Technical Perspective Research Articles

Improving Refugees' Integration with Online Resource Allocation: Technical Perspective

Improving Refugees' Integration with Online Resource Allocation: Technical Perspective

Thread Lifting Materials: A Review of Its Difference in Terms of Technical and Mechanical Perspective

Thread Lifting Materials: A Review of Its Difference in Terms of Technical and Mechanical Perspective

Examining the Benefits and Drawbacks of Open Network Architectures in E-Commerce

The advent of digital commerce has revolutionized the way businesses operate and interact with customers. One of the key factors contributing to this transformation is the emergence of open network architectures, which facilitate seamless communication and data exchange between various entities within the e-commerce ecosystem. This research aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of digital commerce. And to investigate how open network architectures promote accessibility and interoperability among diverse systems, enabling seamless integration and collaboration within the e-commerce landscape. Explore the potential of open networks to foster innovation and scalability, empowering businesses to rapidly adapt to changing market demands and technological advancements. Evaluate the economic advantages of open network architectures, including reduced infrastructure costs, improved resource utilization, and streamlined operational processes. Examine the potential vulnerabilities and risks associated with open network architectures, such as data breaches, cyber threats, and privacy issues, and propose strategies to mitigate these challenges. Investigate the role of governance frameworks and industry standards in facilitating the effective implementation and management of open network architectures within the e-commerce domain. Identify and analyze the potential barriers to the widespread adoption for overcoming these challenges. This paper aims to provide a comprehensive analysis of the benefits and drawbacks associated with open network architectures in the context of e-commerce. Through real-world case studies, theoretical frameworks, and a multi-faceted approach encompassing technical, business, and strategic perspectives, we strive to equip e-commerce stakeholders with the insights necessary to navigate this complex landscape and make informed decisions that align with their organizational goals and market dynamics.

A technical perspective on integrating artificial intelligence to solid-state welding

AbstractThe implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing their potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy.

Towards human-AI collaborative urban science research enabled by pre-trained large language models

Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, sense public sentiment and so on. They have expanded the breadth of urban research in terms of content, increased the depth and efficiency of the application of multi-source big data in urban research, and enhanced the interaction between urban research and other disciplines. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future.

Natural Climate Protection through Peatland Rewetting: A Future for the Rathsbruch Peatland in Germany

Natural Climate Protection through Peatland Rewetting: A Future for the Rathsbruch Peatland in Germany

Environmental assessment of an innovative adhesive for the footwear industry: road map for product development

Abstract Purpose This work presents an environmental assessment of two adhesives to evaluate the replacement of the traditional adhesive (PU) used in the footwear industry by a novel adhesive based on a microencapsulation approach (PUMC) which is more favorable from the safety and technical perspectives. The PU adhesive is polyurethane-based, while the PUMC adhesive is polyurethane-based but with its isocyanate compounds microencapsulated, increasing storage life and reducing risks related to the exposure of workers with the adhesives. Methods The potential environmental impact of the adhesives was evaluated using the life cycle assessment (LCA) methodology. A detailed process model was developed (both for laboratory and for a pilot-scale implementation) to investigate the environmental impacts associated with these processes. The functional unit was one kilogram of adhesive (PU and PUMC adhesives) produced. A cradle-to-customers’ gate approach was defined. The system boundary starts from extraction of resources, through material production, until adhesive use. This includes microcapsule production in the case of the PUMC adhesive. This study investigates the important drivers behind the environmental impacts to help guide commercialization efforts. A scenarios study/sensitivity analysis was conducted to determine the response of the PUMC adhesive system to the variability of the model, scenarios, and parameters. Results The results show that the PU adhesive environmental impact is due to acetone and polyol consumption in the production stage. In the PUMC adhesive system, acetone consumption and microcapsule production are the major factors responsible for the environmental impact. Polybutylene adipate terephthalate (PBAT), dichloromethane (DCM), and isophorone diisocyanate (IPDI) consumption are the major factors responsible for the environmental impact of the microcapsules’ production. A sensitivity analysis was conducted using three alternative scenarios focused on the reduction in material consumption and increase in material recuperation, as well as using an alternative renewable energy source. Although the traditional PU adhesive has a lower impact, it was found that the three alternative PUMC adhesive systems can become comparable to the traditional PU adhesive. Conclusions This study shows the advance and development of a new technology for microencapsulation of isocyanate in adhesives and its environmental advantages and disadvantages with respect to a traditional product that uses non-encapsulated isocyanate. Finally, it was shown that there is significant potential for minimizing some environmental impacts of the PUMC adhesive, such as optimizing the microcapsules’ production stage, increasing the production efficiency to decrease the required material consumption.

