Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Antidiabetic Profile of Ayurvedic Herbo-mineral Formulation and its Constituents on Normal and Streptozotocin-induced Diabetic Rats

Madhusunya is a commercially available Ayurvedic polyherbo-mineral preparation composed of nine medicinal plants i.e. Gymnema sylvestre, Momordica charantia, Syzygium cumini, Azadirachta indica, Withania somnifera, Hibiscus rosa-sinensis, Ocimum sanctum, Pterocarpus marsupium and Coccinia indica and a herbomineral substance ‘Shilajeet’. It improved oral glucose tolerance post sucrose load on both normal as well as STZ-induced diabetic rats, however, the effects of its constituents on oral glucose tolerance was not well marked at the concentration present in the formulation. Madhusunya treatment also resulted in lowering of elevated blood glucose and glycosylated hemoglobin (HbA1c) levels and increasing of declined serum insulin levels of STZ-induced diabetic rats when given for 21 consecutive days. 'Madhusunya' treatment also decreased the elevated levels of triglycerides, total cholesterol, LDL-cholesterol and increased the declined HDL-cholesterol levels in sera of STZ-induced diabetic rats. The renal as well as hepatic pathology were also found improved in the STZ-induced diabetic rats treated with Madhusunya. However, the effect of constituents except on hepatic parameters and renal function parameters was not prominent. This study provides at least some logic for using antidiabetic polyherbomineral formulation rather than the individual plant powder in the treatment of dysglycemia and associated complications in T2DM patients in the ayurvedic system of medicine.
Evaluation of HVDC cable impedance and admittance matrices by finite element method

In this work, a 2D Finite Element Method (FEM) is used to calculate the frequency dependent impedance and admittance matrices of underground and submarine cables. A harmonic magnetodynamic formulation is used to calculate the series impedances, and for the calculation of the parallel admittance an electrodynamic formulation is applied. The frequency response of the cable was obtained by coupling the FEM method with an external electric circuits. This technique allows the physical representation of the nonlinear behavior of the frequency dependence of the cable parameters. The numerical results are compared with those obtained from commonly used analytical formulations and other methods available in the literature. Different surrounding environment cases are treated, and their effect is shown in frequency. Time domain simulations under fault circumstances point out the necessity of an accurate cable model. Current and voltage measurements are the input of protection algorithm and breaking decision depends on these values. Therefore, the use of a precise method is mandatory.
Overview of the Meso-NH model version 5.4 and its applications

Abstract. This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.
Use of NHANES Data to Link Chemical Exposures to Chronic Diseases: A Cautionary Tale

Background The National Health and Nutrition Examination Survey (NHANES) is one example of cross-sectional datasets that have been used to draw causal inferences regarding environmental chemical exposures and adverse health outcomes. Our objectives were to analyze four NHANES datasets using consistent a priori selected methods to address the following questions: Is there a consistent association between urinary bisphenol A (BPA) measures and diabetes, coronary heart disease (CHD), and/or heart attack across surveys? Is NHANES an appropriate dataset for investigating associations between chemicals with short physiologic half-lives such as BPA and chronic diseases with multi-factorial etiologies? Data on urinary BPA and health outcomes from 2003–2004, 2005–2006, 2007–2008, and 2009–2010 were available. Methodology and Findings Regression models were adjusted for creatinine, age, gender, race/ethnicity, education, income, smoking, heavy drinking, BMI, waist circumference, calorie intake, family history of heart attack, hypertension, sedentary time, and total cholesterol. Urinary BPA was not significantly associated with adverse health outcomes for any of the NHANES surveys, with ORs (95% CIs) ranging from 0.996 (0.951–1.04) to 1.03 (0.978–1.09) for CHD, 0.987 (0.941–1.04) to 1.04 (0.996–1.09) for heart attack, and 0.957 (0.899–1.02) to 1.01 (0.980–1.05) for diabetes. Conclusions Using scientifically and clinically supportable exclusion criteria and outcome definitions, we consistently found no associations between urinary BPA and heart disease or diabetes. These results do not support associations and causal inferences reported in previous studies that used different criteria and definitions. We are not drawing conclusions regarding whether BPA is a risk factor for these diseases. We are stating the opposite–that using cross-sectional datasets like NHANES to draw such conclusions about short-lived environmental chemicals and chronic complex diseases is inappropriate. We need to expend resources on appropriately designed epidemiologic studies and toxicological explorations to understand whether these types of chemicals play a causal role in chronic diseases.
DeepEmo: Learning and Enriching Pattern-Based Emotion Representations

