bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization and performance evaluation of lightweight thermal insulation recycled concrete
Authors: Xu Chenyang , Pan Wangjie , Zhang Pengfei , Zhang Zhizhu , Wang Zixu , Su Dunlei , Shao Changhai , Li Qiuyi , Guo Yuanxin 
Published: May 2024

AbstractTo enhance forestry solid waste reuse, reduce building energy consumption, and increase building capacity, producing lightweight concrete with new materials has garnered recent attention. This study used waste wood and expanded perlite (EP) to design lightweight thermal insulation recycled concrete (LTIRC) with varying volume admixtures. Compared to mineral aggregate, wood aggregate (WA) and EP exhibit significant differences in water absorption, particle morphology, density, and crushing index. This study aimed to prepare LTIRC using two lightweight aggregates (WA & EP) and evaluate their dry density, mechanical properties, thermal properties, chloride ion permeability, and frost resistance. The results showed WA and EP introduction effectively reduced concrete bulk weight and met the dry density standard for lightweight concrete. In terms of thermal insulation performance, both WA and EP are characterized by porous, low–density, and low thermal conductivity. Consequently, LTIRC thermal conductivity was reduced by up to 76.5% versus conventional concrete, effectively increasing resistance to heat flow through concrete and providing potential for building energy savings. Additionally, WA and EP addition caused LTIRC to experience mechanical and durability property deterioration. However, some LTIRC achieved over 80% of the strength of natural aggregate concrete. Moreover, WA addition inhibited internal crack generation in LTIRC and slowed concrete damage from increased WA and EP dosage. The maximum mass loss of LTIPC was 2.72% after 100 freeze–thaw cycles. LTIPC precast panels are suitable for preparing low-carbon insulated building wall panels.
Characterization of Respirable Coal Mine Dust Recovered from Fibrous Polyvinyl Chloride Filters by Scanning Electron Microscopy
Authors: Greth August , Afrouz Setareh Ghaychi , Keles Cigdem , Sarver Emily 
Published: May 2024

AbstractThe characterization of respirable dust on the basis of constituent fractions and particle sizes is increasingly of concern for evaluating exposure hazards. For high-resolution particle analysis, scanning electron microscopy with energy dispersive X-ray (SEM-EDX) can be an effective tool. However, it requires particles to be deposited on a smooth, uniform substrate such as a polycarbonate (PC) filter for optimal results. While direct sampling onto PC is possible, this is not the standard approach in many situations. For example, in coal mines, respirable dust samples have typically been collected onto polyvinyl chloride (PVC) filters because they are intended for gravimetric and/or infrared spectroscopy analysis. Such fibrous substrates are not ideal for SEM-EDX (or other microscopy), but an effective method to recover and redeposit the dust particles could render such samples suitable for the additional analysis. Here, we present a simple method and compare SEM-EDX results for paired samples analyzed directly on PC and following recovery from PVC and redeposition on PC. Both laboratory-generated dust samples (n = 10 pairs) and field samples of respirable coal mine dust (n = 44 pairs) are included in this study. Although some changes in particle size distributions were observed between samples analyzed directly and those that were recovered and redeposited prior to analysis, the results indicate the dust recovery method generally yields a representative sample in terms of mineral constituents. That said, results also highlighted the effects of high particle loading density on individual particle analysis. Considering all sample pairs, those with similar loading density between the directly analyzed and recovered sample tended to exhibit similar mineralogy distributions. This was generally the case for the lab-generated sample pairs, and the Freeman-Halton exact test of independence indicated that the samples in just three (of 10) pairs were in disagreement in terms of their mineralogy distributions. On the other hand, for the field samples, the directly analyzed sample often had higher loading density than the recovered sample; and the Freeman-Halton test showed that 25 (of 44) pairs were in disagreement. However, the effect of possible particle agglomeration on the directly analyzed samples cannot be ruled out—and exploration of this factor was beyond the scope of the current study.
Meta Heuristic Technique with Reinforcement Learning for Node Deployment in Wireless Sensor Networks
Authors: Kusuma S. M. , Veena K. N. , Kumar B. P. Vijaya , Naresh E. , Marianne Lobo Athena 
Published: May 2024

