Miracle-dz/UniFAR-FTUs huggingface dataset:
Adhesion Prevention Strategies in Laparoscopic Surgery

Adhesions are defined as abnormal attachments between tissues and organs [1]. Intra-ab‐ dominal adhesions may be classified as congenital or acquired [2]. Congenital adhesions are a consequence of embryological anomaly in the development of the peritoneal cavity. Ac‐ quired adhesions result from the inflammatory response of the peritoneum that arises after intra-abdominal inflammatory processes (e.g. acute appendicitis, pelvic inflammatory dis‐ ease, exposure to intestinal contents and previous use of intrauterine contraceptive devices), radiation and surgical trauma [3]. It has been reported that the majority of acquired adhe‐ sions (about 90%) are post-surgical [2].
Influence of Sewage Sludge Biochar on the Microbial Environment, Chinese Cabbage Growth, and Heavy Metals Availability of Soil

The effects of sewage sludge biochar (SSB) on the microbial environment, Chinese cabbage yield, and heavy metals (HMs) availability of soil were comprehensively investigated in this study. Results showed that the concentrations of the dehydrogenase (DHA) and urease in the soil added with 10% SSB were 3.60 and 1.67 times as high as that of the control soil, respectively, after planting; the concentrations of the bacteria, fungi, ammonia-oxidizing archaea (AOA), and ammonia-oxidizing bacteria (AOB) in the soil added with 10% SSB after planting reached 2.84, 2.62, 1.76, and 2.23 times, respectively, compared with those of the control group; the weights of the aboveground and underground parts of Chinese cabbage were 5.82 and 8.67 times as high as those of the control group, respectively. Moreover, the addition of SSB enhanced the immobilization of Cr, Ni, and Cd. All in all, SSB can improve the microbial environment of soil and inhibit the availability of HMs, which is very important for their utilization in barren soil.
Síndrome metabólico II: del concepto a la acción en alimentación y nutrición

El reto para los profesionales de la salud es grande, no solo en la prevención de las enfermedades crónicas relacionadas con lo alimentario y nutricional, como el síndrome metabólico (SM) y sus patologías asociadas, sino con el mantenimiento de la salud desde las etapas tempranas de la vida. El SM caracterizado por la presencia de varios de los problemas relacionados con la resistencia a la insulina, demanda de un tratamiento ajustadoa las necesidades de cada paciente, en el que interactúan dieta, actividad física y de ser necesario farmacoterapia. No hay una dieta única para el tratamiento del SM, es relevante identificar cada alteración metabólica y hacer recomendaciones dietarias específicas y contextualizadas a la situación del paciente. En general, se requiere de una dieta balanceada que cumpla con los requerimientos calóricos, sin incurrir en excesos, ni deficiencia de ningún macro o micronutriente. Un tratamiento alimentario y nutricional exitoso requiere de la definición de metas y de la identificación de barreras que pueda tener el individuo para el cambio. Para generar una identificación de los factores de riesgo es preciso motivar al paciente, para el desarrollo de estrategias que contribuyan a la modificación de sus hábitos alimentarios y de su estilo de vida.
Using a null model approach to explore how dynamic interactions in wildlife are analyzed

The nature of interactions between individuals of an animal population is a fundamental aspect of a species' behavioural ecology and information on the frequency and duration of these interactions is vital to understanding mating and territorial behaviour, resource use, and infectious disease epidemiology. There have been two main ways to quantify interactions in wildlife studies: 'static interactions', which involve some quantification of home range overlap (and are therefore just spatial), and 'dynamic interactions' which are defined as occurring within a spatial and temporal threshold. Dynamic interactions can provide information on possible attraction and avoidance of individuals that are in the same area at the same time and are far more useful for understanding how two individuals interact in the context of disease transmission and behavioural ecology, but they are more problematic to measure. Few studies have tested different dynamic interaction metrics using the same data, and when they have been compared, the results have been quite variable (Miller, 2012, Long and Nelson, 2014). Most currently used dynamic interaction metrics lack a benchmarking framework that deals with null models or expected values for neutral interaction in order to facilitate more meaningful interpretation of their values (e.g., what are the values for each metric that would be associated with social behaviors be consistent with 'attraction,' 'repulsion,' or 'no interaction'? ). The research presented here borrows from the null model approach commonly used in community ecology to compare observed (empirical) dynamic interaction values with distributions of expected values generated by using different null models.
Conceptualising contemporary friendship : A closeness-location model of friendship

