Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying the Horizon Based Tomography Method to Update Interval Velocity Model, Identify The Structure of Pre-Stack Depth Migration 3D and Estimate The Hydrocarbon Reserve In SBI Field of North West Java Basin

Seismic data processing is one of the three stages in the seismic method that has an important role in the exploration of oil and gas. Without good data processing, it is impossible to get seismic image cross section for good interpretation. A research using seismic data processing was done to update the velocity model by horizon based tomography method in SBI Field, North West Java Basin. This method reduces error of seismic wave travel time through the analyzed horizon because the existence velocity of high lateral variation in research area. There are three parameters used to determine the accuracy of the resulting interval velocity model, namely, flat depth gathers, semblance residual moveout that coincides with the axis zero residual moveout, and the correspondence between image depth (horizon) with wells marker (well seismic tie). Pre Stack Depth Migration (PSDM) form interval velocity model and updating using horizon-based tomography method gives better imaging of under-surfaced structure results than PSDM before using tomography. There are three faults found in the research area, two normal faults have southwest-northeast strike and the other has northwest-southeast strike. The thickness of reservoir in SBI field, North West Java Basin, is predicted between 71 to 175 meters and the hydrocarbon (oil) reserve is predicted about with 22.6% porosity and 70.7% water saturation.
Profitability of the Moving Average Strategy and the Episodic Dependencies: Empirical Evidence from European Stock

Numerous recent studies are emphasizing the existence of different stock price behaviors, namely long random walk sub periods alternating with short ones characterized by strong linear and/or nonlinear correlations. All these studies suggest that these serial dependencies have an episodic nature. In this paper we investigate the profitability of an optimum moving average strategy selected from 15,000 combinations on the main European capital markets considering the episodic character of linear and/or nonlinear dependencies, the period under study being 1997-2008. The empirical results are consistent the assumptions made by the Adaptive Markets Hypothesis (AMH) of Lo (2004) regarding the fact that profit opportunities do exist from time to time. More than that, the paper proves that the profitability of those strategies is mainly due to nonlinear episodic dependencies.
Texture based Anisotropic Diffusion for Real Ultrasound Image Despeckling

This paper presents a new texture based anisotropic diffusion method for real ultrasound image despeckling. Texture information is obtained by a real ultrasound image model. Unlike traditional anisotropic diffusion methods usually taking image gradient as a diffusion index, we take the image texture as a new diffusion index. The results comparing our new method with others on both simulated image and real ultrasound images are reported, and our method shows the superiority in keeping important features of real ultrasound images.
The α-helical FXXΦΦ motif in p53: TAF interaction and discrimination by MDM2

Transcriptional activation domains share little sequence homology and generally lack folded structures in the absence of their targets, aspects that have rendered activation domains difficult to characterize. Here, a combination of biochemical and nuclear magnetic resonance experiments demonstrates that the activation domain of the tumor suppressor p53 has an FXXΦΦ motif (F, Phe; X, any amino acids; Φ, hydrophobic residues) that folds into an α-helix upon binding to one of its targets, hTAFII31 (a human TFIID TATA box-binding protein-associated factor). MDM2, the cellular attenuator of p53, discriminates the FXXΦΦ motif of p53 from those of NF-κB p65 and VP16 and specifically inhibits p53 activity. Our studies support the notion that the FXXΦΦ sequence is a general α-helical recognition motif for hTAFII31 and provide insights into the mechanistic basis for regulation of p53 function.
An overview of integrative analysis in cancer studies

Recently, many different molecular datasets from cancers, such as genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA (mRNA), and microRNA (miRNA) have been generated. These datasets have provided many key insights into cancer subtypes, molecular heterogeneity, cancer pathogenesis and cancer progression. Although many datasets and tools have been developed for cancer analysis, there are still strong needs for more efficient computational methods. In response, many researchers have developed more effective and robust tools that analyze these datasets. Until now, the main focus of the methodological development was to increase the efficiency for analyzing the individual dataset. However, recent methods being developed are capable of integrative analysis of datasets. In this short review, we provide an overview of three commonly used analysis of integration in cancer studies. The first case of commonly used tool is integrating different types of molecular datasets. The second tool is integrating one type of molecular datasets and biological information. The last case is integrating molecular datasets and clinical information.
A Modern Approach for Software Cost Estimation using Neural Networks

Programming planning estimation and examination especially, cost estimation exercises have been in the point of convergence of thought for a few associations. Maker explores the usage of the ace result declaration and machine learning methods using intelligent framework and moreover focusing COCOMO II method to manage estimate the cost of programming. Few basic techniques in the usage of neural framework in surveying programming cost. Made to great degree exact results, however the genuine incident in their work was a direct result of the way that the precision of the report depended enthusiastically on the degree of the planning set [4]. Getting the hardship in implementing neural frameworks, the maker makes a dynamic neural framework that would at first use COCOMO II. Sorting out upgrades and its results the amount of instructive gathering augmentations with commitment from ace finalizing that effects the studying strategy.
Ginkgo bilobaL.: Phytochemical components and antioxidant activity