AbstractWireless Sensor Networks (WSNs) are vital in applications like environmental monitoring, smart homes, and battlefield surveillance. Comprising small devices with limited resources, WSNs require efficient node deployment for power optimization and prolonged network lifetime, ensuring sufficient coverage and connectivity. This study introduces an Intelligent Satin Bower Bird Optimizer augmented with reinforcement learning (ISBO-RL), enhancing coverage and connectivity. ISBO-RL focuses on optimal sensor placement for improved coverage and connectivity, using an Optimum Position Finding (OPF) method to identify key sensor node locations. Reinforcement learning is integrated into the ISBO algorithm, allowing nodes to adapt based on performance and changing conditions. Experimental results on diverse platforms highlight ISBO-RL’s efficacy and its superior coverage and connectivity performance as compared to other algorithms. ISBO-RL represents a significant advancement in the field of Wireless Sensor Networks, offering a promising solution to address the challenges of efficient node deployment and network optimization in various critical applications.
Legal Design Patterns: New Tools for Analysis and Translations Between Law and Technology
Authors: Koulu Riikka , Pohle Jörg 
Published: May 2024

AbstractDesign patterns, a concept originated in urban architecture and adopted also in software engineering, provides a potential approach also for translations between law and technology. This approach will be examined and elaborated from various viewpoints in this topical collection, for which this introductory article provides an overall framework. Here, we discuss design patterns as documentations of living practice, which embed legal concepts, rules, and thinking and between internal and external perspectives to law. We argue that design patterns provide a structured format for interdisciplinary discussions and enhance problem-solving and self-reflecting capabilities of legal scholarship.
Nanofiltration & reverse osmosis technical assessment for pesticides removal
Authors: Rodríguez-Alegre Rubén , Pérez Megías Laura , Sanchis Sonia , Andecochea Saiz Carlos , You Xialei 
Published: May 2024

AbstractThe increasing food demand for a growing population has resulted in the intensification and modernization of agriculture leading to an increasing use of pesticides to protect crops against insects, weeds, fungi, and other pests. These chemical compounds are time-persistant as they usually report low biodegradability and can cause adverse effects on the environment due to their toxicity. This study assesses the use of membranes designed for urban wastewater and drinking water treatment -DuPont FilmTec™ NF270 and FilmTec™ XLE membranes- for the removal of six pesticides (atrazine, simazine, isoproturon, metolachlor ESA, 2,4-D, and chlorothalonil) from aqueous streams. The results reported average rejection rates of 29–89% in the case of nanofiltration membranes and > 97% for reverse osmosis membranes. In addition, it was observed adsorption of pesticides within membranes’ active layer, which should be taken into account for the assessment of membranes performance when a fresh membrane is used. From this study can be concluded that membrane-based technology is effective for the removal of these pollutants from aqueous streams, but a customised selection of the membrane (nanofiltration/reverse osmosis) should be performed depending on the targeted pollutants in order to balance the pesticide rejection and energy consumption for each market application.
Numerical modelling and analysis of porous surface-enhanced jet cooling using copper inverse opals in single-phase flow
Authors: Lyu Shuhang , Wu Qianying , Wei Tiwei 
Published: May 2024

Abstract
In this study, we propose a novel cooling scheme that utilizes copper inverse opals (CIOs) for surface enhancement in a single-phase impingement jet cooling system. We perform computational fluid dynamics simulations to evaluate the cooling performance of the CIO jet coolers. Our modelling results indicate that the proposed CIO-coated cooler can significantly reduce the average temperature and improve the temperature uniformity across the entire chip surface. The average Nusselt number of the CIO-coated cooler can reach up to 2.8 times that of the flat surface jet cooler. However, the porous structure of the CIO-coated cooler increases the total pressure drop. To determine designs with high cooling performance and low energy consumption, we investigate two crucial design factors, namely the inlet velocity and the nozzle-to-CIO distance. Our analysis reveals that increasing the inlet velocity further enhances the heat transfer, but at the expense of high pressure drop. On the other hand, a larger nozzle-to-CIO distance results in a lower pressure drop but also reduces the heat transfer coefficient. The effects of nozzle-to-CIO distance are further understood by studying the flow resistance network. Furthermore, we present a reduced-order model that accurately captures the thermofluidic characteristics of the proposed design.
Nonrandom Tweet Mortality and Data Access Restrictions: Compromising the Replication of Sensitive Twitter Studies
Authors: Küpfer Andreas 
Published: May 2024

