Miracle-dz/UniFAR-FTUs huggingface dataset:
Objective Methods of Muscle Tone Diagnosis and Their Application—A Critical Review

“Muscle tone” is a clinically important and widely used term and palpation is a crucial skill for its diagnosis. However, the term is defined rather vaguely, and palpation is not measurable objectively. Therefore, several methods have been developed to measure muscle tone objectively, in terms of biomechanical properties of the muscle. This article aims to summarize these approaches. Through database searches, we identified those studies related to objective muscle tone measurement in vivo, in situ. Based on them, we described existing methods and devices and compared their reliability. Furthermore, we presented an extensive list of the use of these methods in different fields of research. Although it is believed by some authors that palpation cannot be replaced by a mechanical device, several methods have already proved their utility in muscle biomechanical property diagnosis. There appear to be two issues preventing wider usage of these objective methods in clinical practice. Firstly, a high variability of their reliability, and secondly, a lack of valid mathematical models that would provide the observed mechanical characteristics with a clear physical significance and allow the results to be compared with each other.
Identification Customer Needs of Shuttle Travel by Using Servqual and Kano Methods

Bhinneka Shuttle is a companies which is engaged in customer delivery transportation services and was founded in 1972. Throughout 2022, Bhinneka Shuttle experienced an unstable amount of revenue and did not achieve the target number of passengers. Based on the results of a comparison of the four branches of Bhinneka Shuttle outlets in Bandung City, it is known that Bhinneka Shuttle Mekar Wangi needs to get improvement priority. This is supported by the low Google review rating and many complaints about the performance of the services provided. Customer dissatisfaction and high competition in the transportation business require Bhinneka Shuttle Mekar Wangi to improve and increase the quality of its services. This study aims to identify True Customer Needs by using the integration of the Servqual method and the Kano Model so that priority attributes are obtained for improvement and become basic recommendations for designing company service quality improvements. The attribute needs were obtained by conducting a study of the literature and Voice of Customer which were classified into five Servqual dimensions, that is Reliability, Comfort, Extent of Service, Safety, and Affordability (RESCA). According to research result, 11 attributes True Customer Needs that needed to be improved which states that the services provided by Bhinneka Shuttle still does not provide satisfaction to consumers. Bhinneka Shuttle are suggested to pay more attention to weak attributes that are included in the must be category because they are basic need in company’s services.
Structure Aware Multi-Graph Network for Multi-Modal Emotion Recognition in Conversations

Multi-Modal Emotion Recognition in Conversations (MMERC) is an increasingly active research field that leverages multi-modal signals to understand the feelings behind each utterance. Modeling contextual interactions and multi-modal fusion lie at the heart of this field, with graph-based models recently being widely used for MMERC to capture global multi-modal contextual information. However, these models generally mix all modality representations in a single graph, and utterances in each modality are fully connected, potentially ignoring three problems: 1) the heterogeneity of the multi-modal context, 2) the redundancy of contextual information, and 3) over-smoothing of the graph networks. To address these problems, we propose a Structure Aware Multi-Graph Network (SAMGN) for MMERC. Specifically, we construct multiple modality-specific graphs to model the heterogeneity of the multi-modal context. Instead of fully connecting the utterances in each modality, we design a structure learning module that determines whether edges exist between the utterances. This module reduces redundancy by forcing each utterance to focus on the contextual ones that contribute to its emotion recognition, acting like a message propagating reducer to alleviate over-smoothing. Then, we develop the SAMGN via Dual-Stream Propagation (DSP), which contains two propagation streams, i.e., intra- and inter-modal, performed in parallel to aggregate the heterogeneous modality information from multi-graphs. DSP also contains a gating unit that adaptively integrates the co-occurrence information from the above two propagations for emotion recognition. Experiments on two popular MMERC datasets demonstrate that SAMGN achieves new State-Of-The-Art (SOTA) results.
A Corporate Mentoring Program Cross-Cultural Experience and the Impact on Self-Identity for Students From an Urban Community

