bluuebunny/crossref_metadata_2025 huggingface dataset:
What I Wish You Knew: Insights on Burnout, Inertia, Meltdown, and Shutdown From Autistic Youth
Authors: Phung Jasmine , Penner Melanie , Pirlot Clémentine , Welch Christie
Published: November 2021

Introduction: Burnout, inertia, meltdown, and shutdown (BIMS) have been identified as important parts of some autistic people’s lives. This study builds on our previous work that offered early academic descriptions of these phenomena, based on the perspectives of autistic adults.

Objectives: This study aimed to explore the unique knowledge and insights of eight autistic children and youth to extend and refine our earlier description of burnout, inertia, and meltdown, with additional exploration of shutdown. We also aimed to explore how these youth cope with these phenomena and what others around them do that make things better or worse, with a hope to glean knowledge to design better supports.

Methods: One-to-one interviews were conducted with eight children and youth, who shared their experience with BIMS. To match individual communication strengths of children and youth, we took a flexible approach to interviews, allowing for augmentative communication systems and use of visual images to support verbal interviews, as needed. We conducted a reflexive, inductive thematic analysis, using an iterative process of coding, collating, reviewing, and mapping themes.

Findings: Our analysis has identified that these youth describe BIMS as a multi-faceted experience involving emotional, cognitive and physical components. Moreover, these multifaceted experiences are often misunderstood by neurotypical adults, which contributes to inadequate support in managing BIMS. Of the four experiences, these youth identified meltdowns as most common.

Conclusion: By gaining first-hand perspectives, we have identified novel insights into BIMS and developed a more holistic understanding of these phenomena. These youths’ descriptions of supportive strategies for BIMS stress the importance of compassion and collaboration from trusted adults. This new knowledge will provide a foundation for how to better support autistic children and youth. Further research is required to develop an understanding of BIMS, especially with respect to how it is experienced by children and youth. Future research should leverage the insights and experiential knowledge of autistic children and youth to co-design support tool(s) for BIMS.
How Technology Tools Impact Writing Performance, Lexical Complexity, and Perceived Self-Regulated Learning Strategies in EFL Academic Writing: A Comparative Study
Authors: Han Yangxi , Zhao Shuo , Ng Lee-Luan
Published: November 2021

Students experience different levels of autonomy based on the mediation of self-regulated learning (SRL), but little is known about the effects of different mediation technologies on students' perceived SRL strategies. This mixed explanatory study compared two technology mediation models, Icourse (a learning management system) and Icourse+Pigai (an automatic writing evaluation system), with a control group that did not use technology. A quasi-experimental design was used, which involved a pre and post-intervention academic writing test, an SRL questionnaire, and one-to-one semi-structured student interviews. The aim was to investigate 280 Chinese undergraduate English as a foreign language (EFL) students' academic writing performance, lexical complexity, and perceptions of self-regulated strategies in academic writing. One-way ANCOVA of writing performance, Kruskal-Wallis test of lexical complexity, ANOVA of the SRL questionnaire, and grounded thematic content analysis revealed that, first, both Icourse and Icourse+Pigai provided significant support for the development of SRL strategies vs. the control group, although there was no significant difference between the two groups. Second, Icourse+Pigai-supported SRL was more helpful for improving students' academic writing performance because it enabled increased writing practice and correction feedback. Third, Icourse+Pigai-supported SRL did not significantly improve students' lexical complexity. In conclusion, we argue that both learning management systems and automated writing evaluation (AWE) platforms may be used to assist students' SRL learning to enhance their writing performance. More effort should be directed toward developing technological tools that increase both lexical accuracy and lexical complexity. We conclude that the technical tools used in this study were positively connected to the use of SRL techniques. However, when creating technologically mediated SRL activities, students' psychological study preferences should be considered.
Effect of Bow Camber and Mass Distribution on Violinists' Preferences and Performance
Authors: Tomezzoli Aurélie , Michaud Benjamin , Gagné Eric , Begon Mickaël , Duprey Sonia
Published: November 2021

