bluuebunny/crossref_metadata_2025 huggingface dataset:
Hierarchical framework to improve individualised exercise prescription in adults: a critical review
Authors: Lehtonen Elias , Gagnon Dominique , Eklund Daniela , Kaseva Kaisa , Peltonen Juha Evert
Published: June 2022

Physical activity (PA) guidelines for the general population are designed to mitigate the rise of chronic and debilitating diseases brought by inactivity and sedentariness. Although essential, they are insufficient as rates of cardiovascular, pulmonary, renal, metabolic and other devastating and life-long diseases remain on the rise. This systemic failure supports the need for an improved exercise prescription approach that targets the individual. Significant interindividual variability of cardiorespiratory fitness (CRF) responses to exercise are partly explained by biological and methodological factors, and the modulation of exercise volume and intensity seem to be key in improving prescription guidelines. The use of physiological thresholds, such as lactate, ventilation, as well as critical power, have demonstrated excellent results to improve CRF in those struggling to respond to the current homogenous prescription of exercise. However, assessing physiological thresholds requires laboratory resources and expertise and is incompatible for a general population approach. A case must be made that balances the effectiveness of an exercise programme to improve CRF and accessibility of resources. A population-wide approach of exercise prescription guidelines should include free and accessible self-assessed threshold tools, such as rate of perceived exertion, where the homeostatic perturbation induced by exercise reflects physiological thresholds. The present critical review outlines factors for individuals exercise prescription and proposes a new theoretical hierarchal framework to help shape PA guidelines based on accessibility and effectiveness as part of a personalised exercise prescription that targets the individual.
T cell repertoire in peripheral blood as a potential biomarker for predicting response to concurrent cetuximab and nivolumab in head and neck squamous cell carcinoma
Authors: Wang Xuefeng , Muzaffar Jameel , Kirtane Kedar , Song Feifei , Johnson Matthew , Schell Michael J , Li Jiannong , Yoder Sean J , Conejo-Garcia Jose R , Guevara-Patino Jose A , Bonomi Marcelo , Bhateja Priyanka , Rocco James W , Steuer Conor E , Saba Nabil F , Chung Christine H
Published: June 2022

Background

T cell receptor (TCR) signaling profile is a fundamental property that underpins both adaptive and innate immunity in the host. Despite its potential clinical relevance, the TCR repertoire in peripheral blood has not been thoroughly explored for its value as an immunotherapy efficacy biomarker in head and neck squamous cell carcinoma (HNSCC). The purpose of the present study is to characterize and compare the TCR repertoire in peripheral blood mononuclear cells (PBMC) from patients with HNSCC treated with the combination of cetuximab and nivolumab.

Methods

We used the immunoSEQ assay to sequence the TCR beta (TCR-B) chain repertoire from serially obtained PBMC at baseline and during the treatments from a total of 41 patients who received the combination (NCT03370276). Key TCR repertoire metrics, including diversity and clonality, were calculated and compared between patients with different therapy responses and clinical characteristics (eg, human papillomavirus (HPV) status and smoking history). Patient survival outcomes were compared according to patient groups stratified by the TCR-B clonotyping. To confirm the observed patterns in TCR spectrum, samples from patients who achieved complete response (CR) and partial response (PR) were further profiled with the immunoSEQ deep resolution assay.

Results

Our data indicated that the patients who achieved CR and PR had an increased TCR sequence diversity in their baseline samples, this tendency being more pronounced in HPV-negative patients or those with a smoking history. Notably, the CR/PR group had the lowest proportion of patients with oligoclonal TCR clones (2 out of 8 patients), followed by the stable disease group (9 out of 20 patients) and lastly the progressive disease group (7 out of 10 patients). An overall trend toward favorable patient survival was also observed in the polyclonal group. Finally, we reported the shared TCR clones across patients within the same response group, as well as the shared clones by aligning immunoSEQ reads with TCR data retrieved from The Cancer Genome Atlas- head and neck squamous cell carcinoma (TCGA-HNSC) cohort.

Conclusions

Our data suggest that, despite the great clinical heterogeneity of HNSCC and the limited responders in the present cohort, the peripheral TCR repertoires from pretreatment PBMC may be developed as biomarkers for the benefit of immunotherapy in HNSCC.
Data-directed search for new physics based on symmetries of the SM
Authors: Birman Mattias , Nachman Benjamin , Sebbah Raphael , Sela Gal , Turetz Ophir , Bressler Shikma
Published: June 2022

