Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravity Law without Universalism is Solving Many Tasks

My MOND proposal includes General Relativity as a special case, i.e. I have effects of General Relativity in many areas of spacetime. I argue that my proposal can describe Dark Matter as well if one understands the modification of gravity as a tensor field X^{\mu\nu}(t,x,y,z) in the Einstein equations, i.e. as an additional mathematical parameter filling the Universe without correspondence to new particles. Notably, there are many different fields in nature, e.g. the Higgs field, the inflaton field, and the temperature distribution field T(t,x,y,z). My testable prediction that they will never find weakly interacting Dark Matter particles is well-realized up to today; therefore, Popper's falsifiability criterion is satisfied because the underground detectors could report the signal. On the other hand, the testable proof would be the indirect discovery of the sterile neutrino. As it does not interact with visible matter even weakly, it is an example of X^{\mu\nu}. This justified theory, applied to key problems of Physics, provides satisfactory answers.
AMPHIBIAN VOCALIZATION: IMPLICATIONS OF A NOVEL LARYNGEAL MUSCLE IN THE CALLING MECHANISMS OF THE TU ́NGARA FROG ENGYSTOMOPS PUSTULOSUS

By Amy D. Lagorio University of the Pacific 2020 The current functional model of the anuran larynx includes four pairs of laryngeal muscles. Their contractions do not account, however, for the behavioral control of call complexity observed in male túngara frogs (Engystomops pustulosus), which optionally add a secondary note with distinct harmonic structure to their advertisement call. Examination of the túngara frog's laryngeal morphology through dissection, microtomography, and resin histology has revealed that the m. dilatator laryngis is divided into two separate bundles (superficial and deep). The superficial bundle closely matches the typical description of the m. dilatator laryngis and is well positioned to open the glottis. The deep bundle is exclusively innervated by the short laryngeal nerve and has an attachment to the fibrous mass, an internal laryngeal structure necessary for complex call production. This attachment indicates a separate role for the deep bundle in controlling the complexity of the call. Based on physical separation, exclusive attachments, distinct fiber orientation, exclusive innervation, and potential action, this study recognizes the deep bundle of the m. dilatator laryngis as a separate muscle. It also revalidates the name m. arylabialis which had been previously used to describe it. The split of the m. dilatator laryngis into two muscles results in a laryngeal innervation pattern that closely matches that of mammals. This study identified a novel laryngeal muscle in túngara frogs, a potential
USING MACHINE LEARNING TO PREDICT PRICE DISPERSION

Theory suggests two sources of price dispersion amongst homogenous goods: market frictions or product heterogeneity. We collected posted-price listings for Kindle Fire tablets from eBay to determine if listing heterogeneity can explain the high degree of dispersion we observe. Using a basic set of controls and empirical techniques in line with the previous literature, we can explain only 13% of variation in posted prices, which is also in keeping with previous research. However, we can explain 42% of the dispersion by applying machine learning to a richer set of variables, which we extract from raw downloaded HTML pages. We interpret this number as a bound on the role of market frictions in driving price dispersion. Variables describing the amount of information in the listings, the style of the listings, and the content of the listings’ text are effective price predictors independently of one another. Our analysis suggests that the content of the listings’ text plays a primal role in generating the predictions of the machine learning estimator. We repeat our analysis on a cross-section of products across a variety of categories on eBay, including household products, sporting goods, and other consumer electronics, and we find a comparable degree of price predictability across all of the products.
Short term effectiveness of extra corporeal shock wave therapy for plantar fasciitis: A systemic review and meta-analysis

