bluuebunny/crossref_metadata_2025 huggingface dataset:
Variable inflaton equation-of-state and reheating
Authors: Di Marco Alessandro , Pradisi Gianfranco
Published: May 2021

We explore the consequences of a time-dependent inflaton equation-of-state (EoS) parameter in the context of the post-inflationary perturbative Boltzmann reheating. In particular, we numerically solve the perturbative coupled system of Boltzmann equations involving the inflaton energy density, the radiation energy density and the related entropy density and temperature of the produced particle thermal bath. We exploit reasonable Ansätze for the EoS and discuss the robustness of the Boltzmann system. We also comment on the possible microscopic origin related to a time-dependent inflaton potential, discussing the consequences on a preheating stage and the related (primordial) gravitational waves.
AN AGE STRUCTURE MODEL WITH IMPULSE ACTIONS FOR LEPTOSPIROSIS IN LIVESTOCK CATTLE
Authors: ASLAN IBRAHIM HALIL , BACA-CARRASCO DAVID , LENHART SUZANNE , VELASCO-HERNANDEZ JORGE X.
Published: May 2021

As a zoonotic disease, leptospirosis has now been identified as one of the emerging infectious diseases. In this paper, we analyze the propagation of leptospirosis and find a schedule for control programs to eradicate the disease in a cattle ranch. A mathematical model has been built with ordinary differential equations (ODEs) to understand the epidemiology of leptospirosis and main factors on its transmission. We investigated the disease free equilibrium of the model and its stability using the basic reproduction number, [Formula: see text], and target reproduction number, [Formula: see text]. Then, the model incorporates vaccination and recruitment control programs in the form of impulse actions as measures to prevent the propagation of leptospirosis in the ranch. Parameter estimation and sensitivity analysis are presented as a part of this study.
Discrete-time quantum walk on circular graph: Simulations and effect of gate depth and errors
Authors: Slimen Iyed Ben , Gueddana Amor , Lakshminarayanan Vasudevan
Published: May 2021

We investigate the counterparts of random walks in universal quantum computing and their implementation using standard quantum circuits. Quantum walks have been recently well investigated for traversing graphs with certain oracles. We focus our study on traversing a 1D graph, namely a circle, and show how to implement a discrete-time quantum walk in quantum circuits built with universal CNOT and single qubit gates. We review elementary quantum gates and circuit decomposition techniques and propose a generalized version of all CNOT-based circuits of the quantum walk. We simulated these circuits on five different qubits IBM-Q quantum devices. This quantum computer has nonperfect gates based on superconducting qubits, and, therefore, we analyzed the impact of the CNOT errors and CNOT-depth on the fidelity of the circuit.
Impact of Knowledge Management Capabilities on New Product Development and Company Performance
Authors: Yildirmaz Hakki , Öner M. Atilla , Herrmann Nicole
Published: May 2021

Managing knowledge is a critical challenge for organizations while it is a major potential for gaining competitive advantage. This empirical study confirms and improves a framework which proposes a link between knowledge management enablers and knowledge creation process. The model introduces improvements on the process-oriented perspective of knowledge by using the knowledge creation model by adding business strategies and market dynamism to the model. As an inter-mediator, new product development is accepted. This research provides a structural equation model (SEM) that can be referred by academicians and enables managers a tool that can be used for seeking sound strategies.
Synthesis and conductive performance about a kind of high-proton conductor, Dawson structure heteropoly acid H6P2W16Mo2O40 ⋅ 29H2O
Authors: Wu Xuefei , Wu Qingyin
Published: May 2021

A heteropoly acid (HPA) with Dawson structure, H6P2W[Formula: see text]Mo2O[Formula: see text]⋅29H2O, is synthesized with characterization and the investigation toward its proton conductive behavior. Actually, at 18[Formula: see text]C with 80% relative humidity (RH), H6P2W[Formula: see text]Mo2O[Formula: see text] ⋅ 29H2O displays the conductivity as 2.30 × 10[Formula: see text] S ⋅ cm[Formula: see text], indicating an excellent protonic conductor. The proton conduction activation energy is 32.19 kJ ⋅ mol[Formula: see text], implying proton migration following vehicle mechanism. During the measured range, higher temperature can boost the conductivity.
Online Tracking: When Does it Become Stalking?
Authors: Amarasekara Bede Ravindra , Mathrani Anuradha , Scogings Chris
Published: May 2021

Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-traffic generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by traffic generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the efficacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that define the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.
Predicting the trend of stock index based on feature engineering and CatBoost model
Authors: Xu Renzhe , Chen Yudong , Xiao Tenglong , Wang Jingli , Wang Xiong
Published: May 2021

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.
A Systematic Literature Review on Federated Machine Learning
Authors: Lo Sin Kit , Lu Qinghua , Wang Chen , Paik Hye-Young , Zhu Liming
Published: May 2021

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.
Popularity Bias in False-positive Metrics for Recommender Systems Evaluation
Authors: Mena-Maldonado Elisa , Cañamares Rocío , Castells Pablo , Ren Yongli , Sanderson Mark
Published: May 2021

We investigate the impact of popularity bias in false-positive metrics in the offline evaluation of recommender systems. Unlike their true-positive complements, false-positive metrics reward systems that minimize recommendations disliked by users. Our analysis is, to the best of our knowledge, the first to show that false-positive metrics tend to penalise popular items, the opposite behavior of true-positive metrics—causing a disagreement trend between both types of metrics in the presence of popularity biases. We present a theoretical analysis of the metrics that identifies the reason that the metrics disagree and determines rare situations where the metrics might agree—the key to the situation lies in the relationship between popularity and relevance distributions, in terms of their
agreement
and
steepness
—two fundamental concepts we formalize. We then examine three well-known datasets using multiple popular true- and false-positive metrics on 16 recommendation algorithms. Specific datasets are chosen to allow us to estimate both biased and unbiased metric values. The results of the empirical study confirm and illustrate our analytical findings. With the conditions of the disagreement of the two types of metrics established, we then determine under which circumstances true-positive or false-positive metrics should be used by researchers of offline evaluation in recommender systems.
1
A Survey on Conversational Recommender Systems
Authors: Jannach Dietmar , Manzoor Ahtsham , Cai Wanling , Chen Li
Published: May 2021

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.
Survey on Blockchain Networking
Authors: Dotan Maya , Pignolet Yvonne-Anne , Schmid Stefan , Tochner Saar , Zohar Aviv
Published: May 2021

Blockchains, in general, and cryptocurrencies such as Bitcoin, in particular, are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however, can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This article is motivated by these differences and, in particular, by the observation that many fundamental design aspects of these networks are not well-understood today. To support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery, over block and transaction propagation, to sharding and off-chain networks, also reviewing existing empirical results from different measurement studies. In particular, for each of these domains, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our article can hence also be seen as a call-to-arms to improve the foundation on top of which blockchains are built.
Assuring the Machine Learning Lifecycle
Authors: Ashmore Rob , Calinescu Radu , Paterson Colin
Published: May 2021

Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic, and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence, and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our article provides a comprehensive survey of the state of the art in theassurance of ML, i.e., in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of themachine learning lifecycle, i.e., of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The article begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.
Cation Dynamics in Hybrid Halide Perovskites
Authors: Mozur Eve M. , Neilson James R.
Published: May 2021

Hybrid halide perovskite semiconductors exhibit complex, dynamical disorder while also harboring properties ideal for optoelectronic applications that include photovoltaics. However, these materials are structurally and compositionally distinct from traditional compound semiconductors composed of tetrahedrally coordinated elements with an average valence electron count of silicon. The additional dynamic degrees of freedom of hybrid halide perovskites underlie many of their potentially transformative physical properties. Neutron scattering and spectroscopy studies of the atomic dynamics of these materials have yielded significant insights into their functional properties. Specifically, inelastic neutron scattering has been used to elucidate the phonon band structure, and quasi-elastic neutron scattering has revealed the nature of the uncorrelated dynamics pertaining to molecular reorientations. Understanding the dynamics of these complex semiconductors has elucidated the temperature-dependent phase stability and origins of defect-tolerant electronic transport from the highly polarizable dielectric response. Furthermore, the dynamic degrees of freedom of the hybrid perovskites provide additional opportunities for application engineering and innovation.
Chemistry Under Shock Conditions
Authors: Hamilton Brenden W. , Sakano Michael N. , Li Chunyu , Strachan Alejandro
Published: May 2021