We propose exploiting symmetries (exact or approximate) of the Standard Model (SM) to search for physics Beyond the Standard Model (BSM) using the data-directed paradigm (DDP). Symmetries are very powerful because they provide two samples that can be compared without requiring simulation. Focusing on the data, exclusive selections which exhibit significant asymmetry can be identified efficiently and marked for further study. Using a simple and generic test statistic which compares two matrices already provides good sensitivity, only slightly worse than that of the profile likelihood ratio test statistic which relies on the exact knowledge of the signal shape. This can be exploited for rapidly scanning large portions of the measured data, in an attempt to identify regions of interest. We also demonstrate that weakly supervised Neural Networks could be used for this purpose as well.
INFRASTRUCTURE-FDI CURSE AND INSTITUTIONAL QUALITY IN BELT AND ROAD HOST COUNTRIES
Authors: RAZZAQ ASIF , AN HUI , ABBAS QAISAR , YANG HUANG
Published: June 2022

Most of the Belt and Road host Countries (B&RCs) have weak infrastructure and gained investor’s attention in infrastructure financing. This potential is mainly tapped by Chinese outward foreign direct investment (FDI) under the Belt and Road Initiative (BRI). Therefore, this study analyzes the role of institutions in explaining the infrastructure-FDI framework across the B&RCs from 2004 to 2019. Initially, it constructs composite infrastructure development and institutional quality indices to integrate their multidimensional attributes. Later, System Generalized Method of Moments estimator is employed, and it proposes three key findings. First, infrastructure development of the host country crowd-out Chinese outward FDI, indicating the presence of the infrastructure-FDI curse. Second, the host country’s institutional quality contributes to stimulating Chinese outward FDI and helps to mitigate the infrastructure-FDI curse. Third, the infrastructure-FDI curse (does not) holds in Post (Pre) Belt and Road period. These results offer valuable policy suggestions for participating B&RCs and highlight the region’s prevailing infrastructure gaps.
Machine learning in CMS
Authors: May Samuel
Published: June 2022

Advanced machine learning (ML) methods are increasingly used in CMS physics analyses to maximize the sensitivity of a wide range of measurements. The landscape is diverse in terms of both methods and applications. Deep learning methods, from recurrent long short-term memory (LSTM) architectures for classification tasks to deep autoencoders for data quality monitoring, have greatly improved the physics results delivered from the CMS experiment. Algorithms are developed both for collaboration-wide use as well as for individual physics analyses. Results from CMS, such as the measurement of the Higgs boson’s properties in the diphoton decay channel, exploit a variety of ML algorithms to reduce uncertainties on measurements.
Tunneling analysis of regular black holes with cosmic strings-like solution in Newman–Janis algorithm
Authors: Ali Riasat , Babar Rimsha , Asgher Muhammad , Xia Tie-Cheng
Published: June 2022

We consider the regular black holes (BHs) solution with cosmic strings (RBHCS) in the rotation parameter by assuming the Newman–Janis method. After this, we study thermodynamical property (i.e. Hawking temperature [Formula: see text]) for the RBHCS in the presence of spin parameter. Moreover, we study the graphical interpretation of Hawking temperature with event horizon to check the physical and stable form of RBHCS under the effect of Newman–Janis algorithm. We graphically show that the RBHCS in the context of Newman–Janis algorithm are colder than the Schwarzschild BH. Furthermore, we investigate the quantum-corrected temperature for RBHCS in Newman–Janis method by incorporating generalized uncertainty principle. We have also analyzed the graphical interpretation of corrected temperature [Formula: see text] versus [Formula: see text] and study the stable condition of RBHCS in Newman–Janis method in the presence of gravity parameter effects. Finally, the corrected entropy for RBHCS with rotation parameter is analyzed.
Study of semi-boosted top quark reconstruction performance on the line shape of a tt̄ resonance
Authors: Pácalt Josef , Kvita Jiri
Published: June 2022

In this paper, we study the top quark pair events production in pp collisions in the [Formula: see text] channel at the energy of [Formula: see text] TeV for Standard Model as well as new physics processes. We explore the usage of semi-boosted topologies where the top quark decays into a high-transverse momentum (boosted) hadronic W-jet and an isolated b-jet and study their performance in the [Formula: see text] events kinematic reconstruction. An important event fraction is recovered and the correlation of selected kinematic variables between the detector and particle level is studied. Quality of the reconstructed mass line shape of a hypothetical scalar resonance decaying into [Formula: see text] is evaluated and compared for regimes of a different degree of the transverse boost. Unfolding performance is checked in terms of comparing the excess of events in spectra before and after the unfolding, concluding with the proof of a signal significance loss after the unfolding procedure for both energy and angle related observables, with possible applications for current LHC experiments.
Human–Object Interaction Detection Based on Star Graph
Authors: Cai Shuang , Ma Shiwei , Gu Dongzhou , Wang Chang
Published: June 2022