AbstractUsed by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.
The Wrong Winners: Anti-Corporate Animus and Attitudes Towards Trade
Authors: Menon Anil , Osgood Iain 
Published: May 2024

AbstractGlobalization creates winners and losers, and recent research emphasizes that large corporations are among the biggest beneficiaries of trade while smaller firms may be harmed. How do these redistributive effects impact trade attitudes? Because a growing share of Americans hold highly unfavourable views of big corporations, we argue that the belief that large firms win from trade will provoke hostility towards trade and globalization. To test this theory, we show experimentally that informing people that large corporations benefit from trade makes them markedly more hostile towards trade compared to a treatment emphasizing that firms in exporting industries benefit. Using subgroup and mediation analysis, we find that anti-corporate sentiment drives this effect, particularly concern about corporations’ power in society. Our findings illustrate how distributive consequences and attitudes towards the winners and losers from policy change interact to shape public opinion on economic policy.
Characterization of continuous homomorphisms on entire slice monogenic functions
Authors: Pinton Stefano , Schlosser Peter 
Published: May 2024

AbstractThis paper is inspired by a class of infinite order differential operators arising in quantum mechanics. They turned out to be an important tool in the investigation of evolution of superoscillations with respect to quantum fields equations. Infinite order differential operators act naturally on spaces of holomorphic functions or on hyperfunctions. Recently, infinite order differential operators have been considered and characterized on the spaces of entire monogenic functions, i.e. functions that are in the kernel of the Dirac operators. The focus of this paper is the characterization of infinite order differential operators that act continuously on a different class of hyperholomorphic functions, called slice hyperholomorphic functions with values in a Clifford algebra or also slice monogenic functions. This function theory has a very reach associated spectral theory and both the function theory and the operator theory in this setting are subjected to intensive investigations. Here we introduce the concept of proximate order and establish some fundamental properties of entire slice monogenic functions that are crucial for the characterization of infinite order differential operators acting on entire slice monogenic functions.
CPR-SLAM: RGB-D SLAM in dynamic environment using sub-point cloud correlations
Authors: Yu Xinyi , Zheng Wancai , Ou Linlin 
Published: May 2024

AbstractThe early applications of Visual Simultaneous Localization and Mapping (VSLAM) technology were primarily focused on static environments, relying on the static nature of the environment for map construction and localization. However, in practical applications, we often encounter various dynamic environments, such as city streets, where moving objects are present. These dynamic objects can make it challenging for robots to accurately understand their own position. This paper proposes a real-time localization and mapping method tailored for dynamic environments to effectively deal with the interference of moving objects in such settings. Firstly, depth images are clustered, and they are subdivided into sub-point clouds to obtain clearer local information. Secondly, when processing regular frames, we fully exploit the structural invariance of static sub-point clouds and their relative relationships. Among these, the concept of the sub-point cloud is introduced as novel idea in this paper. By utilizing the results computed based on sub-poses, we can effectively quantify the disparities between regular frames and reference frames. This enables us to accurately detect dynamic areas within the regular frames. Furthermore, by refining the dynamic areas of keyframes using historical observation data, the robustness of the system is further enhanced. We conducted comprehensive experimental evaluations on challenging dynamic sequences from the TUM dataset and compared our approach with state-of-the-art dynamic VSLAM systems. The experimental results demonstrate that our method significantly enhances the accuracy and robustness of pose estimation. Additionally, we validated the effectiveness of the system in dynamic environments through real-world scenario tests.
A machine learning-based approach for automatic motion/constraint and mobility analysis of parallel robots
Authors: Huo Xinming , Song Zehong , Sun Tao 
Published: May 2024