Little is known about how bow mechanical characteristics objectively and quantitatively influence violinists' preferences and performance. Hypothesizing that the bow shape (i.e., camber) and mass distribution modifications would alter both violinists' appreciations of a bow and objective assessments of their performance, we recruited 10 professional violinists to play their own violin using 18 versions of a single bow, modified by combining three cambers and six mass distributions, in random order. A musical phrase, composed for this study, was played legato and spiccato at three octaves and two tempi. Each violinist scored all 18 bows. Then, experts assessed the recorded performances according to criteria inspired by basic musical analysis. Finally, 12 audio-descriptors were calculated on the same note from each trial, to objectivise potential acoustic differences. Statistical analysis (ANOVA) reveals that bow camber impacted the violinists' appreciations (p < 0.05), and that heavier bow tips gave lower scores for spiccato playing (p < 0.05). The expert evaluations reveal that playing with a lighter bow (tip or frog), or with a bow whose camber's maximum curvature is close to the frog, had a positive impact on some violinists' performance (NS to p < 0.001). The “camber-participant” interaction had significant effects on the violinists' appreciations (p < 0.01 to p < 0.001), on the expert's evaluation and on almost all the audio-descriptors (NS to p < 0.001). While trends were identified, multiple camber-participant interactions suggest that bow makers should provide a variety of cambers to satisfy different violinists.
Epidemiological Characteristics of Infectious Diseases Among Travelers Between China and Foreign Countries Before and During the Early Stage of the COVID-19 Pandemic
Authors: Luo Zheng , Wang Wei , Ding Yibo , Xie Jiaxin , Lu Jinhua , Xue Wen , Chen Yichen , Wang Ruiping , Li Xiaopan , Wu Lile
Published: November 2021

Background: International travel during the Coronavirus disease 2019 (COVID-19) pandemic carries a certain magnitude of infection risk both to travelers and their destination, which may be difficult to assess in the early stage. The characteristics of common infectious diseases of tourists may provide some clues to identify the high-risk travelers and protect susceptible population.

Methods: From among 48,444 travelers screened at Shanghai Port, we analyzed 577 travelers with 590 infectious diseases for age, sex, disease type, and World Health Organization (WHO) regions. We used the Joinpoint Regression Program to identify the average percent changes (APC) in the various trends among these individuals.

Results: Hepatitis B, syphilis, and HIV were the most common infectious diseases in travelers entering China, and Hepatitis B, pulmonary tuberculosis, and syphilis in Chinese nationals traveling abroad (overall detection rates, 1.43 and 0.74%, respectively; P < 0.05). Africa (2.96%), the Americas (1.68%), and the Western Pacific (1.62%) exhibited the highest detection rates. This trend did not decrease since the COVID-19 pandemic (P > 0.05) and rather showed an upward trend with increasing age [APC 95% CI = 5.46 (3.41,7.56)%, P < 0.05]. However, there were no evident trends in monthly infection rates of travelers exiting and entering China from different WHO regions (all P > 0.05).

Conclusion: Travelers always carry a transmission risk of common infectious diseases. It may be reasonable to adjust strategies for airport screening and quarantine according to the age and departure area of travelers to prevent and control new infectious diseases.
A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods
Authors: Vilone Giulia , Longo Luca
Published: November 2021

Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who, subsequently, perceive them as black-boxes and might not trust their predictions. Therefore, scholars have proposed several methods for extracting rules from data-driven machine-learned models to explain their logic. However, limited work exists on the evaluation and comparison of these methods. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by five model-agnostic, post-hoc rule extractors by employing eight quantitative metrics. Eventually, the Friedman test was employed to check whether a method consistently performed better than the others, in terms of the selected metrics, and could be considered superior. Findings demonstrate that these metrics do not provide sufficient evidence to identify superior methods over the others. However, when used together, these metrics form a tool, applicable to every rule-extraction method and machine-learned models, that is, suitable to highlight the strengths and weaknesses of the rule-extractors in various applications in an objective and straightforward manner, without any human interventions. Thus, they are capable of successfully modelling distinctively aspects of explainability, providing to researchers and practitioners vital insights on what a model has learned during its training process and how it makes its predictions.
Robot Evolution: Ethical Concerns
Authors: Eiben Ágoston E. , Ellers Jacintha , Meynen Gerben , Nyholm Sven
Published: November 2021

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.
Inter-annual Variability of Snowfall in the Lower Peninsula of Michigan
Authors: Meng Lei , Ayon Bandhan Dutta , Koirala Nirjala , Baker Kathleen M.
Published: November 2021