Plantar Fasciitis is a common foot disorder in which symptoms may become chronic and functionally disabling [1]. About 10% of people experience Plantar Fasciitis during globally their life, and 20% 30% of cases have a bilateral involvement [2]. The Plantar fascia is a thick ibrous tissue on the bottom of the foot that protects sensitive plantar structures such as nerves, vessels, muscles and tendons, and in addition, is responsible for maintaining the plantar arch[3]. Plantar Fasciitis usually diagnosed clinically based on the history of morning heel pain made worse with ambulation on hard surfaces and by the physical indings of pain over the medial aspect of the plantar fascia [4]. There is maximal tenderness at the Plantar fascial origin on the medial process of the calcaneal tuberosity, and pain increases with passive stretching of the plantar fascia [5]. A Calcaneal spur may be present in 50% of patients with painful heel [6]. The conservative treatment approach to the plantar fasciitis is focused at decreasing the pain and improving the foot function and easy weight bearing with different treatment strategies from prescribing NonSteroidal anti-in lammatory drugs to the orthotic support to correct the foot wear and maintain good arch support, Physical therapy which has a major role in management of PF is considered to be a cost effective option throughout the world which includes different modalities and prescribing remedial exercises to stretch the tightened fascia and improve joint mobility and function. The results from such treatment Abstract
On the Variations and Evolutions of API Usage Patterns: Case Study on Android Applications

Software developers can reduce the implementation cost by calling already provided functions through accessing library Application Programming Interface (API). APIs are often used in combination but how to combine them are not well-documented. Existing researches focused on how to extract API usage patterns or how to detect API misuse from existing software. This kind of research might be affected by dataset to analyze, so to improve mining results and to understand how the difference of API usage patterns affect the software health are important tasks. We conducted an analysis on variations of API usage pattern among software projects and their version history with Android SDK APIs and Android applications. Based on our analysis results, we made some suggestions for further API analysis. For example, there are many project-specific API usage patterns and long-life uncommon API usage patterns so that they might affect the mining result or checking software health status.
Evaluation of Decolorization, Mineralization, and Toxicity Reduction of an Azo Dye C.I. Reactive Black 5 in a Countercurrent Bubble Column by Ozone

A countercurrent and continuous-flow design bubble column reactor (BCR) was used to investigate variations in traditional parameters of water-containing Reactive Black 5 (RB5) under different gas/liquid flow rates. RB5 is a long-chain diazo dye with a high molecular weight that is widely applied in industry. The results revealed that the variations were functions of the flow rates of the liquid and/or gas and of operation column height. Pseudo-first-order or global-second-order reaction types were used to describe the variations, and the monitored parameters i.e., total organic carbon (TOC), sulfate, and nitrate. Not only did flow patterns affect the RB5 removal and the mineralization of derivatives, but column height might also have had an influence on ozone consumption. It was observed that the extent of decolorization and mineralization decreased as the sampling port height increased, indicating that the column height may reflect the retention time of ozone gas and the contacting time between ozone and.
University of Groningen Differentiable Kernels in Generalized Matrix Learning Vector Quantization

In the present paper we investigate the application of differentiable kernel for generalized matrix learning vector quantization as an alternative kernel-based classifier, which additionally provides classification dependent data visualization. We show that the concept of differentiable kernels allows a prototype description in the data space but equipped with the kernel metric. Moreover, using the visualization properties of the original matrix learning vector quantization we are able to optimize the class visualization by inherent visualization mapping learning also in this new kernel-metric data space.
Effcient Continuous Pareto Exploration in Multi-Task Learning

give rise to a set of solutions, known as the Pareto set, with Tasks in multi-task learning often correlate, con-fict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimality to this feld and directly cast multi-task learning as multi-objective optimization problems, but solutions returned by existing methods are typically fnite, sparse, and discrete. We present a novel, effcient method that generates locally continuous Pareto sets and Pareto fronts, which opens up the possibility of continuous analysis of Pareto optimal solutions in machine learning problems. We scale up theoretical results in multi-objective optimization to modern machine learning problems by proposing a sample-based sparse linear system, for which standard Hessian-free solvers in machine learning can be applied. We compare our method to the state-of-the-art algorithms and demonstrate its usage of analyzing local Pareto sets on various multi-task classifcation and regression problems. The experimental results confrm that our algorithm reveals the primary directions in local Pareto sets for trade-off balancing, fnds more solutions with different trade-offs effciently, and scales well to tasks with millions of parameters.
Numerical Optimization of Single-chamber Mufflers Using Neural Networks and Genetic Algorithm