Shock loading takes materials from ambient conditions to extreme conditions of temperature and nonhydrostatic stress on picosecond timescales. In molecular materials the fast loading results in temporary nonequilibrium conditions with overheated low-frequency modes and relatively cold, high-frequency, intramolecular modes; coupling the shock front with the material's microstructure and defects results in energy localization in hot spots. These processes can conspire to lead to a material response not observed under quasi-static loads. This review focuses on chemical reactions induced by dynamical loading, the understanding of which requires bringing together materials science, shock physics, and condensed matter chemistry. Recent progress in experiments and simulations holds the key to the answer of long-standing grand challenges with implications for the initiation of detonation and life on Earth.
A Two-Dimensional DOA Estimation Method Based on Virtual Extension of Sparse Array
Authors: Wang Guibao , Zhao Peiyao , Wang Lanmei , Wang Xiangyu , Wang Huan , Zhang Zhongpeng
Published: May 2021

The interelement spacing of a coprime array breaks through the half-wavelength limitation, so that a larger array aperture can be obtained with a fixed number of array elements. In this paper, the symmetry of the noncircular signal is used to virtually expand the L-shaped array into an orthogonal cross array. Furthermore, the virtual received signal of the augmented array is obtained by the second-order statistic of the received data. Decoupling and dimension reduction of elevation and azimuth are realized by a z-axis subarray and y-axis subarray. Finally, the sparse reconstruction of the signal is realized by the minimum absolute convergence and selection operator method. This method can enlarge the aperture and freedom of array and has higher accuracy and resolution of DOA estimation. It has the advantages of automatic parameter pairing without additional pairing operation and is effective for coherent and incoherent signals. The final numerical simulation results prove the effectiveness of the method in this paper.
MicroRNA‐145‐Mediated KDM6A Downregulation Enhances Neural Repair after Spinal Cord Injury via the NOTCH2/Abcb1a Axis
Authors: Gao Changzhao , Yin Fei , Li Ran , Ruan Qing , Meng Chunyang , Zhao Kunchi , Zhu Qingsan
Published: May 2021

Spinal cord injury (SCI) causes a significant physical, emotional, social, and economic burden to millions of people. MicroRNAs are known players in the regulatory circuitry of the neural repair in SCI. However, most microRNAs remain uncharacterized. Here, we demonstrate the neuroprotection of microRNA‐145 (miR‐145) after SCI in vivo and in vitro. In silico analysis predicted the target gene KDM6A of miR‐145. The rat SCI model was developed by weight drop, and lipopolysaccharide‐ (LPS‐) induced PC12 cell inflammatory injury model was also established. We manipulated the expression of miR‐145 and/or KDM6A both in vivo and in vitro to explain their roles in rat neurological functional recovery as well as PC12 cell activities and inflammation. Furthermore, we delineated the mechanistic involvement of NOTCH2 and Abcb1a in the neuroprotection of miR‐145. According to the results, miR‐145 was poorly expressed and KDM6A was highly expressed in the spinal cord tissue of the SCI rat model and LPS‐induced PC12 cells. Overexpression of miR‐145 protects PC12 cells from LPS‐induced cell damage and expedites neurological functional recovery of SCI in rats. miR‐145 was validated to target and downregulate the demethylase KDM6A expression, thus abrogating the expression of Abcb1a by promoting the methylation of NOTCH2. Additionally, in vivo findings verified that miR‐145 expedites neuroprotection after SCI by regulating the KDM6A/NOTCH2/Abcb1a axis. Taken together, miR‐145 confers neuroprotective effects and enhances neural repair after SCI through the KDM6A‐mediated NOTCH2/Abcb1a axis.
Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method
Authors: Yang Zhou , Pak Unsong , Kwon Cholu
Published: May 2021