Winter snowfall, particularly lake-contributed snowfall, has a significant impact on the society and environment in the Great Lakes regions including transportation, tourism, agriculture, and ecosystem. Understanding the inter-annual variability of snowfall will provide sound basis for local community safety management and reduce its environmental impacts on agriculture and ecosystems. This study attempts to understand the trend and inter-annual variability in snowfall in the Lower Peninsula of Michigan (LPM) using statistical analysis based on snowfall measurements from eight weather stations. Our study demonstrates that snowfall has significantly increased from 1932 to 2015. Correlation analysis suggests that regional average air temperatures have a strong negative relationship with snowfall in the LPM. On average, approximately 27% of inter-annual variability in snowfall can be explained by regional average air temperatures. ENSO events are also negatively related to snowfall in the LPM and can explain ~8% of inter-annual variability. The North Atlantic Oscillation (NAO) does not have strong influence on snowfall. Composite analysis demonstrates that on an annual basis, more snowfall occurs during the years with higher maximum ice cover (MIC) than during the years with lower MIC in Lake Michigan. Higher MIC is often associated with lower air temperatures which are negatively related to snowfall. This study could provide insight on future snow related climate model improvement and weather forecasting.
Transition Design in Latin America: Enabling Collective Learning and Change
Authors: Juri Silvana , Zurbriggen Cristina , Bosch Gómez Sofía , Ortega Pallanez Marysol
Published: November 2021

Latin American societies currently confront numerous social, economic, and environmental issues. The complex and interlinked nature of these issues demands responses that fully and equally engage with the social and environmental domains in creative and integrative ways that blur traditional dichotomies and disciplinary boundaries. Most importantly, they need to weave in and reflect the plurality and specificity of the contexts in an autonomous and non-colonialist fashion. In this paper, we draw inspiration from approaches that emerged in the Global North such as Resilience Thinking, Policy Design, and Transition Design, all of which strive for knowledge plurality and synthesis applied to systemic transformational processes. To contribute to this pluralistic motivation and to promote critical reflection and learning, in this work we outline the main contributions of such approaches and have them converse with Latin American perspectives and practices. Through the application of a Transition Design lens, a practice-oriented perspective aimed at catalyzing societal transitional processes towards sustainable futures, we act as practitioners and interlocutors that adopt, adapt, and expand its theoretical and methodological applications in collective learning spaces, processes, and platforms. The action-oriented nature of this approach allows us to analyze particular cases of application, their contexts, and their theoretical or methodological nuances which determine their potential or degree of success in generating actual change. The structure of this article moves from outlining and introducing the main frameworks and notions relevant for adopting a Transition Design approach in Latin America, to describing cases developed in different pedagogical or action-research platforms, culminating with a collection of reflections stemming from our experiences applying Transition Design in Latin America. The first section offers a theoretical compass to expand a more robust framework that supports and enables socio-environmental transitions in the region. The second part presents three case studies to illustrate the application and interpretation of different methods and the challenges and opportunities presented. We conclude by offering insights into potential future pathways for embracing and deepening holistic and systemic approaches like Transition Design in Latin American settings.
Sitting for Too Long, Moving Too Little: Regular Muscle Contractions Can Reduce Muscle Stiffness During Prolonged Periods of Chair-Sitting
Authors: Kett Alexander R. , Milani Thomas L. , Sichting Freddy
Published: November 2021

In modern Western societies, sedentary behavior has become a growing health concern. There is increasing evidence that prolonged sitting periods can be associated with musculoskeletal disorders. While it is generally recognized that back muscle activity is low during chair-sitting, little is known about the consequences of minor to no muscle activity on muscle stiffness. Muscle stiffness may play an important role in musculoskeletal health. This study investigated the effects of regular muscle contractions on muscle stiffness in a controlled experiment in which participants sat for 4.5 h. Neuromuscular electrical stimulation in the lumbar region of the back was applied to trigger regular muscle contractions. Using stiffness measurements and continuous motion capturing, we found that prolonged sitting periods without regular muscle contractions significantly increased back muscle stiffness. Moreover, we were able to show that regular muscle contractions can prevent those effects. Our results highlight the importance of consistent muscle activity throughout the day and may help explain why prolonged periods of chair-sitting increase the susceptibility to common pathological conditions such as low back pain.
Efficacy of Intraoperative Recurrent Laryngeal Nerve Monitoring During Thoracoscopic Esophagectomy for Esophageal Cancer: A Systematic Review and Meta-Analysis
Authors: Wang Xinxin , Guo Haixie , Hu Quanteng , Ying Yongquan , Chen Baofu
Published: November 2021

Background: Recurrent laryngeal nerve paralysis (RLNP), a severe complication of mini-invasive esophagectomy, usually occurs during lymphadenectomy adjacent to recurrent laryngeal nerve. This systematic review and meta-analysis aimed to evaluate the efficacy of intraoperative nerve monitoring (IONM) in reducing RLNP incidence during mini-invasive esophagectomy.