To simplify the optimization process, a simplified mathematical model of a muffler is constructed using a neural network and a series of input design data (muffler dimensions) and output data (theoretical sound transmission loss) that are obtained by utilizing a theoretical mathematical model (TMM). To assess an optimal muffler, a neural network model (NNM) is used as the objective function in conjunction with a genetic algorithm (GA). Before the GA operation can be carried out, however, the accuracy of the TMM must be checked and be in accord with the experimental data. Additionally, the NNM must also be in agreement with the TMM. Also discussed are the numerical cases of sound elimination relative to the various parameter sets and pure tones (500, 1000, and 2000 Hz). The results reveal that the maximum value of the sound transmission loss (STL) can be accurately obtained at the desired frequencies. Consequently, the algorithm proposed in this study can provide an efficient way to develop optimal silencers for the requisite industries.
CNN-based Approach for Opti-Acoustic Reciprocal Feature Matching

—Sonar and optical images are widely utilized imaging sensors in underwater research. Despite their popularity, the optic camera’s limited view and acoustic camera’s high level noise may hinder their utility in a single-sensor application. To alleviate such challenges, complementary exploitation of the two sensors was introduced and suggested for opti-acoustic approaches in underwater sensing. However, deﬁning the features and matching the two different sensor modalities is challenging. In this paper, we propose to automatically match the features between the optic image and the acoustic image by using the opti-acoustic image conversion that is based on a neural network. The main focus of this paper is to generate the optic-style image, given an acoustic image via the Convolutional Neural Network (CNN)-based neural style transfer. In doing so, we acquired the contour of the object in sonar image with content feature extraction and an embodied optic image style with style feature extraction. The generated image has the content of the sonar image, yet, the style is the optic image. Finally, we veriﬁed the utility of out result by calculating the cosine similarity and the number of matched features between two images.
In vitro and in vivo antiplasmodial assays of selected Nigerian commercial herbal formulations

Introduction: Malaria remains a life-threatening disease, mainly in tropical and sub-tropical countries of the world. The problem caused by the disease is further compounded by the emergence and spread of multidrug resistant Plasmodium falciparum. Coupled with the poor distribution of modern health facilities, there is resurgence in the use of herbal remedies to treat malaria. In this study, we evaluated the antiplasmodial activities of six commercially available herbal formulations using in vivo and in vitro methods to assess their claimed antimalarial properties. Methods: The antiplasmodial activities of the six herbal formulations were assessed using Chloroquine sensitive P. falciparum parasite strain 3D7 using the SYBR Green in vitro method and the in vivo curative test (established infection) in Plasmodium berghei infected Mus musculus. Results: The six herbal formulations had values of IC50 > 100 μg/mL on 3D7 strain of P. falciparum compared to controls which had IC50 values of 6.92nM (Chloroquine) and 0.75nM (Artesunate). In the curative evaluation (in vivo) the herbal formulations significantly reduced parasitaemia on day 4 (26.3%-77.3 %) and day 7 (45.54%-94.81%) post-treatments (P < 0.05) when compared to the untreated group, which recorded high mortality rate. Conclusion: Findings made in this study lend support to the claim that these herbal formulations have antiplasmodial activities. Percentage inhibitions of parasitaemia of the formulations were all above 50% except M? T capsule which had lower percentage inhibition of parasitaemia.
Initial‐value predictability of Antarctic sea ice in the Community Climate System Model 3