Graph attention networks (GATs)-based method performs well in Human–Object Interaction (HOI) detection due to its ability to aggregate contextual information. However, the traditional GATs-based methods are computationally intensive, and the graph’s structure cannot effectively represent the HOI in an image. Meanwhile, the graph models are unable to predict the interactions that involve less contextual information correctly. In this paper, we design a method based on graph models called V-SGATs, which stands for Visual Branch and Star Graph Attention Networks. The human-centric star graph and object-centric star graph are adopted to reduce computational complexity, and the logical structure of the star graph can represent HOI more reasonably. Meanwhile, the visual branch that recognizes the interaction without utilizing the contextual information is designed to aid the graph model in predicting the interactions that involve less contextual information. Experiments are carried out on two large-scale HOI public benchmarks V-COCO and HICO-DET, and the results show that the proposed method performs better than most of the existing methods based on GATs.
Towards a Reliable Text Summarization Evaluation Metric Using Predictive Models
Authors: Zhao Bo , Lui Yui Man
Published: June 2022

With an increasing number of new summarization systems proposed in recent years, an automatic text evaluation metric that can accurately and reliably rate the performance of summarization systems has been a pressing need. However, current automatic text evaluation metrics can only measure one or certain aspects of the quality between two summary texts and do not agree with human judgments consistently. In this paper, we show that combining multiple well-chosen evaluation metrics and training predictive models using human annotated datasets can lead to more reliable evaluation scores than using any individual automatic metric. Our predictive models trained on a human annotated subset of the CNN/DailyMail corpus demonstrate significant improvements (e.g. approximately 25% along coherence dimension) over selected individual metrics. Furthermore, a concise meta-evaluation on automatic metrics is provided along with an analysis of the performance of 12 predictive models. We also investigate the sensitivity of automatic metrics when mixed together for training these models. We have made the code, the instructions for experiment setup, and the trained models available as a tool for comparing and evaluating text summarization systems. a

a https://github.com/bzhao2718/ReliableSummEvalReg .
A Novel Deep Learning-Based Bidirectional Elman Neural Network for Facial Emotion Recognition
Authors: Alamgir Fakir Mashuque , Alam Md. Shafiul
Published: June 2022

Facial emotion recognition (FER) is an interesting area of research. It has a wide range of applications, but there is still a deficiency of an accurate approach to provide better results. A novel FER system to maximize classification accuracy has been introduced in this paper. The proposed approach constitutes the following phases: pre-processing, feature extraction, feature selection, and classification. Initially, the images are pre-processed using the extended cascaded filter (ECF) and then the geometric and appearance-based features are extracted. An enhanced battle royale optimization (EBRO) for feature selection has been proposed to select the relevant features and to reduce the dimensionality problem. Then, the classification is carried out using a novel bidirectional Elman neural network (Bi-ENN) that offers high classification results. The proposed Bi-ENN-based emotion classification can accurately discriminate the input features. It enabled the model to predict the labels for classification accurately. The proposed model on evaluations attained an accuracy rate of 98.57% on JAFFE and 98.75% on CK+ datasets.
Deep Learning-Based Verification of Iridology in Diagnosing Type II Diabetes Mellitus
Authors: Sruthi K. , Vijayakumar J. , Thavamani S.
Published: June 2022

Type II Diabetes Mellitus (Type II DM) is a chronic condition that has detrimental effect on vital organs if left untreated, necessitating early diagnosis and treatment. Iridology, a subset of Complementary and Alternative Medicine (CAM), has the potential to serve as a tool for noninvasive early diagnosis of Type II DM. Iridology involves analyzing the characteristics of iris such as color and pattern for detection of organ and system defects. Deep learning algorithm is one of the promising methods in diagnosing various health-related issues. In this study, we have demonstrated the efficiency of iridology in diagnosing Type II DM using deep learning algorithms. Near Infra-Red images of iris were captured using iris scanner from 178 voluntary subjects belonging to two categories namely, Type II DM (95 subjects) and nondiabetic or healthy category (83 subjects). We have developed an algorithm using Fully Convolutional Neural network for effective iris segmentation. Normalized iris images were used to crop out our region of interest, pancreas, based on the iridology chart. Classification networks such as AlexNet, VGG-16, and ResNet-50 were used to classify Type II DM versus healthy category. Our proposed model for iris segmentation achieved an accuracy and sensitivity of 0.99, specificity and F-Score of 0.98, and a precision of 0.97. Results obtained using AlexNet classifier exhibits better classification accuracy of 95.85% for Zero-padding based resized image. The classifier yielded a sensitivity, specificity, and precision of 95.80%, 95.85%, and 96.11%, respectively. Our study results establish the efficacy and emphasize the importance of the proposed algorithm for diagnosing Type II DM.
Ergonomic Assessment Method of Risk Factors for Musculoskeletal Disorders Associated with Sitting Postures
Authors: Li Jianwei , Huang Sihan , Wang Faming , Chen Sixi , Zheng Huiru
Published: June 2022