Methods: Systematic literature search of PubMed, EMBASE, EBSCO, Web of Knowledge, and Cochrane Library until June 4, 2021 was performed using the terms “(nerve monitoring) OR neuromonitoring OR neural monitoring OR recurrent laryngeal nerve AND (esophagectomy OR esophageal).” Primary outcome was postoperative RLNP incidence. Secondary outcomes were sensitivity, specificity, and positive and negative predictive values for IONM; complications after esophagectomy; number of dissected lymph nodes; operation time; and length of hospital stay.

Results: Among 2,330 studies, five studies comprising 509 patients were eligible for final analysis. The RLNP incidence was significantly lower (odds ratio [OR] 0.33, 95% confidence interval [CI] 0.12–0.88, p < 0.05), the number of dissected mediastinal lymph nodes was significantly higher (mean difference 4.30, 95%CI 2.75–5.85, p < 0.001), and the rate of hoarseness was significantly lower (OR 0.14, 95%CI 0.03–0.63, p = 0.01) in the IONM group than in the non-IONM group. The rates of aspiration (OR 0.31, 95%CI 0.06–1.64, p = 0.17), pneumonia (OR 1.08, 95%CI 0.70–1.67, p = 0.71), and operation time (mean difference 7.68, 95%CI −23.60–38.95, p = 0.63) were not significantly different between the two groups. The mean sensitivity, specificity, and positive and negative predictive values for IONM were 53.2% (0–66.7%), 93.7% (54.8–100%), 71.4% (0–100%), and 87.1% (68.0–96.6%), respectively.

Conclusion: IONM was a feasible and effective approach to minimize RLNP, improve lymphadenectomy, and reduce hoarseness after thoracoscopic esophagectomy for esophageal cancer, although IONM did not provide significant benefit in reducing aspiration, pneumonia, operation time, and length of hospital stay.
High-Throughput Sequencing of Environmental DNA as a Tool for Monitoring Eukaryotic Communities and Potential Pathogens in a Coastal Upwelling Ecosystem
Authors: Ríos-Castro Raquel , Romero Alejandro , Aranguren Raquel , Pallavicini Alberto , Banchi Elisa , Novoa Beatriz , Figueras Antonio
Published: November 2021

The marine environment includes diverse microeukaryotic organisms that play important functional roles in the ecosystem. With molecular approaches, eukaryotic taxonomy has been improved, complementing classical analysis. In this study, DNA metabarcoding was performed to describe putative pathogenic eukaryotic microorganisms in sediment and marine water fractions collected in Galicia (NW Spain) from 2016 to 2018. The composition of eukaryotic communities was distinct between sediment and water fractions. Protists were the most diverse group, with the clade TSAR (Stramenopiles, Alveolata, Rhizaria, and Telonemida) as the primary representative organisms in the environment. Harmful algae and invasive species were frequently detected. Potential pathogens, invasive pathogenic organisms as well as the causative agents of harmful phytoplanktonic blooms were identified in this marine ecosystem. Most of the identified pathogens have a crucial impact on the aquacultural sector or affect to relevant species in the marine ecosystem, such as diatoms. Moreover, pathogens with medical and veterinary importance worldwide were also found, as well as pathogens that affect diatoms. The evaluation of the health of a marine ecosystem that directly affects the aquacultural sector with a zoonotic concern was performed with the metabarcoding assay.
Expression, Purification, and Characterization of Bovine Leukemia Virus-Like Particles Produced in Drosophila S2 Cells
Authors: Olivero-Deibe Natalia , Tomé-Poderti Lorena , Carrión Federico , Bianchi Sergio , Fló Martín , Prieto Daniel , Rammauro Florencia , Addiego Andrés , Ibañez Natalia , Portela Madelón , Duran Rosario , Berois Mabel , Pritsch Otto
Published: November 2021