Curative effects of Ginkgo biloba L. have been recognized for centuries, dating back to traditional Chinese medicine which used crushed leaves to treat several health problems. Although G. biloba L. has several known and investigated activities, the antioxidant activity of its extract (EGb 761) is particularly relevant because reactive oxygen (ROS) and reactive nitrogen (RNS) species are constantly produced in aerobic organisms. Currently, the exploitation of the antioxidant activity of G. biloba extract Egb 761 has been of particular pharmacological importance because oxidative stress may be harmful to cells and may trigger the development of many disorders. The antioxidant activity of the EGb extract against oxidative stress has been associated with several therapeutic effects and currently, Egb761 is indicated to treat labyrinthitis, headache, memory disturbance, intermittent claudication, dementia, Alzheimer’s disease, glaucoma, cardiovascular disorders, cerebral ischemia, increased libido and sexual activity, and psychiatric diseases, such as depression. This study is a review of basic and clinical studies related to antioxidant properties of G. biloba L. Key words: Ginkgo biloba L. , EGb 761, antioxidant activity, phytotherapic drug.
Modeling of asymmetrical boost converters

The asymmetrical interleaved dual boost (AIDB) is a fifth-order DC/DC converter designed to interface photovoltaic (PV) panels. The AIDB produces small current harmonics to the PV panels, reducing the power losses caused by the converter operation. Moreover, the AIDB provides a large voltage conversion ratio, which is required to step-up the PV voltage to the large dc-link voltage used in grid-connected inverters. To reject irradiance and load disturbances, the AIDB must be operated in a closed-loop and a dynamic model is required. Given that the AIDB converter operates in Discontinuous Conduction Mode (DCM), classical modeling approaches based on Continuous Conduction Mode (CCM) are not valid. Moreover, classical DCM modeling techniques are not suitable for the AIDB converter. Therefore, this paper develops a novel mathematical model for the AIDB converter, which is suitable for control-pur-poses. The proposed model is based on the calculation of a diode current that is typically disregarded. Moreover, because the traditional correction to the second duty cycle reported in literature is not effective, a new equation is designed. The model accuracy is contrasted with circuital simulations in time and frequency domains, obtaining satisfactory results. Finally, the usefulness of the model in control applications is illustrated with an application example.
Web Usage Mining: Finding Usage Patterns from Web Logs

Web usage mining is an important research domain of Web mining which focuses on learning about the user's behavior while interacting with the websites. It is the application of data mining techniques on web log data in order to find the usage patterns of web users. A web log file represents activities of the users accessing a web site and hence provides vital information about user access patterns. Web usage mining includes three main phases namely: data collection and preprocessing, pattern discovery and pattern analysis. This paper gives an overview of web log file, describes its various types and gives a detail of the process of Web usage mining. In the end it also outlines future research directions in area of web usage mining.
R&D, Innovation, and Technological Progress: A Test of the Schumpeterian Framework Without Scale Effects

I use U.S. manufacturing industry data to estimate a system of three equations implied by a model of R&D-induced growth in steady state. These three equations relate R&D intensity to patenting, patenting to technological progress, and technological progress to economic growth. In each case, I find evidence of positive impact. Thus, I reject the null hypothesis that growth is not induced by R&D in favor of the Schumpeterian endogenous growth framework without scale effects. I also find strong support for technological spillovers from aggregate research intensity to industry-level innovation success.
On low cost model-based monitoring of industrial robotic arms using standard machine vision

This paper contributes towards the development of a computer vision system for telemonitoring of industrial articulated robotic arms. The system aims to provide precision real time measurements of the joint angles by employing low cost cameras and visual markers on the body of the robot. To achieve this, a mathematical model that connects image features and joint angles was developed covering rotation of a single joint whose axis is parallel to the visual projection plane. The feature that is examined during image processing is the varying area of given circular target placed on the body of the robot, as registered by the camera during rotation of the arm. In order to distinguish between rotation directions four targets were used placed every 90° and observed by two cameras at suitable angular distances. The results were deemed acceptable considering camera cost and lighting conditions of the workspace. A computational error analysis explored how deviations from the ideal camera positions affect the measurements and led to appropriate correction. The method is deemed to be extensible to multiple joint motion of a known kinematic chain.
The Networking Abilities of Craft Entrepreneurs