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network‐based important sampling method (ANN‐IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN‐based reliability analysis methods. Because the process of FEA is time‐consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF‐IS and BP‐IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF‐IS method is closer to that of the Monte‐Carlo simulation method (MCS) than those of other methods (including BP‐IS). In addition, the RBF‐IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF‐IS method is well suited to structure reliability problems.
Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning
Authors: Liang Shuai
Published: May 2021

This paper studies the feature extraction and middle‐level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of the depth descriptor, Hellinger kernel and Principal Component Analysis (PCA) are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral‐ (PVB‐) laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high‐speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four‐frame plate glass is supported by sharp dagger‐like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four‐point supporting glass specimen is generally stronger than that of the four‐frame supporting glass.
Hypo‐EP Matrices of Adjointable Operators on Hilbert C∗‐Modules
Authors: Li Xiaopeng , Huang Junjie , Chen Alatancang
Published: May 2021

This paper introduces and studies hypo‐EP matrices of adjointable operators on Hilbert C∗‐modules, based on the generalized Schur complement. The necessary and sufficient conditions for some modular operator matrices to be hypo‐EP are given, and some special circumstances are also analyzed. Furthermore, an application of the EP operator in operator equations is given.
Hierarchical Characteristics and Proximity Mechanism of Intercity Innovation Networks: A Case of 290 Cities in China
Authors: Cao Xianzhong , Zeng Gang , Lin Lan , Zou Lin
Published: May 2021

The formation mechanism of innovation networks is one of the core issues in the current research of innovation networks, and proximity plays an important role in the formation and development of innovation networks; however, which proximity is more important and how different proximities interact remain to be further researched. This study conducts a social network analysis and adopts a spatial interaction model to examine innovation networks among 290 Chinese cities. The results reveal that, first, the hierarchical characteristics of Chinese cities’ innovation networks reflect a core periphery structure and the spatial patterns of large dispersion and small agglomeration. Further, bound by the Hu line, the hierarchy is high in the east and low in the west. Second, geographical, institutional, and cognitive proximities positively affect Chinese cities’ innovation networking. Cognitive proximity, particularly, has the highest impact. Geographical proximity reinforces the effect of institutional proximity, and thus, their interactions are complementary.
Protective Role of Platelets in Myocardial Infarction and Ischemia/Reperfusion Injury
Authors: Hałucha Kornela , Rak-Pasikowska Alina , Bil-Lula Iwona
Published: May 2021

Thrombotic occlusion of the coronary artery is a key component in the pathogenesis of myocardial ischemia and myocardial infarction (MI). The standard therapy for ischemia is revascularization and restoration of blood flow to previously ischemic myocardium. Paradoxically, reperfusion may result in further tissue damage called ischemia/reperfusion injury (IRI). Platelets play a major role in the pathogenesis of MI and IRI, since they contribute to the thrombus and microthrombi formation, inflammation, release of immunomodulatory mediators, and vasoconstrictive molecules. Antiplatelet therapies have proven efficacy in the prevention of thrombosis and play a protective role in cardiac IRI. Beyond the deterioration effect of platelets in MI and IRI, in the 90s the first reports on a protective effect of molecules released from platelets during MI appeared. However, the role of platelets in cardioprotection is still poorly understood. This review describes the involvement of platelets in MI, IRI, and inflammation. It mainly focuses on the protective role of platelets in MI and IRI. Platelets are involved in cardioprotection based on platelet-releasing molecules and antiplatelet therapy, apart from antiaggregatory effects. Additionally, the use of platelet-derived microparticles as possible markers of MI, with and without comorbidities, and their role in cardioprotection are discussed. This review is aimed at illustrating the present knowledge on the role of platelets in MI and IRI, especially in a context of cardioprotection.
Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis
Authors: Xu Bojun , Wang Lei , Zhan Huakui , Zhao Liangbin , Wang Yuehan , Shen Meng , Xu Keyang , Li Li , Luo Xu , Zhou Shasha , Tang Anqi , Liu Gang , Song Lu , Li Yan
Published: May 2021