Musculoskeletal disorders (MSDs) are associated with sitting postures. The assessment and prevention of risk factors for workplace exposure are indispensable aspects of reducing the occurrence of MSDs. This paper proposes an ergonomic assessment method of risk factors for MSDs associated with sitting postures in the actual working conditions. A Kinect sensor with the RULA method was primarily used to collect the data and evaluate the relevant postures. The results obtained were compared with the evaluation results by a human expert. Additionally, we verified the capability and effectiveness of this method. A program system for human joint recognition and acquisition was implemented. The results indicated that the Kinect joint data is generally accurate and can adequately complete the RULA evaluation table. The results from the front and right-hand side obtained by the Kinect were consistent with the results of the expert evaluation, and no significant difference was observed between them ([Formula: see text]). However, when the participants faced the Kinect, the sensor performed better, and the evaluation result was more accurate. A high consistency was observed between the evaluation results obtained from the front and the expert (proportion agreement [Formula: see text], Cohen’s [Formula: see text]). Only a slight consistency was observed between the evaluation results obtained from the right-hand side and the expert (proportion agreement [Formula: see text], Cohen’s [Formula: see text]). This research created a new ergonomic method for the risk assessment of MSDs associated with sitting postures. The combination of theory and practice is crucial in the risk assessment of sitting postures in workplaces.
Higher Kiss terms
Authors: Moorhead Andrew
Published: June 2022

We show that the modular term condition higher commutator is equal to the modular hypercommutator. As a consequence, we arrive at a new proof that HC8 holds for modular varieties. Next, we develop a procedure for a modular variety for producing the higher dimensional congruences that characterize the hypercommutator. This procedure allows us to demonstrate that every modular variety has an infinite sequence of what we call higher dimensional Kiss terms. We use these results to extend the scope of a theorem of Opršal from permutable varieties to modular varieties.
ANALYZING AND MODELING LOCALIZED CORROSION BEHAVIOR OF Ni COATING ON PIPELINE STEEL
Authors: DAI TING , ZHAO JIE , GU YANHONG , GUO QIANG , YANG XIAOYU , ZHANG LEI
Published: June 2022

To improve the corrosion resistance of X70 pipeline steel in seawater, nickel coating was prepared on the surface of X70 steel by electroplating method. The corrosion properties of the samples in simulated seawater were studied by macroscopic electrochemical experiment and micro-scanning electrochemical experiment. The systematic characterization of the samples was conducted using a scanning electron microscope (SEM), energy disperse spectroscopy (EDS), and X-ray diffractometer (XRD) techniques. The time-dependent model was established to simulate the localized corrosion by COMSOL Multiphysics. The characterization results show that the nickel coating prepared at 0.045 A is compact and thickest. In the macroscopic electrochemical results, the impedance value of the nickel layer prepared at 0.045 A is 34% and 36% higher than that of the other two coatings, and the current density is 25% and 66% lower than that of the other two coatings. In the micro-scanning electrochemical results, the impedance value of the nickel layer prepared at 0.045 A is 5% and 40% higher than that of the other two coatings, and the current density is 14% and 26% lower than that of the other two coatings. Therefore, 0.045 A is the best electroplating current for preparing nickel coating. The simulation results show that the micropores on the surface of nickel-plated X70 are easy to induce localized corrosion, and the degree of localized corrosion decreases with the increase of micropore diameter.
PRELIMINARY INVESTIGATION OF POWDER-MIXED MICROELECTRICAL DISCHARGE DRILLING OF CFRP COMPOSITE
Authors: DUTTA HRISHIKESH , DEBNATH KISHORE , SARMA DEBA KUMAR
Published: June 2022

This work investigates the fabrication of microholes in carbon-fiber-reinforced polymer (CFRP) composite by powder-mixed microelectrical discharge drilling (PM[Formula: see text]EDD) method. Initially, pilot experiments were carried out to understand the effect of powder concentration on the machining time and material removal mechanism. Then the full-factorial experimental analysis was performed to investigate the micromachining performance of CFRP composite. The input factors considered for the full-factorial analysis were powder concentration (2, 4, and 6 g/L), capacitance (100, 1000, and 10,000 [Formula: see text]F), and tool rotation (300, 400, and 500 rpm). The regression analysis was carried out to assess the significance of the chosen input factors. The surface morphology of the fabricated microhole was extensively studied to establish the material removal mechanism and failure mechanisms of the composite constituents.
Flexural Wave Band Gaps in Periodic Bi-Directionally Orthogonal Stiffened Plates with Holes
Authors: Guo Wenjie , Nie Rui , Zhu Xiang , Mao Yida , Song Lizhong , Zhang Hong
Published: June 2022