Exploring hydrogen metallurgy to CO2 emissions reduction in China’s iron and steel production: An analysis based on the life cycle CO2 emissions – LEAP model

Previous research has primarily assessed the environmental impact of hydrogen metallurgy from technical or project perspectives, lacking a life cycle perspective. The impact of continued cleanliness of the hydrogen production mix and power generation structure on carbon dioxide (CO2) emissions from the hydrogen metallurgy production process has not been investigated on a medium to long-term scale, which does not provide an adequate and comprehensive view of the contribution of hydrogen metallurgy to China’s emissions reduction, resulting in short-sighted technology choices. To address this lack, we construct a bottom-up energy system model that combines life cycle CO2 emissions with long-range energy alternatives planning (LEAP), including iron and steel (IS) production, power generation, and hydrogen production. Setting development paths of two typical hydrogen metallurgy technologies, including business-as-usual (BAU) and hydrogen metallurgy (HM) scenarios, we carefully evaluate the CO2 emissions impact of hydrogen metallurgy from a whole industry perspective and in each IS production stage. The results suggest that the hydrogen metallurgy transition will help the iron and steel industry (ISI) to achieve peak and deep carbon reduction. Under the HM scenario, CO2 emissions from the ISI would decrease to 816.28 million tons (Mt) in 2050, representing an additional 281.13 Mt reduction over the BAU scenario. Hydrogen metallurgy can accelerate CO2 reduction in raw material acquisition and material processing phases but may increase CO2 emissions in manufacturing and recycling phases. In 2050, hydrogen metallurgy will contribute an additional 12.49 Mt and 300.95 Mt of CO2 reduction to the raw material acquisition and material processing phases, respectively. However, CO2 emissions in the manufacturing and recycling phases would be 32.15 Mt and 0.22 Mt more than in the BAU scenario due to the increased electricity consumption and scrap demand of electric arc furnace (EAF) steelmaking, respectively. Over time, the optimization of hydrogen production and power supply mix will gradually reduce CO2 emissions in each IS production process. In 2050, the CO2 intensity of the hydrogen-rich shaft furnace direct reduced iron (H2-DRI)-EAF process will drop to 753.41 kgCO2/t, which is lower than all other processes except the 100 % scrap-based EAF process. Advancing the development of the H2-DRI-EAF process can effectively achieve CO2 emissions reduction while alleviating dependence on scrap resources. The results are expected to inform government policy on the development of hydrogen metallurgy in China and other countries.

Biological feasibility of discharge a local WTTP sludge to sewer network and centralized WWTP; a case study: Tehran, Iran

Over the recent years, ever-increasing population growth and higher wastewater production has been a challenge for decentralized wastewater treatment plants (WWTPs). In addition, sludge treatment due to high cost for equipment and place make authorities to find a sustainable approach in both of economical and technical perspectives. One of the proposed solutions is transferring the sludge produced from decentralized WWTP to centralized WWTP. However, the appropriate proportional ratio of raw sludge to raw sewage is a challenge, otherwise, it make anaerobic conditions and sewage rotting along the sewer network based on permissible limit of dihydrogen sulfide (H2S) gas (5 ppm). In the present study, seven reactors with different ratios of sludge to raw sewage (0, 15, 20, 25, 50, 75, 100) were used to stimulate the feasibility of transferring Shahrake Gharb WWTP sludge along the wastewater transfer pipe to the centralized sewage treatment south Tehran WWTP plant in Tehran, Iran. The septic situation and H2S emission of different reactors within 7 h (Time to reach the compound in the south treatment plant) was analyzed by gas meter. The results indicated that the optimum ratio of sludge to raw sewage was 15% without H2S production during 7 h. In addition, due to the high volume of sludge produced by the Shahrake Gharb WWTP, the optimal ratio of lime to total solids (TS) in sludge (gr/gr) (0.6) increased the sludge loading rate from 15 to 30% without any H2S emission during the stimulation study period. Therefore, the lime stabilization and transfer of sludge from a decentralized WWTP to a centralized WWTP is a feasible way to manage the sludge and enhance the treatment capacity in local WWTP.

