Miracle-dz/UniFAR-FTUs huggingface dataset:
Catch and Catch per unit efforts of Acipenser persicus ( Borodin , 1897 ) in southern of the Caspian Sea ( Mazandaran waters )

This study has been conducted in the southern coast of Caspian Sea in Mazandaran waters from 2009 to 2012. The sampling was done monthly through two fishing methods; through landing and beach seine nets. After being caught, the samples were delivered to breeding and reproduction center in Sari in order to be artificially propagations for the purpose of protection and restocks. The purpose of this study is to investigate the distribution, abundance, age, fork length, and body, meat and caviar weight, catch condition, catch per unit effort in the beach seine and landing. The results shown that 1283 caught A. persicus, 732 had caviars. Totally, the average fork length, body weight, meat and caviar weight were 150.5±17.6 cm, 24.4±9 kg, 18.6±6.4 kg and 4.8±1.7 kg, respectively. The contribution of landing in caviar production was 73.1%. There was a significant difference in the amounts of caviar and meat and the body weight between landing and beach seine nets (P>0.05). The ages of the sturgeons caught in the domain were from 11 to 23 years old. The most frequent age group was 14-15 years which had a frequency of about 44%. The catch per unit effort had decreasing trend from 2009 to 2012 with 0.289 and 0.124 kg/boat per day, respectively. The Persian sturgeon constituted 69% of the whole caviars caught during 20092012. The reason was the increase of the release of the juvenile of A. persicus in comparison with the other species of sturgeon. As this species is critically endangered, for the purpose of protecting and reviving it in the Caspian Sea, its commercial trade catch projects must be banned and its generators catch projects must be implemented for artificial propagations purposes. Furthermore, because this species is anadrumous, the preservation and revival of the rivers must be noticed by the authorities.
Evaluation of Dirui ® H 13-Cr and H 12-800 MA strips for albuminuria detection Emis

Increased urine albumin excretion is suggested to be an early marker of glomerular damage. Evaluation of a test’s performance characteristics such as diagnostic sensitivity and specifictiy is a necessity before application into routine practice. To our knowledge up to now there is no study evaluating Dirui test strips for albuminuria determination. Therefore the objective of this study was to test these strips for agreement with the established method used in our routine laboratory. Freshly collected 177 urine specimens were included in the study. Strips were read on instruments Dirui H-500 and Dirui H-800 using reflectance photometry. For DiruiH13-Cr strip albumin to creatinine ratio (ACR) and for DiruiH12-800MA strip albumin determinations were compared with the analyses performed using immunoturbidimetry and kinetic Jaffe method. Sensitivity values for estimated ACR using DiruiH13-Cr strip and albumin concentration using DiruiH12-800MA strip were calculated as 80%, 72%, respectively. Specificity values were as; 71% for Dirui H13-Cr strip and 78% for Dirui H12-800MA strip. Screening tests using urine test strips should be validated before acceptance in the routine use. Dirui strips evaluated in this study seem to have a lower sensitivity than suggested and excess number of false negatives seems to limit utility of these strips.
Perspectives of Parkinson ’ s Disease Therapies using Induced Pluripotent Stem Cells

Parkinson’s Disease (PD) is an intractable disease resulting in localized neurodegeneration of dopaminergic neurons of the substantia nigra pars compacta. Many current therapies of PD are symptomatic, but no current option for clinical-grade disease modifying treatment exists. Fortunately, recent advances in the field of cellular reprogramming now allow for previously unattainable cell therapies and modeling of PD using induced pluripotent stem cells (iPSCs) to potentially restore a disease-free state. iPSCs can be selectively differentiated into a dopaminergic neuron fate to model endogenous physiology and pathogenesis. iPSC lines can also be established with genetically-linked PD. These patient-specific cell lines are then genetically corrected in mutations of influence and can be subsequently transplanted back into the patient to reestablish function. This year, induced pluripotent stem cells iPSCs entered the first human trial for PD therapy. This form of cell therapy has shown promising results in other model organisms and is currently one of our best options in slowing or even halting the progression of PD. Here we examine the genetic contributions that have reshaped our understanding of PD, as well as the advantages and applications of iPSCs for modeling disease and clinical therapies.
Food poverty and its causes in Pakistan.

