Search Results (1,477)

This paper utilizes the digital integrated circuit testing model to compute the test yield curve of future wafers and explore the influence of test guardband (TGB) on quality and yield. With the passage of three years since the COVID-19 pandemic disrupted semiconductor production lines, the semiconductor manufacturing industry still faces chip shortages. Although initiatives such as the CHIPS and Science Act in the United States have helped stabilize chip supply chains, manufacturers still face inventory shortages and delayed deliveries. Moreover, the backwardness and inaccuracy of semiconductor test equipment have led to a decline in both test yield and wafer quality, resulting in reduced shipments. Therefore, to mitigate yield losses and enhance the test yield and shipment volume of semiconductor products, this paper proposes a diverse test method (DTM) to improve test outcomes through the alteration of the testing strategy and TGB adjustment. Furthermore, according to the wafer estimation table published in the IEEE International Roadmap for Devices and Systems (2023), the proposed DTM can effectively enhance the test yield of wafers and improve the testing capabilities of ATE testers (automatic test equipment). Consequently, suppliers can stabilize the chip supply chain and enhance their companies’ profits and reputation by improving chip test yield. Full article

A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997–1998 to 2021–2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention. Although data solely from specimens submitted to the NYS PHL were insufficient to meet surveillance goals, when supplemented with testing data from clinical laboratories participating in surveillance programs, the recommended surveillance goals were achieved. Despite the sudden decline in influenza cases in 2020–2021, impacted by the COVID-19 mitigation measures, the dramatic increases in influenza cases surrounding the coronavirus pandemic reveal that influenza remains a national and international public health threat. Sample submissions to public health laboratories must be encouraged to facilitate monitoring for emerging viruses and preparedness for another pandemic. Full article

This study explores the implementation and impact of autonomous vehicle (AV) systems, particularly in developing countries. While AVs promise enhanced road safety by reducing crashes, injuries, and fatalities, their adoption faces significant challenges, including public acceptance and infrastructure readiness. A mixed methods approach was employed, combining quantitative data from surveys of approximately 1500 randomly selected individuals and qualitative insights from in-depth interviews with policymakers, traffic engineers, and industry representatives. The quantitative analysis revealed high levels of perceived usefulness (78.8%), positive attitudes (87.78%), and expected benefits (86.09%) among respondents, indicating optimism about AVs’ potential to improve traffic efficiency and safety. However, concerns about technical reliability, cybersecurity, and the cost of infrastructure upgrades persist. Comparative analysis of physical and digital infrastructure highlighted significant gaps, particularly in road quality, markings, and internet connectivity. Policy implications emphasize the need for targeted public education to build trust and address safety concerns, regulatory reforms to ensure cybersecurity and ethical compliance, and strategic investments in infrastructure to meet AV requirements. Drawing on lessons from international contexts, the study recommends proactive stakeholder engagement and community outreach to align technological advancements with societal needs. These findings provide a roadmap for policymakers to navigate the challenges of AV adoption in Ethiopia and similar contexts, ensuring the integration of automation into sustainable and efficient transportation systems. Full article

This study presents a novel sustainability plan tailored for Istanbul Airport, leveraging advanced decision-making methodologies to address the urgent need for sustainable practices in aviation. By integrating the entropy weighting and 2-tuple linguistic T-spherical fuzzy multi-criteria decision-making (MCDM) models, the study offers a comprehensive approach to evaluating and prioritizing sustainability criteria based on expert input from 12 professionals. The novelty of this research lies in its unique combination of advanced MCDM techniques with cutting-edge technologies, including IoT-enabled monitoring systems, digital twin models, blockchain-based sustainability reporting, and carbon capture initiatives, tailored specifically for large-scale airport operations. The study develops a phased implementation roadmap comprising three stages: (1) a short-term focus on energy efficiency and renewable energy infrastructure, achieving significant cost reductions within a 3–7.5-year payback period; (2) medium-term initiatives integrating IoT and digital twins to enhance operational efficiency; and (3) long-term measures incorporating carbon capture and blockchain for transparency and compliance. Key implementation steps include upgrading energy systems, deploying IoT sensors, creating digital replicas of airport infrastructure, and establishing regulatory and stakeholder collaboration frameworks. This research contributes a replicable framework for airports worldwide, bridging theoretical models with actionable solutions. Full article

