bluuebunny/crossref_metadata_2025 huggingface dataset:
Is it appropriate to use external loads during side-lying hip abduction exercise for weakened gluteus medius?
Authors: Nam Soo-Jin , Oh Duck-Won
Published: June 2021

BACKGROUND: Side-lying hip abduction exercise could be beneficial to improve the neuromuscular control of the hip abductor; however, there has been limited information available to determine the exercise load during the exercise. OBJECTIVE: This study aimed to demonstrate the effects of using external loads on the hip abductor muscles during side-lying hip abduction exercises in females with gluteus medius (GM) weakness. METHODS: This study enrolled 24 females with weakness in the GM. Electromyographic (EMG) data of the quadratus lumborum (QL) and GM muscles were recorded during the exercise under three load conditions: no-load, external load-1 (3% of body weight), and external load-2 (5% of body weight). RESULTS: During the exercise, the EMG activities of the QL were significantly different under all three conditions (p< 0.05), with greater activity observed in the external load-2 condition (92.05 ± 65.93% maximal voluntary isometric contraction [MVIC]) as compared to the others, and in the external load-1 condition (82.47 ± 57.36% MVIC) as compared to the no-load condition (48.94 ± 45.09% MVIC). Furthermore, the GM/QL ratios showed significant differences between no-load (1.78 ± 1.47) and external load-1 conditions (0.93 ± 0.60), and between no-load and external load-2 (0.85 ± 0.45) conditions (p< 0.05). CONCLUSION: These findings suggest that greater load could be a factor to increase the QL activity during the exercise in females with weakened GM.
Quantitative analysis of KLF4 and SOX2 expression in oral carcinomas reveals independent association with oral tongue subsite location and histological grade
Authors: Paparella María Luisa , Ferri Dario Martin , Villegas Krissya María , Raimondi Ana Rosa
Published: June 2021

BACKGROUND: Stemness factors associated with tumorigenesis in different types of cancers have not been specifically studied in oral tongue SCC (OTSSC). Here, we aimed to quantify expression levels and distribution of KLF4 and SOX2, two relevant stemness factors, in oral SCC including OTSCC samples from different subsites. METHODS AND RESULTS: We determined KLF4 and SOX2 expression levels by immunostaining 35 biopsies of OSCC. Stained wholeslide images were digitized and subjected to automatic cell detection and unbiased quantification using Qupath software. We found statistically significant reduction in KLF4 positive cells density (p= 0.024), and fraction (p= 0.022) in OTSCC from tongue borders compared with other tongue subsites. Instead, quantitative SOX2 analysis did not show differences in expression levels between OTSCC from the borders versus OTSCC developed in others subsites. Notably SOX2 expression was revealed increased in moderately and poorly differentiated OSCC compared with well differentiated ones (positive cells density p= 0.025, fraction p= 0.006). No significant correlation between KLF4 and SOX2 expression was observed, neither in OSCC nor in OTSCC. CONCLUSIONS: KLF4 and SOX2 exhibit opposite expression profiles regarding subsite localization and differentiation level in OSCC. Our study prompts future OTSCC prospective studies looking for clinical prognosis to incorporate detailed subsite information in the analysis.
Relationship between explosive strength characteristics with change of direction speed in junior and cadet karate athletes
Authors: Ojeda-Aravena Alex , Herrera-Valenzuela Tomás , Valdés-Badilla Pablo , Azócar-Gallardo Jairo , Campos-Uribe Victor , García-García José Manuel
Published: June 2021

BACKGROUND:

Explosive strength and change of direction speed (CODS) are relevant physical abilities in karate.

OBJECTIVE:

To examine the relationship between the characteristics of explosive strength and the 5-m linear sprint (5M) with CODS performance and ii) to examine the influential characteristics of explosive strength on CODS performance.

METHODS:

Eighteen cadet and junior karate athletes, eight females and ten males were evaluated. The physical abilities assessments included: squat jump (SJ), countermovement jump (CMJ), 5M and CODS. Also, pre-stretch percentage increase (PSA), eccentric utilization index (EUR) reactive strength index (RSI) were calculated.

