bluuebunny/crossref_metadata_2025 huggingface dataset:
Interferensi Sintaksis Bahasa Indonesia pada Keterampilan Berbicara Bahasa Inggris Mahasiswa Non-Jurusan Bahasa Inggris
Authors: Hamzah Amir , Hidayat Muhamad Taufik , Hasim Wahid
Published: July 2021

This study aims to analyze the Indonesian language syntactic interference on English Speaking Skills of English Language Students. The research subjects were students of semester 3 at a non-English language college in Garut. This study uses a qualitative descriptive approach with equivalent and distribution methods. Meanwhile, the corpus was obtained by using the listening technique (SBLC), recording and taking notes from students' conversation assignments about their past activities in the form of videos. The results showed that the syntactic interference in this study includes the inaccuracy of word forms that are influenced by the past, inaccurate subject-verb agreements, inaccuracies, absence of articles, sentence patterns that do not match S + V + O, inaccurate use of the words 'no' and 'not'.Keywords: interference, syntax, speaking skills
Efektivitas Pelatihan Dalam Meningkatkan Kompetensi Sumber Daya Manusia
Authors: Hayati Neneng , Yulianto Erwin
Published: July 2021

Sumber daya manusia di perusahaan berperan sebagai penentu keberhasilan perusahaan untuk mencapai hasil yang diharapkan. Sumber daya manusia pada perusahaan perlu dikelola secara profesional agar terwujud keselarasan antara kompetensi pegawaidengan dengan tuntutan dan tujuan perusahaan. Salah satu proses pengembangan dan peningkatan kompetensi pegawai dapat dilakukan dengan melaksanakan kegiatan pendidikan dan pelatihan. Pelatihan bagi para pegawai dapat dilakukan dengan berbagai cara, antara lain dengan kegiatan on job training dan off the job training. Dengan adanya pelatihan diharapkan produktivitas pegawai akan lebih meningkat serta dapat bekerja secara lebih efektif dan efisien terutama untuk tantangan dalam mencapai tujuan perusahaan ke depan. Evaluasi pelatihan merupakan salah satu cara mengukur efektifitas pelatihan sehingga memberikan feedback bagi perusahaan dalam rangka meningkatkan kompetensi sumber daya manusia.
Nonexistence result for the generalized Tricomi equation with the scale-invariant damping, mass term and time derivative nonlinearity
Authors: Ben Hassen Moahmed Fahmi , Hamouda Makram , Hamza Mohamed Ali , Teka Hanen Khaled
Published: July 2021

In this article, we consider the damped wave equation in the scale-invariant case with time-dependent speed of propagation, mass term and time derivative nonlinearity. More precisely, we study the blow-up of the solutions to the following equation: [Formula: see text] that we associate with small initial data. Assuming some assumptions on the mass and damping coefficients, ν and [Formula: see text], respectively, we prove that blow-up region and the lifespan bound of the solution of [Formula: see text] remain the same as the ones obtained for the case without mass, i.e. [Formula: see text] with [Formula: see text] which constitutes itself a shift of the dimension N by [Formula: see text] compared to the problem without damping and mass. Finally, we think that the new bound for p is a serious candidate to the critical exponent which characterizes the threshold between the blow-up and the global existence regions.
Kinesio taping as an alternative treatment for manual laborers with carpal tunnel syndrome: A double-blind randomized clinical trial
Authors: Aminian-Far Atefeh , Pahlevan Daryoush , Kohnegi Fatemeh Maleki
Published: July 2021

BACKGROUND: Carpal tunnel syndrome (CTS) is one of the most common forms of peripheral neuropathies due to median nerve compression. Occupational factors, such as repetitive hand motions are believed to be associated with this condition. OBJECTIVE: This clinical trial assessed the effect of Kinesio taping (KT) of hand flexor muscles on the management of mild to moderate CTS in Iranian manual laborers. METHODS: Thirty manual laborers with mild to moderate CTS participated in this clinical trial. They were assigned to an intervention or a control group. The treatment consisted of a 2-week KT followed by a 4-week follow-up. The control group received sham KT without tension applied. Boston Carpal Tunnel Questionnaire scores, pinch and grip force tests, and electrophysiological examination of the median nerve were performed for each participant at baseline, 24 to 48 hours, and two to four weeks of follow-up. The statistical analysis of variance was performed (2 groups × 5 times) for all participants, comparing differences in the data within and between the two groups. RESULTS: The baseline assessment revealed no inter-group differences in the clinical outcomes (P> 0.05). Comparisons of the changes in the electro-physiological and functional variables indicated significantly greater improvement in the KT group than in the sham counterpart (P< 0.05). No statically significant improvement was evident by the electrodiagnostic criteria in the sham-KT group (P> 0.05). CONCLUSIONS: KT is an effective, safe, reliable, and conservative therapeutic choice for the management of patients with CTS. The treatment does not restrict the daily activities of patients nor has it any side effects.
Functional exercise capacity in rheumatoid arthritis unrelated to lung injury: A comparison of women with and without rheumatoid disease
Authors: Assis Ana Carolina Brandão , Lopes Agnaldo José
Published: July 2021