We assess initial‐value predictability characteristics of Antarctic sea ice from climate simulations. The integrations are initialized on 1 January with identical ice‐ocean‐terrestrial conditions and integrated forward for two years. We find that the initialized ice‐ocean state provides predictive capability on the ice‐edge location around Antarctica for the first several months of integration. During the ice advance season from April to September, significant predictability is retained in some locations with an eastward propagating signal. This is consistent with previous work suggesting the advection of sea ice anomalies with the mean ocean circulation. The ice‐edge predictability is then generally lost during the ice retreat season after October. However, predictability reemerges during the next year's ice advance starting around June in some locations. This reemergence is associated with ocean heat content anomalies that are retained at depth during the austral summer and resurface during the following autumn as the ocean mixed layers deepen.
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing

We consider revenue maximization in online auction/pricing problems. A seller sells an identical item in each period to a new buyer, or a new set of buyers. For the online pricing problem, both when the arriving buyer bids or only responds to the posted price, we design algorithms whose regret bounds scale with the best fixed price in-hindsight, rather than the range of the values. Under the bidding model, we further show our algorithms achieve a revenue convergence rate that matches the offline sample complexity of the single-item single-buyer auction. We also show regret bounds that are scale free, and match the offline sample complexity, when comparing to a benchmark that requires a lower bound on the market share. We further expand our results beyond pricing to multi-buyer auctions, and obtain online learning algorithms for auctions, with convergence rates matching the known sample complexity upper bound of online single-item multi-buyer auctions. These results are obtained by generalizing the classical learning from experts and multiarmed bandit problems to their multi-scale versions. In this version, the reward of each action is in a different range, and the regret with respect to a given action scales with its own range, rather than the maximum range. We obtain almost optimal multi-scale regret bounds by introducing a new Online Mirror Descent (OMD) algorithm whose mirror map is the multi-scale version of the negative entropy function. We further generalize to the bandit setting by introducing the stochastic variant of this OMD algorithm.
Fabrication and Characterization of CH3NH3PbI3 Perovskite Solar Cells Added with Polysilanes

Effects of polysilane additions on CH3NH3PbI3 perovskite solar cells were investigated. Photovoltaic cells were fabricated by a spin-coating method using perovskite precursor solutions with polymethyl phenylsilane, polyphenylsilane, or decaphenyl cyclopentasilane (DPPS), and the microstructures were examined by X-ray diffraction and optical microscopy. Open-circuit voltages were increased by introducing these polysilanes, and short-circuit current density was increased by the DPPS addition, which resulted in the improvement of the photoconversion efficiencies to 10.46%. The incident photon-to-current conversion efficiencies were also increased in the range of 400~750 nm. Microstructure analysis indicated the formation of a dense interfacial structure by grain growth and increase of surface coverage of the perovskite layer with DPPS, and the formation of PbI2 was suppressed, leading to the improvement of photovoltaic properties.
Foliar study on effect of iron oxide nanoparticles as an alternate source of iron fertilizer to cotton

An experiment was carried out under pot culture to investigate the effects of different iron sources of fertilizers like iron sulphate, iron oxide and nano sized iron oxide particles viz., haematite and magnetite with different levels of concentration viz., 2, 4, 6, 8 and 10 g/lit on cotton growth, development and yield of cotton. Treatments were replicated into three and foliar sprayed (20 ml per pot) on square formation (45 DAS) and boll formation (90 DAS) stages. The study showed that significant increment of plant height (37 cm), leaf area (45.4 cm/plant), number of symbodial branches per plant (15.1), seed cotton yield (14.9 g/pot) and boll weight (3.5 g/boll) were attained due to foliar application of magnetite nanoparticles during both square and flower formation stages. It is concluded that oxide type iron nanoparticles particularly magnetite was superior than normal iron oxide on increasing plant growth parameters as well as seed cotton yield but it was onpar with sulphate type fertilizers.
on Intelligent Manufacturing and Automation , 2013 Guidelines for Applying Statistical Quality Control Method to Monitor Autocorrelated Prcoesses

