Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Active Release Technique verses Conventional Physiotherapy in Management of Upper Cross Syndrome

Background: In upper cross syndrome (UCS), weaker neck flexors, anterior and middle serratus and lower trapezius along with rhomboids usually develop, and tightness of the levator scapulae, pectoralis major and upper trapezius are biomechanically adapted. Active Release Technique (ART) helps to reduce discomfort and improve the range of movement. Also, Active Release Technique (ART) is a manual procedure which is also being used for other soft tissue rehabilitation as well as for the management of the scar tissues. UCS and neck pain is common with uncomfortable job postures as well as in stress and anxiety, due to which muscle dysfunction starts around the neck. Aim and Objective: To evaluate the effects of Active Release Technique verses conventional physiotherapy in management of upper cross syndrome physiotherapy. Methodology: 35 patients were grouped in two groups. Both groups were treated for six sessions. Group A was treated with active release technique and static stretching along with hydrocollator pack. The patients coming under Group B, conventional physiotherapy was given which include stretching, levator scapulae and pectoralis major as well as rhomboid strengthening, deep neck flexors, lower trapezius along with the hydrocollator pack. Result: Both the treatment protocol were beneficial for the UCS patients but ART yields greater reduction in pain and increase in ROM than the conventional physiotherapy. Conclusion: this study concludes that administering ART among the UCS patients has shown positive trends and is beneficial for the patient as compared to the conventional
Application of the Ordinary Kriging method for prediction of the positive spread of Covid-19 in West Java

The Ordinary Kriging method is a method used to predict an observation at an unobserved location based on observed points that are spatially related. Corona Virus Disease 19 (Covid-19) is a contagious disease and viral pathogenic infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). This virus spreads almost all over the world including Indonesia and it gives an influence between location. In this study, a case study was carried out with predictions on the positive distribution data of Covid-19 at 27 districts/cities in West Java. The process of calculating predictions of the positive distribution Covid-19 is started by the determination of the experimental semivariogram model. The Covid-19 data have an Exponential Semivariogram model which is used as an input of the Ordinary Kriging. Furthermore, using the R program for the Ordinary Kriging, we can predict the observation of positive Covid-19 at unobserved locations in West Java.
RF magnetron sputtered Cd doped ZnO thin films for gas-sensing applications

ABSTRACT RF magnetron sputtering has been employed to manufacture undoped and Cd doped (3%, 10%, and 20%) ZnO (CZO) films. X-ray diffraction (XRD) results revealed that the films have preferential orientation along (002) plane with hexagonal structure, and there are no secondary peaks. It is observed from the Hall Effect measurement that CZO films posses n-type conductivity, and the carrier concentration does not vary much with Cd concentration. On the other hand, the band gap estimated from Tauc’s plot shows that band gap decreased with increase in Cd concentration. Red shift was observed in photoluminescence spectrum. Ethanol, methanol, and ammonia at gas concentration range of 50–200 ppm were exposed on the grown CZO films at different temperatures. Among the various concentrations of the films, CZO having 20% Cd exhibited better gas response toward methanol, while CZO with 10% Cd showed maximum gas response toward ammonia gas. This has been explained by grain boundary barrier (GBB) and double Schottky potential barrier mechanisms.
Bayesian Modeling for Exposure Response Curve via Gaussian Processes: Causal Effects of Exposure to Air Pollution on Health Outcomes

