Correlation Between Proprioceptive Impairment and Motor Deficits After Stroke: A Meta-Analysis Review

Introduction: Proprioceptive impairment is a common symptom after stroke. Clarifying how proprioception correlates with motor function after stroke may be helpful in optimizing proprioception-augmented movement training. Previous studies have shown inconsistent findings. A meta-analysis is an optimal method to explore the correlation and identify the factors contributing to these inconsistencies.Objective: To explore the correlation between proprioception and motor function after stroke through a meta-analysis, taking into account characteristics of the measurements used in these studies.Methods: We searched multiple databases until November 2021 for eligible studies that measured both proprioception and motor functions in persons with stroke and reported their correlation or data for correlation analysis. A meta-analysis of the correlations was performed. The subgroup analysis and meta-regression were further conducted to investigate potential factors contributing to the heterogeneity of correlation strength, based on the participants' characteristics, proprioception, and motor function measures.Results: In total, 28 studies comprising of 1,829 participants with stroke were included in the meta-analysis. The overall correlation between proprioception and motor function was significant (r = 0.267, p < 0.05), but there was heterogeneity across studies (I2 = 45%, p < 0.05). The results of the subgroup analysis showed proprioception of the axial segment in weight-bearing conditions (r = 0.443, p < 0.05) and upper limb without weight-bearing (r = 0.292, p < 0.05) had a stronger correlation with motor function than proprioception of the lower limb without weight-bearing. The proprioception measured through ipsilateral matching (r = 0.412, p < 0.05) showed a stronger correlation with motor function than through contralateral matching. The International Classification of Functioning, Disability, and Health (ICF) domains of motor function, movement function (r = 0.338, p < 0.05), activity performance (r = 0.239, p < 0.05), and independence (r = 0.319, p < 0.05) showed a stronger correlation with proprioception than with other domains.Conclusion: There is a significant correlation between proprioception and motor dysfunction after stroke. The proprioception measured in the axial segment under weight-bearing conditions or measured with ipsilateral matching, and motor function, specifically in the ICF domains of movement function, activity performance, and independence showed a positive contribution to the association between proprioception and motor function. The correlation does not imply causation and might be underestimated by attributes of current tests for proprioception and motor function. Further studies are needed to clarify the cause-effect relationship.

Road infrastructure and intra-community trade in the Central African Economic and Monetary Community

We examine the contribution of domestic and interstates road infrastructures to trade flow between member countries of the Central African Economic and Monetary Community (CEMAC). Theories of international economics as well as those of the new economic geography suggest a positive contribution of both road infrastructures to intraregional trade. A gravity model of international trade is estimated to evaluate this theoretical prediction in the CEMAC zone. Results confirm a positive contribution of interstate road infrastructure to intra-Community trade, but show no evidence of a positive contribution of domestic road infrastructure to intra- CEMAC trade.

Social Media Strategy to Improve Brand Image: in the Context of Students’ Decision Making

This study aims to examine and analyze the influence of social media on brand image and student decision-making. This study involved as many as 210 students from the health sciences and business studies program. Of the total 210 data obtained, the remaining 202 data can be used for further testing and data analysis, this is because there are a number of data that are categorized as outliers so that they must be eliminated. This study is a quantitative research category using a survey approach as a method of data collection. Then, regarding the process of testing and analyzing data, this study uses a structural equation modeling (SEM) approach with AMOS software. The findings of this study confirm that social media has a significant positive effect on brand image and decision making, besides that brand image has a positive and significant effect on student decision making. The findings of this study make a positive contribution to the development of science, especially in the field of marketing strategies through social media in improving brand image and decision making. The findings of this study are expected to make a practical contribution to increasing product competitiveness and consumer confidence.

Analysis of Sustainable Business Models: Exploratory Study in Two Brazilian Logistics Companies

Over the years, researchers have presented different concepts regarding business models. There is no unanimity, but there is a general agreement that a business model is the representation of how a company conducts its business. Subsequently, interest in the topic has increased and has come to be considered by companies that, driven to incorporate a positive contribution to sustainability into their values, began to face economic development that considers social and environmental aspects instead of worrying only about profit for the shareholders. The general objective of this research is to identify how the merger of two Brazilian logistics companies with different customer segments can positively contribute to the defense of sustainability, considering social and environmental aspects. As a methodology, a literature review on business models, conceptual business models, and sustainable business models was used to legitimize issues relevant to the themes. The case studies allowed for a measurement of the productivity of the areas of the chosen companies, and a survey of used business processes and technological systems, in addition to diagnosing the level of maturity of these areas. An analysis of the strengths and weaknesses in these areas, within the studied companies, resulted in a solution that allowed for the optimization of processes and savings in human and material resources, and the identification of qualitative benefits arising from mergers, improvements in productivity and services, the standardization of processes, and the implementation of innovative digital technologies.