AbstractMotion and constraint identification are the fundamental issue throughout the development of parallel mechanisms. Aiming at meaningful result with heuristic and visualizable process, this paper proposes a machine learning-based method for motions and constraints modeling and further develops the automatic software for mobility analysis. As a preliminary, topology of parallel mechanism is characterized by recognizable symbols and mapped to the motion of component limb through programming algorithm. A predictive model for motion and constraint with their nature meanings is constructed based on neural network. An increase in accuracy is obtained by the novel loss function, which combines the errors of network and physical equation. Based on the predictive model, an automatic framework for mobility analysis of parallel mechanisms is constructed. A software is developed with WebGL interface, providing the result of mobility analysis as well as the visualizing process particularly. Finally, five typical parallel mechanisms are taken as examples to verify the approach and its software. The method facilitates to attain motion/constraint and mobility of parallel mechanisms with both numerical and geometric features.
Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review
Authors: Alrawashdeh Ahmad , Alqahtani Saeed , Alkhatib Zaid I. , Kheirallah Khalid , Melhem Nebras Y. , Alwidyan Mahmoud , Al-Dekah Arwa M. , Alshammari Talal , Nehme Ziad 
Published: May 2024

AbstractObjective:The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS).Methods:Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains.Results:This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms.Conclusion:Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.
Identifying phosphorus use efficient genotypes by evaluating a chickpea reference set across different phosphorus regimes
Authors: Kumar Aiswarya S. , Singh Satinder , Sharma Parul , Singh Inderjit , Salaria Sonia , Srinivasan Samineni , Thudi Mahendar , Gill Balwinder Singh , Singh Sarvjeet 
Published: May 2024

AbstractLow phosphorus use efficiency (PUE) is one of the abiotic factors that hamper yield and production potential in chickpea (Cicer arietinum L.). Higher yield coupled with improved PUE can make this crop more adaptive and competitive to wide cropland area, especially on marginal soils having low-level phosphorus (P). To identify chickpea germplasm lines that assimilate phosphorus more efficiently under P-deficient soils, 288 diverse genotypes of chickpea belonging to reference set were evaluated for yield component traits and PUE under field conditions for two consecutive years at two phosphorus levels (low P – no phosphorus application and high P – phosphorus application at 40 kg/ha). Based on 2-year evaluation of data under high and low P soil conditions, we identified strong correlations for traits like number of primary and secondary branches, number of pods, biological yield and seed yield indicating that these traits can be used as proxy traits for PUE. ICC 6571 was the best performing genotype under low P conditions while ICC 6579 yielded maximum under high P regime. We report 16 genotypes namely ICC 1052, ICC 1083, ICC 1098, ICC 1161, ICC 2072, ICC 4418, ICC 4567, ICC 4991, ICC 5504, ICC 5639, ICC 7413, ICC 8350, ICC 9590, ICC 9702, ICC 11584 and ICC 13357 as phosphorus use efficient genotypes based on their better performance for yield and yield-contributing traits under low P compared to high P conditions. These genotypes can be exploited in future as potential donors for development of phosphorus use efficient chickpea cultivars.
Neutralizing the sacred space: pre-election messages in a typical Indonesian city
Authors: Lussier Danielle N. , Ahnaf Mohammad Iqbal 
Published: May 2024

AbstractDespite frequent claims that religion serves as a motivator of political behavior in Muslim-majority contexts, empirical evidence of political messaging in sacred spaces is scant. Building on earlier studies on political messaging in both churches and mosques, we examine the presence and nature of political messages across Muslim and Christian religious contexts in a typical Indonesian city, Yogyakarta. Through an analysis of 71 sermons that took place in mosques and churches prior to elections in 2017 and 2019, we analyze the frequency, style, tone, and consistency of political content. We find that political messages within individual houses of worship were often inconsistent from week to week, reducing their potential as a mobilizing force for political action. We also find that houses of worship frequently sought to present themselves as politically neutral spaces in an electoral context in which religious themes are politicized.