Motivated by environmental health research on air pollution, we address the challenge of estimation and uncertainty quantification of causal exposure-response function (CERF). The CERF describes the relationship between a continuously varying exposure (or treatment) and its causal effect on a outcome. We propose a new Bayesian approach that relies on a Gaussian process (GP) model to estimate the CERF. We parametrize the covariance (kernel) function of the GP to mimic matching via a Generalized Propensity Score (GPS). The tuning parameters of the matching function are chosen to optimize covariate balance. Our approach achieves automatic uncertainty evaluation of the CERF with high computational efficiency, enables change point detection through inference on derivatives of the CERF, and yields the desired separation of design and analysis phases for causal estimation. We provide theoretical results showing the correspondence between our Bayesian GP framework and traditional approaches in causal inference for estimating causal effects of a continuous exposure. We apply the methods 1 ar X iv :2 10 5. 03 45 4v 2 [ st at . M E ] 9 J un 2 02 1 to 520,711 ZIP-code-level observations to estimate the causal effect of long-term exposures to PM2.5 on all-cause mortality among Medicare enrollees in the United States.
An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Text Summarization is an essential area in text mining, which has procedures for text extraction. In natural language processing, text summarization maps the documents to a representative set of descriptive words. Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents. Text summarization has two main areas such as abstractive, and extractive summarization. Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles. All such text extractions have limitations in providing the basic theme of the underlying documents. In this paper, we have employed text summarization by TF-IDF with PageRank keywords, sentence score algorithm, and Word2Vec word embedding. The study compared these forms of the text summarizations with the actual text, by calculating cosine similarities. Furthermore, TF-IDF based PageRank keywords are extracted from the other two extractive summarizations. An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed. This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document. This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text. It also solves the issue of deciding the number of representative keywords for a specific text document. To evaluate the technique, the study used a sample of more than eighteen hundred text documents. The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization. The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.
Some multivariate goodness of fit tests based on data depth

Using notions of depth functions in the multivariate setting, we have constructed several new multivariate goodness of fit (GoF) tests based on existing univariate GoF tests. Since the exact computation of depth is difficult, depth is estimated based on a large random sample drawn from the null distribution. It has been shown that test statistics based on estimated depth are close to those based on the true depth. Some two-sample tests based on data depth are also discussed for scale differences. These tests are distribution-free under the null hypothesis. Finite sample properties of the proposed tests are studied using several numerical examples. A real-data example is discussed to illustrate the usefulness of the proposed GoF tests.
Double-Auction Mechanisms for Resource Trading Markets

We consider a double-auction mechanism, which was recently proposed in the context of rate allocation in mobile data-offloading markets; our mechanism is also applicable to the problem of bandwidth allocation in network slicing markets. Network operators (users) derive benefit from offloading their traffic to third party WiFi or femtocell networks (link-suppliers). Link-suppliers experience costs for the additional capacity that they provide. Users and link-suppliers (collectively referred to as agents) have their pay-offs and cost functions as private knowledge. A network-manager decomposes the problem into a network problem (with surrogate pay-offs and surrogate cost functions) and agent problems (one per agent). The surrogate pay-offs and cost functions are modulated by the agents’ bids. Agents’ payoffs and costs are then determined by the allocations and prices set by the network-manager. Under this design, so long as the agents do not anticipate the effect of their actions on the prices set by the network-manager (i.e., price-taking agents), a competitive equilibrium exists as a solution to the network and agent problems, and this equilibrium optimizes the sum utility of all agents. However, this design fails when the agents (including the link-supplier) are all strategic (price-anticipating). Specifically, the presence of a strategic link-supplier drives the system to an undesirable equilibrium with zero participation resulting in an efficiency loss of 100%. This is in stark contrast to an earlier setting where the users alone are strategic but the link-supplier is not − the efficiency loss is known to be at most 34%. The paper then proposes the following Stackelberg game modification with asymmetric information structures for link-supplier and users in order to alleviate the efficiency-loss problem: the network-manager first announces the allocation and payment functions; he then invites the link-supplier to announce its bid, following which the users are invited to respond with their bids. The resulting Stackelberg games’ efficiency losses can be characterized in terms of the link-supplier’s cost function when the users’ pay-off functions are linear. Specifically, when the link-supplier’s cost function is quadratic, the worst case efficiency loss is 25%. Further, the loss in efficiency improves for polynomial cost functions of higher degree. For non-linear utility functions (e.g., $\alpha $ -fair and $\log $ utilities), we demonstrate the efficacy of the proposed mechanism via. a detailed numerical study.
Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts

The use of machine translation as artificial intelligence (AI) keeps increasing and the world’s most popular translation tool is Google Translate (GT). This tool is not merely used for the benefits of learning and obtaining information from foreign languages through translation but has also been used as a medium of interaction and communication in hospitals, airports and shopping centers. This paper aims to explore machine translation accuracy in translating French-Indonesian culinary texts (recipes). The samples of culinary text were taken from the internet. The research results show that the semiotic model of machine translation in GT is the translation from the signifier (forms) of the source language to the signifier (forms) of the target language by emphasizing the equivalence of the concept (signified) of the source language and the target language. GT aids to translate the existing French-Indonesian culinary text concepts through words, phrases and sentences. A problem encountered in machine translation for culinary texts is a cultural equivalence. GT machine translation cannot accurately identify the cultural context of the source language and the target language, so the results are in the form of a literal translation. However, the accuracy of GT can be improved by refining the translation of cultural equivalents through words, phrases and sentences from one language to another.
Accuracy assessment of applied supervised machine learning models on usual data probability distributions