Research on Development Mode of Quality Education Based on the Perspective of Chat GPT

The development of modern educational technology greatly influences education, becoming a prevalent topic in the field of high-quality education. This article examines Chat GPT's current status in high-quality education in China and discusses its significance in promoting educational progress and development. Explored challenges in delivering high-quality education from policy and technical perspectives and proposed viable solutions. Through this exploration, our goal is to acquire a deeper understanding of the challenges and opportunities that exist for Chat GPT in the field of high-quality education. We strive to enhance modern educational technology to better cater to the needs of Chinese students and contribute to their growth and development.

Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

Recycling potential of carbon fibres in the construction industry: From a technical and ecological perspective

Recycling potential of carbon fibres in the construction industry: From a technical and ecological perspective

Investigation of earthquake-damaged and affected structures

Recent earthquakes in Zinda Jan district, Herat province during the fall season of  2023 resulted in more than 2000 casualties and the destruction of numerous houses and buildings in the villages of Zinda Jan. From a technical perspective, notable factors in this tragedy include traditional and improper construction methods, the formation of structures with metal frames, concrete structures, and load-bearing walls without proper earthquake design. Based on surveys in the affected villages of Zinda Jan, the buildings can be categorized into four types: traditional mud-brick houses, masonry structures, concrete buildings, and metal structures. In the research, more than 95% of mud-brick houses were destroyed due to their lack of resilience against earthquakes, causing the highest number of casualties. Timber-framed structures and load-bearing walls ranked second, causing the most damage after mud-brick houses. However, fortunately, fewer roofs collapsed, resulting in the preservation of human lives. In the studied areas, concrete buildings and metal structures are also noticeable, with some of these structures suffering severe damage while a few remained intact without significant damage. This study discusses the vulnerability of buildings and houses, as well as the reasons for the incurred damages, which have yielded desirable results. It is undeniable that shortcomings in planning, design, calculations, execution, and supervision all played a role in this major incident because only a few structures remained undamaged in this earthquake.

Assessing sustainable supply chain transparency practices in Taiwan semiconductor industry: A hierarchical interdependence approach.

This study contributes both theoretical and practical perspectives by providing sufficient resolution for detecting sustainable supply chain transparency structures, particularly when transparency has stretched beyond current supply chain operation, which involves various uncertain and complex factors. Sustainable supply chain transparency is established to enhance the disclosure and visibility of sustainability information within and outside the supply chain. However, the complexity and uncertainty of accessing supply chain information make such assessments challenging. This study integrates the balanced scorecard as the theoretical foundation in addition to the technical perspective for a performance model, employing a hybrid of the fuzzy Delphi method, a fuzzy decision-making trial and evaluation laboratory, an analytic network process and Choquet Integral. The analysis aims to identify valid sustainable supply chain transparency attributes, examine causal relationships, assess hierarchical interdependencies, and indicate priority practices for Taiwan's semiconductor industry. The findings reveal six aspects and 20 indicators as a valid hierarchical structure, with causal aspects such as stakeholder efforts, information processing and analytics, and digital capability. The crucial managerial implications that focus on production and logistics processes, government regulation, safety and data security include connectivity and data confidentiality. The prioritizing practices of disclosure, traceability, and visibility are indicated to improve sustainable supply chain transparency in the industry.

Acquisition Devices for Fetal Phonocardiography: A Scoping Review.

Timely and reliable fetal monitoring is crucial to prevent adverse events during pregnancy and delivery. Fetal phonocardiography, i.e., the recording of fetal heart sounds, is emerging as a novel possibility to monitor fetal health status. Indeed, due to its passive nature and its noninvasiveness, the technique is suitable for long-term monitoring and for telemonitoring applications. Despite the high share of literature focusing on signal processing, no previous work has reviewed the technological hardware solutions devoted to the recording of fetal heart sounds. Thus, the aim of this scoping review is to collect information regarding the acquisition devices for fetal phonocardiography (FPCG), focusing on technical specifications and clinical use. Overall, PRISMA-guidelines-based analysis selected 57 studies that described 26 research prototypes and eight commercial devices for FPCG acquisition. Results of our review study reveal that no commercial devices were designed for fetal-specific purposes, that the latest advances involve the use of multiple microphones and sensors, and that no quantitative validation was usually performed. By highlighting the past and future trends and the most relevant innovations from both a technical and clinical perspective, this review will represent a useful reference for the evaluation of different acquisition devices and for the development of new FPCG-based systems for fetal monitoring.