RESULTS:

Superior performance ([Formula: see text] 0.05) was documented in SJ, CMJ and CODS in male vs. female. Also, significant correlations between CODS with SJ and CMJ ([Formula: see text]0.70 to [Formula: see text]0.80; [Formula: see text]0.51 to [Formula: see text]0.73; [Formula: see text] 0.05, respectively) and correlations ([Formula: see text]0.14 to [Formula: see text]0.22; [Formula: see text] 0.01 to 0.04; [Formula: see text] 0.05) between CODS with RSI, EUR and PSA. Multiple regression model documented that only SJ significantly influenced CODS performance in male ([Formula: see text] 60%; [Formula: see text] 0.009) and female ([Formula: see text] 71%; [Formula: see text] 0.001).

CONCLUSION:

CODS correlate with SJ and CMJ. In particular, SJ influence CODS independently of gender.
Strength normalized to muscle volume rather than body weight is more accurate for assessing knee strength following anterior cruciate ligament reconstruction
Authors: Ohji Shunsuke , Aizawa Junya , Hirohata Kenji , Ohmi Takehiro , Mitomo Sho , Koga Hideyuki , Yagishita Kazuyoshi
Published: June 2021

BACKGROUND:

Knee strength weakness is a major problem frequently observed in patients during postoperative rehabilitation following anterior cruciate ligament reconstruction (ACLR).

OBJECTIVE:

To investigate whether knee strength normalized to muscle volume could better detect side-to-side differences than that normalized to body weight following ACLR.

METHOD:

This study included 17 patients who had undergone primary ACLR (11.6 [Formula: see text] 2.3 months). Body weight and total muscle volume were measured using a bioelectrical impedance analysis composition scale. Isokinetic knee extension and flexion moment were measured at 60[Formula: see text]/s and 180[Formula: see text]/s, respectively. Bivariate correlation analysis was used to examine correlations between body composition and knee strength. Differences in knee strength between the operated and unoperated legs were analyzed using a paired [Formula: see text]-test, which calculated the effect size.

RESULTS:

There was a significant correlation between knee strength and body weight ([Formula: see text] 0.53–0.67); however, a stronger correlation was observed between knee strength and total muscle volume ([Formula: see text] 0.80–0.87). The effect size was larger for knee strength expressed as % total muscle volume than for knee strength expressed as % body weight.

CONCLUSION:

Strength expressed as % total muscle volume may be more accurate than that expressed as % body weight for detecting side-to-side differences in knee strength following ACLR.
Interaction Between APOE Genotype and Diabetes in Longevity
Authors: Shinohara Mitsuru , Suzuki Kaoru , Bu Guojun , Sato Naoyuki
Published: June 2021

Background: While both apolipoprotein E (APOE) genotype and diabetes affect longevity as well as Alzheimer’s disease, their relationship remains to be elucidated. Objective: The current study investigated the potential interaction between diabetes and APOE for lifespan and their relationship with cognitive status. Methods: We reviewed the National Alzheimer’s Coordinating Center (NACC) dataset, which documents longitudinally clinical records of 24,967 individuals with APOE genotype and diabetic status. Results: Diabetes was associated with shorter lifespan in APOE3 carriers (n = 12,415, HR = 1.29, 95%CI = 1.17–1.42, p < 0.001) and APOE2 carriers (n = 2,390, HR = 1.37, 95%CI = 1.10–1.69, p = 0.016), while such associations were weaker and not significant in APOE4 carriers (n = 9,490, HR = 1.11, 95%CI = 0.99–1.24, p = 0.162). As there is a significant interactive effect of cognitive status and diabetes on lifespan (p < 0.001), we stratified subjects by cognitive status and observed persistent APOE-dependent harmful effects of diabetes in nondemented individuals but not demented individuals. Notably, questionnaire-based activity status, with which we previously observed an association between APOE genotype and longevity, was also significantly affected by diabetes only in non-APOE4 carriers. Conclusion: The effects of diabetes on longevity vary among APOE genotype. These effects are observed in nondemented individuals and are potentially associated with activity status during their lifespan.
Improved calendar disc scheduler for LTE advanced networks with HARQ
Authors: Shakir Shaffath Hussain , Rajesh A.
Published: June 2021