The implementation of statistical control charts under autocorrelated situations is a critical issue since it has a significant impact on the monitoring capability of manufacturing processes. The objective of this study is to assess the performance of control charts under different scenarios and to optimize the design of control charts to best deal with autocorrelated processes. To achieve the proposed objective, two autoregressive integrated moving average models, ARIMA (1, 0, 1) and ARIMA (0, 1, 1), are utilized to characterize stationary and non-stationary processes. These process models were simulated to achieve the response, average run length (ARL), which is the performance measure of this study. The factorial design of experiment was conducted to quantify the effect of critical factors, i.e., ARIMA coefficients, types of charts (exponentially weighted moving average: EWMA and moving range: MR) and shift sizes on the ARL. The experimental results show that EWMA chart is the most appropriate control chart to monitor autocorrelated observations. Additionally, both AR and MA parameters along with shift sizes have a significant effect on the performance of control charts. Therefore, this study has pointed out a suitable tool for use under the different scenarios of autocorrelation. The validation of the above experimental results was conducted on another ARIMA model, ARIMA (1, 0, 0). If the performance of control charts under autocorrelated disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when deploying SPC. © 2014 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of DAAAM International Vienna.
ION TRACKING IN A HIGH-VOLTAGE DC PHOTO-GUN

The photocathode lifetime of GaAs in a high voltage DC gun is limited primarily by ion back bombardment. Ions produced in collisions of the electron beam with residual gas in cathode-anode gap and downstream of the anode are accelerated toward the cathode and strike the cathode surface. Systematical studies suggest that ion back bombardment is determined by gas pressure, gun voltage, laser spot and electric field profile. This paper presents a study of ion back bombardment in a high average current DC photocathode gun.This study is based on numerical analysis and particle tracking simulation. The results implies that ion generation can be suppressed by improving vacuum condition as well as gun voltage, and the back bombardment can be reduced by the optimization of laser spot position and electric field profile.
The Geoaccumulation Index of Some Heavy Metals in Al-Hawizeh Marsh, Iraq

Heavy metals have a great ecological significance due to their toxicity and accumulative behavior. The geoaccumulation index (I geo ) in 10 stations in Al- Hawizeh Marsh, (i.e Al-Adaim (I & II), Um Al-Neaj (I & II), Um-Awarded, North Al-Soudah, South Al-Soudah, Al-Beda, Lissan Ejerdah and Majnoon) were calculated in this article. The sediment pollution was investigated by following the concentration of six heavy metals (As, Cd, Cr, Co, Cu and Pb). Inductively coupled plasma mass spectroscopy (ICPMS) was used for analysis. According to the geoaccumulation index (I geo ) the results of all the 10 stations were analyzed and discussed in detailed.
Degree and Socio-demographic Predictors of Residents’ Willingness to Participate in Mountain Tourism: The Case of Ado-Awaye Suspended Lake, Nigeria

This study determined the degree and socio-demographic predictors of residents’ willingness to participate in the mountain tourism development of Ado-Awaye Suspended Lake (ASL), Nigeria. A multistage sampling technique was employed to select and administer semi-structured questionnaires to three hundred and sixty-six (366) households. Descriptive statistics and binomial logistic regression algorithm were used to analyze the data. The results revealed that majority of the households were willing to learn about the natural heritage, preserve the culture, provide environmental education for tourists, and participate in mountain tourism-related employment. Among the four indicators of residents’ willingness to participate in mountain tourism development, households were willing to participate in mountain tourism planning. The linear regression analysis shows that predictors varied with the four critical indicators willingness to conserve the natural resources, to preserve the cultural tradition, to sustainable community development, and to participate in the mountain tourism planning and management. This implied that the socio-demography of the residents is pertinent and should be put into consideration for the successful integration of the host community into the decision making framework as well as sustainable planning and development of the ASL for mountain tourism.