Bovine leukemia virus(BLV) is an oncogenic deltaretrovirus that infects cattle worldwide. In Uruguay, it is estimated that more than 70% of dairy cattle are infected, causing serious economic losses due to decreased milk production, increased calving interval, and livestock losses due to lymphosarcoma. Several attempts to develop vaccine candidates that activate protective immune responses against BLV were performed, but up to date, there is no vaccine that ensures efficient protection and/or decreased viral transmission. The development and application of new vaccines that effectively control BLV infection represent a major challenge for countries with a high prevalence of infection. In this study, we generated twoDrosophila melanogasterS2 stable cell lines capable of producing BLV virus-like particles (BLV-VLPs). One of them, BLV-VLP1, expressed both Gag and Env wild-type (Envwt) full-length proteins, whereas BLV-VLP2 contain Gag together with a mutant form of Env non-susceptible to proteolytic maturation by cellular furin type enzymes (EnvFm). We showed that Envwt is properly cleaved by cellular furin, whereas EnvFm is produced as a full-length gp72 precursor, which undergoes some partial cleavage. We observed that said mutation does not drastically affect its expression or its entry into the secretory pathway of S2 insect cells. In addition, it is expressed on the membrane and retains significant structural motifs when expressed in S2 insect cells. Morphology and size of purified BLV-VLPs were analyzed by transmission electron microscopy and dynamic light scattering, showing numerous non-aggregated and approximately spherical particles of variable diameter (70–200 nm) as previously reported for retroviral VLPs produced using different expression systems. Furthermore, we identified two N-glycosylation patterns rich in mannose in EnvFm protein displayed on VLP2. Our results suggest that the VLPs produced inDrosophilaS2 cells could be a potential immunogen to be used in the development of BLV vaccines that might contribute, in conjunction with other control strategies, to reduce the transmission of the virus.
Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times
Authors: Jeong BongJoo , Han Jun-Hee , Lee Ju-Yong
Published: November 2021

This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.
Animal-Based Indicators for On-Farm Welfare Assessment in Goats
Authors: Minnig Adrian , Zufferey Romane , Thomann Beat , Zwygart Sibylle , Keil Nina , Schüpbach-Regula Gertraud , Miserez Raymond , Stucki Dimitri , Zanolari Patrik
Published: November 2021

This review describes the current state of knowledge relating to scientific literature on welfare indicators for goats. Our aim was to provide an overview of animal-based indicators for on-farm welfare assessments. We performed a literature search and extracted 96 relevant articles by title, abstract, and full-text screening. Out of these articles, similar indicators were aggregated to result in a total of 32 welfare indicators, some of which were covered in multiple articles, others in only a single one. We discuss a set of three established assessment protocols containing these indicators, as well as all individual indicators which were covered in more than one article. As single indicators, we identified lameness, body condition score (BCS), qualitative behaviour assessment (QBA), and human–animal relationship (HAR) tests with substantial evidence for sufficient validity to assess welfare in goats. A multitude of indicators (e.g., hair coat condition) was studied less intensively but was successfully used for welfare assessments. For some indicators (e.g., oblivion, lying behaviour), we highlight the need for future research to further validate them or to optimise their use in on-farm welfare assessments. Moreover, further investigations need to include kids, bucks, and meat and fibre goats, as well as extensively kept goats as the literature predominantly focuses on dairy goats in intensive production systems.
Antimicrobial Activity of Aztreonam in Combination with Old and New β-Lactamase Inhibitors against MBL and ESBL Co-Producing Gram-Negative Clinical Isolates: Possible Options for the Treatment of Complicated Infections
Authors: Morroni Gianluca , Bressan Raffaela , Fioriti Simona , D’Achille Gloria , Mingoia Marina , Cirioni Oscar , Di Bella Stefano , Piazza Aurora , Comandatore Francesco , Mauri Carola , Migliavacca Roberta , Luzzaro Francesco , Principe Luigi , Lagatolla Cristina
Published: November 2021

Metallo-β-lactamases (MBLs) are among the most challenging bacterial enzymes to overcome. Aztreonam (ATM) is the only β-lactam not hydrolyzed by MBLs but is often inactivated by co-produced extended-spectrum β-lactamases (ESBL). We assessed the activity of the combination of ATM with old and new β-lactamases inhibitors (BLIs) against MBL and ESBL co-producing Gram-negative clinical isolates. Six Enterobacterales and three non-fermenting bacilli co-producing MBL and ESBL determinants were selected as difficult-to-treat pathogens. ESBLs and MBLs genes were characterized by PCR and sequencing. The activity of ATM in combination with seven different BLIs (clavulanate, sulbactam, tazobactam, vaborbactam, avibactam, relebactam, zidebactam) was assessed by microdilution assay and time–kill curve. ATM plus avibactam was the most effective combination, able to restore ATM susceptibility in four out of nine tested isolates, reaching in some cases a 128-fold reduction of the MIC of ATM. In addition, relebactam and zidebactam showed to be effective, but with lesser reduction of the MIC of ATM. E. meningoseptica and C. indologenes were not inhibited by any ATM–BLI combination. ATM–BLI combinations demonstrated to be promising against MBL and ESBL co-producers, hence providing multiple options for treatment of related infections. However, no effective combination was found for some non-fermentative bacilli, suggesting the presence of additional resistance mechanisms that complicate the choice of an active therapy.
Performance Comparison for Single-User and Multi-User Network MIMO Cellular Systems with Power Management
Authors: Sheu Jeng-Shin , Huang Kuan-Ming
Published: November 2021