Long Term Evolution Advanced (LTE-A) is a broadband wireless technology that supports variety of services with different data rate. In order to achieve this the evolved Node B (eNB) uses different features provided in the 3GPP standards. Features like Carrier Aggregation (CA), Multiple input and Multiple output (MIMO) and Hybrid Automatic Repeat Request (HARQ) help to increase the throughput and spectral efficiency. In this paper, a novel two-level calendar disc algorithm with HARQ is introduced at the eNB for effectively scheduling real time and non-real time traffic with different service types. The algorithm also uses a burst profile management module that analyzes the current user profile and notifies the scheduler about the need to change in profile based on power boosting. The Calendar Disc Scheduler (CDS) is improved by adding HARQ retransmission index as a parameter in calculating the metric weight. The scheduler was tested for both adaptive and non-adaptive methods of HARQ in both synchronous and asynchronous modes. The proposed improved CDS scheduler was simulated with LTESim simulator and compared with calendar disc algorithm without HARQ improvements. Results show that the proposed scheduling method provides increased performance in terms of goodput, delay and spectral efficiency.
Intelligent energy-aware multiple quality of service restraints based secured optimal routing protocol with dynamic mobility estimation for wireless sensor networks
Authors: Selvakumar K. , SaiRamesh L. , Ayyasamy A. , Archana M.
Published: June 2021

This research work confronts a sender-based responsive and novel protocol named “Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)” protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of ‘n’ number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node’s trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols.
An adaptive approach for estimation of transition probability matrix in the interacting multiple model filter
Authors: Cosme Luciana Balieiro , D’Angelo Marcos Flávio Silveira Vasconcelos , Caminhas Walmir Matos , Camargos Murilo Osorio , Palhares Reinaldo Martínez
Published: June 2021

The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approach based on the IMM filter and retention models to determine the TPM adaptively and automatically with relatively low computational cost and no need for complex operations or storing the measurement history. The proposed method is compared to the traditional IMM filter, IMM with Bayesian Network (BNs) and a state-of-the-art Adaptive TPM-based parallel IMM (ATPM-PIMM) algorithm. The experiments were carried out in an artificial numerical example as well as in two real-world health monitoring applications: the PRONOSTIA platform and the Li-ion batteries data set provided by NASA. The Retention Interacting Multiple Model (R-IMM) results indicate that a better prediction performance can be obtained when the TPM is not properly adjusted or not precisely known.
Integrating a prospect theory-based consensus-reaching process into large-scale quality function deployment and its application in the evaluation of contingency plan
Authors: Zhu Yan , Fan Chuanhao , Xiao Jing , Liu Shenghua
Published: June 2021

Quality function deployment (QFD) is a quality management tool that aims to improve customer satisfaction by transforming customer requirements into technical characteristics. It is a crucial procedure to obtain the prioritization of technical characteristics for the products or services in QFD. Traditional QFDs are often implemented by a small number of QFD members. However, with the increasing complexity of product and service design, QFD requires the participation of more QFD members from dispersed departments or institutions. Additionally, the evaluation information given by QFD members may widely differ due to their different knowledge and background. Furthermore, the psychological behaviours of QFD members also greatly influence the final prioritization of technical characteristics. Hence, this paper proposes a novel QFD framework to prioritize technical characteristics using a consensus-reaching process and prospect theory when large numbers of QFD members are involved. In the large-scale QFD framework, prospect theory is generally utilized to depict the psychological behaviours of QFD members. Then, QFD members are divided into several clusters. Eventually, a consensus-reaching process is established to assist QFD members in reaching a consensus. To verify the practicability of the presented framework, this paper applies it to the evaluation of contingency plan to determine the critical measures.
Matrix-based reduction approach for one-sided fuzzy three-way concept lattices
Authors: Zhang Chengling , Li Jinjin , Lin Yidong
Published: June 2021