Economists and a demographer analyzed data from the 1984-1985 Household and Income Expenditure Survey using the Foster and Thorbeke model to measure the poverty level in Pakistan. This model considers the minimum level of food expenditure/adult equivalent required to purchase the required daily allowance of energy intake. The analysts calculated the poverty lines to be Rs 246.00 for urban areas and Rs 149.00 for rural areas. They learned that the real poor and less poor households did not rank expenditures on food as a top priority despite deficient food intake. Thus traditional diet pattern did not differentiate the real poor from the less power, but rather lower purchasing power. They defined real poor households as those who had a negative or O relationship between income and food and less poor households as those who had a positive relationship even though the magnitude of the slope coefficient was extremely small (.024 for urban areas and .082 for rural areas). The larger the family size the more likely a household was poor and the members malnourished. This was especially true if the family size grew because of the addition of more dependents. On the other hand, the more education the head of the household and his wife the less likely the household was poor and the members malnourished. In fact, the negative effect of education on poverty and malnutrition was strongest for the wife. 2% of all urban households and 3% of all rural households ranked as real poor households. 59% and 35% respectively ranked as less poor households. Even though the difference in food poverty between extended and nuclear families was insignificant, extended families fared better than nuclear families. These findings showed that Pakistan should design its food policy to target the real poor by improving their nutritional status without forsaking that of the less poor.
Studies on the Anaerobic Micrococci

Anaerobic members of the genera Staphylococcus and Micrococcus have long been known, but, with the exception of the work of Pr6vot (1933), no serious attempt has ever been made to study these organisms as a group. Our own experience (Foubert and Douglas, 1946) in attempting to identify anaerobic micrococci that had been isolated from plasma and human skin indicated that a further study of the taxonomy of these organisms was needed. This paper presents the results of such a study, together with a determinative key, based primarily on biochemical characteristics, for the identification of these microorganisms.
Presenting a Fast Classifier Based on Unsupervised Learning for Diagnosis Diseases

From long ago, decision support systems (DSS) as a vital tool in many industrials is considered by decision-makers. These systems can aid managers in making better decisions by collecting and interpreting data. Medical decision support systems (MDSS) have critical role in medical practice. They can help physicians for improving the quality of medical diagnosis. Classifiers as main core of MDSS systems play an important role in improving their performance. This paper presents an unsupervised learning-based real time classifier which is able to perform recognizing medical patterns with proper precision and speed. In the training phase, the proposed classifier is capable to obtain reference models related to classes using synergic clustering technique and finding the frequency of attributes. In order to evaluate efficiency of the proposed classifier, the UCI datasets including breast cancer (WBCD), liver disease (ILPD) and diabetic disease (PID) are applied. The obtained results indicate the effectiveness of the proposed method.
Comparison of Heart Rate Variability Measured by ECG in Different Signal Lengths

The measurement of heart rate variability, HRV, provides a noninvasive measurement of the autonomic nervous system (ANS) activity. HRV can be measured with the variation of RR intervals exhibited in a sequence of ECG sample. For a short-term HRV, the measuring time is usually five minutes. However, there are still many expectations of shortening the measuring time to evaluate the ANS. In this paper we analyzed and compared three minute HRV measurement to five minute standard short-term HRV. In order to evaluate the measurement results based on three minute and five minute HRVs, four major measurements were calculated in this study. The first is the standard deviation of normal-beat to normal-beat intervals (SDNN). The second is the square root of the mean squared differences of successive difference normal-beat to normal-beat intervals (RMSSD). The third is the proportion of interval differences of successive normal-beat to normal-beat intervals greater than 50 ms (pNN50). The fourth is the ratio of low frequency energy to high frequency energy (LF/HF) based on Fourier analysis method. Results show that the HRV presented by both SDNN and LF/HF using the three-minute measuring data differs significantly from that using the five-minute measuring data. In addition, the characteristics of HRV under different heart rate conditions shows that faster heart rate will come out smaller HRV. In conclusion, the HRV analyzed based on three minutes measuring data would not be equaled to that of five minutes measuring data. At the same time, the conditions of heart rate need to be considered when heart rate variability was analyzed.
Computer Aided Analysis and Derivation for Artificial Neural Systems

The theoretical analysis and derivation of artificial neural systems, which consists essentially of manipulating symbolic mathematical objects according to certain mathematical and biological knowledge, can be done more efficiently with computer assistance by using and extending methods and systems of symbolic computation. After presenting the mathematical characteristics of neural systems and a brief review on Lyapunov stability theory, the authors present some features and capabilities of existing systems and the extension for manipulating objects occurring in the analysis of neural systems. Some strategies and a toolkit developed in MACSYMA for computer-aided analysis and derivation are described. A concrete example is given to demonstrate the derivation of a hybrid neural system, i.e. a system which in its learning rule combines elements of supervised and unsupervised learning. Future work and research directions are indicated. >
Contact fusion of droplets patterned on opposing plates for cellular transportation and medium exchange for hanging droplet cell culture