Plate frame structure with holes is a novel candidate to reduce the weight and strengthen the architectural structures to fulfill the requirements of practical engineering applications. The designed periodic hole structure can generate a band gap from the perspective of the frequency spectrum. In this study, a new band gap calculation method was proposed under the basic energy method framework for the analysis of hole periodic structures. Gaussian wavelet function with a good localization characteristic was adopted to overcome the inaccurately assumed displacement filed function due to the introduced hole issue. The entire plate structure was divided into cell elements and an artificial spring model was introduced to satisfy the cell coupling conditions. The coupling constraints were converted into elastic potential energy to be added to the energy functional for the entire system. The dispersion curves obtained by this method are in excellent agreement with the results of the finite element method. It was observed that periodic hole plates lead to the generation of complete and directional flexural wave band gaps. By introducing bi-directionally orthogonal stiffeners, the band gap of periodic hole plates could be significantly lengthened and complete band gaps could be successfully generated. Furthermore, geometrical parametric studies on flexural wave band gaps were analyzed. A wide and complete band gap could be realized by increasing the width of the stiffener to a certain extent, but not fully proportional.
An Analytical Solution for Lateral-Torsional Buckling Resistance of Perforated Cold-Formed Steel Channel Beams with Circular Holes in Web
Authors: Yu Nan-Ting , Huang Xu-Hao , Xu Xing-Hao , Chen Ze-Min , Yuan Wei-Bin
Published: June 2022

Perforated cold-formed steel (PCFS) beams are regarded as an economical option for steel construction industry owing to its various advantages. They can provide access for the facilities like electric wires and pipelines to penetrate the web so that extra building space could be saved. However, the web openings might change the web stress distribution as well as reduce the cross-sectional properties, which make the PCFS beams more susceptible to buckling and may have effect on their load-bearing capacity. In this paper, a simplified spring model was proposed based on the use of energy method, to predict the critical moment of lateral-torsional buckling of PCFS channel beams with circular holes in web subject to pure bending. The effect of hole sizes and lateral restraint at the tension flange was examined by using the derived analytical solution. In addition, eigenvalue buckling analysis was conducted by using finite-element package ANSYS to validate the accuracy of the proposed analytical model. The results demonstrated that the proposed analytical solution was reliable and the critical moment predicted can be used for the extension of DSM formulae for the design of PCFS beams when considering the failure caused by lateral-torsional buckling of the beams.
Double Sub-Resonance Mechanism in Torsional–Flexural Vibrations of a Double-Track Short Bridge Under a Moving Train
Authors: Wang Y. J. , Yau J. D. , Shi J. , Urushadze S.
Published: June 2022

Train-induced resonance of a railway bridge occurs when the train speed coincides with the primary resonance speed ([Formula: see text]) of the bridge, from which we can determine the sub-resonance speed as ([Formula: see text]). The primary resonance speed of a short bridge is generally much higher than the operation speeds of current high-speed trains but the corresponding sub-resonance speeds probably lie in the operation speed range. Such a resonance scenario can be observed from the vibration of a double-track bridge subjected to an eccentrically moving train, in which the deck vibration is combined by the vertical-flexural and torsional vibration modes of the bridge. Once the two modal sub-resonance speeds coincide with each other, a double sub-resonance will be developed on the bridge. In this study, an iteration-based train–bridge interaction finite element procedure will be presented to demonstrate the double resonance phenomenon. As expected, the double resonance may bring about a dramatic amplification on deck vibration. Such an excessive vibration is harmful to ballast stability and track maintenance of railway bridges.
Deviation properties for linear self-attracting diffusion process and applications
Authors: Jiang Hui , Pan Yajuan
Published: June 2022

In this paper, we study explicitly the deviation properties, including the deviation inequalities and Cramér-type moderate deviations, for some quadratic functionals of linear self-attracting diffusion process. As applications, Cramér-type moderate deviations for the log-likelihood ratio process and drift parameter estimator are obtained. The main methods consist of the deviation inequalities and Cramér-type moderate deviations for multiple Wiener–Itô integrals, as well as the asymptotic analysis techniques.
A note on the action of the Hecke group H(2) on subsets of the form ℚ∗(n)
Authors: Cimpoeaş Mircea
Published: June 2022

Here, we study the action of the groups [Formula: see text] generated by the linear fractional transformations [Formula: see text] and [Formula: see text] is a positive integer, on the subsets [Formula: see text] and [Formula: see text] is a square-free positive integer. We prove that this action has a finite number of orbits if and only if [Formula: see text] or [Formula: see text]. Moreover, we give an upper bound for the number of orbits for [Formula: see text].
On zero-dimensional linear sections of surfaces of maximal sectional regularity
Authors: Lee Wanseok , Park Euisung
Published: June 2022

Let [Formula: see text] be an [Formula: see text]-dimensional nondegenerate irreducible projective variety of degree [Formula: see text] and codimension [Formula: see text]. For [Formula: see text] and a [Formula: see text]-dimensional linear subspace [Formula: see text] satisfying dim([Formula: see text], [Formula: see text] is defined as the possibly maximal length of the scheme theoretic intersection [Formula: see text]. Then it is well known that [Formula: see text] if [Formula: see text] is a curve. Also it was generalized by Noma [Multisecant lines to projective varieties, Projective Varieties with Unexpected Properties (Walter de Gruyter, GmbH and KG, Berlin, 2005), pp. 349–359] that [Formula: see text] when [Formula: see text] is locally Cohen–Macaulary. On the other hand, the possible values of [Formula: see text] are unknown if [Formula: see text] is not locally Cohen–Macaulay. In this paper, we construct surfaces [Formula: see text] of maximal sectional regularity (which are not locally Cohen–Macaulay) and of degree [Formula: see text] for every [Formula: see text] such that [Formula: see text] for all [Formula: see text].
FINITE ELEMENT APPROACH TO DESIGN OF MODULAR HIP IMPLANTS MINIMIZING FRETTING WEAR
Authors: MECH DHRUBA JYOTI , CHAKRABORTY ARINDAM , CHOWDHURY AMIT ROY , DATTA PALLAB
Published: June 2022