Cellular mobile systems aim at aggressive spectrum reuse to achieve high spectral efficiency. Unfortunately, this leads to unacceptable interference near cell borders. To control this, network multi-input multiple-output (MIMO) can be adopted to improve coverage and cell-edge throughput through multi-cell cooperation. With network MIMO, multiple geographically separated base stations (BSs) cooperatively serve their cell-edge users (CEUs) using their antennas, acting together as a network of distributed antenna array. It can be single-user (SU) or multi-user (MU) network MIMO by coordinating channel allocation in adjacent cells. In this paper, we make a capacity comparison of SU- and MU-network MIMO. In network MIMO, a collaborative BS simultaneously serves its own cell-center users (CCUs) and CEUs, and the CEUs of other partner BSs under a power constraint. As a result, power management among three types of users (intra-cell CCUs/CEUs, inter-cell CEUs) becomes necessary. Accordingly, we propose power management methods to help raise the signal strength of inter-cell CEUs and in the meantime gratify the performance of intra-cell users. Simulation results show that MU-network MIMO with superposition coding offers much better CEU capacity than SU-network MIMO. As for the CCU capacity, MU-network MIMO is generally better than SU-network MIMO.
An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
Authors: Farooq Muhammad Shoaib , Rehman Attique Ur , Idrees Muhammad , Raza Muhammad Ahsan , Ali Jehad , Masud Mehedi , Al-Amri Jehad F. , Kazmi Syed Hasnain Raza
Published: November 2021

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.
Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
Authors: Pollak Artur , Temich Sebastian , Ptasiński Wojciech , Kucharczyk Jacek , Gąsiorek Damian
Published: November 2021

Continuous production maintenance cost is among one of the highest operational expenses for manufacturing companies. Proper planning of maintenance interventions results in optimized equipment use, higher product quality, and reduced costs. For a belt drive usefulness, it is important that it is properly stretched and has no defects. However, manual condition assessment requires a production line stop, which in turn causes production to stop with associated consequences. Continuous fault diagnosis for anomalies is a fundamental step in estimating a component’s remaining service life and then obtaining a reliable predictive maintenance system that reduces production costs. The presented work presents an approach to anomaly detection based on the vibrations obtained from the operation of the belt transmission.
Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
Authors: Ahmad Mahmood , Ahmad Feezan , Wróblewski Piotr , Al-Mansob Ramez A. , Olczak Piotr , Kamiński Paweł , Safdar Muhammad , Rai Partab
Published: November 2021

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.
Influences of Brass Surface Morphology on Leidenfrost Effect during Liquid Nitrogen Cooling
Authors: Li Zhiwei , Yu Dingwen , Cui Jie , Feng Pingfa , Feng Feng
Published: November 2021

Cooling in liquid nitrogen is a typical service condition of high-temperature superconducting wire, and the variation of boiling stages on the wire protective layers such as the brass layers could be crucial for the quench behavior of superconducting devices. In this study, the influence of brass surface morphology (parameters of surface roughness and fractal dimension) on the Leidenfrost effect (including the wall superheat at critical heat flux and the wall superheat at Leidenfrost point, which are respectively characterized by the temperatures of ΔTCHF and ΔTLP) was studied. The surfaces of brass samples were polished by sandpaper to obtain different morphologies, which were characterized by using white light interferometer images, and the boiling curves were recorded and analyzed by Matlab with lumped parameter method. The experimental results demonstrated that the surface morphology of brass samples could influence the ΔTLP significantly, but had no clear relationship with the ΔTCHF. Moreover, the multi-scaled analysis was carried out to explore the influencing mechanism of surface microstructure, the relationship between ΔTLP and scale was more clear when the scale was small, and the fractal dimension was calculated and discussed together with surface roughness. The findings of this study could be instructive for surface treatment of superconducting wires to suppress quench propagation.
Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
Authors: Zhou Deyi , Xu Chongbin , Xin Yuelin , Hou Pengfei , Wu Baoguang , Yu Haiye , Zhang Jinsong , Zhang Qiang
Published: November 2021