BACKGROUND: Rheumatoid arthritis (RA) mainly affects the joints of the upper and lower limbs, so evaluating functional exercise capacity in individuals with RA via dynamic tests of the locomotor system is essential. OBJECTIVES: To compare functional exercise capacity using the Glittre-activities of daily living (ADL) test (G-AT) in women with and without RA in the absence of RA pulmonary disease (RA-PD) and to correlate the findings with hand functioning, physical functioning, handgrip strength (HGS), and quadriceps strength (QS). METHODS: This cross-sectional pilot study evaluated 35 women with RA and 25 healthy controls by assessing hand functioning using the Cochin Hand Functional Scale (CHFS), physical functioning with the Health Assessment Questionnaire Disability Index (HAQ-DI), muscle functioning using HGS and QS, and G-AT results. RESULTS: Compared to the women in the control group, the women with RA presented higher scores for the CHFS (p< 0.0001) and HAQ-DI (p< 0.0001) and lower HGS (p< 0.0001) and QS (p= 0.013) values. The median G-AT time was higher in the RA patients than in the healthy controls [300 (295–420) vs. 180 (155–203) s], p< 0.0001), and the greatest difficulty reported by patients after the G-AT was squatting to perform the shelving tasks. G-AT time was positively correlated with the HAQ-DI (rs= 0.668, p< 0.0001) and CHFS (rs= 0.586, p= 0.0007) and negatively correlated with QS (rs=-0.429, p= 0.037). There was no significant correlation between the G-AT time and HGS. CONCLUSIONS: Women with RA take longer to perform G-AT tasks. Moreover, G-AT time was associated with hand functioning, physical functioning and QS, but not with HGS.
Genetic variations in tumor necrosis factor related apoptosis-inducing ligand receptor-1 (TRAIL-R1) gene and the susceptibility to b-cell non-hodgkin lymphoma (B-NHL) in Egypt1
Authors: Khorshied Mervat , Soliman Nohair , Khorshid Ola , Bakr Salwa
Published: July 2021

BACKGROUND: Dysregulated apoptosis is a hallmark of cancer development and progression. TRAIL and its receptors (R1 and R2) are key players in the extrinsic apoptotic pathway. Genetic alteration or blockade of TRAIL-R1 may alter its apoptotic function, and subsequently provide growth advantage to neoplastic cells. OBJECTIVE: to investigate the possible association between -C626G, -A683C and -A1322G single nucleotide polymorphisms (SNPs) of TRAIL-R1 gene and the susceptibility to B-NHL in a cohort of Egyptians. METHODS: Genotypic analysis was performed for 100 newly diagnosed B-NHL patients and 150 age and gender matched healthy controls. RESULTS: The polymorphic alleles of -C626G and -A1322G conferred almost twofold increased risk of B-NHL (OR = 1.76; 95%CI = 1.01–3.22 and OR = 1.89; 95%CI = 1.01–3.75 respectively). There was no statistical difference in the distribution of TRAIL-R1-A683C alleles/genotypes between B-NHL patients and controls. B-NHL risk increased when -C626G and -A1322G polymorphic genotypes were co-inherited (OR = 3.57; 95%CI = 1.29–9.84). The risk conferred by -C626G SNP increased for DLBCL (OR = 3.39, 95% CI: 1.61–7.16). CONCLUSION: TRAIL-R1–C626G and -A1322G polymorphisms could be considered as molecular risk factors for B-NHL especially DLBCL. The data provided by the current study constitute an initial millstone towards developing a large-scale dataset for genetic variations that could contribute to lymphomagenesis in Egyptian population.
Genetic and epigenetic landscape of leukocyte infiltration identifies an immune prognosticator in lung adenocarcinoma
Authors: Khadirnaikar Seema , Chatterjee Annesha , Shukla Sudhanshu Kumar
Published: July 2021