Researchers examining the composition of people‟s friendship and social networks typically categorise friendship into groups based on the level of closeness in the friendship. These groups often include, for example, best friend, close friend, friend and acquaintance. Research has shown, however, that friendship is a highly nuanced and personal relationship that is difficult to universally define. Using a priori definitions or frameworks of friendship may not capture the broad spectrum of friendship and may limit the scope of the study. Subsequently, further research is needed into people‟s every day, personal conceptions of their friendship networks. Using data from a larger research project, this study examines the friendship and social grouping patterns of a group of 28 Facebook users. Results of a friendship-sorting exercise did not support a closeness-based model of friendship. Rather, results indicated that friendship groupings tend to consist of one closeness-based group with the remainder based on the location of the friend (i.e. where the friend was first met). Based on these results, this paper suggests that a closeness-location model of friendship best represents contemporary friendship.
FUNCTIONAL RECOVERY ENHANCEMENT BY TIGER MILK MUSHROOM, LIGNOSUS RHINOCEROTIS IN A SCIATIC NERVE CRUSH INJURY MODEL AND MORPHOLOGICAL STUDY OF ITS NEUROTOXICITY

Insights into the process of peripheral nerve regeneration are essential for the development of novel therapies. We tested the activity of aqueous extract from Lignosus rhinocerotis mushroom on nerve regeneration in a rat model of sciatic nerve crush injury and to examine (if any) toxicity effect of the extract on nervous tissues. Activities of aqueous extracts were compared to mecobalamin, a drug for peripheral neuropathies. 24 adult rats were randomly divided to 4 groups, the negative controls, positive controls, low and high dosages of aqueous extracts. Functional recovery was evaluated by sensitivity to thermal pain and toe-spreading reflex. Morphological changes in the nervous tissues was assessed by haematoxylin and eosin stain. Hot plate test showed acceleration of sensory recovery in low dosage group compared to other groups. Return of toe-spreading in the crushed limb was accomplished by day 12 and day 16 in low and high dosage groups, respectively after crush injury. Microscopic examination showed normal features of Peripheral and Central Nervous Tissues. There were no abnormal clinical signs detected. Oral administration of low dosage of L. rhinocerotis aqueous extract is capable to enhance motor and sensory functional recovery after nerve injury and had no adverse effects on nervous tissues.
A Low-Energy and High-Performance Voltage Level Shifter Using Dual-Supply Applications

This paper presents a design of low-energy and high-performance voltage levelshifting circuit is to capable of shifting low core voltage to high input-output voltage. The proposed circuit performance is due to the fact not only strengthen the pull-up device and slowly reduced when the pull-down device is pulling output node to down, but the strength of the pull-down device is also increases low-power circuit. Simulation results of the proposed circuit in a 90 nm technology calculate propagation delay of 7 ns and input frequency of 200 GHz, a static power consumption of 0.12 nW, a total power per transition of 135 fJ, and, low supply voltage levels of VDDL=0.9V, and high supply voltage levels of VDDH=1.8 V. Index Terms -Multi supply voltage design, Level converter, low power sub-threshold operation, voltage level shifter.
A Novel Dataset of Brazilian Rhythmic Instruments and Some Experiments in Computational Rhythm Analysis

