Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Strati cation for Renal Ultrasonography in the Evaluation of Acute Kidney Injury

Background Renal ultrasounds (RUS) are commonly ordered in hospitalized patients with acute kidney injury (AKI), however clinical risk prediction could be used to inform which patients require imaging to rule out hydronephrosis. While risk strati cation frameworks have been described, the role of nephrology consultation as an additional driver of RUS ordering has not been comprehensively studied. Methods We performed a cross-sectional study of hospitalized adults at a large, tertiary, academic medical center with AKI who had a RUS ordered. Predictors were high-risk, medium-risk, or low-risk category based on an existing risk strati cation framework and RUS recommended by a consulting nephrology service. Outcomes were presence of unilateral or bilateral hydronephrosis and incidental ndings on RUS. Results Two hundred and eighty-one patients were included in the study cohort; 111 (40%), 76 (27%), and 94 (33%) patients were in the high-, medium-, and low-risk groups for hydronephrosis, respectively, based on the risk strati cation framework. Thirtyve patients (12%) were found to have hydronephrosis, of whom 86% were captured in the high-risk group. A nephrology consult was involved in 168 (60%) patients and RUS was recommended by the nephrology service in 95 (57%) cases. Of the 95 patients recommended for a RUS, 9 patients (9%) had hydronephrosis. Among the patients with a nephrology consultation, 9 (56%) of the 16 total patients with hydronephrosis were recommended to obtain a RUS. Conclusions We further validated a risk strati cation framework for hydronephrosis and found that nephrology consultation was an additional driver of RUS ordering, but predicted hydronephrosis less well than the risk strati cation framework. Our decision framework strengthens the argument for the use of risk strati cation to improve upon consultant recommendations, reduce incidental ndings, and decrease RUS overuse.
Myths in Modern Science: The Hydrogen Bond and its Surroundings Part 2. The Hydrophobic-Bond-Myth

The fashionable but stereotypic thinking on the concept of the so-called “hydrophobic bond” has been examined in light of criticisms raised by many scientists: Hildebrand, Shinoda, Israelachvili, and so on. The author’s comments are given on the harmful influence of the concept of “hydrophobic bond” in chemistry and biochemistry. In my opinion, this concept can be considered as a myth in modern science.
Spatial Classification and Prediction in Hyperspectral Remote Sensing Data using Random Forest by Tuning Parameters

: Over the past decades hyperspectral remote sensing data have been emerging in identifying the geographical patterns and predicting its behaviour. A digital remote sensing data offers, many practices in learning, exploring, monitoring and understanding the behaviour of the earth surfaces. The hyperspectral remote sensing data is an advanced technique where the topological information are collected in spectral with more number of bands. Inculcating the information from the hyperspectral data relies on the data mining approaches where the data contains many spectral bands and huge temporal information. It requires an front running algorithm to yield high accuracy. The random forest technique is one of the best tree based techniques where optimal solutions are captured at high end construction of possible trees. The mission of this research paper is to give detailed analysis of tuning the parameters of random forest technique based on variable importance, conditional inference and quantile forest applied to AVIRIS Indian pine site-3 hyperspectral data to predict the class labels. The experiment result of random forest with variable importance shows high accuracy of 94.93% in predicting the class labels. Conditional inference and quantile forests are also achieved high accuracy with slight difference of 94.65% and 93.46% respectively.
Wavelet transform based compression of electric signal waveforms for smart grid applications

With the advent of smart grid concept a massive wide variety of measurement devices and smart monitors are to be deployed in the distribution network to allow broad observability and real time monitoring of the behavior of power system. The volume of data generated by the smart monitors and measurement devices are a tough challenge for storage, computation and transmission through the power system. As a result, the complexity of the power system increases tremendously. In order to increase the robustness of the power system data compression techniques are evolved, that aims to improve the efficiency of the data transmission and to reduce the storage issues. In the current work, to reduce the issues of data transmission and volume of data in power system, an efficient wavelet transform based data compression is introduced. The compression technique is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction. The proposed technique is applied for Electric signals with disturbances. Simulation results are compared with the conventional discrete cosine transform. It is observed that the proposed data compression technique yields high compression ratio and also this technique can efficiently reconstruct the original signal.
Review Paper on Vedic Multiplier by Using Different Methods

- In digital circuits multiplier has important role. These are the main important blocks in many applications like digital signal processing, microprocessor and microcomputers. The time that required to do calculation in multiplier is reduced by using Vedic multipliers. Vedic Mathematics is the fastest and low power multiplier. Vedic Mathematics have sixteen sutras but “Urdhva Tiryagbhyam” is mainly used . In this paper 16 bit Vedic multiplier is designed by using modified full adders which has used less number of slices and delay is reduced when compared to existing techniques of Vedic Multiplier. Simulation and synthesis are carried on XILINX ISE 14.4 software.
POCKLINGTON EQUATION AND THE METHOD OF MOMENTS

