Prospect and barrier of 3D concrete: a systematic review

This paper aims to explore the current state of the art and potential of 3D concrete printing and its use in large-scale applications. The study analysed 373 academic research, all of which were obtained from the Scopus database. The review conducted on some crucial issues on development of 3D concrete that included materials and their desirable properties, printer nozzle developments, reinforcement in printing, geopolymers as printing materials, and the use of coarse graded aggregates. This study provides researchers and institutions with an in-depth insight into 3D concrete printing and research trends worldwide and assesses the future of 3D concrete printing in large-scale applications. The requirement of more research on the mechanics of 3D printers, standardising a printer nozzle, the automation of reinforcing processes, and use of coarse graded aggregate for large-scale structural application were identified in this review. It also shows how 3D concrete printing has evolved and changed over time and gives an insight into the future of 3D concrete printing—making this scientometric review a framework for future studies

Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development

A Sustainable Cold Mix Asphalt Mixture Comprising Paper Sludge Ash and Cement Kiln Dust

Concerns about the environment, the cost of energy, and safety mean that low-energy cold-mix asphalt materials are very interesting as a potential replacement for present-day hot mix asphalt. The main disadvantage of cold bituminous emulsion mixtures is their poor early life strength, meaning they require a long time to achieve mature strength. This research work aims to study the protentional utilization of waste and by-product materials as a filler in cold emulsion mixtures with mechanical properties comparable to those of traditional hot mix asphalt. Accordingly, cold mix asphalt was prepared to utilize paper sludge ash (PSA) and cement kiln dust (CKD) as a substitution for conventional mineral filler with percentages ranging from 0–6% and 0–4%, respectively. Test results have shown that the incorporation of such waste materials reflected a significant improvement in the mixture’s stiffness and strength evolution. The cementitious reactivity of PSA produces bonding inside the mixtures, while CKD is used as an additive to activate the hydration process of PSA. Therefore, based on the results, it will be easier to build cold mixtures by shortening the amount of time needed to reach full curing conditions

Application of filament winding technology in composite pressure vessels and challenges : A review

The filament winding (FW) technology is one of the emerging manufacturing practices with a high degree of excellence and automation that has revolutionized gas storage and transportation doctrine. Various pressure vessels have evolved in the last few decades, from metal to fiber-reinforced tanks, primarily for weight savings and high-pressure ratings; advantageously, Type 4 composite pressure vessels (CPVs) can affect fuel gas tanks' weight savings to 75% compared to metallic vessels. As a result, composite pipelines and CPV manufacturing through FW technology have proliferated. Though many design and manufacturing challenges are associated with various process factors involved in winding technology, careful considerations are needed to create a reliable product. Therefore, it is essential to comprehend the various process parameters, their combined effects, and the associated challenges while designing and fabricating filament-wound structures. This article reviews the FW technique's utility, its evolution, various process parameters, and the CPVs as an emerging contender for high-pressure gas and cryo fluid storage. In addition, different optimization techniques, numerical analysis strategies, and challenges are summarized with related disputes and suggestions

Estimating the costs of school closure for mitigating an influenza pandemic

BACKGROUND: School closure is a key component of many countries' plans to mitigate the effect of an influenza pandemic. Although a number of studies have suggested that such a policy might reduce the incidence, there are no published studies of the cost of such policies. This study attempts to fill this knowledge gap METHODS: School closure is expected to lead to significant work absenteeism of working parents who are likely to be the main care givers to their dependent children at home. The cost of absenteeism due to school closure is calculated as the paid productivity loss of parental absenteeism during the period of school closure. The cost is estimated from societal perspective using a nationally representative survey. RESULTS: The results show that overall about 16% of the workforce is likely to be the main caregiver for dependent children and therefore likely to take absenteeism. This rises to 30% in the health and social care sector, as a large proportion of the workforce are women. The estimated costs of school closure are significant, at 0.2 pounds bn - 1.2 pounds bn per week. School closure is likely to significantly exacerbate the pressures on the health system through staff absenteeism. CONCLUSION: The estimates of school closure associated absenteeism and the projected cost would be useful for pandemic planning for business continuity, and for cost effectiveness evaluation of different pandemic influenza mitigation strategies

Evaluating Temporal Factors in Combined Interventions of Workforce Shift and School Closure for Mitigating the Spread of Influenza

10.1371/journal.pone.0032203PLoS ONE7

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community

Privacy enhancing technologies (PETs) for connected vehicles in smart cities

This is an accepted manuscript of an article published by Wiley in Transactions on Emerging Telecommunications Technologies, available online: https://doi.org/10.1002/ett.4173 The accepted version of the publication may differ from the final published version.Many Experts believe that the Internet of Things (IoT) is a new revolution in technology that has brought many benefits for our organizations, businesses, and industries. However, information security and privacy protection are important challenges particularly for smart vehicles in smart cities that have attracted the attention of experts in this domain. Privacy Enhancing Technologies (PETs) endeavor to mitigate the risk of privacy invasions, but the literature lacks a thorough review of the approaches and techniques that support individuals' privacy in the connection between smart vehicles and smart cities. This gap has stimulated us to conduct this research with the main goal of reviewing recent privacy-enhancing technologies, approaches, taxonomy, challenges, and solutions on the application of PETs for smart vehicles in smart cities. The significant aspect of this study originates from the inclusion of data-oriented and process-oriented privacy protection. This research also identifies limitations of existing PETs, complementary technologies, and potential research directions.Published onlin

Application of paper sludge ash and incinerated sewage ash in emulsified asphalt cold mixtures

Certain disadvantages could have appeared while using hot mix asphalt (HMA), such as the release of unhealthy gases into the environment (environmental issues), difficulty in sustaining the temperature over long distances (logistical issues), and consuming a sufficient amount of energy while preparing and laying down (practical and economic issues). To overcome the aforementioned issues, this study aimed to develop rapid-curing emulsified asphalt cold mixes (EACM) comprising a cementitious filler made from industrial by-product materials. Paper sludge ash (PSA) is used as an active filler for application in the EACM rather than conventional mineral filler. Additionally, to maximize the effect of PSA’s hydraulic activity, incinerated sewage ash (ISA) is utilized as an activator at a concentration of 0%–4% by mass of the aggregates. The results demonstrate that the use of waste PSA significantly improves the indirect tensile stiffness modulus (ITSM) by around 10 times more after 2 days than the traditional emulsified asphalt cold mixes. In addition, the improvement in ITSM was around 30% and 65% for 6%PSA+1%ISA and 6%PSA+4%ISA mixes, respectively. Furthermore, the rutting for the 6%PSA+1%ISA and 6%PSA+4%ISA mixes decreased to around 19% and 11% in comparison to the traditional 131-pen HMA. The formation of hydration products and rapid demulsification of asphalt emulsion, which results in binding within the mixtures, are responsible for the increased ITSM and rutting resistance. As a result, environmental issues are minimized, and energy preservation may be maintained