BACKGROUND: Leukocyte infiltration plays an critical role in outcome of various diseases including Lung adenocarcinoma (LUAD). OBJECTIVES: To understand the genetic and epigenetic factors affecting leukocyte infiltration and identification and validation of immune based biomarkers. METHOD: Correlation analysis was done to get the associations of the factors. CIBERSORT analysis was done for immune cell infiltration. Genetic and epigenetic analysis were performed. Cox regression was carried out for survival. RESULTS: We categorized the TCGA-LUAD patients based on Leukocyte fraction (LF) and performed extensive immunogenomic analysis. Interestingly, we showed that LF has a negative correlation with copy number variation (CNV) but not with mutational load. However, several individual genetic mutations, including KRAS and KEAP1, were significantly linked with LF. Also, as expected, patients with high LF showed significantly increased expression of genes involved in leukocyte migration and activation. DNA methylation changes also showed a strong association with LF and regulated a significant proportion of genes associated with LF. We also developed and validated an independent prognostic immune signature using the top six prognostic genes associated with LF. CONCLUSION: Together, we have identified clinical, genetic, and epigenetic variations associated with LUAD LF and developed an immune gene-based signature for disease prognostication.
The miRNA content of circulating exosomes in DLBCL patients and in vitro influence of DLBCL-derived exosomes on miRNA expression of healthy B-cells from peripheral blood
Authors: Caner Vildan , Cetin Gokhan Ozan , Hacioglu Sibel , Baris Ikbal Cansu , Tepeli Emre , Turk Nilay Sen , Bagci Gulseren , Yararbas Kanay , Cagliyan Gulsum
Published: July 2021

BACKGROUND: Due to the heterogeneous nature of Diffuse Large B-cell Lymphoma (DLBCL), the mechanisms underlying tumor development and progression have not yet been fully elucidated. OBJECTIVE: This study aimed to compare the characteristics of plasma exosomes of DLBCL patients and healthy individuals and to evaluate the exosomal interactions between DLBCL cell lines and normal B-cells. METHODS: Exosome isolation was performed using an ultracentrifugation-based protocol from plasma of 20 patients with DLBCL and 20 controls. The expression of miRNAs from exosome samples was analyzed using a miRNA expression microarray. The presence of exosome-mediated communication between the lymphoma cells and normal B-cells was determined by the co-culture model. RESULTS: A significant increase in plasma exosome concentrations of DLBCL patients was observed. There was also a significant decrease in the expression of 33 miRNAs in plasma exosomes of DLBCL patients. It was determined that normal B-cells internalize DLBCL-derived exosomes and then miRNA expression differences observed in normal B-cells are specific to lymphoma-subtypes. CONCLUSIONS: MiR-3960, miR-6089 and miR-939-5p can be used as the miRNA signature in DLBCL diagnosis. We suppose that the exosomes changed the molecular signature of the target cells depending on the genomic characterization of the lymphoma cells they have originated.
Knockdown CYP2S1 inhibits lung cancer cells proliferation and migration
Authors: Guo Huan , Zeng Baozhen , Wang Liqiong , Ge Chunlei , Zuo Xianglin , Li Yan , Ding Wanbao , Deng Lei , Zhang Jie , Qian Xiaoling , Song Xin , Zhang Peixian
Published: July 2021

BACKGROUND: The incidence of lung cancer in Yunnan area ranks firstly in the world and underlying molecular mechanisms of lung cancer in Yunnan region are still unclear. We screened a novel potential oncogene CYP2S1 used mRNA microassay and bioinformation database. The function of CYP2S1 in lung cancer has not been reported. OBJECTIVE: To investigate the functions of CYP2S1 in lung cancer. METHODS: Immunohistochemistry and Real-time PCR were used to verify the expression of CYP2S1. Colony formation and Transwell assays were used to determine cell proliferation, invasion and migration. Xenograft assays were used to detected cell growth in vivo. RESULTS: CYP2S1 is significantly up-regulated in lung cancer tissues and cells. Knockdown CYP2S1 in lung cancer cells resulted in decrease cell proliferation, invasion and migration in vitro. Animal experiments showed downregulation of CYP2S1 inhibited lung cancer cell growth in vivo. GSEA analysis suggested that CYP2S1 played functions by regulating E2F targets and G2M checkpoint pathway which involved in cell cycle. Kaplan-Meier analysis indicated that patients with high CYP2S1 had markedly shorter event overall survival (OS) time. CONCLUSIONS: Our data demonstrate that CYP2S1 exerts tumor suppressor function in lung cancer. The high expression of CYP2S1 is an unfavorable prognostic marker for patient survival.
A new semi-supervised algorithm combined with MCICA optimizing SVM for motion imagination EEG classification
Authors: Tan Xuemin , Guo Chao , Jiang Tao , Fu Kechang , Zhou Nan , Yuan Jianying , Zhang Guoliang
Published: July 2021