Three-way concept analysis is a mathematical model of the combination of formal concept analysis and three-way decision, and knowledge discovery plays a significant impact on formal fuzzy contexts since such datasets are frequently encountered in real life. In this paper, a novel type of one-sided fuzzy three-way concept lattices is presented in a given formal fuzzy context with its complement, in which a ternary classification is available. In such case, we comprehensively explore the connections between the proposed models and classical fuzzy concept lattices among elements, sets, and orders. Furthermore, approaches to granular matrix-based reductions are investigated, by which granular consistent sets, and granular reducts via discernibility Boolean matrices are tectonically put forward. At last, the demonstrated results are performed by several experiments which enrich the research of three-way concept analysis.
PCA based SVD fusion for MRI and CT medical images
Authors: Faragallah Osama S. , Muhammed Abdullah N. , Taha Taha S. , Geweid Gamal G.N.
Published: June 2021

This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach.
Minmax fuzzy deterministic policy gradient for zero-sum differential game: Take pursuit-evasion problem as example
Authors: Liao Wei , Wei Xiaohui , Lai Jizhou
Published: June 2021

A novel actor-critic algorithm is introduced and applied to zero-sum differential game. The proposed novel structure consists of two actors and a critic. Different actors represent the control policies of different players, and the critic is used to approximate the state-action utility function. Instead of neural network, the fuzzy inference system is applied as approximators for the actors and critic so that the specific practical meaning can be represented by the linguistic fuzzy rules. Since the goals of the players in the game are completely opposite, the actors for different players are simultaneously updated in opposite directions during the training. One actor is updated updated toward the direction that can minimize the Q value while the other updated toward the direction that can maximize the Q value. A pursuit-evasion problem with two pursuers and one evader is taken as an example to illustrate the validity of our method. In this problem, the two pursuers the same actor and the symmetry in the problem is used to improve the replay buffer. At the end of this paper, some confrontations between the policies with different training episodes are conducted.
Gauss-guided patch-based deep convolutional neural networks for no-reference image quality assessment
Authors: Zhou Ze-Nan , Zhou Zhiheng , Huang Junchu
Published: June 2021

Patch-based deep convolutional neural network (DCNN) has been proved to have advanced performance in no-reference image quality assessment (NR-IQA). However, these methods generally take global quality score as the quality score of each patch mainly since local quality score is not provided. Unfortunately, the perceived quality of image patch is difficult to maintain a high degree of consistency. Thus, the use of the same global quality score in different patches of the same image may hinder training of DCNNs. In this paper, we propose a universal and nearly cost-free model called Gaussian Random Jitter (GRJ). According to the uncertainty of the perceived quality, GRJ divided the training images into high-confidence distorted images and low-confidence distorted images, and reasonably assigned different local quality scores to each patch through specific gaussian functions with the global quality score as the mean value and the undetermined hyperparameter as the standard deviation. We took one of the most advanced patch-based DCNNs models as backbone and tested the improved performance over three widely used image quality databases. We show that our model can further improve the performance of patch-based models and even help them comparable with those of state-of-the-art NR-IQA algorithms.
A microbial treatment population dynamics optimization approach
Authors: Gao Jinding
Published: June 2021

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.
Ranking parametric form of fuzzy numbers by defuzzification based on centroids value and ambiguity
Authors: Botsa Devaki Rani , Peddi Phani Bushan Rao , Boddu Vikas
Published: June 2021