In this paper, an application analysis of supervised classification techniques on several probability distributions is carried out. Accuracy as well as usual standard metrics have been highlighted to rate the performance of generated learning models. Using data that fit different distributions, we investigated whether the application of a classification method had an optimizing impact on the accurateness of its correlated learning model.
Guía de práctica de campo. Monitoreo de rasgos funcionales en los cultivos para la detección de estrés temprano: La conductancia estomática y termografía infrarroja como herramienta de medidas claves

La determinación de momentos idóneos para llevar a cabo el riego en los cultivos es crucial para la optimización del recurso hídrico, además de ser parte fundamental de un sistema de soporte de decisión para el riego. Es importante identificar el momento (el ¿cuándo? ) en el cual debemos regar de tal manera que ahorremos agua y no reduzcamos significativamente el rendimiento del cultivo, como se ha reportado en nuestras investigaciones llevadas a cabo en el Centro Internacional de la Papa (CIP). Este manual práctico ha sido escrito para ser usado por estudiantes, docentes e investigadores interesados en la detección de estrés temprano en los cultivos, y pretende brindar un conjunto de herramientas recientemente publicadas y generadas por el CIP con el financiamiento del Programa Nacional de Innovación Agraria (PNIA) y el Programa de Investigación en Raíces, Tubérculos y Bananas del CGIAR (RTB). Nuestros alcances han partido de la medición de la conductancia estomática máxima a luz saturada, considerada como el principal indicador del estado hídrico de las plantas, y su relación con la temperatura del follaje. Dicha temperatura es obtenida por medio de imágenes térmicas que son procesadas utilizando un software de acceso abierto “TIPCIP” (Thermal Image Processor). Esto con la finalidad de calcular el índice de estrés hídrico del cultivo (CWSI), una variable que nos indica el grado de estrés hídrico del cultivo y el momento apropiado del riego. En este manual se resume de manera práctica los procedimientos publicados recientemente en revistas internacionales, con la finalidad de que sean aplicados de manera práctica en otros cultivos propiciando la investigación en el campo de la agricultura de ahorro de agua mediante el uso de herramientas ecofisiológicas.
Carrot seeds vigor on plant performance and crop yield

Recebido: 17/07/2020 Aprovado: 26/12/2020 Rapid emergence and uniform seedling growth are essential attributes in the production of vegetables such as carrots, whose sowing is direct in the field. The experiment was conducted to evaluate the effect of carrot seed vigor on seedling emergence, plant development, and yield. Four seed lots of carrot, cv. Brasília were characterized for their physiological potential by germination and vigor tests. A field experiment was conducted, where the seeds of each lot were sown in in the field to evaluate seedling emergence at 10 and 14 days, the length of the shoot and root at 30, 45 and 60 days after sowing, the crop growth rate, commercial, non-commercial and commercial total roots yield. Seed vigor significantly affected the seedling emergence and development of carrot plants in the field. The less vigorous lot showed lower final emergence than the most vigorous lots and these negative vigor effects extended to the most advanced stages of plant development. Carrot seed vigor affects seedling emergence and plant growth in the field. Vigorous seeds causes greater emergence, plant height and plant dry matter during the vegetative phase. The use of less vigorous seeds causes yield losses of up to 27%.
Fluid structure coupling simulation analysis of vibration assisted 3D bioprinting extruder

Aiming at the problem of high wall shear stress at the nozzle of micro extrusion biological 3D printing technology, this paper proposes a method of reducing the viscosity of biological ink through vibration, so as to reduce the shear stress and improve printing efficiency and cell vitality. The 3D bioprinting extrusion device is taken as the research object, and the influence of vibration parameters on the printing process is studied on by fluid structure coupling simulation of the extrusion device.
DocSCAN: Unsupervised Text Classification via Learning from Neighbors