This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-consuming training process and enhances the adaptability of Brain Computer Interface (BCI), but also utilizes the MCICA to optimize the parameters of SVM in the semi-supervised process. This algorithm combines mutual information and cross validation to construct objective function in the semi-supervised training process, and uses the constructed objective function to establish the semi-supervised model of MCICA for optimizing the parameters of SVM, and finally applies the selected optimal parameters to the data set Iva of 2005 BCI competition to verify its effectiveness. The results showed that the proposed algorithm is effective in optimizing parameters and has good robustness and generalization in solving small sample classification problems.
Human interaction recognition method based on parallel multi-feature fusion network
Authors: Ye Qing , Zhong Haoxin , Qu Chang , Zhang Yongmei
Published: July 2021

Human activity recognition is a key technology in intelligent video surveillance and an important research direction in the field of computer vision. However, the complexity of human interaction features and the differences in motion characteristics at different time periods have always existed. In this paper, a human interaction recognition algorithm based on parallel multi-feature fusion network is proposed. First of all, in view of the different amount of information provided by the different time periods of action, an improved time-phased video down sampling method based on Gaussian model is proposed. Second, the Inception module uses different scale convolution kernels for feature extraction. It can improve network performance and reduce the amount of network parameters at the same time. The ResNet module mitigates degradation problem due to increased depth of neural networks and achieves higher classification accuracy. The amount of information provided in the motion video in different stages of motion time is also different. Therefore, we combine the advantages of the Inception network and ResNet to extract feature information, and then we integrate the extracted features. After the extracted features are merged, the training is continued to realize parallel connection of the multi-feature neural network. In this paper, experiments are carried out on the UT dataset. Compared with the traditional activity recognition algorithm, this method can accomplish the recognition tasks of six kinds of interactive actions in a better way, and its accuracy rate reaches 88.9%.
TextureMask: A merged architecture for low-resolution instance segmentation
Authors: Da Ting , Yang Liang
Published: July 2021

Instance segmentation has a wide range of applications, including video surveillance, autonomous driving, and behavior analysis. Nevertheless, as a type of pixel-level segmentation, its prediction performance in practice is substantially affected by low-resolution (LR) images resulting from the limitations of image acquisition equipment and poor acquisition conditions. Moreover, because their immense computational costs prevent the implementation of existing segmentation models on embedded devices, the development of a lightweight segmentation model has become an urgent necessity. However, it is challenging to achieve sound results with high efficiency and portability. From another perspective, to improve understanding of detailed objects, an architecture is needed that promotes an advanced interpretation of the segmentation, that is, a refined mask with texture. Our main contribution, called TextureMask, consists of the MobileNet-FPN for Mask R-CNN methods, segmentation with cropping, and a gradient sensitivity map, which are then merged into a unified map to refine and enrich the mask with texture information. Furthermore, preprocessing and post-processing algorithms are incorporated. Experiments demonstrated that our technique exhibits good pixel-level segmentation performance in terms of both accuracy and computational efficiency for a given LR input, and it can be easily implemented in embedded platforms.
Development of a data generator for multivariate numerical data with arbitrary correlations and distributions
Authors: Vahldiek Kai , Zhou Libing , Zhu Wenfeng , Klawonn Frank
Published: July 2021

Artificial or simulated data are particularly relevant in tests and benchmarks for machine learning methods, in teaching for exercises and for setting up analysis workflows. They are relevant when real data may not be used for reasons of data protection, or when special distributions or effects should be present in the data to test certain machine learning methods. In this paper a generator for multivariate numerical data with arbitrary marginal distributions and – as far as possible – arbitrary correlations is presented. The data generator is implemented in the open source statistics software R. It can also be used for categorical variables, if data are generated separately for the corresponding characteristics of a categorical variable. Additionally, outliers can be integrated. The use of the data generator is demonstrated with a concrete example.
The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map
Authors: Zhang Nannan , Yao Xixi , Luo Chao
Published: July 2021

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.
Handling incomplete data classification using imputed feature selected bagging (IFBag) method
Authors: Khan Ahmad Jaffar , Raza Basit , Shahid Ahmad Raza , Kumar Yogan Jaya , Faheem Muhammad , Alquhayz Hani
Published: July 2021

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.
Content-aware data distribution over cluster nodes
Authors: Krechowicz Adam
Published: July 2021