It has been claimed that networking capability is one of the core competences of firms and one of the critical competences of entrepreneurs. Yet very few studies to date have focused on conceptualising network competence. Thus, the aim of this study is to deepen our understanding of the capabilities needed in establishing and maintaining business relationships and nets. In our study, the network competence concept refers to the ability and willingness of craft entrepreneurs to act in versatile nets. The study was conducted as a multiple case study (14 craft firms). After identifying the requisite skills for network competence, this research was able to construct a new theoretical framework or local theory on the network competence of craft entrepreneurs. The framework consists of two bundles of abilities: those needed in establishing relationships and those needed in maintaining relationships and nets. We argue that the fundamental requirement for establishing relationships is confidence in networks as a system, and that the key skill in maintaining relationships and nets is an ability to take the partner’s interests into consideration.
Does Quality Control Matter? a Revisit of Surface Urban Heat Island Intensity Estimated by Satellite-Derived Land Surface Temperature Products

Abstract. Temporally regular and spatially continuous monitoring of surface urban heat island (SUHI) has been extremely difficult until the advent of spaceborne land surface temperature (LST) products. The higher errors of these LST products compared with in-situ measurements, nevertheless, have resulted in a comparatively inaccuracy and may distort the interpretation of SUHI. Although reports have shown that LST quality matters to the SUHI interpretation, a systematic investigation on how the SUHI indicators are responsive to the LST quality across cities within dissimilar bioclimates remains rare. With regard to this issue, our study chose eighty-six major cities across the mainland China and analyzed the SUHI intensity (SUHII) discrepancies (referred to as ΔSUHII) between using and not using quality control (QC) flags from Moderate Resolution Imaging Spectroradiometer data. Our major findings include: (1) the SUHII can be significantly impacted by the MODIS QC flags, and the associated seasonal ΔSUHIIs generally account for 25.5 % (29.6 %) of the total intensity in the day (night). (2) The ΔSUHIIs differ season-by-season and significant discrepancies also appear among northern and southern cities, with northern ones often possessing a higher annual mean ΔSUHII. (3) The internal ΔSUHIIs within an individual city are also heterogeneous, with the variations exceeding 5.0 K (3.0 K) in northern (southern) cities. (4) The ΔSUHII is significantly negatively related to the SUHII and cloud cover percentage mostly in transitional seasons. Our findings highlight that one needs to be very careful when using the LST-product-based SUHII to interpret the SUHI.
Validation of the Clock Drawing Test Scoring Method in older adults with neurocognitive disorder

Introduction. The Clock Drawing Test (CDT) is a widely used instrument for identifying neurocognitive disorders (NCDs) in older adults. However, there is insufficient evidence to determine the best scoring method, since current quantitative methods involve the assignment of numerical values, while qualitative ones do not allow for objectivity in the diagnosis. Parsey and Schmitter-Edgecombe (2011) proposed a scoring scheme which, in addition to providing a score of the patient’s performance, permits error analysis, thereby making it possible to identify potential underlying cognitive difficulties. Objective. The purpose of this study was to validate the CDT scoring scheme proposed by Parsey and Schmitter-Edgecombe (2011) for screening for NCDs in Mexican older adults. Method. There were 167 participants: 58 cognitively healthy subjects (CH), 52 with mild neurocognitive disorder (mild-NCD), and 57 with major neurocognitive disorder (major-NCD). The CDT scoring method was compared with the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment in Spanish (MoCA-S). Inter- and intra-observer reliability and construct validity were determined and the sensitivity and specificity of this method were calculated. Results. The  age was 75 years (SD ± 8 years) and the  educational attainment was 10.7 years (SD ± 5.2 years). Internal reliability was . 750, with an intraclass correlation coefficient of . 774. The cut-off point for the CDT in mild-NCD was 14 points (sensitivity: 40%, specificity: 70%) and 12 points for major-NCD (sensitivity: 90%, specificity: 95%). The most frequent errors in the CDT were: graphic, conceptual, spatial, and/or planning difficulties. Discussion and conclusion. This method makes it possibly to quickly and easily explore the cognitive status of the patient. It contains ideal psychometric properties for the detection of patients with major-NCD, in addition to offering the possibility of analyzing performance errors and underlying cognitive difficulties.
LINEAR ELASTIC ACTUATOR OF A BIPED ROBOT 'ROTTO'

The actuators with the serial elastic elements are been increasingly used in manipulators and walking robots nowadays. The elasticity allows not only storing energy, but also adds special characteristics to the actuator. The problem of a personal safety by casual contact with the industrial manipulators is the important issue. The solution is found in the control of the system impedance. The daunting problem of the walking mechanisms is a problem of the contact with the surface, where the big forces occur in short time intervals. The elastic element of the actuator allows the partial damping of the contact energy and gives the possibility to react on perturbation.
Concentrations and cycling of DMS, DMSP, and DMSO in coastal and offshore waters of the Subarctic Pacific during summer, 2010‐2011