Cross-cultural mentoring can lead to personal development through experiencing cultural differences that cultivate individual virtues and values. Culture is an aspect of self-identity. Self-identity for adolescents is important for their long-term development. This study examines a corporate mentoring program that offers a cross-cultural experience for participants. It aimed to determine if the mentoring program influenced participants’ self-identity and if there was a correlation between culture and their self-identity. The study was guided by identity control theory. A 10-question Likert scale survey was administered to the 20 urban high school students at the completion of their mentoring program for the academic year, which included nine sessions. The study found that the corporate mentoring program did impact the self-identity of urban youth; however, there was no correlation between the cross-cultural experience and their self-identity. This finding adds to the research on youth identity formation. Further research is needed on how different cultural influences may contribute to youth identity formation.
Assessing the Prevalence and Potential Risks of Salmonella Infection Associated with Fresh Salad Vegetable Consumption in the United Arab Emirates

This study aimed to investigate the occurrence and characteristics of Salmonella isolates in salad vegetables in the United Arab Emirates (UAE). Out of 400 samples tested from retail, only 1.25% (95% confidence interval, 0.41–2.89) were found to be positive for Salmonella, all of which were from conventional local produce, presented at ambient temperature, and featured as loose items. The five Salmonella-positive samples were arugula (n = 3), dill (n = 1), and spinach (n = 1). The Salmonella isolates from the five samples were found to be pan-susceptible to a panel of 12 antimicrobials tested using a disc diffusion assay. Based on whole-genome sequencing (WGS) analysis, only two antimicrobial resistance genes were detected—one conferring resistance to aminoglycosides (aac(6′)-Iaa) and the other to fosfomycin (fosA7). WGS enabled the analysis of virulence determinants of the recovered Salmonella isolates from salad vegetables, revealing a range from 152 to 165 genes, collectively grouped under five categories, including secretion system, fimbrial adherence determinants, macrophage-inducible genes, magnesium uptake, and non-fimbrial adherence determinants. All isolates were found to possess genes associated with the type III secretion system (TTSS), encoded by Salmonella pathogenicity island-1 (SPI-1), but various genes associated with the second type III secretion system (TTSS-2), encoded by SPI-2, were absent in all isolates. Combining the mean prevalence of Salmonella with information regarding consumption in the UAE, an exposure of 0.0131 salmonellae consumed per person per day through transmission via salad vegetables was calculated. This exposure was used as an input in a beta-Poisson dose–response model, which estimated that there would be 10,584 cases of the Salmonella infection annually for the entire UAE population. In conclusion, salad vegetables sold in the UAE are generally safe for consumption regarding Salmonella occurrence, but occasional contamination is possible. The results of this study may be used for the future development of risk-based food safety surveillance systems in the UAE and to elaborate on the importance for producers, retailers, and consumers to follow good hygiene practices, particularly for raw food items such as leafy salad greens.
MariClus: Your One-Stop Platform for Information on Marine Natural Products, Their Gene Clusters and Producing Organisms

Background: The marine environment hosts the vast majority of living species and marine microbes that produce natural products with great potential in providing lead compounds for drug development. With over 70% of Earth’s surface covered in water and the high interaction rate associated with liquid environments, this has resulted in many marine natural product discoveries. Our improved understanding of the biosynthesis of these molecules, encoded by gene clusters, along with increased genomic information will aid us in uncovering even more novel compounds. Results: We introduce MariClus (https://www.mariclus.com), an online user-friendly platform for mining and visualizing marine gene clusters. The first version contains information on clusters and the predicted molecules for over 500 marine-related prokaryotes. The user-friendly interface allows scientists to easily search by species, cluster type or molecule and visualize the information in table format or graphical representation. Conclusions: This new online portal simplifies the exploration and comparison of gene clusters in marine species for scientists and assists in characterizing the bioactive molecules they produce. MariClus integrates data from public sources, like GenBank, MIBiG and PubChem, with genome mining results from antiSMASH. This allows users to access and analyze various aspects of marine natural product biosynthesis and diversity.
Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study

In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.
Comparative Assessment of Different Ultrasound Technologies in the Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

