semantic features of big data, technological limitations, and complexities with algorithm selection. Given these challenges, this research proposed a framework emphasizing multilayer data fusion, the integration of open building information modeling (openBIM) and geographic information system (GIS) for immersive visualization and stakeholder engagement, and the adoption of extended industry foundation classes (IFC) for data integration throughout the asset lifecycle.

Sniffer Faster R-CNN ++: An Efficient Camera-LiDAR Object Detector with Proposal Refinement on Fused Candidates

In this paper we present Sniffer Faster R-CNN++, an efficient Camera-LiDAR late fusion network for low complexity and accurate object detection in autonomous driving scenarios. The proposed detection network architecture operates on output candidates of any 3D detector and proposals from regional proposal network of any 2D detector to generate final prediction results. In comparison to the single modality object detection approaches, fusion based methods in many instances suffer from dissimilar data integration difficulties. On one hand, fusion based network models are complicated in nature and on the other hand they require large computational overhead and resources, processing pipelines for training and inference specially, the early fusion and deep fusion approaches. As such, we devise a late fusion network that in-cooperates pre-trained, single-modality detectors without change, performing association only at the detection level. In addition to this, lidar based method fail to detect distant object due to its sparse nature so we devise proposal refinement algorithm to jointly optimize detection candidates and assist detection for distant objects. Extensive experiments on both the 3D and 2D detection benchmark of challenging KITTI dataset illustrate that our proposed network architecture significantly improves the detection accuracy, accelerating the detection speed.

A Review of Urban Heat Studies in Recife—PE (Brazil): Urban Heat Scales, Landscape Inequalities and Future Perspectives

The objective of this article is to discuss the main studies related to urban heat in the city of Recife, the capital of the state of Pernambuco in Brazil, and contribute to providing theoretical and methodological perspectives for future research and the understanding of the history of these studies. Recife is the core of the largest metropolitan region in the northeastern region of Brazil, characterized by a complex urban landscape marked by inequalities and socio-spatial challenges. Moreover, it is recognized as one of the most vulnerable cities to climate change in the world. Analyzing urban heat in Recife through a descriptive review has the potential to provide a comprehensive analysis of the interactions between physical, social, and environmental elements with urban heat in this city, as well as to identify shortcomings and trends in ongoing initiatives and future perspectives. The discussions encompass the following themes: (a) urban heat scales, ranging from macro to global, in both temporal and spatial aspects; (b) the regional and local contexts of urban growth in Recife; (c) the challenges in urban heat studies in the city, including difficulties in data acquisition and integration of approaches; and (d) current efforts and future perspectives in addressing urban heat in this city. The main challenges for future heat studies include developing means for applicability in urban planning, enhancing observations of the interaction with the urban landscape, overcoming the lack of data sources, and understanding the interaction between scales to develop approaches that integrate the social and physical dimensions of urban climate and its relationship with climate change. Such reflections can also be extended to other geographic areas that share similar patterns of urban growth and socio-spatial complexities, helping to enhance the representation of the Global South in this field.

Physics, Structures, and Applications of Fluorite-Structured Ferroelectric Tunnel Junctions.

The interest in ferroelectric tunnel junctions (FTJ)has been revitalized by the discovery of ferroelectricity in fluorite-structured oxides such asHfO2 and ZrO2 . In terms of thickness scaling, CMOS compatibility, and 3D integration, these fluorite-structured FTJs provide a number of benefits over conventional perovskite-based FTJs. Here, recent developments involving all FTJ devices with fluorite structures are examined. The transport mechanism of fluorite-structured FTJs is explored and contrasted with perovskite-based FTJs and other 2-terminal resistive switching devices starting with the operation principle and essential parameters of the tunneling electroresistance effect. The applications of FTJs, such as neuromorphic devices, logic-in-memory, and physically unclonable function, are then discussed, along with several structural approaches to fluorite-structure FTJs. Finally, the materials and device integration difficulties related to fluorite-structure FTJ devices are reviewed. The purpose of this review isto outline the theories, physics, fabrication processes, applications, and current difficulties associated with fluorite-structure FTJs while also describing potential future possibilities for optimization.