This paper proposes a new method to rank the parametric form of fuzzy numbers based on defuzzification. The defuzzification process use centroids, value, ambiguity and decision levels on fuzzy number developed from the parametric form of a generalized fuzzy number. The proposed method avoids reducing function to remove lower alpha levels and can overcome the shortcomings in some of the existing fuzzy ranking methods. The proposed method can effectively rank symmetric fuzzy numbers with the same core and different heights, fuzzy numbers with the same support and different cores, crisp numbers, crisp numbers having the same support and different heights, and fuzzy numbers having compensation of areas. A demonstration of the proposed method through examples and a comparative study with other methods in the literature shows that the proposed method gives effective results.
RSNet: Rail semantic segmentation network for extracting aerial railroad images
Authors: Rampriya R.S. , Sabarinathan  , Suganya R.
Published: June 2021

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.
Research on teaching quality evaluation of college english based on the CODAS method under interval-valued intuitionistic fuzzy information
Authors: Wang Qian
Published: June 2021

With the rapid development of China’s economic globalization in the new era, the demand for English majors is obviously on the rise, which puts forward new and higher requirements for application-oriented undergraduate colleges to train compound English majors. However, from the perspective of teaching quality evaluation of English majors in application-oriented undergraduate colleges, the results are not optimistic. Therefore, it is an important task for higher education research in China to explore the problems existing in the process of teaching quality evaluation for English majors in application-oriented undergraduate colleges and how to better train qualified and versatile talents for English majors to adapt to the economic and social development in the new era. The teaching quality evaluation of college English is frequently viewed as a multi-attribute group decision-making (MAGDM). Thus, a novel MAGDM method is used to tackle it. Depending on the conventional CODAS method and interval-valued intuitionistic fuzzy sets (IVIFSs), this paper designs a novel distance based IVIF-CODAS method to assess the teaching quality evaluation of college English. First of all, a related literature review is conducted. What’s more, some necessary theories related to IVIFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IVIFSs, the conventional CODAS method is extended to the IVIFSs to calculate assessment score of every alternative. Therefore, all alternatives can be ranked and the one with the best teaching quality. Eventually, an application about teaching quality evaluation of college English and some comparative methods have been employed to show the superiority of the developed method. The results illustrate that the defined framework is very useful for assessing the teaching quality of college English.
Application of digital image processing technology in online education under COVID-19 epidemic
Authors: Jia Baoxian , Zhang Wunong
Published: June 2021

In the current epidemic situation, appropriate learning resources are the prerequisite and basis for effective online education. Current online education, teacher teaching, and student learning lack a sense of immersion and participation. The reason is that the technology of online education is lagging. The thesis introduces augmented reality technology to enhance the immersion and interactivity of online education; introduces the technology based on Hadhoop to store image resources and improve the speed of reading image resources; the use of random forest algorithm for image recognition digital information Mining to recommend the most suitable online resources. Image processing technology can develop appropriate interactions and functions according to different scenarios to enhance the immersion and participation of online teaching, comprehensively improve the quality of teaching and learning, and provide technical support for the improvement of online learning.
BP neural network model for comprehensive evaluation of rural financial ecological environment
Authors: Ren Xiaolin
Published: June 2021

For this reason, the key to solve the problem of rural financial development is to solve the problem of rural financial development. At present, the state has given important instructions to improve the rural financial ecological environment, but the relevant research on the evaluation of rural financial ecological environment in China is still insufficient. In view of this situation, this paper puts forward a BP neural network model for the comprehensive evaluation of rural financial ecological environment. First of all, this paper studies the relevant basic theory of financial ecology and ecological environment comprehensive evaluation. Through the research, this paper believes that the construction of rural financial ecological environment involves many factors, and each factor has a mutual influence. It is difficult to determine the influence of a single factor on the final result. Therefore, in view of this complex situation, this paper establishes a set of multi factor evaluation index systems including economy, policy, law, culture, etc. And these complex factors are trained by BP neural network. The training results were normalized to quantify the specific impact of each index on the rural financial ecological environment. Finally, in order to verify the actual evaluation effect of this model, a number of comparative experiments including validity verification, stability analysis, comparison and verification of different model error rates are carried out. Through the analysis of experimental data, we can see that the BP neural network evaluation model in this paper has good comprehensive performance and significantly improves the calculation accuracy compared with the traditional analytic hierarchy process.
Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network
Authors: Si Lipeng , Liu Baolong , Fu Yanfang
Published: June 2021