This study analyzed the engine operating condition curve of the corn kernel harvester. Field experiments identified the feed rate, concave clearance, and cylinder speed as the main factors affecting operating quality and efficiency. A ternary quadratic regression orthogonal center-of-rotation combined optimization test method was used to determine the feed rate, cylinder speed, and concave clearance as the influencing factors, and the engine speed variation rate, crushing rate, impurity rate, loss rate, and cylinder speed variation rate as the objective functions. A mathematical regression model was developed for the combination of operating quality indicators, efficiency indicators, and operating parameters of the corn kernel harvester. A non-linear optimization method was used to optimize the parameters of each influencing factor. The results showed that with a feed rate of 12 kg/s, a forward speed of 5 km/h, a cylinder speed of 360 r/min, and a concave clearance of 30 mm, the average crushing rate was 3.91%, the average impurity rate was 1.71%, and the kernel loss rate was 3.1%. This model could be used for the design and development of intelligent control systems.
Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
Authors: Zhu Zong-Wu , Huang Ru-Wei
Published: November 2021

Aiming at the problems of large ciphertext size and low efficiency in the current secure multi-party computation (SMC) protocol based on fully homomorphic encryption (FHE), the paper proves that the fully homomorphic encryption scheme that supports multi-bit encryption proposed by Chen Li et al. satisfies the key homomorphism. Based on this scheme and threshold decryption, a three-round, interactive, leveled, secure multi-party computation protocol under the Common Random String (CRS) model is designed. The protocol is proved to be safe under the semi-honest model and the semi-malicious model. From the non-interactive zero-knowledge proof, it can be concluded that the protocol is also safe under the malicious model. Its security can be attributed to the Decisional Learning With Errors (DLWE) and a variant of this problem (some-are-errorless LWE). Compared with the existing secure multi-party computation protocol based on fully homomorphic encryption under the CRS model, the ciphertext size of this protocol is smaller, the efficiency is higher, the storage overhead is smaller, and the overall performance is better than the existing protocol.
Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting
Authors: Lin Wen-Hui , Wang Ping , Chao Kuo-Ming , Lin Hsiao-Chung , Yang Zong-Yu , Lai Yu-Huang
Published: November 2021

Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24–72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.
Prevalence of invA Gene of Salmonella spp. in Fish and Fishery Resources from Manila Bay Aquaculture Farms Using Real-Time PCR
Authors: Tanyag Bryan , Quiambao Jerick Jann , Ko Aira Angeline , Singh Amarjet , Cambia Flordeliza , Montojo Ulysses
Published: November 2021

Manila Bay contributes significantly to the Philippines’ economy through industry, trade, tourism, and agriculture. However, microbiological contamination in the bay is putting it at risk in the present. Pathogen contamination of the water poses a serious threat to food safety, implicating public health. To address these concerns, the present study examined the prevalence of Salmonella in six different aquaculture commodities, Perna viridis (mussel), Crassostrea iridalei (oyster), Scylla serrata (crab), Penaeus spp. (shrimp), Oreochromis niloticus (tilapia), and Chanos chanos (milkfish), as well as in environmental water from growing areas. A tandem approach of culture-based and real-time PCR methods for the isolation and identification of Salmonella was carried out. To accurately identify the isolates, a real-time PCR Taqman assay based on the invA gene was used. Among the fishery resources examined, no positive samples were observed from C. chanos and O. niloticus. In addition, Salmonella was found in twenty (20) samples, representing 16.26% of all aquaculture commodities collected. Furthermore, Salmonella was found in 13.58% of the 81 environmental water samples examined. P. viridis had the highest prevalence of 55.55% out of 18 samples examined. Samples contaminated with Salmonella failed to meet the regulatory limits set by BFAR FAO 210 series 2001 and EC No. 2073/2005. In addition, it was observed that the sample matrix had a significant impact on the presence of Salmonella (p < 0.05). However, the spatial and temporal distribution of Salmonella did not vary greatly (p > 0.05). This study underscores the importance of imposing strict policies by regulatory bodies to prevent diseases, thus avoiding severe health implications.
Benefits of Using Plants in Indoor Environments: Exploring Common Research Gaps
Authors: Persiani Sandra G. L.
Published: November 2021