Simple Summary Prostate cancer (PCa) is considered as one of main causes of death in men globally. More research is required on the diagnostic accuracy of mpUS and advanced modalities in prostate cancer detection, which may provide insightful information into the diagnostic accuracy and clinical utility of this technique. Therefore, we have conducted a systematic review and meta-analysis to assess the diagnostic test accuracy of different ultrasound scanning technologies (shear-wave elastography, contrast enhanced, micro-ultrasound) and grayscale ultrasound technology in the detection of prostate cancer. This will assist in determining whether this new method of detecting prostate cancer is effective. Our results showed that some studies proved that advanced ultrasound modalities are promising methods for the detection of prostate cancer. Abstract The present study aimed to assess the diagnostic test accuracy of different ultrasound scanning technologies in the detection of prostate cancer. A systematic search was conducted using the Cochrane Guidelines for Screening and Diagnostic Tests. We performed a systematic search in the international databases PubMed, Medline, Ovid, Embase and Cochrane Library. Searches were designed to find all studies that evaluated Micro-US, mpUS, SWE and CEUS as the main detection modalities for prostate cancer. This study was registered with Research Registry of systematic review and meta-analysis. The QUADAS-2 tool was utilized to perform quality assessment and bias analysis. The literature search generated 1376 studies. Of these, 320 studies were screened for eligibility, with 1056 studies being excluded. Overall, 26 studies with a total of 6370 patients met the inclusion criteria. The pooled sensitivity for grayscale, CEUS, SWE, Micro-US and mpUS modalities were 0.66 (95% CI 0.54–0.73) 0.73 (95% CI 0.58–0.88), 0.82 (95% CI 0.75–0.90), 0.85 (95% CI 0.76–0.94) and 0.87 (95% CI 0.71–1.03), respectively. Moreover, the pooled specificity for grayscale, CEUS, SWE, Micro-US and mpUS modalities were 0.56 (95% CI 0.21–0.90), 0.78 (95% CI 0.67–0.88), 0.76 (95% CI 0.65–0.88), 0.43 (95% CI 0.28–0.59) and 0.68 (95% CI 0.54–0.81), respectively. In terms of sensitivity, substantial heterogeneity between studies was detected (I2 = 72%, p = 0.000 < 0.05). In relation to specificity, extreme heterogeneity was detected (I2 = 93%, p = 0.000 < 0.05). Some studies proved that advanced ultrasound modalities such as mpUS, Micro-US, shear-wave elastography, contrast enhanced and micro-ultrasound are promising methods for the detection of prostate cancer.
Few shot image segmentation with using U-net

Image segmentation is a typical task in the field of computer vision. Compared to the task of image segmentation within the framework of the hypothesis with a large set, recognition with a small data set can not only identify known categories in the training data set, but also assign colored labels to objects. In real-world segmentation tasks, due to various objective factors, it is usually difficult to collect exhaustive training samples for all categories when training a neural network. During training, there is a complete knowledge of categories about the world, and during testing, the algorithm is provided with information about the object (object mask). Segmentation by neural networks is required for accurate segmentation of existing categories in the training data set by assigning labels to them. In recent years, with the development of deep learning, deep learning for image segmentation relies heavily on various neural network models existing today. Although neural networks are very efficient, good results can rarely be explained. A recent method for solving the object segmentation problem involves two main stages: preparation of the initial images of a data set with specially prepared masks; and then training a compact neural network model using a specially prepared dataset. For an instance of a test object, when the probability is below the threshold value of a known class, it is determined that the object belongs to the category seen in the training data set. However, this method does not take into account information about the image data of unknown categories in the process of training the model.
Temporary love: A love-hate transition among luxury fashion consumers in a mediation of brand jealousy and moderation of value-expressiveness

ABSTRACT There is a limited amount of research to investigate emotional transition (i.e. love to hate) among consumers. Moreover, can value-expressiveness motivate consumers to not to share negative word of mouth (NWOM) despite hating a brand? This study aims at addressing these important research questions. Survey-based data is collected from 413 Indian luxury fashion consumers and analyzed via structural equation modelling technique, using AMOS 27.0. Findings reveal that brand jealousy mediates the relationship between brand love and brand hate. Furthermore, when value expressiveness is low, the relationship between brand hate and negative word of mouth is stronger. Brand marketers should strengthen symbolic brand identity to sustain positive consumer-brand emotions. Moreover, some relationship dyads can be used to establish the brand image in a family-oriented, collectivist culture. The mediation of brand jealousy and the moderation of NWOM are unique contributions of this study in an Indian context.
A novel second-order nonstandard finite difference method preserving dynamical properties of a general single-species model

In this paper, we extend the Mickens' methodology to construct a second-order nonstandard finite difference (NSFD) method, which preserves dynamical properties including positivity, local asymptotic stability and especially, global asymptotic stability of a general single-species model. This NSFD method is based on a novel weighted non-local approximation of the right-hand side function in combination with the renormalization of the denominator function. The weight guarantees the dynamic consistency and the nonstandard denominator function ensures the convergence of order 2 of the NSFD method. The result is that we obtain a second-order and dynamically consistent NSFD method. It is proved that the NSFD method is simple and efficient and can be extended for solving a broad range of mathematical models arising in real-world applications. Also, we combine the constructed second-order NSFD method with Richardson's extrapolation technique to generate high-order numerical approximations. Finally, the theoretical findings are illustrated and supported by numerical experiments.
Representation Learning Framework of Object Recognition via Feature Construction