We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most state-of-the-art emotion recognition techniques.
Greening Internet of Things for Smart Everythings with A Green-Environment Life: A Survey and Future Prospects

Tremendous technology development in the field of Internet of Things (IoT) has changed the way we work and live. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also consumes energy, embraces toxic pollution and E-waste. These place new stress on the environments and smart world. In order to increase the benefits and reduce the harm of IoT, there is an increasing desire to move toward green IoT. Green IoT is seen as the future of IoT that is environmentally friendly. To achieve that, it is necessary to put a lot of measures to reduce carbon footprint, conserve fewer resources, and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, communications, sensors, clouds, and internet are alongside energy efficiency and reducing carbon emission. This paper presents a thorough survey of the current on-going research work and potential technologies of green IoT with an intention to provide some clues for future green IoT research.
A Unified Theory of Regression Adjustment for Design-based Inference

Under the Neyman causal model, it is well-known that OLS with treatment-by-covariate interactions cannot harm asymptotic precision of estimated treatment effects in completely randomized experiments. But do such guarantees extend to experiments with more complex designs? This paper proposes a general framework for addressing this question and defines a class of generalized regression estimators that are applicable to experiments of any design. The class subsumes common estimators (e.g., OLS). Within that class, two novel estimators are proposed that are applicable to arbitrary designs and asymptotically optimal. The first is composed of three Horvitz-Thompson estimators. The second recursively applies the principle of generalized regression estimation to obtain regression-adjusted regression adjustment. Additionally, variance bounds are derived that are tighter than those existing in the literature for arbitrary designs. Finally, a simulation study illustrates the potential for MSE improvements.
Using locality of reference to improve performance of peer-to-peer applications

Peer-to-peer, or simply P2P, systems have recently emerged as a popular paradigm for building distributed applications. One key aspect of the P2P system design is the mechanism used for content location. A number of different approaches are currently in use. In particular, the location algorithm used in Gnutella, a popular and extensively analyzed P2P file sharing application, is based on flooding of messages in the network, which results in significant processing overhead on the participant nodes and thus, poor performance.In this paper, we provide an extensive performance evaluation of alternative algorithms for content location and retrieval in P2P systems, in particular, the Freenet and Gnutella systems. We compare the original Freenet and Gnutella algorithms, a previously proposed interest-based algorithm and two new algorithms which also explore locality of interest among peers to efficiently allow content location. Unlike previous proposals, the new algorithms organize the peers into communities that share interests. Two peers are said to have common interest if they share some of the locally stored files.In order to evaluate the performance of these algorithms, we use a previously developed Freenet simulator and build a new Gnutella simulator, which includes several realistic system characteristics. We show that the new community-based algorithms improve the original Gnutella content location latency (and thus the system QoS) and system load by up to 31% and 30%, respectively. Our algorithms also reduce the average Freenet request and response path lengths by up to 39% and 31%, respectively. Furthermore, we show that, compared to the previously proposed interest-based algorithm, our new algorithms improve query latency by up to 27% without a significant increase in the load.
Physicians disciplined by a state medical board.