Proper data items distribution may seriously improve the performance of data processing in distributed environment. However, typical datastorage systems as well as distributed computational frameworks do not pay special attention to that aspect. In this paper author introduces two custom data items addressing methods for distributed datastorage on the example of Scalable Distributed Two-Layer Datastore. The basic idea of those methods is to preserve that data items stored on the same cluster node are similar to each other following concepts of data clustering. Still, most of the data clustering mechanisms have serious problem with data scalability which is a severe limitation in Big Data applications. The proposed methods allow to efficiently distribute data set over a set of buckets. As it was shown by the experimental results, all proposed methods generate good results efficiently in comparison to traditional clustering techniques like k-means, agglomerative and birch clustering. Distributed environment experiments shown that proper data distribution can seriously improve the effectiveness of Big Data processing.
C_CART: An instance confidence-based decision tree algorithm for classification
Authors: Yu Shuang , Li Xiongfei , Wang Hancheng , Zhang Xiaoli , Chen Shiping
Published: July 2021

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.
Manifold regularization ensemble clustering with many objectives using unsupervised extreme learning machines
Authors: Homayouni Haleh , Mansoori Eghbal G.
Published: July 2021

Spectral clustering has been an effective clustering method, in last decades, because it can get an optimal solution without any assumptions on data’s structure. The basic key in spectral clustering is its similarity matrix. Despite many empirical successes in similarity matrix construction, almost all previous methods suffer from handling just one objective. To address the multi-objective ensemble clustering, we introduce a new ensemble manifold regularization (MR) method based on stacking framework. In our Manifold Regularization Ensemble Clustering (MREC) method, several objective functions are considered simultaneously, as a robust method for constructing the similarity matrix. Using it, the unsupervised extreme learning machine (UELM) is employed to find the generalized eigenvectors to embed the data in low-dimensional space. These eigenvectors are then used as the base point in spectral clustering to find the best partitioning of the data. The aims of this paper are to find robust partitioning that satisfy multiple objectives, handling noisy data, keeping diversity-based goals, and dimension reduction. Experiments on some real-world datasets besides to three benchmark protein datasets demonstrate the superiority of MREC over some state-of-the-art single and ensemble methods.
Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation
Authors: Zeeshan Zeeshan , ul Ain Qurat , Bhatti Uzair Aslam , Memon Waqar Hussain , Ali Sajid , Nawaz Saqib Ali , Nizamani Mir Muhammad , Mehmood Anum , Bhatti Mughair Aslam , Shoukat Muhammad Usman
Published: July 2021

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.
The effect of muscular strength and strength asymmetry on jumping height in soccer players
Authors: Misjuk Mikola , Rannama Indrek
Published: July 2021

BACKGROUND:

Lower limb muscular asymmetry is not well studied and may have a negative impact on performance.

OBJECTIVE:

To estimate how muscular strength and strength asymmetry affect jumping performance in soccer players.

METHODS:

Twenty-eight male professional soccer players took part in the study. The countermovement jump (CMJ) without arm swing was used to determine jumping height. Muscle strength was measured concentrically at 60 and 300[Formula: see text]/s.

RESULTS:

The peak moment of the knee extensors was positively and significantly correlated with the CMJ; [Formula: see text] 0.608 at 300[Formula: see text]/s and [Formula: see text] 0.489 at 60[Formula: see text]/s. The asymmetry of the knee flexors between the stronger and weaker leg was negatively and moderately correlated with the CMJ at 300[Formula: see text]/s ([Formula: see text]0.396). The regression model ([Formula: see text] 0.474) showed that an increase of 0.18-Nm/kg in the relative strength of the knee extensors at 300[Formula: see text]/s (by one SD) was related to an increase of 3-cm in the CMJ. Reducing the asymmetry of the knee flexors by 6.8 percentage points (by one SD) was related to a rise of 1.7-cm in the CMJ.

CONCLUSIONS:

Greater strength in the knee extensors, preferably tested at higher velocity, and reduced asymmetry in the strength of the lower hamstring muscles have a statistically significant effect on the CMJ.
Effect of plyometric training on dynamic leg strength and jumping performance in rhythmic gymnastics: A preliminary study
Authors: Nitzsche Nico , Siebert Tobias , Schulz Henry , Stutzig Norman
Published: July 2021

BACKGROUND:

Reactive performance is an important component of rhythmic gymnastics. So far, it is unclear whether additional plyometric training in female gymnasts shows an increase in performance.

OBJECTIVE:

The aim of the study was to examine the effect of additional plyometric training in rhythmic gymnastics on the reactive jumping performance and strength of the lower leg muscles.