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.
Empowered Hybrid Parent Selection for Improving Network Lifetime, PDR, and Latency in Smart Grid
Authors: Srisomboon Kanabadee , Dindam Tinnaphob , Lee Wilaiporn
Published: May 2021

To support the constraints of smart meters—low power and memory—of AMI network, RPL is considered as the most suitable routing protocol to be implemented in practice. Network lifetime, PDR, and latency are the critical issues to be focused on and addressed. Generally, single parent selection scheme cannot satisfy all expected performance requirements of RPL based on AMI network due to tradeoff between workload balancing and transmission performance, PDR and latency. Moreover, the single parent also suffers from the package size and transmission range. Then, multiparent solution is proposed to overcome these demerits using multipath transmission strategy. Although the existing multiparent solutions, MELT and MAHP, overcome the issue of transmission performance, they present low network lifetime since multiparent solution consumes high energy in data transmission. In this paper, we propose an “empowered hybrid parent selection (EHPS)” that exploits the merits of multiparent solution and the single parent with cognitive radio technology in a hybridizing scheme. To split the data packet efficiently under multipath transmission strategy, a fuzzy AHP (FAHP) is adopted; therefore, EHPS balances the workload effectively and maximizes the network lifetime over long transmission range and large data size. Moreover, by exploiting cognitive radio, EHPS is flexible to the transmission range and data size since it achieves the highest transmission performance, highest PDR, and lowest latency among others, while maintaining high network lifetime.
Clinicians’ Views on the Need for Cultural Adaptation of Intervention for Children with ADHD from the Ultraorthodox Community
Authors: Golos Anat , Mor Risa , Fisher Orit , Finkelstein Adi
Published: May 2021

Culture is a core context within occupational therapy, with a recent literature emphasizing the importance of cultural competence, as well as culturally sensitive assessment and intervention. The recent literature has indicated the efficacy of the Cognitive-Functional intervention (Cog-Fun) for children with ADHD among the general Israeli population, yet no studies to date have examined the necessity of cultural adaptations for minority groups. The current study examines the necessity of adapting the intervention protocol and process to the Ultraorthodox (UO) population, as perceived by occupational therapists. The study included 28 occupational therapists certified to use the Cog-Fun intervention, who reported using this approach with UO children. Participants responded to an online questionnaire developed for this study, regarding characteristics of the UO population and necessary adaptions to the Cog-Fun intervention process and protocol. Findings were analyzed using descriptive statistics and qualitative content analysis. Results of the study point to the necessity of addressing various features of the UO community, including daily routines and habits, cultural values, knowledge regarding ADHD, and accessibility of information. Participants also reported a need to adapt the graphic content of the intervention materials. The qualitative data expanded on the perceptions of the participants through four main themes: (a) knowledge regarding ADHD diagnosis and intervention; (b) perceptions and attitudes regarding ADHD diagnosis and medication; (c) factors affecting communication between the OT, parents, and teachers; and (d) adapting the intervention protocol to habits, routines, and lifestyle of UO families. This study has direct implications for therapists utilizing the Cog-Fun with UO children and may also provide insights relevant to occupational therapists using other treatment approaches with children from this culture, as well as other minority or traditional groups. Furthermore, this study may serve as an important addition to the limited literature describing cultural adaptations of evidence-based interventions.
Influence of Narrow Rectangular Channel (
AR
=
1
:
4
) on Heat Transfer and Friction for V- and W-Shaped Ribs in Turbine Blade Applications
Authors: Krishnaswamy Karthik , Sivan Suresh , Ali Hafiz Muhammad
Published: May 2021