Total hip replacement is a commonly used technique for the treatment of degenerative joint diseases. Several design innovations are pursued to meet their growing clinical need. Modular hip implants have emerged to become a mainstay in design as these offer a large array of femoral offsets, length adjustment and greater stability. A major disadvantage of modular implants is that each interface becomes a potential site for corrosion, fretting and fatigue between mating surfaces, which eventually leads to mechanical-assisted crevice corrosion. Though experimental studies have revealed the role of different implant design or manufacturing factors that affect MACC, finite element simulation models developed so far factor in only a few design modification in order to improve implant life. The objective of this study is to investigate the effect of bulk porosity, surface roughness and strength of the bone at the implantation site on implant performance in terms of contact pressure, micromotion and wear depth. It is observed that a three times increase in surface roughness increases wear depth by 1.2 times, increases porosity by 120%, causes wear to change two-fold, while weaker bone shows appreciably higher wear. The findings of this study provide a comprehensive tool to design hip implant with reduced probability of failure.
OPTIMIZATION ASSISTED HYBRID INTELLIGENT SYSTEM FOR HEART DISEASE PREDICTION
Authors: KUMAR PARVATHANENI RAJENDRA , RAVICHANDRAN SUBAN , NARAYANA S.
Published: June 2022

This research work proposes a new intelligent technique for predicting heart disease. The heart disease prediction model consists of three major processes like feature extraction, optimal feature selection, and classification. The input data are first fed into the feature extraction procedure, which extracts features based on central tendency, degree of dispersion, qualitative variation, Chi-squared feature, ReliefF, and symmetrical uncertainty (SU). However, the ’curse of dimensionality is the fundamental concern in this circumstance. Thus, it becomes essential to choose the optimal or best features to proceed with the disease prediction. To choose the right feature, this study uses a new hybrid algorithm called the Sea Lion Adopted Dragonfly Algorithm (SADA). Following that, the selected characteristics are fed into a hybrid classifier that combines the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). The suggested approach optimizes the count of filter size in CNN to make the system more accurate in disease prediction (SADA). Finally, the performance of the proposed work is analyzed and verified using existing models for specific performance metrics.
BIOMECHANICAL EVALUATION OF MANDIBULAR OVERDENTURES SUPPORTED BY MINI-IMPLANTS: A FINITE ELEMENT ANALYSIS
Authors: GANESH THIAGARAJAN , SCULLY JESSICA , WALKER MARY P. , PETRIE CYNTHIA S.
Published: June 2022

This paper presents the effects of using two or four mini-implants to support a mandibular overdenture for edentulous patients. The stresses on the mini-implants and the strains on the peri-implant and surrounding bone were evaluated and compared to known values of strength for the mini-implants and physiologic bone strain to potentially stimulate and maintain bone volume of the edentulous ridge. Two finite element models created were identical with the exception of the number of denture-supporting mini-implants. Both models contained modeled mucosa, cortical bone, cancellous bone, and two anterior mini-implants (approximating the position of the mandibular canine teeth). The second model also contained two posterior mini-implants (in the molar region). Clinical masticatory loading of three 70N loads bilaterally was used. The anterior mini-implants in both models showed stresses below the material’s fracture threshold. In contrast, posterior mini-implants experienced stresses above the implant’s yield limit, indicating that the posterior mini-implants could be at risk for failure. Strains in the peri-implant and surrounding bone around the posterior mini-implants were above the physiologic limit, indicating that this region might be at risk for bone resorption. The addition of the posterior mini-implants did not appear beneficial to maintain edentulous bone. Based on the limitations of this study, anterior mini-implants can be safely used for a mandibular overdenture whereas posterior mini-implants may not be a wise treatment option for edentulous patients due to risks of implant fracture and pathologic bone resorption. Dentists must weigh risks and benefits of using posterior mini-implants placed in the molar region.
The Effect of Lead Halide on Characterization of Methyl Ammonium Iodine MAI2 Perovskite
Authors: Abood Mohammad Malik , Dakhil Osama Abdul Azeez , Baron Aref S. , Mohammed Kahtan A.
Published: June 2022