The introduction of green plants in indoor spaces has raised a great amount of interest motivated by plants’ supposed capacity to improve the quality of indoor built environments. Subsequent studies have covered a broad range of topics, testing plants in indoor environments for their climate-mitigating effects, acoustic benefits, potential energy savings and the enhancement of the indoor microbial communities. Despite the diversity of focus in these studies, no major breakthroughs have been made involving the use of plants in indoor environments after nearly thirty years of research. To identify major inconsistencies and gaps in the research, this review, of an explorative nature, presents an analysis of plant-related parameters reported in 31 cases of experimental research involving the use of plants in indoor environments. The papers were identified by searching the online databases Google Scholar, ResearchGate, Scopus and MDPI and were selected based on their relevance to the topic and diversity of focus. Two classifications in table form provide an overview of the 38 plant-related parameters used in the reviewed research. The conclusions drawn from the analysis of the tables highlight a strongly anthropocentric frame of reference across the majority of the studies, which prioritize human and experimental convenience above plant physiology, and display an overall scarcity and inconsistency in the plant-related parameters reported.
Thermo-Hygrometric Variability on Waterfronts in Negative Radiation Balance: A Case Study of Balneário Camboriú/SC, Brazil
Authors: Wollmann Cássio Arthur , Hoppe Ismael Luiz , Gobo João Paulo Assis , Simioni João Paulo Delapasse , Costa Iago Turba , Baratto Jakeline , Shooshtarian Salman
Published: November 2021

Extensive urbanization around the world has resulted in the consumption of massive vegetated areas and natural resources. To this end, one strategy for urban development is to consolidate urban areas. In Balneário Camboriú/SC, Brazil, this trend has transformed the city into a vertical built-up area on its coastal strip, accommodating a large amount of buildings both in terms of quantity and number of floors. This research aims to quantify the thermo-hygrometric fluctuation on the waterfront of Balneário Camboriú, in negative radiation balance. To acquire the data on air temperature (Ta) and relative humidity (RH), two mobile transects and measuring at two fixed points were made in a situation of negative radiation balance on 26 August 2019, in the winter period of the Southern Hemisphere. The collection work began at 06:00:00 a.m. (before sunrise, the peak of the negative radiation balance), on Atlântica Avenue (waterfront) and Brasil Avenue (parallel to the waterfront). It was verified that the Ta varied from 16.0 °C to 19.0 °C, and the RH remained over 80% during the entire route. At the meteorological shelters, the temperature presented a variation from 14.4 °C to 17.7 °C, and the RH ranged from 79.6% to 91.3% between the two points. The spatial variability in the Ta and RH along the paths travelled and at the fixed points is directly related to the land cover, represented especially by the buildings’ verticalization and data collection time.
Observations of Gas-Phase Alkylamines at a Coastal Site in the East Mediterranean Atmosphere
Authors: Tzitzikalaki Evangelia , Kalivitis Nikos , Kanakidou Maria
Published: November 2021

Atmospheric amines are ubiquitous compounds in the atmosphere, having both natural and anthropogenic origin. Recently, they have been identified as important contributors to new particle formation in the atmosphere, but observations of their atmospheric concentrations are scarce. In the present study we introduce the first systematic long-term observations of gas-phase amines measurements in the East Mediterranean atmosphere. Air samples were collected at the Finokalia monitoring station of the University of Crete during a 3.5-year period from January 2013 to July 2016, and analyzed after extraction using a high-performance liquid chromatography triple quadrupole mass spectrometer. The detected alkylamines were the sum of dimethylamine and ethylamine (DMA+EA), trimethylamine (TMA), diethylamine (DEA) and triethylamine (TEA). DMA+EA and TMA were the most abundant alkylamines, with concentrations spanning from the detection limit to 78.0 and 69.8 pptv, and average concentrations of 7.8 ± 12.1 and 7.5 ± 12.4 pptv, respectively. Amines showed pronounced seasonal variability with DMA+EA and TMA concentrations being higher in winter. Statistical analysis of the observations showed different sources for each of the studied amines, except for DMA+ΕA and DEA that appear to have common sources in the region. This analysis points to a marine source of TMA and animal husbandry in the area as a potential source of TEA. None of the alkylamines is correlated with other anthropogenic sources. Furthermore, no clear association was found between the seasonality of NPF events and alkylamines concentrations, while a clear correlation was detected between the seasonality of nucleation mode particle (dp < 25 nm) number concentrations and alkylamine concentrations, indicating that amines may contribute to nucleation mode particles’ production.