With the intensification of aquaculture to meet the rising demands of fish and shellfish, disease outbreaks during the larval and adult stages are a major challenge faced by aqua culturists. As the prophylactic use of vaccines and antibiotics has several limitations, research is now focused on sustainable alternatives to vaccines and antibiotics, e.g., medicinal plants, probiotics, postbiotics, prebiotics, and synbiotics, as promising candidates to strengthen the immune response of fish and shellfish and to control disease outbreaks. With respect to probiotics, numerous studies are available revealing their health-promoting and beneficial impacts in aquaculture. However, most studies focus on Bacillus and Lactobacillus species. Keeping in view the positive effects of probiotic lactic acid bacteria in aquaculture, researchers are now looking for other probiotic bacteria that can be used in aquaculture. Recently, many non-lactic acid bacteria (non-LAB), which are mainly host-associated, have been reported to reveal beneficial effects in fish and shellfish aquaculture. The main non-LAB probiotic genera are Bifidobacterium, Clostridium, Microbacterium, Micrococcus, Paenibacillus, Acinetobacter, Alcaligenes, Enterobacter, Phaeobacter Pseudoalteromonas, Pseudomonas, Pseudomonas, and Vibrio. Despite the promising effects of non-LAB probiotics, comparably, there is limited available information in this context. This review focuses only on probiotic strains that are non-LAB, mostly isolated from the host digestive tract or rearing water, and discusses their beneficial effects in fish and shellfish aquaculture. This review will provide detailed information on the use of various non-LAB bacteria and provide a roadmap to future studies on new probiotics for sustainable aquaculture. Full article

The Fourth Industrial Revolution (Industry 4.0) has profoundly transformed industries worldwide through the integration of advanced digital technologies, including artificial intelligence, digital twins, building information modeling (BIM), and the Internet of Things (IoT). The Architecture, Construction, and Engineering (ACE) sectors are increasingly adopting these innovations to meet the evolving demands of the global market. Within this dynamic context, Saudi Arabia has emerged as a front-runner and significant investor in this sector, as evidenced by the launch of ambitious mega-projects such as NEOM and The Line. These developments prompt valuable discussions about the readiness of graduates to adapt to rapid technological advancements and meet the current demands of the Saudi market. Although numerous studies have explored this issue, the Saudi context presents unique challenges and opportunities due to the accelerated pace of change within the ACE sectors, driven by the goals of Vision 2030. For this reason, this paper aims to address this gap by exploring the readiness of architectural programs in the context of Saudi Arabia to meet the demands of Industry 4.0. To achieve this, a comprehensive literature review was conducted, developing an analytical framework. Subsequently, a multiple-cases approach was employed, with an overall top-level discussion on the undergraduate architecture program subjects available in the five regions in Saudi Arabia. A combination of field observations, domain expertise, and evidence-based coding methods was employed to develop the SWOT analysis. The SWOT framework was utilized to identify key strengths, weaknesses, opportunities, and threats within the current academic programs. The findings were then analyzed in a comprehensive discussion, highlighting necessary transformations in existing programs. The methodology employed in our study involves prolonged engagement and persistent observation to enhance the quality and credibility of the discussion. This paper serves as a roadmap for guiding future educational reforms and aligning architectural education with emerging industry demands and technological advancements in the field. Four key themes are essential for aligning architectural education with Industry 4.0: sustainability in the built environment, innovation and creativity, digital applications in the built environment, and entrepreneurship and leadership in venture engineering. It also strongly emphasized sustainability courses and noted notable deficiencies in preparing students for a digitally driven professional landscape. For example, the average program comprises 162 credit hours and 58 courses, with only six related to Industry 4.0. The top five institutions offering Industry 4.0 courses ranked from highest to lowest are ARCH-U11, ARCH-U8, ARCH-U3, ARCH-U4, and ARCH-U15. ARCH-U11 offers the most Industry 4.0 courses, totaling 15, which account for 26.8% of its courses and 15% of its credit hours, in contrast to ARCH-U20, which offers no courses. The novelty of this research lies in its comprehensive analysis of the readiness of architecture program curricula from 20 Saudi universities to meet the requirements of Industry 4.0. Importantly, these findings support previous studies that established guidelines that mandate the inclusion of sustainability, innovation, and digital skills in architectural education programs. Contribution to the knowledge and findings is valuable for educational institutions, policymakers, and industry leaders, offering insights into evolving architectural education to meet future industry demands and foster technological innovation and sustainable development. Moreover, it provides actionable recommendations for curriculum development in alignment with Vision 2030. Contrary to expectations, findings show that lower-ranked universities offer more Industry 4.0-related courses than higher-ranked ones, emphasizing the need to align university evaluation standards with labor market demands. Full article