A hybrid method is proposed to synthesize methylammonium lead iodine, bromine and chlorine MAPbI3, MAPbBr2I and MAPbCl2I Perovskite by mixing two-step methods. The descriptions of perovskite samples included structural, morphological and optical properties. The intensity and orientation in X-ray diffraction (XRD) patterns appear clearly and squeaky. FESEM images of the perovskite films appeared as a rough surface due to iodine and more crystalline size using bromine and chlorine. Optical properties shifted towards low wavelengths (blue shift) and the value changed from 830 nm to 420 nm when the lead halide is changed from iodine to bromine and then to chlorine.
New 2D Material: Two-Dimensional Black Phosphorus (2D BP)
Authors: Vittoria Urso
Published: June 2022

In this article, I want to show some properties of black phosphorus (BP) and some of its applications. In particular, in Sec. 1, I give an introduction to the topic and some historical notes, in Sec. 2, the two-dimensional crystal structure of the BP is explained, in Sec. 3, the optical and electronic properties of the BP are shown, in Sec. 4, the biomedical applications of the BP are listed and finally in Sec. 5, there are conclusions.
Institutionalization as an Innovation Process: Insight Into ICT Use in Agricultural Advisory System
Authors: Gouroubera Mori W. , Idrissou Latifou , Moumouni Ismail M. , Okry Florent , Baco Mohamed Nasser
Published: June 2022

This article critically examines the relationship between institutionalization and the innovation process, traditionally regarded distinctively, leading to theoretical and methodological biases. It is also difficult to find how empirical research implements institutional theories. Drawing on the innovation and institutional theories, and a systematic literature review on ICT usage in the agricultural advisory system (AAS), our study reveals that ICT is becoming established within AAS. Two dominant isomorphisms guided ICT usage in AAS: coercive and normative isomorphism. We argue that the institutionalization process is a continuum of the innovation process. From this perspective, technology is conceptualized as potentially useful knowledge that undergoes a series of changes during its institutionalization. We propose an analytical framework that could induce further theoretical refinement and progress.
Industrial Sustainability in Architectural Paints: A Bibliometric Research
Authors: Alencar Junior André Armando Mendonça , Delesposte Jamile Euletério , Mainier Fernando Benedicto , Mattos Lisiane Veiga
Published: June 2022

Sustainability is one of the main challenges of civil construction. Most architectural paints release volatile organic compounds (VOCs) that harm the environment and human health. This paper presents the main discussions and theoretical approaches through bibliometrics on the theme of industrial sustainability in architectural paints. The study presents an analysis of citations, the main topics associated with this specific line of research and a conceptual framework, as well as the identification of the most explored approaches in the literature and the main gaps. The Bibliometrix library from the R package was used to organize the published data used and process the generated analyses. The analysis of 773 international scientific documents from January 1985 to November 2019 allowed an overview of the research field related to industrial sustainability in architectural paints. The main findings are summarized as follows: most documents are focused on the study of new paint compositions, observing sustainability criteria in their development; there was little presence of documents dealing with architectural paintings; most publications focus on the development and testing of new paint formulations, which are obtained from the substitution of toxic substances, which indicates that there is also a need to diversify research approaches in this field; few publications were found addressing the subject, which configures a gap in the literature related to this field of research.
Suppression of transmembrane sodium currents on the freshly isolated hippocampal neuron cell with continuous infrared light
Authors: Kong Fanyi , Li Xinyu , Jiao Ruonan , Liu Kun , Han Xue , Sun Changkai , Sun Changsen
Published: June 2022

Physiotherapeutic effects of infrared lasers have been proved in clinic. These infrared-based regulations of the bioelectrical activities can roughly be classified into enhancement and suppression of action potential (AP), which are described by sodium (Na) and potassium (K) transmembrane current equations, named as Hodgkin and Huxley (HH)-model. The enhancement effect is able to evoke or strengthen the AP when infrared light is applied. Its corresponding mechanism is commonly ascribed to the changes of the cell membrane capacitance, which is transiently increased in response to the infrared radiation. The distinctive feature of the suppression effect is to inhibit or reduce the AP by the designed protocols of infrared radiation. However, its mechanism presents more complexity than that in enhancement cases. HH-model describes how the Na current determines the initial phase of AP. So, the enhancement and suppression of AP can be also ascribed to the regulations of the corresponding Na currents. Here, a continuous infrared light at the wavelength of 980[Formula: see text]nm (CIS-980) was employed to stimulate a freshly isolated hippocampal neuron in vitro and a suppression effect on the Na currents of the neuron cell was observed. Both Na and K currents, which are named as whole cell currents, were simultaneously recorded with the cell membrane capacitance current by using a patch clamp combined with infrared irradiation. The results demonstrated that the CIS-980 was able to reversibly increase the capacitance currents, completely suppressed Na currents, but little changed K currents, which forms the steady outward whole cell currents and plays a major role on the AP repolarization. A confirmation experiment was designed and carried out by synchronizing tens of milliseconds of infrared stimulation on the same kinds of hippocampal neuron cells. After the blocked K channel, a reduction of Na current amplitude was still recorded. This proved that infrared suppression of Na current was irrelevant to K channel. A membrane capacitance mediation process was preliminarily proposed to explain the Na channel suppression process.
The sparse array elements selection in sparse imaging of circular-array photoacoustic tomography
Authors: Qin Zezheng , Liu Yang , Chi Junke , Ma Yiming , Sun Mingjian
Published: June 2022