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.
Simulation of optimal selection algorithm for wireless sensor cluster head node Bayesian statistical network
Authors: Xu Ying
Published: June 2021

This paper proposes a routing algorithm of cluster tree network and further combines the hierarchical structure of clustering with that of neural network, and designs a data fusion algorithm based on clustering routing protocol. Then, aiming at the difficulty in selecting the weights of neural network, a weight optimization neural network based on particle swarm optimization algorithm is proposed and applied to multi-sensor fusion. The simulation results show that the number of cluster heads of ACEC protocol is more concentrated on the expected value and has good stability. The algorithm selects cluster head nodes by non-uniform clustering and dynamic threshold, which ensures the balanced distribution of cluster head nodes in the network, reduces the network energy consumption and prolongs the service life of the network. The success rate of ancec protocol is similar to debug protocol, but with the increase of transmission time, LEACH protocol and debug protocol do not consider the link quality factor when forwarding data, so the communication link quality is uneven when selecting the next hop relay point in each round, so the data transmission success rate has a relatively obvious downward trend. The fusion result is clearly better than the poor two sensors, but inferior to the best sensor. This is due to the low SNR of sensors SNL and SN2, so their recognition effect is relatively poor, which also conforms to the rule of multi-sensor fusion. The results show that the method based on qdpso-bp network fusion is feasible.
Delivering behavior analytic services in an inclusive job setting for a young adult with autism spectrum disorder: A case example
Authors: Jenssen Kayla J. , Van Stratton Jessica E.
Published: June 2021

BACKGROUND: The rate of employment for adults diagnosed with Autism Spectrum Disorder (ASD) is low. This may be due in part to repetitive or disruptive behaviors associated with the ASD diagnosis and challenges to delivering services in the workplace. OBJECTIVE: This paper outlines a behavior analytic approach to providing job coaching supports in collaboration with employers in an inclusive setting. METHODS: A case example for a 26-year-old male (22 at the time of this project) diagnosed with ASD, employed as a data entry specialist, is highlighted. An A-B design was used to evaluate the impact of behavior analytic interventions on the rate of disruptive vocalizations. Behavior analysts and job coaches from a university-based team worked with employers to identify feasible and effective strategies. RESULTS: Rates of vocalizations decreased and maintained for 5 months following intervention fading. Social acceptability ratings from key office personnel reflected improved coworker relations and a high degree of acceptability for procedures used by the team. CONCLUSIONS: Employer collaboration was necessary for intervention design and decision making. Service providers should consider response effort for employers, the degree of normalization of strategies, how to limit disruption to productivity and the work setting, and methods for fading interventions or components of interventions.
Assessment of family needs of children with cerebral palsy in Northern-Nigeria: A cross-sectional study
Authors: Umar Abdullahi B. , Yakasai Abdulsalam M. , Danazumi Musa S. , Shehu Usman T. , Badaru Umaru M. , Kaka Bashir
Published: June 2021