DETERMINING PRE-SERVICE SCIENCE TEACHERS' UNDERSTANDING ABOUT STEM EDUCATION

STEM education is included in education programs by many countries on a global scale. Pre-service teachers are also expected to apply STEM education in their future classrooms. The aim of the research was to determine how pre-service science teachers perceived STEM education, whether they adopted it or not, whether they thought of themselves as sufficient, and the environment and situations that affected their STEM experience. The understanding of the pre-service science teachers was tried to be determined before the theoretical STEM education, after the theoretical education and after the STEM application. The study group of the research consisted of a total of 66 pre-service teachers. Content analysis results of the interviews were carried out in three stages. It was found that there was no single STEM definition that pre-service science teachers agreed on. The training provided increased the STEM competency levels of pre-service science teachers. However, the majority of pre-service science teachers defined themselves as having intermediate competence in STEM education. Pre-service teachers adopt STEM education and believe that it will contribute to students. Pre-service science teachers had the most difficulty in disciplinary integration during the STEM theory and practice education. The most preferred model after both theoretical knowledge and application was the problem-based STEM model. Keywords: content analysis, pre-service science teachers, STEM education, STEM understanding

Integrating Solar Heaters with Building Energy Systems : A Simulation Study

This paper looks at the topic of high-tech solar water heating systems being incorporated into existing building energy infrastructure. Hybrid systems that use solar water heating in conjunction with other renewable energy sources are also discussed, as are technical developments in collector designs, the use of cutting-edge control and monitoring systems, and the like. Reduced carbon emissions and optimized resource utilization are only two of the environmental advantages highlighted in the report. It also emphasizes the need of precise system sizing and regional life cycle assessments (LCAs) in achieving maximum energy efficiency. The paper highlights knowledge gaps in the areas of performance analysis, localized environmental impact studies, integration difficulties, and economic assessments. By filling up these spaces, it hopes to promote more eco-friendly and economical construction methods. Sustainable construction, energy savings, less of an influence on the environment, and new innovations in solar water heating are some of the terms that come to mind.

The intervention effect of traditional Chinese clothing elements into healing clothing for autistic groups

BackgroundAutism is a developmental disorder that affects an individual’s ability to socialize, communicate, and behave. Autistic groups often face difficulties in social integration and psychological pressure. As a fashion style focusing on comfort, warmth and relaxation, healing clothing is gradually being accepted and loved by people. Traditional clothing elements represent unique Chinese cultural symbols and aesthetic values, and their integration into healing clothing may have a positive intervention effect on autistic groups.Subjects and MethodsThe experiment involved 50 people with common autism as research subjects, half of whom served as the experimental group, wearing healing clothing with elements of traditional Chinese dress. The control group had the same number of people as the experimental group and wore normal healing clothing. The participants’ social interaction, emotional performance and self-identity were observed and recorded for a certain period of time, and the effect was tested by the Stanford Acute Stress Response Questionnaire (SASRQ).ResultsAfter wearing the healing clothing with traditional Chinese clothing elements, the experimental group had more active social behaviors and positive emotions, and the average questionnaire score was 10 points higher than that of the control group. However, most of the patients in the control group were still in a depressed state and their activity was not high.ConclusionsThis study proposed to integrate traditional Chinese clothing elements into healing clothing, which has a very good intervention effect on the autistic group, and the research results are expected to provide better support and care for the autistic group.AcknowledgementJiangsu University Philosophy and Social Science Research Project (No. 2020SJA1286).