Effective cooling of blades with a nominal pressure drop is essential for performance augmentation and thermal management of gas turbines. Hence, present work is aimed at determining the heat transfer enhancement and friction for W- and V-shaped ribs inside a rectangular cooling channel having hydraulic diameter (

D

h

) of 0.048 m and aspect ratio (

AR

) 1 : 4. Ribs are fixed facing downstream with angle of attack (

α

) 45° on opposite walls. Pitch (

P

) between two successive ribs is 25 mm for both cases. Continuous V- and W-shaped ribs with height to channel hydraulic diameter ratio (

e
/

D

h

) 0.052 and 0.0416 and pitch to height ratio (

P
/
e

) 10 and 12.5, respectively, have been examined for Reynolds number (

Re

) range 20000-80000. Heat transfer augmentation achieved at

Re

80000 is 1.94 and 1.8 times higher than

Re

20000 for V- and W-shaped ribs, respectively. Streamwise and spanwise variations in local Nusselt number ratio are highest for V-shaped ribs, which are estimated to be 31% and 12%. For W-shaped ribs, variations are 17.5% and 3.5%. Nusselt number (

Nu

) is highest along span length 0.5

w

for V-shaped ribs due to dominance of apex induced secondary flow. For W-shaped ribs, Nusselt number along the span lengths is found to be nearly same view uniformity in secondary flow. Maximum enhancement (

Nu
/
N

u

o

) estimated for both the rib shapes is 3.9 at

Re

20000. Due to increased rib height, friction losses for V-shaped ribs are higher than W-shaped ribs. Maximum friction loss increment is estimated to be 85% for V-shaped ribs and 42% for W-shaped ribs between

Re

20000 and 40000. For both rib shapes, impact of ribs is found to be greatest at

Re

40000. Thermohydraulic performance (

THP

) for W-shaped ribs is superior to V-shaped ribs. Best

THP

achieved for W- and V-shaped ribs are 3.7 and 3.4 at

Re

20000.
Score for Predicting Active Cancer in Patients with Ischemic Stroke: A Retrospective Study
Authors: Jiang Jiwei , Shang Xiuli , Zhao Jinming , Cao Meihui , Wang Jirui , Li Runzhi , Wang Yanli , Xu Jun
Published: May 2021

Background. We aimed to examine the differences of clinical characteristics between patients with ischemic stroke with active cancer and those without cancer to develop a clinical score for predicting the presence of occult cancer in patients with ischemic stroke. Methods. This retrospective study enrolled consecutive adult patients with acute ischemic stroke who were admitted to our department between December 2017 and January 2019. The demographic, clinical, laboratory, and neuroimaging characteristics were compared between patients with ischemic stroke with active cancer and those without cancer. Multivariate analysis was performed to identify independent factors associated with active cancer. Subsequently, a predictive score was developed using the areas under the receiver operating characteristic curves based on these independent factors. Finally, Bayesian decision theory was applied to calculate the posterior probability of active cancer for finding the best scoring system. Results. Fifty‐three (6.63%) of 799 patients with ischemic stroke had active cancer. The absence of a history of hyperlipidemia (odds ratio (OR) = 0.17, 95% confidence interval (CI): 0.06–0.48, P < 0.01), elevated serum fibrinogen (OR = 1.72, 95% CI: 1.33–2.22, P < 0.01) and D‐dimer levels (OR = 1.43, 95% CI: 1.24–1.64, P <0.01), and stroke of undetermined etiology (OR = 22.87, 95% CI: 9.91–52.78, P < 0.01) were independently associated with active cancer. A clinical score based on the absence of hyperlipidemia, serum fibrinogen level of ≥4.00 g/L, and D‐dimer level of ≥2.00 μg/mL predicted active cancer with an area under the curve of 0.83 (95% CI: 0.77–0.89, P < 0.01). The probability of active cancer was 59% at a supposed prevalence of 6.63%, if all three independent factors were present in a patient with ischemic stroke. Conclusions. We devised a clinical score to predict active cancer in patients with ischemic stroke based on the absence of a history of hyperlipidemia and elevated serum D‐dimer and fibrinogen levels. The use of this score may allow for early intervention. Further research is needed to confirm the implementation of this score in clinical settings.
Acoustic Scene Classification and Visualization of Beehive Sounds Using Machine Learning Algorithms and Grad-CAM
Authors: Kim Jaehoon , Oh Jeongkyu , Heo Tae-Young
Published: May 2021

Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.
Evaluation of Fibroblast Viability Seeded on Acellular Human Amniotic Membrane
Authors: Moravvej Hamideh , Memariani Hamed , Memariani Mojtaba , Kabir-Salmani Maryam , Shoae-Hassani Alireza , Abdollahimajd Fahimeh
Published: May 2021

Background. Investigating the viability and proliferative rates of fibroblast cells on human amniotic membrane (HAM) as a scaffold will be an important subject for further research. The aim of this study was to assess the fibroblast viability seeded on acellular HAM, since foreskin neonatal allogenic fibroblasts seeded on HAM accelerate the wound healing process. Methods. Fibroblasts were retrieved from the foreskin of a genetically healthy male infant, and we exploited AM of healthy term neonates to prepare the amniotic scaffold for fibroblast transfer. After cell culture, preparation of acellular HAM, and seeding of cells on HAM based on the protocol, different methods including 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide (MTT), 4 ′,6‐Diamidino‐2‐phenylindole dihydrochloride (DAPI), and propidium iodide (PI) staining were employed for assessment of fibroblast viability on HAM. Results. Based on the results obtained from the DAPI and PI staining, the percentage of viable cells in the former staining was clearly higher than that of the dead cells in the latter one. The results of DAPI and PI staining were in accordance with the findings of MTT assay, confirming that fibroblasts were viable and even proliferate on HAM. Conclusion. Our findings showed the viability of fibroblasts seeded on the acellular HAM using MTT assay, DAPI, and PI staining; however, this study had some limitations. It would be an interesting subject for future research to compare the viability and proliferation rate of fibroblasts seeded on both cellular and acellular HAM.
Analysis of Facial Skeletal Morphology: Nasal Bone, Maxilla, and Mandible
Authors: Chen Han-Sheng , Hsiao Szu-Yu , Lee Kun-Tsung
Published: May 2021

The growth and development of facial bones are closely related to each other. The present study investigated the differences in the nasomaxillary and mandibular morphology among different skeletal patterns. Cephalograms of 240 participants were divided into 3 groups based on the skeletal pattern (Class I, Class II, and Class III). The dimensions of nasomaxilla (nasal bone length, nasal ridge length, nasal depth, palatal length, and maxillary height) and mandible (condylar length, ramus length, body length, symphysis length, and entire mandibular length) were measured. One‐way analysis of variance and Pearson’s correlation test were used for statistical analysis. No significant differences were observed among the skeletal patterns in terms of nasal bone length, palatal length, maxillary height, or condylar length. Class II had a significantly shorter ramus, mandibular body, and entire mandibular length compared with those of Class I and Class III. Nasal ridge length exhibited a significant moderate correlated with nasal bone length (correlation coefficient: 0.433) and maxillary height (correlation coefficient: 0.535). The entire mandibular length exhibited a significant moderate correlated with ramus length (correlation coefficient: 0.485) and body length (correlation coefficient: 0.536). In conclusion, nasal and maxillary dimensions exhibited no significant difference among the 3 skeletal patterns. Mandibular body and entire mandibular lengths were significantly positively correlations with Class III skeletal patterns.
Parameter Optimization of Droop Controllers for Microgrids in Islanded Mode by the SQP Method with Gradient Sampling
Authors: Li Peijie , Yang Ziyi , Huang Shuchen , Zhang Jun
Published: May 2021