The importance of the electronics sector in the modern world is unquestionable, as it demonstrates clean technology, dry processes, and efficient design, which favor Industry 4.0 and sustainability. Nonetheless, the large number of instruments developed, and their correspondent quick obsolescence, imply an increment in electronic waste. Therefore, in this work, with the aim of diminishing obsolescence, we developed and customized one application that runs independently of systems and takes advantage of the existing computing structures. The application is a new edge computing structure (the AIFC) that is based on an enterprise service bus (ESB) developed in decentralized microservices. In this study, we conducted action research involving the collaboration of researchers and practitioners, and the tests involved six different scenarios; they used existing low-cost, basic computing environments and ranged from the proof of concept, prototype, minimum viable product, and scalability to the roadmap for the structure implementation. The six scenarios emulated sections of a small and medium-sized enterprise (SME), and all the developed microservices communicate with each other to perform data filtering, processing, storage, query, and sensor data acquisition. The results show that it is possible to carry out these functions with low latency and without any decrement or even increase in performance when compared with more conventional cloud computing structures, and it is also possible to manipulate different products that do not have single, consolidated structures. Moreover, there is no need to update machines or communication structures, which are the main factors of rapid obsolescence. Therefore, following the steps of the AIFC development, the results from the proof of concept to the minimum viable product and scalability tests correspond to a roadmap for a sustainable solution and are an important tool for both Industry 4.0 and SMEs. Full article

Increasing feed efficiency in beef cattle is critical for meeting the growing global demand for beef while managing rising feed costs and environmental impacts. Challenges in recording feed intake and combining genomic and nutritional models hinder improvements in feed efficiency for sustainable beef production. This review examines the progression from traditional data collection methods to modern genetic and nutritional approaches that enhance feed efficiency. We first discuss the technological advancements that allow precise measurement of individual feed intake and efficiency, providing valuable insights for research and industry. The role of genomic selection in identifying and breeding feed-efficient animals is then explored, emphasizing the benefits of combining data from multiple populations to enhance genomic prediction accuracy. Additionally, the paper highlights the importance of nutritional models that could be used synergistically with genomic selection. Together, these tools allow for optimized feed management in diverse production systems. Combining these approaches also provides a roadmap for reducing input costs and promoting a more sustainable beef industry. Full article

Wastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field. Full article

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This study systematically reviews the global SSB patent landscape using a cross-sectional bibliometric and thematic analysis to identify innovations addressing key technical challenges. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including year, geographic distribution, inventor engagement, award latency, and technological focus. The analysis reveals significant advancements in electrolyte materials, electrode designs, and manufacturability. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions. Full article

Tibet, with its abundant hydraulic, solar, and wind resources, stands at the forefront of China’s renewable energy development. This paper provides a comprehensive analysis of the current state of clean energy development in Tibet, highlighting the region’s vast potential and the challenges it faces. We find that, while Tibet has made significant strides in harnessing its natural endowments, infrastructural limitations, seasonal fluctuations, and technological hurdles constrain the development of clean energy. This paper offers a multifaceted set of recommendations aimed at accelerating clean energy development in Tibet, including policy reforms, infrastructure enhancements, and technological innovations. Our study’s unique contributions lie in its holistic approach to clean energy development, its detailed analysis of the regional energy policies, and its forward-looking recommendations that balance ecological protection with energy security. By adhering to the principle of ecological priority and conducting innovative research in clean energy development, Tibet can leverage its carbon sequestration capabilities for environmental protection while promoting sustainable economic and social development. This paper provides valuable insights for policymakers and scholars, offering a roadmap for the sustainable development of Tibet’s economy and a reference for similar regions embarking on clean energy transitions. Full article

Integrating shared autonomous vehicles (SAVs) in urban transportation systems holds transformative potential but is accompanied by notable challenges. This study, conducted in Saudi Arabia (KSA), aims to address these challenges by identifying and prioritizing the key barriers and policies that are necessary if we are to successfully adopt SAVs. A comprehensive analysis was performed through a literature review and expert consultations, revealing 24 critical barriers and 10 policies for solving them. The research employed a three-phase methodology to evaluate and rank the policies proposed to overcome these barriers. Initially, the study assessed the specific barriers and policies related to SAVs. Subsequently, the Fuzzy Analytic Hierarchy Process (FAHP) was employed to evaluate the relative importance of these barriers. Finally, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) was applied to rank the policies; the process identified government-backed investment, urban planning integration, and funding for research and development in sensor and hardware technologies as the most effective policies. The study underscores the importance of targeted policies in addressing technical and infrastructural challenges. Emphasizing system reliability, cybersecurity, and effective integration of SAVs into urban planning, the findings advocate for robust government support and continued technological innovation. These insights offer a roadmap for policymakers and industry leaders in the KSA to foster a more sustainable and resilient urban transportation future. Full article