Photoacoustic imaging (PAI) has been developed, and photoacoustic computed tomography (PACT) is widely used for in vivo tissue and mouse imaging. Simulated annealing (SA) algorithm solves optimization problems, and compressed sensing (CS) recovers sparse signals from undersampled measurements. We aim to develop an advanced sparse imaging framework for PACT, which invloves the use of SA to find an optimal sparse array element distribution and CS to perform sparse imaging. PACT reconstructions were performed using a dummy and porcine liver phantoms. Compared to traditional sparse reconstruction algorithms, the proposed method recovers signals using few ultrasonic transducer elements, enabling high-speed, low-cost PACT for practical application.
Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning
Authors: Carvalho Silvestre , Gomes Elsa Ferreira
Published: June 2022

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.
Graphene as a Perfect Conductor
Authors: Vyas Vivek M. , Panigrahi Prasanta K.
Published: June 2022

Here, we show that the graphene sheet, which is designed to behave a band insulator, can be turned into a perfect conductor, when the difference of the current from the two valleys gets coupled to an Abelian gauge field. The graphene sheet in such a case is found to display infinite DC conductivity, Debye screening and magnetic flux expulsion.
Deep Reinforcement Learning-Based Diving/Pull-out Control for Bioinspired Morphing UAVs
Authors: Ye Bobo , Li Jie , Li Juan , Liu Chang , Li Jichu , Yang Yachao
Published: June 2022

Next-generation unmanned aerial vehicles (UAVs) should possess the capability to autonomously perform multiple tasks with agility and efficiency in complex obstructive environments and over open terrains. Morphing UAVs can autonomously transform in response to changes in flight environments and tasks, always maintaining an optimal aerodynamic profile. The falcon-inspired morphing UAV folds/twists its wings/tail simultaneously to accomplish diving/pull-out flight during the predation process. In the diving/pull-out flight, falcon-inspired morphing UAVs are able to balance maneuverability and stability, which is hard to realize by current control methods. This paper proposes a deep reinforcement learning (DRL)-based diving/pull-out cooperative control strategy. Considering the continuity of the state space and the action space of morphing UAVs, the deep deterministic policy gradient (DDPG) algorithm based on the actor-critic (AC) network is adopted and refined. With the aim of ensuring the smoothness of the flight action, the proposed DRL-based strategy is tasked with controlling multiple data frames of airspeed, altitude and pitch angle to desired reference values. Numerical experiments have been conducted on fixed-speed ascent/descent flight and diving/pull-out maneuvers flight missions. The results demonstrate the superiority of the proposed DRL-based control strategy compared with a classical proportional-integral-derivative (PID) control strategy. Furthermore, the proposed DRL controller is shown to generalize well to the random white noises which are added to gyroscope measurements and wind disturbance in flight.
Osteoporosis in Rheumatic Diseases
Authors: Mok Chi Chiu
Published: June 2022

The risk of osteoporosis and fragility fracture is increased in patients with autoimmune rheumatic diseases. Although the use of glucocorticoids is the major contributing factor, inflammation mediated by cytokines and growth factors and other medications, including the biologic and targeted disease-modifying antirheumatic drugs, also play important roles in bone remodeling. Pro-inflammatory cytokines such as IL-1, IL-6, IL-17, and TNF[Formula: see text] increase RANK expression and promote osteoclast activity while inhibiting osteoblast-mediated bone formation through the Dickkopf-1 pathway. Certain autoantibodies stimulate differentiation of the osteoclasts, resulting in localized bone resorption. This article covers the prevalence and risk factors for osteoporosis in patients with common rheumatic diseases and the role of inflammatory cytokines and other clinical factors. Controlling disease-related inflammation and optimizing the diagnostic and therapeutic instrumentation is needed to reduce fragility fractures in patients with rheumatic diseases.
Behavioural Aspects of Religiosity in Finance: A Brief Survey on Conventional Versus Islamic Finance
Authors: Ozturk Huseyin
Published: June 2022

Religious beliefs are accepted to be one of the main motivations for financial actions. The economic theory fails to provide an adequate foundation to study the link between religiosity and economic behaviour. A number of empirical studies discusses the role of religion on the financial behaviour of economic agents yet more research needs to be conducted. This study provides a brief review on how religiosity might affect financial behaviour by the inclusion of recent studies. In addition to the empirical research that is based on European countries, we discuss the differences in different geographies and finance schemes. We also debate the potential factors that affect differing behavioural aspects with respect to religiosity.
Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications
Authors: Singh Shashank Sheshar , Srivastava Vishal , Kumar Ajay , Tiwari Shailendra , Singh Dilbag , Lee Heung-No
Published: June 2022

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more.
Social network analysis
(SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.