For enhancing the stability of the microgrid operation, this paper proposes an optimization model considering the small‐signal stability constraint. Due to the nonsmooth property of the spectral abscissa function, the droop controller parameters’ optimization is a nonsmooth optimization problem. The Sequential Quadratic Programming with Gradient Sampling (SQP‐GS) is implemented to optimize the droop controller parameters for solving the nonsmooth problem. The SQP‐GS method can guarantee the solution of the optimization problem globally and efficiently converges to stationary points with probability of one. In the current iteration, the gradient of the nonsmooth function can be evaluated on a set of randomly generated nearby points by computing closed‐form sensitivities. A test on the microgrid system shows that the optimality and the efficiency of the SQP‐GS are better than those of the heuristic algorithms.
The Extended Oxidative and Sensory Stability of Traditional Dairy-Based Oil with Steam-Distilled Essential Oils Extracted from the Bioactive-Rich Leaves of Ziziphora tenuior, Ferulago angulata, and Bunium persicum
Authors: Mohammadi Mehrdad , Khorshidian Nasim , Gharibzahedi Seyed Mohammad Taghi
Published: May 2021

The oxidation rate and overall sensory acceptability of Iranian animal oil (IAO) during storage were evaluated after adding the bioactive essential oils extracted from medicinal herbs of Ziziphora tenuior, Ferulago angulata, and Bunium persicum. Results showed that the most dominant chemical constituents in Z. tenuior, F. angulata, and B. persicum essential oils were pulegone (12.77%), ferulagon (14.97%), and (+)-trans-carveol (57.70%), respectively. IAO contained more saturated fatty acids (67.43%, mainly palmitic and myristic acids) than unsaturated (32.27%, mostly oleic acid) ones. B. persicum essential oil compared to the other two oils significantly had more total flavonoid (1.08 mg quercetin equivalent/g), phenolic (123.2 mg GAE/g), carotenoid (591.31 mg/kg), and chlorophyll (24.32 mg/kg) contents. A significant dose-dependent increase in the overall sensory acceptability of IAO was found by increasing the concentration of B. persicum essential oil. Similar to tertiary butyl hydroquinone, the oil blend of IAO+10% B. persicum essential oil obtained the maximum overall sensory acceptability scores during 28 d cold storage due to the remarkable in vitro DPPH inhibition (83.45%) and ferric-reducing power (0.754 at Å700nm). A much slower formation rate in primary and secondary oxidation compounds in IAO rich in B. persicum essential oil during the storage was associated with the overall sensory acceptability data (

p
<
0.01

, r = 0.951). Thus, this bioactive additive as a bio-preservative may well stabilize crude oils and emulsions.
Analysis of the Outer Contour Characteristics of Roadway Forming under the Influence of Cutting Head Vibration
Authors: Liu Zhixiang , Wang Shuai , Xie Miao , Xie Chunxue
Published: May 2021

Considering that the trajectory of the cutting head of the longitudinal axis roadheader directly affects the surface morphology of the roadway, the formation mechanism of the outer profile of the roadway obtained by the cutting head is analyzed, and the dynamic model of the cutting part was constructed. The variation law of the vibration swing angle of the cutting head under different working conditions was obtained, and the coordinate of the cutting head of the roadheader in the roadway space coordinate system was determined by using the robot kinematics analysis method. The mathematical model of cutting the outer outline of the roadway by the cutting head is constructed. Through the numerical calculation and analysis, the characteristics of the formed outer profile of the roadway were analyzed considering the influence of the vibration of the cutting head. The results show that the difference between the “pit” and the “peak ridge” obtained by considering the vibration of the cutting head is larger than that without considering the vibration of the cutting head, which shows that the outer outline of the roadway obtained by the real cutting is more rougher than that without considering the vibration of the cutting head. With the increase of the cutting lifting angle, the maximum and average value of the difference between the “pit” and the uplift “peak ridge” on the coal and rock shows an increasing trend, and with the increase of the cylinder pressure, the maximum values of the horizontal swing angle “pit” and the uplift “peak ridge” gradually increase. The vibration of the cutting head when cutting coal and rock will have a great influence on the outer profile of the roadway, which cannot be ignored in the research of accurate autonomous cutting control algorithm of roadheader. The research results of this paper can provide a basis for the optimization of cutting parameters and high‐precision forming control method of roadheader.