METHODS:

Fifteen rhythmic gymnasts (age: 12.3 [Formula: see text] 2.6 years, height: 1.47 [Formula: see text] 0.12 m, body weight: 37.3 [Formula: see text] 9.3 kg, BMI: 16.7 [Formula: see text] 2.1 kg*m[Formula: see text]; competition level: national and international championships, Tanner stages I–III) participated in the study. The athletes were assigned to an experimental (EG) and a control group (CG). The EG performed plyometric exercises three times per week in addition to the regular training. Before and after six weeks of training the reactive jump performance, the work of dorsi flexors and plantar flexors performed during isokinetic plantarflexion, as well as the performance in two sport-specific tests were measured.

RESULTS:

In contrast to the CG, in the EG the jump height (pre: 24.8; post: 27.25 cm; [Formula: see text] 0.05) and the reactive-strength-index (pre: 1.01; post: 1.19; [Formula: see text] 0.01) increased significantly. The EG achieved significant improvements in the counter movement jump test (pre: 27.0 cm; post: 31.5 cm; [Formula: see text] 0.01) and in the sport specific double rope jump test (jumps per minute, pre: 18.0; post: 23.0; [Formula: see text] 0.01). Furthermore, a significant increase in work performed during plantarflexion was found in the EG for the right leg (pre: 24.9 J; post: 29.7 J; [Formula: see text] 0.01) and a tendency to increase for the left leg (pre: 26.4 J; post: 37.7 J; [Formula: see text] 0.05).

CONCLUSION:

Both reactive strength and dynamic force can be efficiently increased by plyometric training. It may be recommended to include plyometric exercises in the training regime of rhythmic gymnasts.
Differences in strength and landing biomechanics between female jumpers and swimmers
Authors: Ruan Mianfang , Zhang Qiang , Zhang Xin , Hu Jing , Wu Xie
Published: July 2021

BACKGROUND:

It remains unclear if plyometric training as a single component could improve landing mechanics that are potentially associated with lower risk of ACL injury in the long term

OBJECTIVE:

The purpose of this study was to investigate the influence of experience undertaking plyometrics on landing biomechanics in female athletes.

METHODS:

Non-jumpers with little experience in plyometric training (12 female college swimmers) and jumpers with five years of experience in plyometric training (12 female college long jumpers and high jumpers) were recruited to participate in two testing sessions: an isokinetic muscle force test for the dominant leg at 120[Formula: see text]/s and a 40-cm drop landing test. An independent t test was applied to detect any significant effects between cohorts for selected muscle force, kinematic, kinetic, and electromyography variables.

RESULTS:

While female jumpers exhibited greater quadriceps eccentric strength ([Formula: see text] 0.013) and hamstring concentric strength ([Formula: see text] 0.023) during isokinetic testing than female swimmers, no significant differences were observed in kinematics, kinetics, and muscle activities during both drop landing and drop jumping.

CONCLUSIONS:

The results suggest that the female jumpers did not present any training-induced modification in landing mechanics regarding reducing injury risks compared with the swimmers. The current study revealed that plyometric training as a single component may not guarantee the development of low-risk landing mechanics for young female athletes.
Correlation between the strength of muscle actions of the paretic lower-limb and gait speed after stroke: Results of a meta-analysis of six studies
Authors: Bohannon Richard W.
Published: July 2021

BACKROUND AND OBJECTIVE:

Lower limb strength after stroke, particularly of the more paretic side, is known to correlate with comfortable gait speed. This meta-analysis sought to determine the relative value of 6 muscle group strengths as explanators of comfortable gait speed.

METHODS:

Relevant literature was sought using PubMed, CINAHL Scopus, and a hand search. Information on samples, measurements, and correlations were extracted. Correlational data were subjected to meta-analysis.

RESULTS:

Results from 6 studies were consolidated. The summary correlations between paretic lower limb strength and comfortable gait speed ranged from 0.45 to 0.61. Data were highly heterogeneous but did not show publication bias.

CONCLUSIONS:

The correlation between the lower limb strength and comfortable gait speed strength was moderate. However, it does not provide an adequate explanation to guide clinical practice.
Power, politics, and the institutionalisation of information systems for promoting digital transformation in the public sector: A case of the South African’s government digital transformation journey
Authors: Manda More Ickson
Published: July 2021