Cell-based test methods with a phenotypic readout are frequently used for toxicity screening. However, guidance on how to validate the hits and how to integrate this information with other data for purposes of risk assessment is missing. We present here such a procedure and exemplify it with a case study on neural crest cell (NCC)-based developmental toxicity of picoxystrobin. A library of potential environmental toxicants was screened in the UKN2 assay, which simultaneously measures migration and cytotoxicity in NCC. Several strobilurin fungicides, known as inhibitors of the mitochondrial respiratory chain complex III, emerged as specific hits. From these, picoxystrobin was chosen to exemplify a roadmap leading from cell-based testing towards toxicological predictions. Following a stringent confirmatory testing, an adverse outcome pathway was developed to provide a testable toxicity hypothesis. Mechanistic studies showed that the oxygen consumption rate was inhibited at sub-µM picoxystrobin concentrations after a 24 h pre-exposure. Migration was inhibited in the 100 nM range, under assay conditions forcing cells to rely on mitochondria. Biokinetic modeling was used to predict intracellular concentrations. Assuming an oral intake of picoxystrobin, consistent with the acceptable daily intake level, physiologically based kinetic modeling suggested that brain concentrations of 0.1–1 µM may be reached. Using this broad array of hazard and toxicokinetics data, we calculated a margin of exposure ≥ 80 between the lowest in vitro point of departure and the highest predicted tissue concentration. Thus, our study exemplifies a hit follow-up strategy and contributes to paving the way to next-generation risk assessment. Full article

In the era of Industry 4.0 and industrial digitization, augmented reality (AR) is a powerful technology with the potential to revolutionize numerous sectors. However, despite a proliferation of supporting tools and hardware and demonstrated benefits in effectiveness, intuitiveness, and ease of use, the practical implementation of AR within the chemical industries remains surprisingly limited. This indicates a potential shortfall in research and development initiatives aimed at fully exploiting the capabilities of AR for industrial applications. This manuscript presents a comprehensive review of the existing landscape of AR within the industry, aiming to shed light on this intriguing paradox. After providing an extensive overview of the current state of AR in industry, we propose a schematic guideline as a systematic approach for introducing AR into industrial operations. The objective of this guide is to bridge the gap between AR’s evident potential and its actual application, fostering a broader adoption of this innovative technology in the industrial sector. Our work offers valuable insights and a practical roadmap for stakeholders aiming to leverage the transformative power of AR in industrial activities. Full article

Remote sensing provides an efficient roadmap in geological analysis and interpretation. However, some challenges arise when remote sensing techniques are integrated with machine learning in geological surveys. Factors including irregular spatial distribution, sample imbalance, interclass resemblances, regolith, and geochemical similarities impede geological feature diagnosis, interpretation, and identification across varied remote sensing datasets. To address these limitations, a hybrid-attention-integrated long short-term memory (LSTM) network is employed to diagnose, interpret, and identify lithological feature representations in a remote sensing-based geological analysis using multisource data fusion. The experimental design integrates varied datasets including Sentinel-2A, Landsat-9, ASTER, ALOS PALSAR DEM, and Bouguer anomaly gravity data. The proposed model incorporates a hybrid attention mechanism (HAM) comprising channel and spatial attention submodules. HAM utilizes an adaptive technique that merges global-average-pooled features with max-pooled features, enhancing the model’s accuracy in identifying lithological units. Additionally, a channel separation operation is employed to allot refined channel features into clusters based on channel attention maps along the channel dimension. The comprehensive analysis of results from comparative extensive experiments demonstrates HAM-LSTM’s state-of-the-art performance, outperforming existing attention modules and attention-based models (ViT, SE-LSTM, and CBAM-LSTM). Comparing HAM-LSTM to baseline LSTM, the HAM module’s integrated configurations equip the proposed model to better diagnose and identify lithological units, thereby increasing the accuracy by 3.69%. Full article