This paper describes and releases the Brazilian Rhythmic Instruments Dataset, assembled for research in Music Information Retrieval. It is comprised of 274 soloand 93 multiple-instrument recorded tracks of 10 different instrument classes with variations (e.g., material, size, stick) playing in 5 main rhythm classes (samba, partido alto, samba-enredo, capoeira, and marcha). We describe the dataset recording process and content, and a few examples of challenges it launches for computational rhythm analysis.
2 Datasets With Extractive Answers

Machine reading comprehension aims to teach machines to understand a text like a human, and is a new challenging direction in Artificial Intelligence. Datasets play an important role while describing or building an algorithm for machine reading comprehension. The type of answers we required from developed algorithm depends on datasets.The datasets are classified into two types, namely datasets with extractive answers and datasets with descriptive answers. This article summarize both datasets with an example of each type to get better insight of datasets in machine reading comprehension and which datasets to use depending the requirements.
Importancia de la biología molecular para la Fisioterapia moderna

RESUMEN Para que el cuerpo de conocimiento de una profesion crezca y se fortalezca debe estar al dia con losavances cientificos y tecnologicos que surgen continuamente para incluirlos en el repertorio de recursosque usa para la investigacion de problemas especificos de su saber. Recientemente el desciframiento delcodigo genetico y la secuenciacion del genoma humano creo la base para el surgimiento de metodologiasy tecnicas en el area de la biologia molecular, las cuales permitieron profundizar en el conocimiento dela estructura y funcion de los tejidos humanos y tambien mejoraron el entendimiento de los mecanismospor los cuales actuan formas de intervencion usadas cotidianamente por profesionales en salud. LaFisioterapia utiliza modalidades fisicas que interactuan con los tejidos corporales, por ello la biologiamolecular permite un mejor entendimiento de los efectos que las dichas modalidades generan en eltejido sobre el cual son aplicadas. Por tanto el objetivo de este articulo es reflexionar sobre la necesidadde que el Fisioterapeuta se apropie del conocimiento en esta area de las ciencias basicas, usarlo comoherramienta para la solucion de preguntas relevantes de su quehacer clinico y asi contribuir de maneraefectiva con la generacion de nuevo conocimiento que promueva la practica basada en la evidencia yfomente el crecimiento de la profesion. Salud UIS 2011; 43 (3): 317-320 Palabras clave: Fisioterapia, biologia molecular, practica basada en la evidencia ABSTRACT A profession can be improved through the development and application of scientific and technologicaladvances around the issues relating to their expertise. Recently, the deciphering of the genetic code andhuman genome sequencing creates the basis for the development of methodologies and techniques ofmolecular biology. These resources have allowed a deeper understanding of the human tissue structureand function, and intervention mechanisms used by health professionals. Physiotherapy uses physicalmodalities affecting the tissues of the body, for that reason, molecular biology offers professionals abetter understanding of the effect of these types of interventions implemented in different tissues. Thus,the Physical therapists should be aware about the importance of this basic science and its clinical usein everyday problem solving that generate a new evidence-based practice to contribute to professionaldevelopment. Salud UIS 2011; 43 (3): 317-320 Key words: Physical therapy, molecular biology, evidence-based practice
Influence of Student Engagement on Learning Gains