The institutionalisation of new technologies, information systems, norms, practices and other innovations for improving governance, planning, operational efficiency and service delivery in the public sector remain a challenge. Power dynamics, and politics have also been recognised as playing a critical role in the institutionalisation of information systems for promoting digital transformation of the public sector. This study used data collected through an extensive review of literature and empirical data from a case study of South Africa’s government digital transformation journey. The study explored power dynamics and the role of politics in the institutionalisation of reforms and deinstitutionalisation of institutionalised practices that constrain transformation in institutions. Power dynamics, and politics in institutions were found to have a significant bearing on the institutionalisation of reforms that include information systems in the digital transformation of government. The study found that the digital transformation of the public is more than the implementation technology and requires the holistic view of institutions as social, economic, and political structures.
Lost in the diffusion chasm: Lessons learned from a failed robot project in the public sector
Authors: Aaen Jon , Nielsen Jeppe Agger
Published: July 2021

Public sector organizations increasingly engage in robotic innovation projects to assist or substitute for humans in service delivery. However, transitioning small-scale development projects into a large-scale context is a notoriously difficult task that often fails, with many promising robotic projects becoming lost in the diffusion “chasm.” We investigate a failed robotic diffusion project to analyze what went wrong and what can be learned from it. Despite an increased interest in learning from public sector digitalization failure, little attention has been paid to how and why seemingly successful service robot initiatives fail to move beyond the pilot stage. We identify three types of explanations for diffusion failure using an in-depth case study of a service robot initiative in the Danish eldercare sector that had a high degree of management support and commitment from key stakeholders. Our analysis demonstrates how the failure was caused by interrelated and context-specific reasons regarding the lack of technological maturity of the service robot (technology-oriented explanations), inadequate problem-solution fit in the conceptual design (scope-oriented explanations), and misalignment between the robot company and public sector organization mindsets (competing logic-oriented explanations). We outline the lessons learned for public sector digitalization and discuss the paradox between the hype surrounding robot innovations and their slow diffusion.
Psychometric Properties of EQ-5D-3L and EQ-5D-5L in Cognitively Impaired Patients Living with Dementia
Authors: Michalowsky Bernhard , Hoffmann Wolfgang , Xie Feng
Published: July 2021

Background: Assessing health-related quality of life in dementia poses challenges due to patients’ cognitive impairment. It is unknown if the newly introduced EQ-5D five-level version (EQ-5D-5L) is superior to the 3-level version (EQ-5D-3L) in this cognitively impaired population group. Objective: To assess the psychometric properties of the EQ-5D-5L in comparison to the EQ-5D-3L in patients living with dementia (PwD). Methods: The EQ-5D-3L and EQ-5D-5L were assessed via interviews with n = 78 PwD at baseline and three and six months after, resulting in 131 assessments. The EQ-5D-3L and EQ-5D-5L were evaluated in terms of acceptability, agreement, ceiling effects, redistribution properties and inconsistency, informativity as well as convergent and discriminative validity. Results: Mean index scores were higher for the EQ-5D-5L than the EQ-5D-3L (0.70 versus 0.64). Missing values occurred more frequently in the EQ-5D-5L than the EQ-5D-3L (8%versus 3%). Agreement between both measures was acceptable but poor in PwD with moderate to severe cognitive impairment. The index value’s relative ceiling effect decreased from EQ-5D-3L to EQ-5D-5L by 17%. Inconsistency was moderate to high (13%). Absolute and relative informativity increased in the EQ-5D-5L compared to the 3L. The EQ-5D-5L demonstrated a lower discriminative ability and convergent validity, especially in PwD with moderate to severe cognitive deficits. Conclusion: The EQ-5D-5L was not superior as a self-rating instrument due to a lower acceptability and discriminative ability and a high inconsistency, especially in moderate to severe dementia. The EQ-5D-3L had slightly better psychometric properties and should preferably be used as a self-rating instrument in economic evaluations in dementia.
A Theil coefficient-based combination prediction method with interval heterogeneous information for wind energy prediction
Authors: Wu Qiongling , Lin Jian , Zhang Shaohan , Tian Zhiyong
Published: July 2021

This paper constructs the continuous-Young optimal weighted arithmetic averaging (C-YOWA) operator and the continuous-Young optimal weighted geometric (C-YOWG) operator based on definite integral and Young inequality. A series of special cases and main properties of the proposed aggregation operators are also investigated. In order to integrate heterogeneous interval data and obtain more accurate prediction results, the heterogeneous interval combination prediction (HICP) model based on C-YOWA operator, C-YOWG operator and Theil coefficient is proposed. The HICP model consider not only the existence of both additive and multiplicative interval information, but also the preference information of experts. Finally, the model is applied to the empirical analysis of wind energy prediction. The comparison of results shows that the established model can effectively improve the accuracy of prediction.
Hamacher heronian mean operators for multi-critria decision-making under multi-valued picture fuzzy uncertain lingsuitic environment
Authors: Li Baolin , Yang Lihua
Published: July 2021