Concentrations of dimethylsulfide (DMS), measured in the Subarctic Pacific during summer 2010 and 2011, ranged from ∼1 to 40 nM, while dissolved dimethylsulfoxide (DMSO) concentrations (range 13-23 nM) exceeded those of dissolved dimethyl sulfoniopropionate (DMSP) (range 1.3–8.8 nM). Particulate DMSP dominated the reduced sulfur pool, reaching maximum concentrations of 100 nM. Coastal and off shore waters exhibited similar overall DMS concentration ranges, but sea-air DMS fluxes were lower in the oceanic waters due to lower wind speeds. Surface DMS concentrations showed statistically significant correlations with various hydrographic variables including the upwelling intensity (r2 = 0.52, p < 0.001) and the Chlorophyll a/mixed layer depth ratio (r2 = 0.52, p < 0.001), but these relationships provided little predictive power at small scales. Stable isotope tracer experiments indicated that the DMSP cleavage pathway always exceeded the DMSO reduction pathway as a DMS source, leading to at least 85% more DMS production in each experiment. Gross DMS production rates were positively correlated with the upwelling intensity, while net rates of DMS production were significantly correlated to surface water DMS concentrations. This latter result suggests that our measurements captured dominant processes driving surface DMS accumulation across a coastal-oceanic gradient.
DIGITAL MAPPING OF FARMLAND CLASSES IN THREE LANDSCAPE IN MEXICO

The cartography of farmland classes allows generating land maps, using a methodology based on local knowledge, rapidly and at low cost, and with a greater number of cartographic units than conventional soil surveys maps. However, the results found when producing these maps with automated cartography techniques are contrasting. Precision and accuracy were evaluated in 324 computer generated farmland class (FLC) maps by applying the Inverse Distance Weighted (IDW) interpolation model. These maps were obtained by varying the sample size for the training, its spatial design, and the Power value of the interpolator. Moreover, the effort needed to obtain maps with acceptable reliability was quantified. The procedure was applied to FLC maps obtained from surveys with producers from three contrasting environmental zones in Mexico. The results show that the best sampling scheme in the three areas is the systematic sampling, and Power 8, giving the maps with the highest reliability. Through the criterion of map reliability and effort needed for sampling, the recommended sample size is 10% to 25% of the total plots.
Automatic Road Extractions from High Resolution Satellite Imagery Using Road Intersection Model in Urban Areas

This paper proposes intersection model and strategy for road extraction from high resolution satellite images. Satellite images are rich in information. For Geographic Information System (GIS), many features require fast and reliable extraction of roads and intersections. They are also complex to analyze. Satellite image provides useful data that is extracted from satellite image of the urban area. Automatic extraction of the road intersections from the urban areas has been a challenging topic because the high resolution satellite images contain multiple layers that represent roads, buildings, and other high density objects. Our goals is to automatically separate the road layer from the other layers then extract the road intersections. Usually traditional image processing methods don't achieve satisfied performance in case of satellite images. This paper proposes a modified and a cost effective method for road extraction from high resolution satellites images. In order to find the precise road intersection of urban areas we have divided whole process into two sequential modules: first, extraction of road line using different Morphological direction filtering to automatically eliminate the other layers from road layer and finally, extraction of road intersections to determine the road orientation and interconnectivity. We applied this method to a set of randomly selected high resolution satellite image from urban and semi urban areas and the correctness of road network extraction reaches 95.71%, significantly higher than those of other existing road extraction methods.
PCNN-Based Image Fusion in Compressed Domain

This paper addresses a novel method of image fusion problem for different application scenarios, employing compressive sensing (CS) as the image sparse representation method and pulse-coupled neural network (PCNN) as the fusion rule. Firstly, source images are compressed through scrambled block Hadamard ensemble (SBHE) for its compression capability and computational simplicity on the sensor side. Local standard variance is input to motivate PCNN and coefficients with large firing times are selected as the fusion coefficients in compressed domain. Fusion coefficients are smoothed by sliding window in order to avoid blocking effect. Experimental results demonstrate that the proposed fusion method outperforms other fusion methods in compressed domain and is effective and adaptive in different image fusion applications.
Insular decision making in the board room: Why boards retain and hire substandard CEOs

This paper explores why a corporate board often fails to replace a substandard CEO. I consider the situation in which the incumbent CEO and directors make decisions in the absence of the new CEO. I show that the board and the CEO maximize the expected utilities of the negotiating parties that do not include the expected utility of the potential CEO. This sometimes results in the retention of an inefficient CEO. I argue this same logic provides a theoretical explanation for how a new CEO is chosen in relation to both the voluntary and enforced replacement of an existing CEO.