The student outcomes of learning and personal development are influenced by the degree to which an institute can channelize the efforts of students towards performing educationally purposeful activities (Astin, 1993; Pascarella & Terenzini, 1991; Pace, 1980). The institutional practices such as active and collaborative learning, student faculty interactions, enriching educational environment, supportive campus, discussions with diversity, are known to lead to higher levels of student engagement (Astin, 1991; Chickering & Reisser, 1993; Kuh, Schuh, Whitt & et.al, 2005; Pascarella & Terenzini, 1991). Chickering and Gamson (1987) had prescribed seven principles of good practices in undergraduate management education that foster higher level of student engagement and learning. These principles include student-faculty contact, cooperation among students, active learning, prompt feedback, time on task, high expectations and, respect for diverse talents and ways of learning. Institutes that are educationally more effective in adding value direct students’ energies towards appropriate activities to engage them at fruitfully. Based on the learning outcomes, such institutes can claim to be of higher quality as compared to other similar institutes. The successful institutes ensure creation and maintenance of an environment which leads to engaged student. An engaged student cocreates better learning with the faculty and peers (Singh & Srivastava,2014; Saha,2014). This study conducted with the responses of 1100 undergraduate students of management classes drawn from five institutions of a private university located in National Capital Region of India attempts to determine if the empirical data supports the six-factor structure of student engagement. Then goes on to test through a structural equation model the twelve hypothesized influences of six student engagement factors on the two learning gains. The result showed that only two factors 'effective teaching practices' and 'supportive environment' significantly influence both the outcomes of 'critical thinking' and 'socio-cultural skills'. Implications of the research have been discussed and directions for future research outlined.
Scalable GP-LSTMs with Semi-Stochastic Gradients

There exists a vast array of machine learning applications where the underlying datasets are sequential. Applications range from the entirety of robotics, to speech, audio and video processing. While neuralnetwork-based approaches have dealt with the issue of representation learning for sequential data, the important question of modeling and propagating uncertainty across time has rarely been addressed by these models. For a robotics application such as a self-driving car, however, it is not just desirable, but essential to have complete predictive densities for variables of interest. When trying to stay in lane and keep a safe following distance from the vehicle front, knowing the uncertainty associated with lanes and lead vehicles is as important as the point estimates.
Sparse Neural Networks for Anomaly Detection in High-Dimensional Time Series

Online anomaly detection in time series is an important component for automated monitoring. In many applications, time series are highdimensional with tens or even hundreds of variables being monitored simultaneously. We note that existing anomaly detection approaches based on recurrent autoencoders may not be very effective for high-dimensional time series. In this work, we propose a simple, yet effective, extension to such approaches for high-dimensional time series. Our approach combines the advantages of non-temporal dimensionality reduction techniques and recurrent autoencoders for time series modeling through an end-to-end learning framework. The recurrent encoder gets sparse access to the input dimensions via a feedforward layer while the recurrent decoder is forced to reconstruct all the input dimensions, thereby leading to better regularization and a robust temporal model. The autoencoder thus trained on normal time series is likely to give a high reconstruction error, and a corresponding high anomaly score, for any anomalous time series pattern. We prove the efficacy of the proposed approach through experiments on a public dataset and two real-world datasets with significant improvement in anomaly detection performance over several baselines. We observe that the proposed approach is able to perform well even without knowledge of relevant dimensions carrying the anomalous signature in a high-dimensional setting.
The Cost of Equity Implications of Accounting for Employee Stock Options

This study examines whether the accounting treatment of employee stock options affects the cost of equity capital. Using the change in accounting imposed by SFAS 123R that required recognition of an expense for the fair value of employee stock option grants, we analyze whether the new accounting standard had an effect on the relation between the value of option grants and the cost of equity. Our analysis is motivated by theoretical models that relate the presentation of accounting information to firm value (Hirschleifer and Teoh 2003) and the effects of accounting information about expected future cash flows and the cost of capital (Lambert et al. 2007), as well as the controversial debate regarding expense recognition leading to implementation of SFAS 123R. The results show that the cost of equity was decreasing in the fair value of option grants before SFAS 123R was implemented, which is consistent with theories of limited information processing of disclosed accounting information. However, we find that once expense recognition was required, the benefit of a lower cost of equity from option grants was eliminated. We also find that the cost of equity benefits under favorable accounting treatment were greater for firms operating in new economy industries, which is consistent with the information effects from expense recognition being more significant for this set of firms.
The Short-Run and Long-Run Effects of Inequality on Inflation