Picture fuzzy set (PFS) and linguistic term set (LTS) are two significant notions in multi-criteria decision-making (MCDM). In practice, decision-makers sometimes need utilize the multiple probable membership degrees for an uncertain linguistic term to express evaluation information. Motivated by these, to better convey the vagueness and uncertainty of cognitive information, multi-valued picture fuzzy uncertain linguistic set combining picture hesitant fuzzy set with uncertain linguistic term set is proposed. We firstly define the concepts of multi-valued picture fuzzy uncertain linguistic set and multi-valued picture fuzzy uncertain linguistic number. Hamacher operations are more general and flexible in information fusion, thus, Hamacher operations and comparison method are developed at the same time. Improved generalized Heronian Mean operator can simultaneously reflect correlations between values and prevent the redundant calculation. Then, two operators of improved generalized weighted Heronian mean and improved generalized geometric weighted Heronian mean in view of Hamacher operations are proposed. Meanwhile, some distinguished properties and instances of two operators are explored as well. Moreover, a novel MCDM approach applying the developed operators is constructed. Ultimately, an illustrative example on vendor selection is performed, and sensitivity analysis and comparison analysis are provided to verify the powerfulness of the proposed method.
On derivatives of fuzzy multi-dimensional mappings and applications under generalized differentiability
Authors: Karbasaki M. Miri , Balooch Shahryari M. R. , Sedaghatfar O.
Published: July 2021

This article identifies and presents the generalized difference (g-difference) of fuzzy numbers, Fréchet and Gâteaux generalized differentiability (g-differentiability) for fuzzy multi-dimensional mapping which consists of a new concept, fuzzy g-(continuous linear) function; Moreover, the relationship between Fréchet and Gâteaux g-differentiability is studied and shown. The concepts of directional and partial g-differentiability are further framed and the relationship of which will the aforementioned concepts are also explored. Furthermore, characterization is pointed out for Fréchet and Gâteaux g-differentiability; based on level-set and through differentiability of endpoints real-valued functions a characterization is also offered and explored for directional and partial g-differentiability. The sufficient condition for Fréchet and Gâteaux g-differentiability, directional and partial g-differentiability based on level-set and through employing level-wise gH-differentiability (LgH-differentiability) is expressed. Finally, to illustrate the ability and reliability of the aforementioned concepts we have solved some application examples.
Research on psychological stress intervention of college students based on Diffie-hellman key exchange algorithm
Authors: Liu Yibo , Hao Xuejing , Mao Yanjun
Published: July 2021

At present, the mental health of college students has also become an important issue that urgently needs attention under the influence of the surrounding environment. It is coupled with the grim employment situation after graduation and the students’ psychological burden is becoming more and heavier. This paper based on Diffie-Hellman key exchange algorithm studied the effect of psychological stress intervention. First, the Diffie-Hellman key exchange algorithm was analyzed, and then the Diffie-Hellman prediction model was established according to the psychological pressure of college students. Secondly, the simulation test was conducted to compare the simulated results with the original data. The conclusion of the data fitting of the network model training set, verification set and test set were good and the error was very small. Finally, the detailed application of the algorithm and the model were described.
Factors affecting recommended childhood vaccine demand
Authors: Dizbay İkbal Ece , Öztürkoğlu Ömer
Published: July 2021

Reaching a high vaccination coverage level is of vital essence when preventing epidemic diseases. For mandatory vaccines, the demand can be forecasted using some demographics such as birth rates or populations between certain ages. However, it has been difficult to forecast non-mandatory vaccine demands because of vaccine hesitation, alongside other factors such as social norms, literacy rate, or healthcare infrastructure. Consequently, the purpose of this study is to explore the predominant factors that affect the non-mandatory vaccine demand, focusing on the recommended childhood vaccines, which are usually excluded from national immunization programs. For this study, fifty-nine factors were determined and categorized as system-oriented and human-oriented factors. After a focus group study conducted with ten experts, seven system-oriented and eight human-oriented factors were determined. To reveal the cause and effect relationship between factors, one of the multi-criteria decision-making methods called Fuzzy-DEMATEL was implemented. The results of the analysis showed that “Immunization-related beliefs”, “Media/social media contents/messaging”, and “Social, cultural, religious norms” have a strong influence on non-mandatory childhood vaccine demand. Furthermore, whereas “Availability and access to health care facilities” and “Political/ financial support to health systems” are identified as cause group factors, “Quality of vaccine and service delivery management” is considered an effect group factor. Lastly, a guide was generated for decision-makers to help their forecasting process of non-mandatory vaccine demands to avoid vaccine waste or shortage.