Method of Moments (MM) is widely used inthe solution of field equations to obtain currentdistribution of wire antennas. Both, Hallen equationdefined by magnetic and electric potentials and theelectric field Pocklington equation, use the MMprocedure. Since the beginning, when Harrington[1] established the method, a main task has beenthe selection of base and weight functions to obtaina computational efficient and reliable solution. Eventhough the actual articles write mainly about MMapplications, we think that there is a pendent issuerelated with the analysis of the best combination ofbase and weight functions. This paper presents acomparative analysis of 16 combinations for the fourmore used functions; as a matter of comparison weuse the feed point impedance obtained for theclassical analysis and the MM solution ofPocklington’s general equation for arbitrary bentwires [2], applied to the well known half wavelengthdipole.
Inattention and Reaction Variability are Linked to Ventromedial Prefrontal Volume in AdolescentsInattention and Ventromedial Prefrontal Volume

BACKGROUND : Neuroimaging studies of attention-deficit/hyperactivity disorder (ADHD) have most commonly reported volumetric abnormalities in the basal ganglia, cerebellum, and prefrontal cortices. Few studies have examined the relationship between ADHD symptomatology and brain structure in population-based samples. Herein, we investigate the relationship between dimensional measures of ADHD symptomatology, brain structure, and reaction time variability—an index of lapses in attention. We also test for associations between brain structural correlates of ADHD symptomatology and maps of dopaminergic gene expression. METHODS : Psychopathology and imaging data were available for 1,538 youths. Parent ratings of ADHD symptoms were obtained using the Development and Well-Being Assessment (DAWBA) and the Strengths and Difficulties Questionnaire (SDQ). Self-reports of ADHD symptomatology were assessed using the youth version of the SDQ. Reaction time variability was available in a subset of participants. For each measure, whole brain voxel-wise regressions with gray matter volume (GMV) were calculated. RESULTS : Parent ratings of ADHD symptoms (DAWBA and SDQ), adolescent self-reports of ADHD symptoms on the SDQ, and reaction time variability were each negatively associated with GMV in an overlapping region of the ventromedial prefrontal cortex (vmPFC). Maps of DRD1 and DRD2 gene expression were associated with brain structural correlates of ADHD symptomatology. CONCLUSIONS : This is the first study to reveal relations between vmPFC structure and multi-informant measures of ADHD symptomatology in a large population-based sample of adolescents. Our results indicate that vmPFC structure is a biomarker for ADHD symptomatology. These findings extend previous research implicating the default mode network and dopaminergic dysfunction in ADHD.
Comparing Competing Explanations of the Link between Conservative Protestants and Conservative Politics in the United States

Using pooled data from the 2000-2004 National Election Studies, we compare the explanatory power of five competing explanations for the links between membership in theologically conservative Protestant denominations and right-of-center political attitudes. These explanations focus, respectively, on: (1) religiosity, (2) moral absolutism, (3) family ideology, (4) class culture, and (5) cultural geography. Moral absolutism is the strongest explanation for conservative political attitudes, both for whites affiliated with theologically conservative Protestant denominations, and for members of other religious traditions. conservative Protestants, causes of links dispute.
Analysis and Design of Operational Amplifier Stability Using Routh-Hurwitz Stability Criterion

This paper proposes to use Routh-Hurwitz stability criterion for analysis and design of the operational amplifier stability; this can lead to explicit stability condition derivation for operational amplifier circuit parameters, and this is very effective to understand which parameter values should be increased or decreased for the operational amplifier stability. The proposed method has been verified by three amplifiers with theoretical analysis and SPICE simulations for three operational amplifier examples.
Characterizing the in ﬂ uence of stress-induced microcracks on the laboratory strength and fracture development in brittle rocks using a ﬁ nite-discrete element method-micro discrete fracture network FDEM- m DFN approach Rock and Geotechnical Engineering