Rolling Shear Properties of Cross-Laminated Timber Made from Australian Plantation Eucalyptus nitens under Planar Shear Test

With the increasing availability of fast-growing Eucalyptus plantation logs in Australia in recent years, the timber manufacturing sector has become interested in discovering the opportunities of producing value-added timber products from this resource. Cross-laminated timber (CLT) could be a potential sustainable product recovered from this resource and supply material for commercial buildings. Shear of the inner cross-laminates, known as rolling shear, is one of the governing factors in serviceability and limit state design for this product under out-of-plane loading. This study evaluated the rolling shear (RS) properties of CLT with heterogonous layup configurations using different structural grade Eucalyptus nitens (E. nitens) timber under the planar shear test. Based on the results, Gr and τr values were shown to be significantly correlated with the density of the CLT panel. There was also a positive correlation between the RS modulus and MOR of the CLT panel. The specimens with high MOE in the top and bottom layers indicated the highest τr and Fmax values. This indicated that using high-grade boards in the top and bottom lamellae plays an important role in increasing the RS strength, whereas using them in the cross-layer has a positive contribution in increasing shear modulus. The maximum observed RS strength and modulus ranged from 2.8–3.4 MPa and 54.3–67.9 MPa, respectively, exceeding the RS characteristic values of the resource. The results obtained in this study were comparable to those recommended in European standards for softwood CLT, demonstrating the potential use for eucalypt timber boards in CLT production. This paper provides an important insight into supporting the potential engineering applications of CLT panel products fabricated with eucalypt plantation.

“This Is the End.” A Case Study of a Japanese Learner’s Experience and Regulation of Anxiety

This study investigates the role of emotions in second language learning, in particular, that of anxiety. Research has shown that positive and negative emotions are interrelated and that negative emotions are negatively correlated with motivation. It is, therefore, important to investigate how learners regulate their emotions. In this case study, one learner was closely observed over a period of 13 years. This learner claims that he has been feeling strong anxiety while learning English, but also that his negative emotion was the source of motivation to proactively study the language. The research used three types of data: (a) language learning historical records, (b) in-depth interviews, and (c) two questionnaires: the Foreign Language Classroom Anxiety Scale and the Strategy Inventory for Language Learning questionnaire. The results reveal the intense experience of the learner’s emotions, as well as the significant shifts therein. It also unearths some of the sources of his emotional experiences and how he regulated these experiences. An important observation was the positive contribution that negative emotions had on some aspects of the participant’s learning.

Quantitative Electroencephalography as a Biomarker for Cognitive Dysfunction in Parkinson’s Disease

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson’s disease (PD).Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD.Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis.Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage.Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.

Entrepreneurship Development in Management of Intra School Student Organizations in SMA/ SMK Level in the East Indonesian Province

Understanding of entrepreneurship and organizational management, especially at the OSIS and MPK levels, is still deficient. With a lack of understanding related to entrepreneurship and organizational management at the school level, lecturers at the Faculty of Social and Political Sciences (FISIP) Unjani organize Education and Training for OSIS at the SMA/SMK in the Eastern Province of Indonesia. The method used in this community service is to provide material related to entrepreneurship development and organizational management, and creativity competitions for OSIS and MPK members between schools. Community service related to entrepreneurship development in the management of Intra-School Student Organizations runs smoothly and in accordance with the desired expectations. Students participate actively in every series of activities held. In addition to participating in the Webinar as the pinnacle of community service, students also participate in the preparation of school creative business proposals, poster making competitions, and Share Content competitions. Thus, community service activities carried out by FISIP Unjani make a positive contribution in providing new understanding and thoughts as well as ideas, ideas, creative innovations that can be applied and implemented in organizations and in the school environment where they study. and form an entrepreneurial spirit, namely a leader who has an entrepreneurial spirit.

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

LIME-Based Data Selection Method for SAR Images Generation Using GAN

Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.