This paper examines the short-run and long-run effects of inequality on inflation. It examines separately data for the United States and several panels of international data. The reason for this strategy is that a variety of measures of inequality are available for the United States for a long period. The panel data sets are limited to a shorter period, but cover a wide range of countries. The cointegration test and the Vector Error Correction Model (VECM) provide evidence that there exists a negative long-run relationship between inequality and inflation in the United States. In the multi-country panels, the Autoregressive Distributed Lag (ARDL) model confirms the negative long-run relationship, and the Arellano-Bond GMM estimator reveals a negative short-run effect. The paper lays out the econometric evidence for this relationship and analyzes the underlying political economy that explains the relationships between inequality and inflation.
Learning andSelectingUsers forAchievingReliability in DemandResponse : AMulti-armedBanditApproach

One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior. This paper focuses on residential demand response (DR) and proposes a closed-loop learning scheme to address these issues. In particular, we consider DR programs where an aggregator calls upon residential users to change their demand so that the total load adjustment is close to a target value. To learn and select the right customers, we formulate the DR problem as a combinatorial multi-armed bandit (CMAB) problem with a reliability objective. We propose a learning algorithm: CUCBAvg (Combinatorial Upper Confidence Bound-Average), which utilizes both upper confidence bounds and sample averages to balance the tradeoff between exploration (learning) and exploitation (selecting). We consider both a fixed time-invariant target and time-varying targets and show that CUCB-Avg achieves O(log T ) and O( √ T log(T ) regrets respectively. We further numerically test our algorithms using synthetic realistic DR data and demonstrate that our CUCB-Avg performs significantly better than the classic CUCB and better than Thompson Sampling.
The Process of Transformational Leadership inAchieving High Performance Workforce that Exceeds Organisational Expectations: A Phenomenological Case Study

Transformational leadership theory has acquired the interest of many researchers in the discipline of organisational leadership since the last three decades. The main foundation of the transformational leadership theory is the leader’s aptitude to stimulate the follower to accomplish more than what the follower planned to accomplish. This study will focus on the main components of transformational leadership namely, idealised influence, inspirational motivation, intellectual stimulation, and individualised consideration. However, for this study, Islamic perspective was added to this research. This addition is mainly because in light of new literature on leadership in Islam, it was found that most of the teachings of Islam embodies many of the concepts of modern leadership theories which are transformational in nature. A number of studies have investigated the effect of transformational leadership on the performance of organisation. However, there is an extensive gap in the research literature regarding the processes through which a transformational leader applies this impact to change his followers to be the best performer that are extremely dedicated to the organisation hence this study will be based on the issue stated above. The qualitative phenomenological case study methodology was chosen based on the objective of conducting this particular research study. The purpose of this study was to explore the process actualised by transformational leaders which will eventually lead to the effectiveness of transformational leadership in accomplishing to support a high-performance workforce that will exceeds the organisational expectations.
IMAGE SEGMENTATION USING MULTILAYER COORDINATED CLUSTERS REPRESENTATION

In this paper we present the rotation-invariant multilayer Coordinated Clusters Representation (CCR) colour texture descriptor. The central idea is to describe the colour content of an image by means of a reduced set of representative colours, and then to split the image into a stack of binary images, one for each colour of the reduced palette. The feature vector for each pixel consists of the codes of the corresponding patterns of binary texture that occur in each layer. The validity of the model has been demonstrated through the segmentation of both synthetic mosaics of OuTex textures and multispectral IKONOS-2 satellite images. Experimental results show that the proposed features have high discriminative power and yield increased accuracy compared to other segmentation methods.
Practical Papercraft Models from Meshes Student :

We present a new approach to generate papercraft models from meshes. The input mesh is approximated by a set of quadric surface proxies. Each quadric proxy is then cut and unfolded into a 2D papercraft pattern. Our method has the following advantages : First, we produce smoother papercraft models than previous methods. Second, the 2D patterns of papercraft models are easy to cut and glue. Finally, the 2D patterns of papercraft models are more meaningful due to the pre-defined cutting rules. We demonstrate this by physically assembling papercraft models from our algorithm.