PURPOSE: This study aimed to identify the needs and priorities of parents of children with cerebral palsy (CP) in order to improve care by increasing family participation in rehabilitation programmes. METHODS: This cross-sectional questionnaire-based study was conducted between January to March 2019. Convenience sampling was used to recruit 43 family members (18 years and above) of children with CP who came to the physiotherapy departments for rehabilitation services for their children. Fisher’s exact test was used to analyse the association between socio-demographic characteristics and each of the need items. RESULTS: Five different items were identified to be the family needs that were most frequently met: a) ‘need for active involvement in the child’s treatment and therapies’ (n= 40; 93.0%), b) ‘need for the provision of standard medical care (n= 39; 90.7%), c) ‘need for questions to be answered honestly’ (n= 38; 88.4%), d) ‘need for healthcare professionals to respect the child’s wishes’ (n= 36; 83.7%), e) ‘need for mothers to discuss their feelings (depression, stress etc.) with someone who has similar experience’ (n= 36; 83.7%). Conversely, three items were the most unmet family needs: a) ‘need to have professionals to consult whenever the child needs help’ (n= 39; 90.7%), b) ‘need to be informed about the child’s prognosis’ (n= 41; 95.3%), and c) ‘need to have financial support to provide the child with adequate care’ (n= 43; 100%). CONCLUSION: All participants overwhelmingly reported that their financial needs were their highest priority. The multiple needs of families of children with disabilities must be assessed and considered in rehabilitation services when treating children with CP.
Suppression criteria for inaccurate estimates
Authors: Marker David A.
Published: June 2021

Statistical offices regularly decide at what level of aggregation to publish results of their data collection. These decisions are typically driven by two separate concerns: first, they do not want to publish estimates with large amounts of uncertainty; second, they do not want to provide potentially-identifying information that could disclose an individual person or company. This article focuses on the first concern, when are data so uncertain that an agency should not publish the results? We focus on policies adopted by 16 statistical offices around the world.
Internet of things-based smart wearable system to monitor sports person health
Authors: Li Fen , Martínez Oscar Sanjuán , Aiswarya R.S.
Published: June 2021

BACKGROUND: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily. OBJECTIVE: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way. METHOD: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents’ vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson. RESULTS: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person’s health to improve sports person performance reliably.
Fog-Internet of things-assisted multi-sensor intelligent monitoring model to analyze the physical health condition
Authors: Li Fen , Shankar Achyut , Santhosh Kumar B.
Published: June 2021

BACKGROUND: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring. OBJECTIVE: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health condition. METHOD: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used. RESULTS: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.
Internet of Things-assisted intelligent monitoring model to analyze the physical health condition
Authors: Tang Xiaowei , Li Fang , Seetharam Tamizharasi G. , Vignesh C. Chandru
Published: June 2021

BACKGROUND: Physical health monitoring may take several forms, from individual quality changes to complex health checks carried out by health staff. Present health issues are detected with monitoring, and potential health problems are expected. Wearable sensors provide users with ease in everyday tracking, although many issues must be addressed in such sensor systems. The devices take a long time to obtain the requisite detection and diagnostic expertise and produce false alarms. OBJECTIVE: In this paper, the Internet of Things-assisted Health Condition Monitoring system (IoT-HCMS) has been proposed to track and analyze the patient physical health condition. METHOD: The proposed IoT-HCMS utilizes the intelligent monitoring model to follow the patient physical health day by day activities and instantaneously generate the health records. The system will indeed support patients in tracking psychological signs to minimize risks to their well-being. RESULTS: The experimental results show that the IoT-HCMS improves accuracy in patient health monitoring and has less response time.
Mapa da violência contra mulheres negras
Authors: Galvão Ianne
Published: June 2021

O presente trabalho tem como objetivo refletir sobre o contexto da violência contra mulheres negras no Brasil e apresentar os altos índices dessas violações. Considerando que as mulheres são oprimidas de modos diferentes, é necessário discutir gênero com recorte de classe e raça. Com isso, a pesquisa baseia-se nos pressupostos teóricos de Sueli Carneiro, Lélia Gonzalez, Djamila Ribeiro e Patrícia Hill Collins. É possível perceber uma diminuição das mortes de mulheres brancas; em contrapartida, as negras ainda têm os maiores índices de suas vidas violadas. Essa realidade é observada através da análise de dados do Fórum Brasileiro de Segurança Pública e do Instituto de Pesquisa Econômica Aplicada (2020). Diante disso, pode-se compreender que o diálogo sobre igualdade de gênero e racial é de extrema importância para incentivar o debate da proteção dos Direitos Humanos, e se destina a modificar essa realidade instaurada há séculos no Brasil.
Ações afirmativas na pós-graduação
Authors: Godoi Marciano Seabra de , Silva Gabriella Véo Lopes da
Published: June 2021