Cooperative exploration model of coal–Ge deposit: A case study of the Wulantuga coal–Ge deposit in Shengli coalfield, Inner Mongolia, China

Germanium (Ge) is an excellent organophilic and dispersed semiconductor resource, which is irreplaceable in emerging industries such as new materials, new energy, aerospace, industrial smelting, national defense, military industry, and modern information technology, it has become an important direction of mineral resources exploration. Therefore, it is urgent to carry out research on the collaborative exploration model of coal and Ge deposits from a technical and economic perspective to lay a theoretical foundation for the collaborative exploration of coal and Ge. In this study, the Wulantuga coal–Ge deposit is taken as an example, according to the distribution characteristics of coal–Ge deposit and the response effect to the exploration technical means, the economical and effective exploration technical means, reasonable exploration project layout and resource estimation parameters of coal–Ge deposit are put forward, and the cooperative exploration mode of coal–Ge deposit is determined, it provides an important scientific basis for improving the strategic coordination ability of Ge mineral resources.

A comprehensive review of artificial intelligence models for screening major retinal diseases

This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.

IDENTIFYING BARRIERS IN PUBLIC SECTOR DIGITAL TRANSFORMATION: A CASE STUDY IN INDONESIA CITY OFFICE FOR POPULATION ADMINISTRATION AND CIVIL REGISTRATION (COPACR) CONTEXT

Purpose: This paper presents a case study of the Surabaya City Office for Population Administration and Civil Registration (COPACR), which is currently undergoing digitization and digital transformation across all its civil registration service lines. This research identifies internal and external barriers relevant to digital transformation in the context of Surabaya COPACR.   Theoretical Framework: This study combines qualitative and quantitative methods to identify internal and external barriers relevant to the implementation of digital transformation in the COPACR sector. The barriers are validated through Focus Group Discussions (FGDs) applying the Analytic Hierarchy Process (AHP) and Expert Choice tools. Priority barriers are determined based on the highest relevance weight values from the perspectives of Top Management and Field Officers.   Design/Methodology/Approach: This research adopts an action research methodology, leveraging the researcher's position within the system to directly intervene and facilitate change. A mixed-method approach is employed, combining qualitative and quantitative methodologies. Twenty literature sources are utilized to identify internal and external barriers within public sectors to adopt digital transformation. A total of 63 barriers (33 internal and 30 external) are categorized and their relevance tested through Focus Group Discussions (FGDs). Barrier priorities are selected using the Analytic Hierarchy Process (AHP) method and expert choice tools.   Findings: The Surabaya COPACR in developing countries exhibits characteristics such as low employee motivation, inadequate officer competency, rigid bureaucratic structures, and limited innovation. With these conditions, the internal priorities for adopting digital transformation in Surabaya COPACR include lack of political support, traditional bureaucracy, organizational complexity, and lack of competency. External barriers include inflexible regulations, bureaucracy, lack of data standardization, and inadequate funding allocation.   Research, Practical & Social Implications: This research contributes theoretical insights into internal and external priority barriers that need to be considered to adopt digital transformation in the COPACR context of developing countries. The identified barriers can serve as references for public sectors with similar organizational characteristics in developing countries to design strategies for adopting digital transformation effectively.   Originality/Value: This study identifies internal and external barriers to digital transformation in the Surabaya COPACR context, involving assessments from both strategic (Top Management) and technical (Field Officer) perspectives.

Systematic literature review on the holistic integration of e-learning in universities: Policy, human, financial, and technical perspectives

This systematic literature review (SLR) explores the integration of e-learning in universities, emphasizing a comprehensive approach that intertwines various mainstream perspectives. Despite numerous studies on e-learning implementation evaluation, few have holistically considered financial, human, technical, and policy factors. This review used PRISMA guidelines and sources from Scopus, Google Scholar, ERIC, SAGE, and ProQuest. Of 26 analyzed studies, seven core themes emerged: Policy, financial, technical, human, institutional factors, others, and an integrated perspective, further distilled into 13 sub-themes. Findings highlight the importance of an integrative framework for evaluating e-learning, underscoring the interplay between macro and institutional policies. Additionally, the authors recommend cross-national comparisons and data synthesis from stakeholders, including students, educators and directors, to fully grasp e-learning implementation dynamics. Distinctively, it adopts an integrated perspective, filling the research gap by emphasizing overlooked financial considerations and presenting a comprehensive view through an SLR. By drawing insights from human, finance, technical, and policy perspective, the study provides a multidimensional lens on e-learning. This forward-looking approach not only captures the current state of e-learning integration but also charts future research directions, establishing its originality and significance in higher education.

A systematic review of green-aware management techniques for sustainable data center

Cloud computing is one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs.