bluuebunny/crossref_metadata_2025 huggingface dataset:
Validation and Determination of Physical Activity Intensity GT3X+ Cut-Points in Children and Adolescents with Physical Disabilities: Preliminary Results in a Cerebral Palsy Population
Authors: Matey-Rodríguez Carmen , López-Ortiz Susana , Peñín-Grandes Saúl , Pinto-Fraga José , Valenzuela Pedro L. , Pico Mónica , Fiuza-Luces Carmen , Lista Simone , Lucia Alejandro , Santos-Lozano Alejandro
Published: February 2023

Background: Children and adolescents with disabilities engage in low levels of moderate-to-vigorous intensity physical activity (MVPA), which may create the onset of a sedentary lifestyle. In light of this, MVPA levels must be quantified with a valid tool such as accelerometry. This study aimed to: (i) analyze the accuracy of Evenson cut-points by estimating MVPA and sedentary behavior (SB) in children and adolescents with disabilities; (ii) define new equations to estimate energy expenditure (EE) with the GT3X+ accelerometer in this population and particularly in those with cerebral palsy (CP); (iii) define specific GT3X+ cut-points to estimate MVPA in those with CP. Methods: A total of 23 children and adolescents with disabilities (10 ± 3 years; 44%females) participated in the study. GT3X+-counts and oxygen uptake (VO2) were measured in four laboratory walking conditions. Results: (i) Evenson cut-points were accurate; (ii) new equations were defined to effectively predict EE; (iii) specific GT3X+ cut-points (VM ≥ 702 counts·min−1; Y-Axis ≥ 360 counts·min−1) were defined for estimating MVPA levels in children and adolescents with CP. Conclusions: The use of specific cut-points for ActiGraph GT3X+ seems to be accurate to estimate MVPA levels in children and adolescents with disabilities and, particularly, in those with CP, at least in laboratory conditions.
Working with Children with Autism Undergoing Health-Care Assessments in a Day Hospital Setting: A Perspective from the Health-Care Professionals
Authors: Davico Chiara , Marcotulli Daniele , Succi Elisa , Canavese Carlotta , Bodea Ancuta F. , Pellegrino Mariacristina , Cuffari Enrica , Cudia Valentina F. , Svevi Barbara , Amianto Federico , Ricci Federica , Vitiello Benedetto
Published: February 2023

Background: Hospitals can be especially stressful for children with autism spectrum disorder (ASD) due to the communication and social skills deficits, lower capacity to adapt to disruption, and sensory hypersensitivity that are typical of these patients. Purpose: This study investigated how health-care professionals (HPs) experienced the clinical care and management of children with ASD undergoing medical testing in a day hospital setting, and assessed the rate of successful completion of laboratory tests and instrumental examinations. Methods: A cross-sectional questionnaire was administered to 45 HPs, inquiring about their experience in obtaining blood and urine tests, ECG, audiometry, and EEG from children with ASD. The clinical sample included 153 consecutively referred children with ASD (74.5% males, mean age 5.6 years) undergoing a medical diagnostic work-up as part of their diagnostic evaluation. The success rate of completing the various assessments was examined. Results: HPs identified aggressive behavior and communication deficits as the major challenges when providing care to children with ASD. The parents were seen as an important resource for managing the children. The completion rate of the laboratory tests and instrumental examinations was high (between 88.5% and 98.4% according to the specific type of examination). The lowest non-completion rate was found for the EEG (12.5%). Conclusions: Despite considerable challenges being reported by HPs in managing children with ASD, the scheduled assessments could be completed in the large majority of cases. Targeted approaches to preventing aggressive behaviors and obviating the communication barriers in children with ASD undergoing hospital exams are warranted.
Physical Self-Concept and Physical Activity in Children with Congenital Heart Defects—Can We Point Out Differences to Healthy Children to Promote Physical Activity?
Authors: Siaplaouras Jannos , Jahn Annika , Helm Paul , Hanssen Kerstin , Bauer Ulrike , Apitz Christian , Niessner Claudia
Published: February 2023

Objective: Children with congenital heart defects (CHD) are at high risk for cardiovascular disease in addition to their congenital disease, so it is important to motivate this group of patients to live a physically active lifestyle. A potential influencing determinant of younger children’s physical performance is the physical self-concept. The objective of the present study was first to evaluate the correlation between the physical self-concept (PSC) and the participation in physical activities (PA) of a representative group of children with congenital heart disease (CHD), and second to point out differences in comparison to their healthy peer group. Methods: Using the database of PA of the S-BAHn-Study we focused on physical self-concept assessed by the German version of the Physical Self-Description Questionnaire. We compare the obtained data of children with CHD to a representative age-matched sample of 3.385 participants of the Motorik Modul Study. Results: N = 1.198 complete datasets could be included in the analyses. The mean age of patients was 11.6 ± 3.1 years. For the total cohort of patients with CHD and the reference group, PA correlated significantly with a positive PSC (p < 0.001). PA was significantly reduced in all groups of patients despite the severity of their heart defect (p < 0.001). Remarkably, PSC did not differ statistically significantly in patients with simple CHD from the reference collective (p > 0.24). Conclusions: According to this representative survey, there is a clear relation between PA and PSC in the cohort of healthy children and the group of children with CHD throughout the severity of their heart defects. Although PSC did not differ in patients with simple CHD and their healthy peer group, PA was significantly reduced. This gap invites us to reflect on how we could break new ground to promote a physically active lifestyle in children with CHD regardless of the severity of their cardiac defects.
Molecular Research in Pancreatic Cancer: Small Molecule Inhibitors, Their Mechanistic Pathways and Beyond
Authors: Shetu Shaila A. , James Nneoma , Rivera Gildardo , Bandyopadhyay Debasish
Published: February 2023

Pancreatic enzymes assist metabolic digestion, and hormones like insulin and glucagon play a critical role in maintaining our blood sugar levels. A malignant pancreas is incapable of doing its regular functions, which results in a health catastrophe. To date, there is no effective biomarker to detect early-stage pancreatic cancer, which makes pancreatic cancer the cancer with the highest mortality rate of all cancer types. Primarily, mutations of the KRAS, CDKN2A, TP53, and SMAD4 genes are responsible for pancreatic cancer, of which mutations of the KRAS gene are present in more than 80% of pancreatic cancer cases. Accordingly, there is a desperate need to develop effective inhibitors of the proteins that are responsible for the proliferation, propagation, regulation, invasion, angiogenesis, and metastasis of pancreatic cancer. This article discusses the effectiveness and mode of action at the molecular level of a wide range of small molecule inhibitors that include pharmaceutically privileged molecules, compounds under clinical trials, and commercial drugs. Both natural and synthetic small molecule inhibitors have been counted. Anti-pancreatic cancer activity and related benefits of using single and combined therapy have been discussed separately. This article sheds light on the scenario, constraints, and future aspects of various small molecule inhibitors for treating pancreatic cancer—the most dreadful cancer so far.
Shared Language: Linguistic Similarity in an Algebra Discussion Forum
Authors: Banawan Michelle P. , Shin Jinnie , Arner Tracy , Balyan Renu , Leite Walter L. , McNamara Danielle S.
Published: February 2023

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals “shared language” among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants’ lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities.
Machine Learning Model for Predicting Epidemics
Authors: Loola Bokonda Patrick , Sidibe Moussa , Souissi Nissrine , Ouazzani-Touhami Khadija
Published: February 2023

COVID-19 has raised the issue of fighting epidemics. We were able to realize that in this fight, countering the spread of the disease was the main goal and we propose to contribute to it. To achieve this, we propose an enriched model of Random Forest (RF) that we called RF EP (EP for Epidemiological Prediction). RF is based on the Forest RI algorithm, proposed by Leo Breiman. Our model (RF EP) is based on a modified version of Forest RI that we called Forest EP. Operations added on Forest RI to obtain Forest EP are as follows: the selection of significant variables, the standardization of data, the reduction in dimensions, and finally the selection of new variables that best synthesize information the algorithm needs. This study uses a data set designed for classification studies to predict whether a patient is suffering from COVID-19 based on the following 11 variables: Country, Age, Fever, Bodypain, Runny_nose, Difficult_in_breathing, Nasal_congestion, Sore_throat, Gender, Severity, and Contact_with_covid_patient. We compared default RF to five other machine learning models: GNB, LR, SVM, KNN, and DT. RF proved to be the best classifier of all with the following metrics: Accuracy (94.9%), Precision (94.0%), Recall (96.6%), and F1 Score (95.2%). Our model, RF EP, produced the following metrics: Accuracy (94.9%), Precision (93.1%), Recall (97.7%), and F1 Score (95.3%). The performance gain by RF EP on the Recall metric compared to default RF allowed us to propose a new model with a better score than default RF in the limitation of the virus propagation on the dataset used in this study.
Design and Prototype Development of Augmented Reality in Reading Learning for Autism
Authors: Khoirunnisa Azizah Nurul , Munir  , Dewi Laksmi
Published: February 2023

(1) Background: Augmented reality is no less popular than virtual reality. This technology has begun to be used in education fields, one of which is special education. Merging the real and virtual worlds is the advantage of augmented reality. However, it needs special attention in making software for children with special needs, such as children with autism. This paper presents an application prototype by paying attention to the characteristics of autistic individuals according to the Autism Guide, that has existed in previous studies. (2) Method: The method used in the development of this prototype is the Linear Sequential Model. Application development is made using Unity3D, Vuforia, and Adobe Illustrator by considering accessibility and other conveniences for developers. (3) Results: The prototype was developed with reference to the Autism Guide, then validated by media experts and autistic experts with the results of the assessment obtaining a score of 87.3/100 which is in the “Very Good” category and is suitable for use. (4) Conclusions: The development of a prototype that refers to the characteristics of children with autism needs to be considered so that what will be conveyed can be easily accepted.
Crystallographic Analysis on the Upper Bainite Formation at the Austenite Grain Boundary in Fe-0.6C-0.8Mn-1.8Si Steel in the Initial Stage of Transformation
Authors: Jimbo Shotaro , Nambu Shoichi
Published: February 2023

A crystallographic analysis was conducted of the upper bainite nucleated at the austenite grain boundary in Fe-0.6C-0.8Mn-1.8Si (in mass %) steel by the EBSD analysis. The effect of the character of the prior austenite grain boundary (PAGB) on the formation of upper bainite was investigated from several perspectives: PAGB plane, grain boundary energy, and so on. BFs form on both sides of the high-angle PAGBs, while BFs do not form at twin boundaries. It is suggested one of the reasons for the suppression of BF formation at twin boundaries is the lower grain boundary energy. At high-angle grain boundaries, there is no difference in the potency for BFs’ nucleation between the tilt-like PAGBs and twist-like PAGBs, and the formation of BF is not affected by the angle between the rotation axis, the PAGB plane, and grain boundary energy. The variant selection of BFs was investigated. The BFs pair, whose misorientation across the PAGB is small, is formed preferentially. When several variant pairs can form having small misorientation across the PAGB, the variant pair that can reduce the elastic strain energy preferentially forms to accommodate the shape strain.
First-Principle Investigation of the Interface Properties of the Core-Shelled L12-Al3M (M = Sc, Zr, Er, Y) Phase
Authors: Song Yu , Zhan Songtao , Nie Baohua , Liu Shuai , Qi Haiying , Liu Fangjun , Fan Touwen , Chen Dongchu
Published: February 2023

The interface structure and segregation behavior of L12-Al3M (M = Sc, Zr, Er, Y) phases were investigated based on first-principles calculations. The results showed that the order of the thermodynamically stable interface was Al3Zr/Al > Al3Sc/Al > Al3Er/ Al > Al3Y/Al. The interfaces of Al3Sc/Al3Zr, Al3Er/Al3Zr, and Al3Y/Al3Er obtained negative interfacial energies and low coherent strain energies and were favorable to form a clear interface. Zr atom tended to segregate to the first atomic layer on the Al side of the Al/Al3Sc, Al/Al3Er, and Al/Al3Y interfaces. The driving effect of the Zr atom segregation to the Al3Y shows was stronger than that to Sc and Er atoms, whereas the high coherent strain energy hindered the formation of Al3Y/Al3Zr interface. Er atom tended to segregate at the Al/Al3Y interface and accelerated the formation of core-shelled Al3Y/Al3Er. Furthermore, the formation of the double core-shelled Al3Y/Al3Er/Al3Zr was discussed.
Two-Dimensional Projection of Distribution and Abundance of Common Species of Spiders and Beetles in South Korea Caused by Climate Change
Authors: Kwon Tae-Sung , Kim Sung-Soo , Choi Won Il , Nam Youngwoo
Published: February 2023

The selection of explanatory variables is very important in a species distribution model for predicting changes in the distribution of organisms caused by climate change. In this study, a two-dimensional prediction of the abundance and distribution of beetles and spiders using temperature and precipitation was compared with the results of previous studies that employed a one-dimensional prediction using temperature. This study used the data from previous surveys for 366 forest sites in South Korea between 2006 and 2009 using pitfall traps. Species distribution models were created for 51 species with a high occurrence (collected from more than 10% of the total sites). A generalized additive model (GAM) was used for the distribution models. The future abundance and distribution based on climate scenarios RCP 4.5 and 8.5 were predicted by selecting 35 species from 51 common species for which climatic factors (temperature and precipitation) had a significant effect on abundance and distribution. In a two-dimensional prediction using temperature and precipitation, the range of change was larger compared with a one-dimensional prediction using temperature, and precipitation had a significant effect on decreasing species.
Contribution to the Knowledge of Cylindrotomidae, Pediciidae and Tipulidae (Diptera: Tipuloidea): First Records of 86 Species from Various European Countries
Authors: Kolcsár Levente-Péter , Oosterbroek Pjotr , Olsen Kjell Magne , Paramonov Nikolai M. , Gavryushin Dmitry I. , Pilipenko Valentin E. , Polevoi Alexei V. , Eiroa Eulalia , Andersson Michael , Dufour Christophe , Syratt Maksymilian , Kurina Olavi , Lindström Mattias , Starý Jaroslav , Lantsov Vladimir I. , Wiedeńska Jolanta , Pape Thomas , Friman Miikka , Peeters Kris , Gritsch Walther , Salmela Jukka , Viitanen Esko , Aristophanous Marios , Janević Dejan , Watanabe Kozo
Published: February 2023

The superfamily Tipuloidea contains the following cranefly families: Cylindrotomidae, Limoniidae, Pediciidae and Tipulidae, with 1267 species known in Europe. Recent studies have increased our knowledge regarding these families substantially, but craneflies still represent an understudied group, even in Europe. A previous paper focused on European Limoniidae, summarizing the faunistic and taxonomic papers concerning the family between 2010 and 2020, and reported additional new country records. In this study, the focus is on the other three cranefly families: Cylindrotomidae, Pediciidae and Tipulidae, summarizing taxonomic and faunistic studies concerning these families in Europe between 2010 and 2022. Also presented are 204 occurrence records belonging to one Cylindrotomidae, 23 Pediciidae and 62 Tipulidae species, which represent first country records from various European countries: three from Albania, three from Belarus, one from Belgium, three from Bosnia and Herzegovina, 13 from Bulgaria, two from Cyprus, two from Denmark, three from Estonia, one from Finland, two from Greece, three from Italy, one from Montenegro, one from North Macedonia, six from Norway, six from Poland, four from Portugal, seven from Serbia, four from Slovenia, two from Spain and one from Sweden, and three from the European territory of Russia. In addition of species known already from Russia, six are presented as new from Central European Russia, 26 from East European Russia, six from North Caucasus and six from North European Russia.
The Disappearance of Small Mammal Carcasses in Human-Dominated Habitats: A Field Experiment in Northeastern Japan
Authors: Shizukuda Kyosuke , Saito Masayuki U.
Published: February 2023

Even in human-dominated regions such as urban and agricultural areas, there are organisms involved in the decomposition of animal carcasses. Therefore, it is possible that these complementary decomposition functions occur in different habitats. Here, we clarified the disappearance patterns of small mammal carcasses in forest and human-dominated (urban and agricultural) habitats in northeastern Japan, based on field experiments. All small mammal carcasses in both summer and autumn were removed by different scavengers within 6 days; therefore, there was little difference in the disappearance rate of carcasses between habitats. The scavenger groups that contributed to carcass removal of remains in the summer survey differed between sites, suggesting that the disappearance process varies with landscape and canopy openness conditions. Although many carcasses were removed by vertebrates during the autumn survey, the vertebrate species involved differed among the survey sites. This study suggests that ecological functions related to the decomposition of small mammal carcasses in anthropogenically modified habitats may be maintained by the complementary activities of vertebrates and invertebrates.
Diversity and Ecology of Charophytes from Vojvodina (Serbia) in Relation to Physico-Chemical and Bioclimatic Habitat Properties
Authors: Marković Aleksandra , Blaženčić Jelena , Tanasković Aljoša , Šinžar-Sekulić Jasmina
Published: February 2023

In Serbia, almost all charophyte species belong to one of the IUCN categories. This study aimed to gather more knowledge about their floristic richness, distribution and ecology. During the survey, 262 localities were investigated in the Vojvodina province, northern Serbia. Seventeen charophyte species were found, out of which 16 were included in the analyses. The environmental matrix included 38 localities, each characterized by habitat type and 35 environmental parameters. The canonical correspondence analysis (CCA) showed six parameters as being statistically significant: substrate, altitude, depth, water temperature, temperature annual range and precipitation of the driest month. Together, these explain the 32.34% variability in the species composition and abundance data. Altitude and substrate contributed the most to this. Two main types of habitats were outlined by the analysis. Small and shallow temporary habitats in the river floodplains, at lower altitudes, with muddy and clayish substrate, belong to one type. These areas are inhabited by either very tolerant species, such as Chara vulgaris and C. globularis, or the so-called “spring” species of the genera Tolypella and Nitella. Habitats which are located at higher altitudes, when deeper and more permanent with a sandy substrate, such as sandpits and river habitats, belong to the second one. These areas are inhabited by species like Chara papillosa, C. hispida, and Nitellopsis obtusa.
Dataset of Partial Analytical Validation of the 1,2-O-Dilauryl-Rac-Glycero-3-Glutaric Acid-(6′-Methylresorufin) Ester (DGGR) Lipase Assay in Equine Plasma
Authors: Peters Laureen Michèle , Howard Judith
Published: February 2023

Laboratory assays require analytical validation to prove they are providing accurate results. This dataset describes the partial analytical validation of lipase activity, measured with the 1,2-o-dilauryl-rac-glycero-3-glutaric acid-(6′-methylresorufin) ester (DGGR) lipase assay in equine plasma. Samples with low (approx. 12 U/L), moderately increased (approx. 79 U/L), and markedly increased lipase activity (approx. 298 U/L) were chosen. Linearity was assessed in samples of ascending dilution prepared by mixing samples with low and high lipase activity in different proportions. Repeatability or intra-assay replication was evaluated by measuring each level in 25 replicates within the same run. Reproducibility or inter-assay replication was calculated by measuring each level in five replicates on five consecutive days. The assay was linear in the range of 12–298 U/L (R2 = 0.9998) with a <2.3% deviation from the calculated value at any point. Within-run coefficients of variation were 4.43%, 0.69%, and 1.00% for the low, medium, and high samples, respectively. Between-run coefficients of variation were 3.57%, 1.42%, and 1.16%, respectively. To our knowledge, these are the first published data on the analytical validation of the DGGR lipase assay in horses, which may be of interest to veterinary clinical pathologists and equine clinicians measuring DGGR lipase in equine blood for diagnostic and research purposes.
A Quantum-Classical Hybrid Solution for Deep Anomaly Detection
Authors: Wang Maida , Huang Anqi , Liu Yong , Yi Xuming , Wu Junjie , Wang Siqi
Published: February 2023

Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notably, image AD is one of the most representative tasks in current deep AD research. ML’s interaction with quantum computing is giving rise to a heated topic named quantum machine learning (QML), which enjoys great prospects according to recent academic research. This paper attempts to address the image AD problem in a deep manner with a novel QML solution. Specifically, we design a quantum-classical hybrid DNN (QHDNN) that aims to learn directly from normal raw images to train a normality model and then exclude images that do not conform to this model as anomalies during its inference. To enable the QHDNN to perform satisfactorily in deep image AD, we explore multiple quantum layer architectures and design a VQC-based QHDNN solution. Extensive experiments were conducted on commonly used benchmarks to test the proposed QML solution, whose results demonstrate the feasibility of addressing deep image AD with QML. Importantly, the experimental results show that our quantum-classical hybrid solution can even yield superior performance to that of its classical counterpart when they share the same number of learnable parameters.
Aggregated Power Indices for Measuring Indirect Control in Complex Corporate Networks with Float Shareholders
Authors: Stach Izabella , Mercik Jacek , Bertini Cesarino , Gładysz Barbara , Staudacher Jochen
Published: February 2023

The purpose of this paper is to introduce new methods to measure the indirect control power of firms in complex corporate shareholding structures using the concept of power indices from cooperative game theory. The proposed measures vary in desirable properties satisfied, as well as in the bargaining models of power indices used to construct them. Hence, they can be used to produce different pictures of the coalitional strength of firms in control of other firms in mutual shareholding networks with the presence of cycles. Precisely, in the framework of Karos and Peters from 2015, ten power indices substitute the original Shapley and Shubik power index in a modular fashion. In this way, we obtain a set of new measures called aggregated indices. The float shareholders typically hold less than 5 percent of the outstanding shares, which is an uncertain element of indirect control in complex shareholding structures. The fuzzy number seems appropriate to model these shareholders’ behavior. The novelty is that we model the behavior of float using Z-fuzzy numbers. The new methods are tested in an example.
The Mediating Effect of the Internal Control System on the Relationship between the Accounting Information System and Employee Performance in Jordan Islamic Banks
Authors: Jarah Baker Akram Falah , Zaqeeba Nidal , Al-Jarrah Mefleh Faisal Mefleh , Al Badarin Abdalla Mohammad , Almatarneh Zeyad
Published: February 2023

The goal of the current study is to determine how the internal control system (ICS) in Jordanian Islamic banks affects the link between the accounting information system (AIS) and employee performance (EP). The study’s target audience is actual Jordanian Islamic Bank workers, and a sample of 92 respondents, representing a response rate of about 70.22%, was selected from a total of 131 participants, themselves divided among 105 branches of Islamic Banks in Jordan. According to the data, there is a clear correlation between the AIS, which comprises information quality (IQ), system quality (SQ1), and service quality (SQ2), and the EP in Jordanian Islamic Banks, at a significance level of 0.00. Furthermore, it was demonstrated that the impact of the ICS on the relationship between AIS and EP in Jordanian Islamic banks was statistically significant.
Intrusion Detection Method Based on CNN–GRU–FL in a Smart Grid Environment
Authors: Zhai Feng , Yang Ting , Chen Hao , He Baoling , Li Shuangquan
Published: February 2023

The aim of this paper is to address the current situation where business units in smart grid (SG) environments are decentralized and independent, and there is a conflict between the need for data privacy protection and network security monitoring. To address this issue, we propose a distributed intrusion detection method based on convolutional neural networks–gated recurrent units–federated learning (CNN–GRU–FL). We designed an intrusion detection model and a local training process based on convolutional neural networks–gated recurrent units (CNN–GRU) and enhanced the feature description ability by introducing an attention mechanism. We also propose a new parameter aggregation mechanism to improve the model quality when dealing with differences in data quality and volume. Additionally, a trust-based node selection mechanism was designed to improve the convergence ability of federated learning (FL). Through experiments, it was demonstrated that the proposed method can effectively build a global intrusion detection model among multiple independent entities, and the training accuracy rate, recall rate, and F1 value of CNN–GRU–FL reached 78.79%, 64.15%, and 76.90%, respectively. The improved mechanism improves the performance and efficiency of parameter aggregation when there are differences in data quality.
A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
Authors: Zhou Tao , Liu Fengzhen , Lu Huiling , Peng Caiyue , Ye Xinyu
Published: February 2023

COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19.
IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives
Authors: Morais Davisson F. T. , Fernandes Gilberto , Lima Gildário D. , Rodrigues Joel J. P. C.
Published: February 2023

The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.
Multi-Array Design for Hydrokinetic Turbines in Hydropower Canals
Authors: Cacciali Luca , Battisti Lorenzo , Dell’Anna Sergio
Published: February 2023

The design of hydrokinetic plants in hydropower canals involves the choice of the array layout, rotor geometry, turbine spacing, and array spacing, and necessitates the control of the resultant backwater to avoid upstream flooding hazards. Several works in the literature have shown that array power optimization is feasible with small spacings between the arrays, disregarding the limitation in the power output induced by backwater upstream. In this study, a 1-D channel model with a Double Multiple Streamtube code and wake sub-models are integrated to predict an array layout that will maximize the array power. The outputs of the conducted sensitivity analysis confirm that this design enabled improved power conversion with closely spaced turbines and largely spaced arrays, thus allowing for a partial recovery of the total head variation for a new array deployed upstream. In addition to the quantitative assessment of the mechanical power converted, the tool enables depth control from the downstream undisturbed flow station to the backwater exhaustion far upstream, thereby increasing its flexibility. Furthermore, it overcomes the limitations of actuator disc models by considering rotor’s fluid dynamic losses. The results show that power output linearly scales for a limited number of arrays (≤5), whilst the variation in water depth variation follows a power law from the most downstream array towards upstream, regardless of the plant size. Finally, the maximum upstream inflow depth is demonstrated to become asymptotic for large multi-array plants under ideal conditions.
Generalised Isentropic Relations in Thermodynamics
Authors: Nederstigt Pim , Pecnik Rene
Published: February 2023

Isentropic processes in thermodynamics are fundamental to our understanding of numerous physical phenomena across different scientific and engineering fields. They provide a theoretical reference case for the evaluation of real thermodynamic processes and observations. Yet, as analytical relations for isentropic transformations in gas dynamics are limited to ideal gases, the inability to analytically describe isentropic processes for non-ideal gases is a fundamental shortcoming. This work presents generalised isentropic relations in thermodynamics based on the work by Kouremenos et al., where three isentropic exponents γPv, γTv and γPT are introduced to replace the ideal gas isentropic exponent γ to incorporate the departure from the non-ideal gas behaviour. The general applicability of the generalised isentropic relations is presented by exploring its connections to existing isentropic models for ideal gases and incompressible liquids. Generalised formulations for the speed of sound, the Bernoulli equation, compressible isentropic flow transformations, and isentropic work are presented thereafter, connecting previously disjoint theories for gases and liquids. Lastly, the generalised expressions are demonstrated for practical engineering examples, and their accuracy is discussed.
Thermodynamic Analysis of an Innovative Cold Energy Storage System for Auto-Cascade Refrigeration Applications
Authors: Liu Zhenzhen , Jiang Jingde , Wang Zilong , Zhang Hua
Published: February 2023

The cooling capacity needed by ultra-low temperature apparatus cannot be reached economically with a single vapor compression refrigeration cycle due to the constraint of the high compressor pressure ratio. The auto-cascade refrigeration cycle is a good alternative. In this work, a novel concept that applies the principle of the auto-cascade refrigeration cycle to store cold energy is conducted. The environment-friendly refrigerants of R600a/R290/R170 zeotropic mixtures are used to study the performance of the modified auto-cascade refrigeration cycle (MACRC) as an alternative for cold-energy applications. The simulation results show that a cooling capacity of 500 W can be provided below −60 °C. The mixture with a mass fraction of 0.25/0.35/0.40 yields a COP of 0.695 and an exergy efficiency of 0.262 at −66 °C. The performance of the MACRC system was investigated at an ambient temperature of 20 to 40 °C for indoor small-scale applications. It is concluded that the performance would be improved by decreasing the ambient temperature. The results of the work should be helpful for the design and optimization of auto-cascade systems.
3D Stitching Double Weave Fabric-Based Elastic Triboelectric Nanogenerator for Energy Harvesting and Self-Powered Sensing
Authors: Chen Lijun , Zhao Yixi , Shen Yunchu , Wang Kai , Ma Pibo , Wang Fumei , Chen Chaoyu
Published: February 2023

With the start of the intelligent age, textiles are no longer limited to safety protection, warmth, and aesthetic purposes. They have become intelligent textiles, which combine functionality, intelligence, and information technology to adapt to the era and enrich our lives, such as wearable textiles and energy harvesting electronics. However, the limited stretchable smart textiles and complex fabrication methods have largely hindered their development. Here, a mass-manufactured 3D stitching double weave fabric-based elastic triboelectric nanogenerator (3DWE-TENG) is developed. Based on its stable electrical output performances and rapid response to external tensile strain, it can be used for energy harvesting and self-powered sensing simultaneously through both the lining layer and the exterior layer. With an advanced 3D structural design and using the improved woven method, 3DWE-TENG can be stretched to 300% and achieves a stable mechanical structure, breathability, and excellent flexibility. Furthermore, it also has low costs, wearable comfortability, and high fabricating efficiency due to the mature woven technique and the common yarns used in the fabric. This work provides more opportunities for stretchable power sources and self-powered sensors with applications in wearable electronics.
Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
Authors: Radhoush Sepideh , Vannoy Trevor , Liyanage Kaveen , Whitaker Bradley M. , Nehrir Hashem
Published: February 2023

Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.
Assessment of Optimal Operating Range and Case Verification of a Waste Heat Air-Source Heat Pump Water Heater Based on a Semiempirical Parametric Model
Authors: Zhou Youxin , Peng Bin , Zhu Bingguo
Published: February 2023

Waste heat air-source heat pump water heater (WH-ASHPWH) systems have been widely used to provide water heating in buildings due to their advantages of high efficiency and low environmental impact. It is important to determine reasonable operating conditions for WH-ASHPWH systems to improve their heating efficiency, especially for systems using industrial waste heat. In this study, a semiempirical parametric model of a WH-ASHPWH system was established to simulate heating performance under different working conditions and determine the optimum operating range of the system. A new discrete model for a tube-in-tube condenser was also generated. Taking the equipment of a pharmaceutical factory as an example, the heat exchanger model was first calibrated; then, the global model was used to study the effects of different air-side and condenser-side waste heat conditions on the heating performance of the system. According to the simulation results, the coefficient of performance (COP) reached a maximum value of 4.5 under the optimum working conditions. The deviation between the COP simulation data and experimental values was less than 8.28%, and the simulation time was less than 8 min. These metrics indicate that the model is reasonable and efficient.
Towards a Power Production from 100% Renewables: The Italian Case Study
Authors: Ferrari Lorenzo , Pasini Gianluca , Desideri Umberto
Published: February 2023

The need to reduce greenhouse gas emissions is driving many actions to decarbonize the most impactful sectors. Among these, the energy sector accounts for almost one third of emissions. Increasing the penetration of renewable energy in the energy mix could easily reduce the emissions of this sector. Theoretically, the target to aim for would be 100% renewable energy production. However, the variable nature of power production from photovoltaic and wind systems, which are expected to play a key role in the energy transition, may pose several limitations to the effective penetration of renewable energy. Many concerns arise when one considers the large diffusion of renewable energy that would be required to meet green targets, and the operating conditions of other systems in charge of compensating for renewable energy variations. This study aims to investigate the potential impact of an increase in the amount of renewable energy installed in a country, particularly in Italy. A simplified approach has been used, based on the assumption of knowing the hourly demand and power generation mix, and multiplying the intermittent power generation by a certain factor. Although not accurate, this approach allows the authors to highlight some critical aspects regarding the potential surplus of renewable energy and the operating conditions of other energy sources. The results of this study may provide a useful basis for a preliminary system evaluation, in particular to assess the feasibility of surplus recovery and the operability of residual generation systems. In addition, it may be easily replicated in other countries for similar estimations.
Research on Optimization Strategy of Battery Swapping for Electric Taxis
Authors: Qiang Hao , Hu Yanchun , Tang Wenqi , Zhang Xiaohua
Published: February 2023

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery’s state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping.
A Computational Scheme for Stochastic Non-Newtonian Mixed Convection Nanofluid Flow over Oscillatory Sheet
Authors: Arif Muhammad Shoaib , Abodayeh Kamaleldin , Nawaz Yasir
Published: February 2023

Stochastic simulations enable researchers to incorporate uncertainties beyond numerical discretization errors in computational fluid dynamics (CFD). Here, the authors provide examples of stochastic simulations of incompressible flows and numerical solutions for validating these newly emerging stochastic modeling methods. A numerical scheme is constructed for finding solutions to stochastic parabolic equations. The scheme is second-order accurate in time for the constant coefficient of the Wiener process term. The stability analysis of the scheme is also provided. The scheme is applied to the dimensionless heat and mass transfer model of mixed convective non-Newtonian nanofluid flow over oscillatory sheets. Both the deterministic and stochastic energy equations use temperature-dependent thermal conductivity. The stochastic model is more general than the deterministic model. The results are calculated for both flat and oscillatory plates. Casson parameter, mixed convective parameter, thermophoresis, Brownian motion parameter, Prandtl number, Schmidt number, and reaction rate parameter all impact the velocities, temperatures, and concentrations shown in the graphs. Under the influence of the oscillating plate, the results reveal that the concentration profile decreases with increasing Brownian motion parameters and increases with increasing thermophoresis parameters. The behavior of the velocity profile for the deterministic and stochastic models is provided, and contour plots for the stochastic model are also displayed. This article aims to provide a state-of-the-art overview of recent achievements in the field of stochastic computational fluid dynamics (SCFD) while also pointing out potential future avenues and unresolved challenges for the computational mathematics community to investigate.