Heterogeneity is an inherent component of rock and may be present in different forms including mineral heterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks are usually observed in crystalline rocks in two forms: natural and stress-induced; the amount of stress- induced microcracking increases with depth and in-situ stress. Laboratory results indicate that the physical properties of rocks such as strength, deformability, P-wave velocity and permeability are in ﬂ uenced by increase in microcrack intensity. In this study, the ﬁ nite-discrete element method (FDEM) is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks using the proposed micro discrete fracture network ( m DFN) approach. The characteristics of the microcracks required to create m DFN models are obtained through image analyses of thin sections of Lac du Bonnet granite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial, triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data. The FDEM- m DFN models indicate that micro-heterogeneity has a profound in ﬂ uence on both the mechanical behavior and resultant fracture pattern. An increase in the microcrack intensity leads to a reduction in the strength of the sample and changes the character of the rock strength envelope. Spalling and axial splitting dominate the failure mode at low con ﬁ nement while shear failure is the dominant failure mode at high con ﬁ nement. Numerical results from simulated compression tests show that microcracking reduces the cohesive component of strength alone, and the frictional strength component remains unaffected. Results from simulated Brazilian tests show that the tensile strength is in ﬂ uenced by the presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. The importance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and its effects on the mechanisms leading to spalling damage near an underground opening are also discussed. laser scanning. A new class of DFN is proposed here, referred to as m DFN, to differentiate the stochastic nature of microscale cracks from large scale discontinuities. At microscale (laboratory scale), data collected from image analysis of thin sections are used to build appropriate m DFN models. Input parameters then include microcrack size, orientation and intensity.
Use of artiﬁcial dermis and recombinant basic ﬁbroblast growth factor for creating a neovagina in a patient with Mayer–Rokitansky–Ku¨ster–Hauser syndrome

Vaginal agenesis is an uncommon, but not rare, condition. Although there are many methods for creating a neovagina, the optimal treatment is unknown. An 18-year-old woman with Mayer–Rokitansky–Ku¨ster–Hauser syndrome received vaginoplasty with a modiﬁed Wharton procedure using an artiﬁcial dermis (atelocollagen sponge). From 10 days after the operation, the patient was administered human recombinant basic ﬁbroblast growth factor (bFGF) spray to accelerate epithelialization on the neovagina. At 50 days after the operation, we conﬁrmed histological squamous epithelialization of the vaginal epithelium. At 12 months after the operation, the neovagina was at least 3.5 cm in width and ~8 cm in length. In this case, use of artiﬁcial dermis and recombinant bFGF to create a neovagina was an easy, less invasive and useful method.
Data Integrity Check in Cloud Computing using Hash Function

- Storage, server, networking, database and more are the services delivered by cloud computing over the internet. Cloud Computing is globally scaled that provides virtualization, scalability, fine grained data access control and performance benefit. Despite all this benefits, there are various issues of security and challenges for cloud. The challenges are that data is no longer in full control of owner, compromised cloud server and access control desired by owner. Customer needs a platform that stores its information and returns it without any changes or modification. Most of the cloud computing environment guarantees safety of data by various advancedencryptions. The information stored is encrypted at server side and is decrypted at client side. This makes data secure so that no third party can access or understand the data. The concern is unfaithful behaviour of the cloud itself. The data stored on cloud may be possible altered or modified without the knowledge of client.There should be a mechanism that verifies the stored and the data being retrieved is same. This paper investigates such mechanism. This research paper points out how data integrity of information stored in cloud can be checked. In this technique after encryption of data, hash is created using hash function. At customer side after decrypting the data each hash is compared with other hash set to verify uniqueness of the data. This verifies is the data has been altered or it is same as the original data stored by the client.
Francisella tularensis Molecular Typing Using Differential Insertion Sequence Ampliﬁcation (cid:1)

Tularemia is a potentially fatal disease that is caused by the highly infectious and zoonotic pathogen Francisella tularensis . Despite the monomorphic nature of sequenced F. tularensis genomes, there is a signiﬁcant degree of plasticity in the organization of genetic elements. The observed variability in these genomes is due primarily to the transposition of direct repeats and insertion sequence (IS) elements. Since current methods used to genotype F. tularensis are time-consuming and require extensive laboratory resources, IS elements were investigated as a means to subtype this organism. The unique spatial location of speciﬁc IS elements provided the basis for the development of a differential IS ampliﬁcation (DISA) assay to detect and distinguish the more virulent F. tularensis subsp. tularensis (subtypes A.I and A.II) and subsp. holarctica (type B) strains from F. tularensis subsp. novicida and other near neighbors, including Francisella philomiragia and Francisella -like endosymbionts found in ticks. Amplicon sizes and sequences derived from DISA showed heterogeneity within members of the subtype A.I and A.II isolates but not the type B strains. These differences were due to a 312-bp fragment derived from the IS element IS Ftu1 . Analysis of wild-type F. tularensis isolates by DISA correlated with pulsed-ﬁeld gel electrophoresis genotyping utilizing two different restriction endonucleases and provided rapid results with minimal sample processing. The applicability of this molecular typing assay for environmental studies was demonstrated by the accurate identiﬁcation and differentiation of tick-borne F. tularensis . The described approach to IS targeting and ampliﬁcation provides new capability for epidemiological inves- tigations and characterizations of tularemia source outbreaks. ethidium bromide, amplicons pCR4-TOPO Carlsbad, and M13 and reverse University of Nebraska Medical Center
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation

Image classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted correlations"data biases,"and the visual features causing data biases"bias factors. "It is challenging to identify and mitigate biases automatically without human intervention. Therefore, we conducted a design study to find a human-in-the-loop solution. First, we identified user tasks that capture the bias mitigation process for image classification models with three experts. Then, to support the tasks, we developed a visual analytics system called DASH that allows users to visually identify bias factors, to iteratively generate synthetic images using a state-of-the-art image-to-image translation model, and to supervise the model training process for improving the classification accuracy. Our quantitative evaluation and qualitative study with ten participants demonstrate the usefulness of DASH and provide lessons for future work.
THE INTERNATIONAL JOURNAL OF HUMANITIES & SOCIAL STUDIES Employees’ Perceptions on the Contribution of Performance Appraisal to Career Growth and Development in Kenya’s Ministry of Education in Nairobi City County, Kenya

limited Abstract: Performance Appraisal System (PAS) is a vital tool to measure the work set by any organization to its employees. PAS is used to track individual contribution and work performance against organizational goals and to identify individual strengths and opportunities for future improvements. Performance Appraisal System (PAS) assesses whether organizational goals are achieved and serves as basis for the organization’s future planning and development. This study determined the effects of employee attitude on the implementation of performance appraisal system among the employees in the Ministry of Education in Nairobi City County, State Department of Early Learning and Basic Education. The study adopted the concurrent mixed methods design where quantitative and qualitative methodologies were employed, simultaneously. The researcher used stratified sampling method to select the samples of the study by stratifying the staff into Education officers and Quality Assurance and Standards officers (QASOs). Semi-structured questionnaires and interview guides were used to collect data from the respondents. The study recommends that ‘Appraisal training by MoE’; and ‘Perceived organization support by MoE’; are the main critical factors that need to be seriously addressed to improve employees’ Attitude on the implementation of Performance Appraisal System. The anticipated key users of the findings include human resources department of the Ministry of Education, the Public service management, other institutions and future researchers.
Implementation and Decoding of Novel Genetic Algorithm for Constraint Based Associative Clustering

: We are introducing a novel Genetic Algorithm for constraint based associative clustering. Subset formation of discretized data set is done. The data points are fitted to appropriate cluster using fitness calculation. Using Bayes factor, crossover computation, mutation and contingency table genetic algorithm is implemented. The performance was tested on UCI database using the evaluation index clustering accuracy. For Big data clustering our algorithm shows better accuracy (75.7%). The paper describes the entire implementation process.
Design and Implementation of a Prediction Failure Handling Component for a Plan-Based Scheduler

The Virtual Resource Manager (VRM) is a Grid management system that aims to bring Service-Level-Agreements (SLA) to the Grid. Clients, as well as service providers could benefit significantly from SLA capabilities, for example by negotiating deadlines as well as penalty fees if that deadline is missed. The VRM architecture utilizes a plan-based scheduling system enabling those capabilities. In order to be able to construct a plan, runtime estimates for jobs are required. Since job behaviour is dynamic and hard to predict, plan deviations of different magnitudes are bound to occur. If the estimation is off by a minor margin, the executing node might choose to accept the deviation and continue running the plan as is. If the deviation is significant, the node has to send a prediction failure signal to the job scheduler. The job scheduler then updates its prediction model and generates a new plan integrating the detected deviation. The goal of this thesis is to design and implement a scheduling component that determines if a detected plan deviation should be considered a minor deviation or a prediction failure. Additionally it is necessary to define associated behaviour, for example how to assign idle time slots or when to preempt a task. The proposed design is implemented based on an existing prototype that is running as a scheduling policy inside the Linux kernel. In this thesis, firstly a systematic analysis is conducted of what conditions warrant a prediction failure signal and which parameters influence those conditions. Then a threshold system is developed that incorporates the various conditions into a coherent component that is implemented as an extension of the existing prototype. The implemented prediction failure handling component is capable of looking at different aspects of the plan and signal a prediction failure if necessary. It also contains self-balancing mechanisms that aim to resolve minor plan deviations locally. The implemented behaviour was developed with the help of a simulation that is intended to give insights into the designed behaviour.