We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN). For each document, we obtain semantically informative vectors from a large pre-trained language model. Similar documents have proximate vectors, so neighbors in the representation space tend to share topic labels. Our learnable clustering approach uses pairs of neighboring datapoints as a weak learning signal. The proposed approach learns to assign classes to the whole dataset without provided ground-truth labels. On five topic classification benchmarks, we improve on various unsupervised baselines by a large margin. In datasets with relatively few and balanced outcome classes, DocSCAN approaches the performance of supervised classification. The method fails for other types of classification, such as sentiment analysis, pointing to important conceptual and practical differences between classifying images and texts.
Edge-Based Hybrid System Implementation for Long-Range Safety and Healthcare IoT Applications

In many conventional Internet-of-Things (IoT) applications, data are transferred directly from the sensor network to the cloud via a gateway for further data processing. However, this typical usage of a gateway is not suitable for every application. For short-range IoT wireless protocols [e.g., Bluetooth low energy (BLE)], multiple gateways are required to achieve broader coverage, which is inconvenient. In this article, an edge-based hybrid network system architecture is presented. The proposed system consists of hybrid routers and an IoT gateway. The router supports two wireless protocols, BLE and long range (LoRa), and is equipped with a solar energy harvester to extend the router’s lifetime. It can extend the coverage of short-range BLE network by utilizing LoRa wireless technology, and support fundamental edge computing tasks such as preliminary data processing. The IoT gateway can support multiple IoT protocols, including LoRa, BLE, and XBee. It can perform more advanced edge computing tasks, such as data filtering, storage, processing, user interface, and cloud connection. Three case studies incorporating a wearable safety monitoring sensor network, a healthcare monitoring application, and a smart hospital application are studied with the proposed edge network system to demonstrate its promising capabilities to support IoT applications. Experimental evaluations indicate that by processing data at the edge, the minimal delay is only 11.5 ms. Furthermore, with the hybrid LoRa network implementation, the BLE network can be extended to 2.4 km.
Selective Probabilistic Classifier Based on Hypothesis Testing

In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By utilizing Z-test over the mean and standard deviation for each class, the proposed method can estimate the statistical significance of the network certainty and reject uncertain outputs. The proposed method was experimented on with different configurations of the COCO and CIFAR datasets. The performance of the proposed method is compared with the Softmax Response, which is a known top-performing method. It is shown that the proposed method can achieve a broader range of operation and cover a lower FPR than the alternative.
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text

Logical reasoning of text requires identifying critical logical structures in the text and performing inference over them. Existing methods for logical reasoning mainly focus on contextual semantics of text while struggling to explicitly model the logical inference process. In this paper, we not only put forward a logic-driven context extension framework but also propose a logic-driven data augmentation algorithm. The former follows a three-step reasoning paradigm, and each step is respectively to extract logical expressions as elementary reasoning units, symbolically infer the implicit expressions following equivalence laws and extend the context to validate the options. The latter augments literally similar but logically different instances and incorporates contrastive learning to better capture logical information, especially logical negative and conditional relationships. We conduct experiments on two benchmark datasets, ReClor and LogiQA. The results show that our method achieves state-of-the-art performance on both datasets, and even surpasses human performance on the ReClor dataset.
Polymer-Directed Assembly of Single Crystal Zinc Oxide/Magnetite Nanocomposites under Atmospheric and Hydrothermal Conditions

Within the field of crystal growth it is recognized that secondary species can sometimes be occluded within a growing crystal according to the crystallization conditions and pairing of the additive and host crystal. This article takes inspiration from this phenomenon to create multifunctional inorganic nanocomposites with unique structures: inorganic single crystals containing embedded inorganic nanoparticles. Using magnetite (Fe3O4)/ZnO as a suitable test system, ZnO crystals are precipitated from aqueous solution at 90 °C and atmospheric pressure in the presence of Fe3O4 nanoparticles functionalized with anionic diblock copolymers. Analysis of product nanocomposite crystals using atomic absorption spectroscopy shows that the Fe3O4 nanoparticles are embedded within the ZnO single crystal hosts at levels of approximately 10 wt %, and TEM analysis shows that there is no apparent discontinuity between the nanoparticles and host crystal matrix. Importantly, we then demonstrate that this occlusion approach ca.