Process Map for Accessing Automatization of Life Cycle Assessment Utilizing Building Information Modeling

Despite the importance of life cycle assessment (LCA) in inspecting environmental impacts, its execution through manual methods affects the quality of the results, besides consuming the time and effort of decision-makers in the construction industry, such as designers, implementers, facility managers, and consultants. When LCA relies on building information modeling (BIM), it encounters many difficulties and obstacles, such as human intervention, conflicts among platforms and environmental databases, and no coordination to integrate or manage information. Thus, the research aims to support LCA through automatized integration with BIM depending on BIM’s role in information management, promoting decisions, and activating BIM implementation success factors to make the BIM model qualified for the LCA. Then, the BIM and LCA overlap methods were analyzed and exemplified. Also, the requirements of their integration strategies were identified for LCA to become automated from now. Accordingly, the process map that supports and coordinates the automatized integration of LCA and BIM, overcomes integration difficulties, and conducts LCA through automated methods was deduced and structured. The analytical hierarchy process was applied through the survey using questionnaires and interviews to validate this map. The survey affirmed that the BIM model acquired new characteristics and will play an essential role in the LCA’s accuracy, reliability, and comprehensiveness. This map supports the standardization and alternative strategies of the workflow. Furthermore, the interconnected stages of this map as a scenario can conduct an LCA within any project’s life stage and guarantee its quality.

Deep Learning and Sustainability in Agriculture: A Systematic Review

Agriculture plays a pivotal role in the global economy, and with the increasing human population, there is substantial pressure to enhance agricultural productivity while minimizing environmental impacts. Digital agriculture and precision farming have emerged as innovative fields leveraging data-intensive approaches to optimize agricultural operations. However, there is a gap in understanding how machine learning (ML) techniques can be effectively integrated across various agricultural domains, including crop management, livestock monitoring, soil analysis, and water management. To address this gap, this study provides a comprehensive review of the application of ML techniques in different agricultural domains. By analyzing publications from 2016 to 2023, we identify key trends, challenges, and future directions for ML in agriculture. The methodology involves categorizing 40 papers from top reputable journals and conferences into four main domains: crop, livestock, soil, and water, and examining the specific ML techniques applied in each domain. Our results show that crop management is the most researched domain, with significant use of machine learning, deep learning, ensemble learning, and transfer learning techniques. Livestock monitoring and soil analysis also employ advanced ML methods, though they face unique challenges related to data quality and model generalizability. Water management benefits from ML for optimizing resource use but encounters difficulties in data integration and scalability. The significance of this study lies in its holistic analysis of ML applications in agriculture, highlighting the most effective techniques and identifying critical challenges that need to be addressed. By providing insights into current research trends and potential future directions, this review serves as a valuable resource for researchers and practitioners aiming to enhance agricultural productivity through advanced ML technologies.

A Dimension-Reduced Line Element Method for 3D Transient Free Surface Flow in Porous Media

In order to reduce the numerical difficulty of the 3D transient free surface flow problems in porous media, a line element method is proposed by dimension reduction. Different from the classical continuum-based methods, homogeneous permeable pores in the control volume are conceptualized by a 3D orthogonal network of tubes. To obtain the same hydraulic solution with the continuum model, the equivalent formulas of flow velocity, continuity equation and transient free surface boundary are derivable from the principle of flow balance. In the solution space of transient free surface flow, the 3D problem is transformed into 1D condition, and then a finite element algorithm is simply deduced. The greatest advantage of the line element method is line integration instead of volume/surface integration, which has dramatically decreased the integration difficulty across the jump free surface. Through the analysis of transient free surface flow in the unconfined aquifer, trapezoidal dam, sand flume and wells, the transient free surface locations predicted from the proposed line element method generally agree well with the analytical, experimental and other numerical data in the available literatures, the numerical efficiency can also be well guaranteed. Furthermore, the hydraulic anisotropy has significant effect on the evolution of free surface locations and the shape of depression cones in spatial. The line element method can be expanded to model the 3D unsaturated seepage flow, two-phase flow and thermos problems in porous media because of the similarity between the similarity of Darcy’s law, Buckingham Law and Fourier’s law.