This paper presents cellular transportation from a droplet on an originator plate to a droplet on an opposing receiver plate by contact fusion of the two droplets. This technique can be applied to the exchange of medium, drug delivery, and in situ characterization for hanging droplet cell cultures. Our study allows patterning of droplet arrays on the receiver plate in ways that facilitate contact fusion of droplets to occur at their locations on the receiver plate. Photoresponsive wettability of titanium dioxide patterns was used for patterning of the droplets. Hanging droplet cell culture successfully demonstrated exchange of medium via the contact fusion of droplets. This paper reports the proposed strategy, droplet array formation, cellular transportation, and liquid transportation for exchange of the medium and drug delivery.
Laterality of Lesions and Trait-Anxiety on Working Memory Performance

An asymmetry of anterior cerebral activation favoring the right hemisphere has been associated with dispositional negative affect including trait-anxiety, while the opposite appears true of cerebral asymmetry favoring the left hemisphere. It was hypothesized that an asymmetry of cerebral activation, as defined by scores on a measure of trait-anxiety, ipsilateral to the side of an anterior brain lesion would be associated with less efficient cognitive processing than greater activation in the hemisphere contralateral to the lesion. Patients with anterior left (n = 16) or right (n = 15) hemisphere lesions completed the State-Trait Anxiety Inventory and several neurocognitive tasks. Of the abilities tested, only Digit Span scores showed an interaction between side of lesion and presumed activation asymmetry. Patients with right- but not with left-hemisphere damage showed significant differences in working memory performance depending on the presumed direction of asymmetry of the two hemispheres, supporting the dual roles of the right hemisphere in affective processing and directed attention.
Extracting Structural Data For Business Intelligence Using Cluster Data Mining

Data mining is a knowledge extraction field that attempt to discover and store the related pattern from the large dataset. Extraction and storing the information is useful for many intellects. Storing of data has been enormously increasing day by dayin many business administrations, so extracting patterns and drifts out of immense data is anexcessive challenge. For the persistence of information finding and information retrieval from such written data text mining is used. The Business Intelligence (BI) system is an effective and ancient data with systematic tools to present valuable and inexpensive information to business developers and decision makers. Many administrations have huge amounts of data in the method of formless text. This large amount of information can lead to the development of new opportunities for the organization. For these issues extracting the unstructured data and form a structured data, here this paper uses the cluster data mining techniques to find out the structured data from the large datasets and provide an effective one to the business Intelligence.
Error Detection in Speech Recognition using CRF Based on Various Linguistic Features

Recently, a learning method of n-gram showing error tendency is focused on. In this method, it is difficult to learn low frequency n-gram appropriately. On the other hand, error detection method for content words was proposed that uses semantic similarity in neighboring recognition result. In this paper, we especially examine semantic score and propose a method for speech recognition error detection that integrates these information. As a result of evaluation experiment using the Corpus of Spontaneous Japanese, error detecting performance was improved for content words in particular. Moreover, semantic score is effectively used by combining with term-weight.
A new framework for assessing classification algorithms for mode choice prediction

Predicting passenger mode choice is an essential task for transport network simulation and operations management. Machine Learning (ML) classifiers trained on trip diary data are increasingly being investigated as an alternative to Discrete Choice Models (DCMs) for mode choice prediction. In order to determine the suitability of a particular algorithm for predicting mode choice, it is crucial to be able to reliably quantify its predictive performance for unknown trips. In this paper we propose and test a new framework for performance estimation of classification algorithms. Firstly, we address three key issues in the literature relating to a) performance metrics for model training, b) sampling methods for model validation, and c) hyperparameter selection for model optimisation. Alternative methods are proposed for each of these issues to form the new framework. The framework is then used to compare the suitability of eight different ML classification algorithms for predicting mode choice. Finally, the implications of the sampling method employed are then investigated experimentally.
Using QMDD in Numerical Methods for Solving Linear Differential Equations via Walsh Functions

This paper discusses the acceleration of computations involved in methods for solving a certain class of differential equations by Walsh series. These methods are based on computations with matrices of relatively large dimensions but having a block structure and including also the dyadic convolution matrices. We propose to represent the involved matrices by Quantum multiple-valued decision diagrams (QMDDs) and perform the computations over them. The structure of the matrices means the QMDDs are reasonably compact and therefore offer possibilities to speed up the overall computations as well as to work with matrices of large dimension which improves accuracy of the approximation of the required solutions by finite Walsh series.
Warming-induced reductions in body size are greater in aquatic than terrestrial species