In this paper, we recognize objects within images by collecting information from a large number of random-size patches of the image. The different backgrounds accompany the foreground object demand to have a learning system to identify each patch as belonging to the object category or to the background category. We strengthen a recent method called Evolution-COnstructed (ECO), which is based on the ensemble learning approach which combines several weak classifier. The improvement is relying on decreasing the overfitting problem. Two different improving ideas are proposed: 1) Pooling operation, which is applied to the weak classifiers data, 2) Random Forest algorithm, which combines the weak classifiers outcomes. Experimental results are reported for classification of 9 categories of Caltech-101 data sets and proved that our modifications boost the performance over the base method and other existing methods.
InSAR Time Series Analysis to Evaluate Subsidence Risk of Monumental Chandigarh City (India) and Surroundings

Groundwater depletion is one of the serious geoenvironmental issues causing ground subsidence, which damages buildings, infrastructures, and causes loss of life. The quantitative and qualitative evaluation of groundwater variability requires multiple approaches to measure hydraulic head level and geodetic deformation. In this study, we have made efforts to integrate multiple hierarchical space-borne data, including Gravity Recovery and Climate Experiment (GRACE), Sentinel-1 interferometric synthetic aperture radar (InSAR), and geological and hydrological data, to quantify subsidence in Chandigarh city and its surroundings. First, we conducted New-Small BAseline Subsets (NSBASs) and pointwise persistent scatterer (PS) InSAR techniques in parallel, using three-year Sentinel-1 data showing a vertical subsidence up to 120 mm/year around fluvial sediment deposits. Furthermore, correlation analysis of hydraulic/climatic measurements clearly shows the subsidence associated with the groundwater depletion. The pattern of PS points shows the instability of structures associated with the ground subsidence over the central city areas. The monumental architectures designed by Le Corbusier in the northern sectors are outside of the main subsidence area. In the target area, the magnitude of subsidence and surface deformation due to groundwater depletion depends on the subsurface geophysical environment and the anthropogenic activities within the region and surroundings. The results provided a case of monitoring scheme using multiresolution satellite data about the subsidence and associated consequences due to groundwater depletion.
Determinants for Awareness of basic Human Rights among University Students

Constitution of a country is meant to protect the citizens’ basic human rights such as freedom of speech, freedom of religion or it can be freedom of movement. The constitution provides fundamental liberties which are also known as basic rights of individuals. However, these basic rights are not absolute and subject to certain restrictions. This study aims to explore the relationships between the determinants (“familiarity” and “restrictions”) against the dependent variable of “awareness”. Findings showed t hat familiarity and restrictions of basic rights have significant effects on the awareness of fundamental liberties among students. This research is significant for policy-makers when drafting curriculums that inculcate education in human rights.
ROCK MASS DISCONTINUITY DETERMINATION WITH TRANSFER LEARNING

Abstract. Rock mass discontinuity and orientation are among the important rock mass features. They are conventionally determined with scan-line surveys by engineering geologists in field, which can be difficult or impossible depending on site accessibility. Photogrammetry and computer vision techniques can aid to automatically perform these measurements, although variations in size, shape and appearance of rock masses make the task challenging. Here we propose an automated approach for the detection of rock mass discontinuities using deep learning and photogrammetric image processing methods. Two deep convolutional neural network (DCNNs) were implemented for this purpose and applied to basalts in Kizilcahamam Guvem Geosite near Ankara, Türkiye. Red-green-blue (RGB) band images of the site were taken from an off-the-shelf camera with 1.7 mm resolution and a 3D digital surface model and orthophotos were produced by using photogrammetric software. The discontinuities were delineated manually on the orthophoto and converted to masks. The first DCNN model was based on the open-source crack dataset consisting of a total of 11,298 road and pavement images, which were used to train the Resnet-18 model (Model-1). The second model (Model-2) was based on fine-tuning of Model-1 using the study data from Kizilcahamam. After fine-tuning, Model-2 was able to achieve high performance with a Jaccard Score of 88% on the test data. The results show high potential of the methodology for transfer learning with fine-tuning of a small amount of data that can be applied to other sites and rock mass types as well.