CONTEXT State medical boards discipline several thousand physicians each year. Although certain subgroups, such as those disciplined for malpractice, substance use, or sexual abuse, have been studied, little is known about disciplined physicians as a group. OBJECTIVE To assess the offenses, contributing factors, and type of discipline of a consecutive series of disciplined physicians. DESIGN Case-control study on publicly available data matching 375 disciplined physicians with 2 groups of control physicians, one matched solely by locale, and a second matched for sex, type of practice, and locale. SUBJECTS All disciplined physicians publicly reported by the Medical Board of California from October 1995 through April 1997. MAIN OUTCOME MEASURES Characteristics of disciplined physicians, offenses leading to discipline, and type of discipline. RESULTS A total of 375 physicians licensed by the Medical Board of California (approximately 0.24% per year) were disciplined for 465 offenses. The most frequent causes for discipline were negligence or incompetence (34%), abuse of alcohol or other drugs (14%), inappropriate prescribing practices (11%), inappropriate contact with patients (10%), and fraud (9%). Discipline imposed was revocation of medical license (21%), actual suspension of license (13%), stayed suspension of license (45%), and reprimand (21%). Type of offense was significantly associated with severity of discipline (P=.03). In logistic regression models comparing disciplined physicians with controls matched by locale, board discipline was significantly associated with physicians' sex (odds ratio [OR] for women, 0.44; 95% confidence interval [CI], 0.28-0.70) and involvement in direct patient care (OR, 2.56; 95% CI, 1.75-3.75). In the regression model with additional matching criteria, disciplinary action was negatively associated with specialty board certification (OR, 0.42; 95% CI, 0.29-0.60) and positively associated with being in practice more than 20 years (OR, 2.02; 95% CI, 1.39-2.92). CONCLUSIONS A small but substantial proportion of physicians is disciplined each year for a variety of offenses. Further study of disciplined physicians is necessary to identify physicians at high risk for offenses leading to disciplinary action and to develop effective interventions to prevent these offenses.
Elastomeric micropillar arrays for the study of protrusive forces in hyphal invasion

Fungi and oomycetes are microorganisms whose pathogenic growth causes significant economic losses and disease. The growth of the hypha of these organisms involves turgor pressure, cell wall yielding and a dynamic cytoskeleton. To study the role of microtubules, actin and turgor in the generation of protrusive force, we have developed a Lab-on-a-Chip platform with integrated force-sensors based on elastomeric micro-pillars. The oomycete Achlya bisexualis was cultured on-chip and a maximum total force of 16 μN was recorded for this organism. The platform provides a useful tool to study the molecular mechanisms for generating protrusive force, such as cytoskeletal dynamics.
Metacognitive Therapy Applications in Social Anxiety Disorder: An Exploratory Study of the Individual and Combined Effects of the Attention Training Technique and Situational Attentional Refocusing

Individuals with Social Anxiety Disorder have difficulty disengaging from self-processing in social situations. Metacognitive therapy interventions for enhancing attentional control were administered to a convenience sample of 24 with a Social Anxiety Disorder diagnosis. Using a cross-over design, 11 participants were given four weekly sessions of Attention Training Technique (ATT), followed by four weekly sessions of Situational Attentional Refocusing (SAR). For the other 13 participants the two treatment components were given in the reverse order. All participants made significant reductions on interview rated and self-reported measures of social and general levels of anxiety by the end of the first intervention (either ATT or SAR). Following completion of the second treatment components, further reductions were observed and 46% (n = 11) of the total sample no longer met DSM-IV criteria for Social Anxiety Disorder diagnosis. Two large order effects were found favoring patients receiving SAR interventions first. Overall these brief techniques aimed at increasing attentional flexibility were associated with large and clinically significant changes in Social Anxiety Disorder symptoms.
Lateral conduction within CNT-based planar microcoils on silicon substrate

Electrodeposition of copper in high aspect ratio (HAR) trenches has always been a challenging step in the fabrication of on-chip microcoils. In contrast, CNTs can be easily grown even in HAR trenches but have an anisotropic behaviour, viz. with large electrical conductivity only along the CNT length. A new hetero-structure consisting of graphite-fused CNTs solves this problem by providing lateral current conduction. This paper reports on-chip planar micro-coils made from the CNT-graphitic heterostructures in HAR trenches and their electrical characterization.
Heuristic evaluation for e-Government websites in Saudi Arabia

This paper reports on an evaluation of two e-Government websites in Saudi Arabia. The aim of the research was to adapt a heuristic evaluation approach that would explain usability problems found on Saudi e-Government websites. The work defines an evaluative approach that offers a detailed view of web design on e-Government websites. The evaluation of the two websites gave a mixed picture with both sites meeting some of the heuristics to a greater or lesser extent. These results will facilitate the improvement of the websites allowing such websites to be more effective in the way they deliver information, and identified the current usability problems found in e-Government websites in Saudi Arabia.
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification

The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predictor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications, e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontarget prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCF-based metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.
Thermal conductivity of unsaturated clay-rocks

Abstract. The parameters used to describe the electrical conductivity of a porous material can be used to describe also its thermal conductivity. A new relationship is developed to connect the thermal conductivity of an unsaturated porous material to the thermal conductivity of the different phases of the composite, and two electrical parameters called the first and second Archie's exponents. A good agreement is obtained between the new model and thermal conductivity measurements performed using packs of glass beads and core samples of the Callovo-Oxfordian clay-rocks at different saturations of the water phase. We showed that the three model parameters optimised to fit the new model against experimental data (namely the thermal conductivity of the solid phase and the two Archie's exponents) are consistent with independent estimates. We also observed that the anisotropy of the effective thermal conductivity of the Callovo-Oxfordian clay-rock was mainly due to the anisotropy of the thermal conductivity of the solid phase.
Consistent Excitement Correlates with Happiness-Predicting Mood Through Body Sensing with Smartwatches

We built a body sensing system using commercially available smartwatches to collect individual mood states and correlate them with body sensing data. In particular, in this first prototype we collected acceleration, heart rate, and light level data from the smartwatch, and location through the GPS sensor of the smartphone. We also polled users on the smartwatch for a week four times per day asking for their mood state. We found that the higher the heart rate, the higher the level of activity sampled through changes in x,y,z coordinates of the accelerometer, and the more consistent the movement, tracked as lower variance in the x-coordinate of the accelerometer was, the higher the individual self-reported level of pleasance and activation was. This system is a first step towards building an automated mood tracking system to be used for better teamwork.
OTyper: A Neural Architecture for Open Named Entity Typing

Named Entity Typing (NET) is valuable for many natural language processing tasks, such as relation extraction, question answering, knowledge base population, and co-reference resolution. Classical NET targeted a few coarse-grained types, but the task has expanded to sets of hundreds of types in recent years. Existing work in NET assumes that the target types are specified in advance, and that hand-labeled examples of each type are available. In this work, we introduce the task of Open Named Entity Typing (ONET), which is NET when the set of target types is not known in advance. We propose a neural network architecture for ONET, called OTyper, and evaluate its ability to tag entities with types not seen in training. On the benchmark FIGER(GOLD) dataset, OTyper achieves a weighted AUC-ROC score of 0.870 on unseen types, substantially outperforming pattern- and embedding-based baselines.
Synthesis and Characterization of Cobalt Ferrite Nanoparticles

Cobalt ferrite can be synthesized using several techniques. However, to have wide applications in electronic, medical fields etc., the synthesis technique should be simple and inexpensive. The synthesis technique used to prepare nanoparticles should not consume lot of time and energy. Also it should yield narrow particle size distribution and homogeneity in the prepared material. It was observed that surface modification such as with silica coating on the cobalt ferrite will have significant effect on the structural and magnetic properties. It is also observed that, silica coated nanoparticles could be used in biomedical applications (Hong et al., 2013). In this work we have chosen sol-gel method to synthesize pure cobalt ferrite (CoFe2O4) and silica coated (CoFe2O4 / SiO2) nanoparticles. To observe the effect of silicate coating on the structural and magnetic properties of CoFe2O4 we have carried out the present study. CoFe2O4 nanoparticles were synthesized with SiO2 coating and in pure form by sol-gel method. The obtained particle sizes were 24 and 26 nm in both the cases. The X-ray diffraction patterns showed the formation of CoFe2O4 spinel structure without any traces of SiO2 in the prepared samples. The Infrared spectra showed the bands corresponding to tetrahedral and octahedral sites as feature of typical spinel ferrites and also band due to SiO2. The particle size and morphology of CoFe2O4 / SiO2 was found to be uniform but in the case of pure CoFe2O4 somewhat agglomerated which is accounted for magnetization of ferrites. The magnetization value for CoFe2O4 / SiO2 showed a drastic decrease when compared to pure CoFe2O4 due to presence of non-magnetic coating layer.