Most ectothermic organisms mature at smaller body sizes when reared in warmer conditions. This phenotypically plastic response, known as the “temperature-size rule” (TSR), is one of the most taxonomically widespread patterns in biology. However, the TSR remains a longstanding life-history puzzle for which no dominant driver has been found. We propose that oxygen supply plays a central role in explaining the magnitude of ectothermic temperature-size responses. Given the much lower oxygen availability and greater effort required to increase uptake in water vs. air, we predict that the TSR in aquatic organisms, especially larger species with lower surface area–body mass ratios, will be stronger than in terrestrial organisms. We performed a meta-analysis of 1,890 body mass responses to temperature in controlled experiments on 169 terrestrial, freshwater, and marine species. This reveals that the strength of the temperature-size response is greater in aquatic than terrestrial species. In animal species of ∼100 mg dry mass, the temperature-size response of aquatic organisms is 10 times greater than in terrestrial organisms (−5.0% °C−1 vs. −0.5% °C−1). Moreover, although the size response of small (<0.1 mg dry mass) aquatic and terrestrial species is similar, increases in species size cause the response to become increasingly negative in aquatic species, as predicted, but on average less negative in terrestrial species. These results support oxygen as a major driver of temperature-size responses in aquatic organisms. Further, the environment-dependent differences parallel latitudinal body size clines, and will influence predicted impacts of climate warming on food production, community structure, and food-web dynamics.
Grasp Recognition using Active Acoustic Sensing

This paper presents a simple and low-cost grasp recognition technique using active acoustic sensing. The technique requires a pair of a speaker and a microphone as its equipment for sensing. We conducted an experiment to measure the recognition rate of our technique. As its results, per–user recognition accuracy with seven grasping postures and with three grasping strengths were 90–99% and 95–100%, respectively. Cross–user recognition accuracy with the seven grasping postures and with the three grasping strengths were 66% and 81%, respectively.
Modeling of High Temperature Superconducting Tapes , Arrays and AC Cables Using COMSOL

In this paper we present a set of numerical models created with COMSOL Multiphysics. The set includes quantitative models of a coated conductor tape, an array of such tapes and a high temperature superconducting cable. Similar models were created in the past. An advantage of our approach is in additional transparency and traceability for a user, these are provided on one hand with existing COMSOL example and on the other hand via step by step validation of each model with trusted experimental and theoretical data.
Design of Low-Voltage High-Speed CML D-Latches in Nanometer CMOS Technologies

This paper presents the design of a novel low-voltage high-speed D-latch circuit suitable for nanometer CMOS technologies. The proposed topology is compared against the low-voltage triple-tail D-latch and its advantages are demonstrated both by simulations, under different performance/power consumption tradeoffs with a 40-nm CMOS technology, and theoretically, thanks to a simple model of the propagation delay derived for both low-voltage topologies. In order to further demonstrate the advantages of the proposed topology, it has also been used to design a D flip-flop (DFF), where thanks to the feature to need just 1 clock differential pair; a further speed improvement is achieved over the conventional triple-tail topology. Indeed, by comparing a two-stage frequency divider designed using both the triple-tail DFF and the proposed folded DFF, a 54% improvement in the maximum operating frequency is found when using the proposed folded DFF.
RETRACTED: AUGMENTATIVE AND ALTERNATIVE COMMUNICATION (AAC) IN NEURODE- VELOPMENTAL DISORDERS: A MINIREVIEW

ACTA MEDICA MEDITERRANEA journal is retracting this paper following on concerns raised by the Journal Owner, Mister Carmelo Pennino, the manager of Carbone Editore company. After an internal investigation that involved all Section Editors, the journal owner detected a suspected misconduct in terms of self-citations activity. The link of the retracted article http://www.actamedicamediterranea. com/archive/2018/medica-5/augmentative-and-alternative-communication-aac-in-neurodevelopmental-disorders-a-minireview
Face recognition with deep learning for mobile applications

There is a lack of mobile open source applications to search and find selected persons in image libraries. Especially if you do not want to give away your data to cloud based services this paper shows a novel approach to use deep learning to train a feature detector in a desktop environment and integrate the results in an Android application. With our approach all privacy critical processing steps are running only locally and stored only on the mobile device. Also the performance of different neural network models are discussed.
CEMENT BASED NANOCOMPOSITES WITH SELF-DIAGNOSTIC CHARACTERISTICS

This study focuses on the development of cement based nanocomposites with self-diagnostic characteristics by reinforcing the cementitious matrix with exfoliated graphene nanoplatelets (xGnPs). The piezoresistive response of the developed cement-based nanocomposites was assessed by monitoring their electrical resistance change as a function of the applied mechanical stress (a) under monotonic compression and (b) compressive loading-unloading in the elastic region (maximum stress applied was 10 MPa). The results were compared with the respective results from reference samples (without reinforcement). The non-reinforced samples didn’t exhibit piezoresistive behaviour, while the incorporation of the xGnPs provided the matrix with self-diagnostic properties. A maximum electrical resistance change of approximately 21% was achieved under monotonic loading. The nanocomposites’ fractional electrical resistance change decreased during compressive loading and increased during unloading. Electrical resistance change was found to return to zero value after every unloading cycle, indicating that no damage has been inflicted in the nanocomposites.