As ações afirmativas no ensino superior brasileiro começaram nos primeiros anos do século XXI, para reformar o crônico confinamento racial do mundo universitário. A lei que impõe a adoção de cotas socioeconômicas e étnico-raciais pelas instituições federais não exige sua aplicação na pós-graduação, mas a partir de 2016 há um processo acelerado de implantação de cotas étnico-raciais na pós-graduação pública, pois a pluralidade de origens sociais e vivências existenciais entre os pesquisadores favorece a construção de um conhecimento científico profundo e especialmente crítico/transformador. Na área do Direito, há uma disparidade muito grande entre a presença de ações afirmativas na pós-graduação pública e privada. É necessário avançar com as ações afirmativas na pós-graduação privada, num processo participativo, dialógico e que considere a necessidade de também dar mais visibilidade acadêmica e pedagógica para epistemologias e linhas de pesquisa voltadas à compreensão e transformação das condições sociais vivenciadas pelos segmentos visados pelas cotas.
Colostro como fonte imunológica e nutricional na criação de bezerras de aptidão leiteira
Authors:   Instituto Federal Farroupilha, Battisti Rutiéli , Gressler Leticia Trevisan ,   Instituto Federal Farroupilha, Teixeira Jullia Sehorek ,   Instituto Federal Farroupilha, Martins Eduarda ,   Instituto Federal Farroupilha, Piaia Juliana Garcia ,   Instituto Federal Farroupilha
Published: June 2021

Facing the absence of transplacental transfer of immunoglobulins to the bovine newborn, the primary objective of this review was to address the colostrum relevance as an immunological source and care needed to perform an adequate colostrum intake. Aiming at the maximum absorption of maternal immunoglobulins, we highlight the maintenance of a colostrum bank in dairy farms, focusing in providing high quality colostrum, in adequate quantity and at the right time after birth. Also, the use of surplus colostrum and transition milk in the form of silage is discussed, being it a restricted nutritional alternative for dairy calves, compared to others, such as whole milk and milk replacers.
CHARIOT RACING AS THE ENTERTAINMENT FORM OF ANCIENT ROME EVENTS IN THE CULTURAL STUDIES DIMENSION
Authors: Goncharova Olena
Published: June 2021

The purpose of the article is the introduction into the cultural discourse of analytically processed and summarized information on the genesis and evolution of chariot racing as the form of entertainment events in ancient Rome, their functional features, specific features of mass events of Antiquity in the context of entertainment culture of Rome. The methodological basis consisted of the methods of critical analysis of cultural, historical, and literary sources, specific and historical analysis, and interdisciplinary synthesis, induction, and deduction. The problematic and chronological, system and structural, comparative, descriptive methods and methods of social and phenomenological analysis were applied from specific and scientific methods. Scientific novelty. The article analyzes the genesis and evolution of chariot racing as a form of events in the context of entertainment culture in ancient Rome. Based on the ancient literary reflection, through the prism of works of culturologists, philosophers, historians, poets, writers of the ancient Rome Gaius Suetonius Tranquillus, annals of Publius Cornelius Tacitus, Cassius Dio, ethic works of Lucius Annaeus Seneca, letters of Gaius Pliny the Younger, poetry pages of Publius Ovidius Naso, epigrams of Marcus Valerius Martialis and others. The author revealed the essence and content of chariot racing as an entertainment form of events in ancient Rome, statistics, and specific features of entertainment events and instruments of ruling the Roman emperors. The author describes the moral aspects of chariot racing in the context of the entertainment culture of antiquity. Conclusions. The place of entertainment culture of Antiquity in the system of cultural knowledge and cultural tradition of their social universe is revealed. The transformations of chariot racing as a social and humanitarian experience of ancient society, the political instrument of government in Rome are explored. The role of entertainment of Antiquity for modern cultural practices is established.