Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Plasticity Associated with Meditation Experience: Neurofunctional Approach and Structural Findings

The study of neurofunctional structure associated with the practice of meditation along with its neural, cognitive and emotional implications, has increased exponentially in recent years, forming a regular line of research in neuroscience production. We review published studies that have used different experimental paradigms (neurophysiological and neuroimaging testing, cognitive tests), which, through indirect evidence and direct findings, highlight both functional and structural changes evidencing a neuroplasticity phenomenon. Thus, our results establish the existence of significant changes in brain plasticity correlated with the time devoted to their practice, and regardless of the specific style or tradition, manifested primarily through (1) changes in the bioelectrical brain pattern, (2) metabolic activation of specific brain areas, (3) cognitive functions supported by idiosyncratic neural networks, and (4) neuroimaging studies that show measurable increases in different subcortical and cortical brain structures, belonging mainly to the prefrontal region. In turn, revealing any shortcomings in this line of research, provides for future initiatives aimed at the optimization of experimental approaches, conceptual clarifications, control of intervening variables and inclusion of new instrumental paradigms.
Exploring employee intimate partner violence (IPV) disclosures in the workplace

ABSTRACT More than 500 employees of a large corporation participated in a web-based survey on workplace health and relationships. Mixed method data were analyzed to better understand intimate partner violence (IPV) workplace disclosures, including disclosure recipients and perceived helpfulness of responses. Statistical comparisons were made to assess the relationship between IPV experiences and disclosures and perceptions of workplace supportiveness. About one in five employees reported experiencing IPV during their work tenure. Survivors were more than twice as likely to disclose to coworkers compared to supervisors, employee assistance, or human resources. Although survivors tended to report helpful workplace responses, nearly 20% of survivors experienced an unhelpful workplace response. Survivors of IPV most frequently cited interpersonal support when asked to describe a helpful workplace response but also valued instrumental support, such as receiving workplace accommodations and resource information. Survivors of IPV perceived the workplace as less supportive and less accommodating than employees who did not report IPV. To successfully address IPV across the organization, all employees need information, training and support, as well as clear, specific IPV policies and protocols. Organizations should promote an organizational culture that supports survivors of IPV to facilitate access to critical resources and enhance employee wellness, safety, and productivity.
Multiple Non-Overlapping RGB-D Sensors for Tracking People

—This work presents the development of a surveillance system for monitoring wide area indoor spaces using multiple Kinect c ï¿¿ devices. The data from these sensors, configured with the widest possible coverage, is integrated into a single coordinate system using a novel calibration technique for non-overlapping range sensors. Moving 3D pixels from each Kinect are transformed into a " plan view " map of activity where the detection and tracking of people is executed. The detection of people is a two step process; data binning and non maxima suppression. The tracking of people is based on the mean-shift algorithm optimized with the prediction step of the Kalman Filter.
Lung cancer risk among female textile workers exposed to endotoxin.

BACKGROUND Reduced risk of lung cancer among workers in the cotton textile industry has been observed since the 1970s. Bacterial endotoxin, a contaminant of raw cotton fiber and cotton dust, has been proposed as a protective agent that may act through the innate and acquired immune systems. We examine the association between endotoxin exposure and lung cancer risk in a cohort of female textile workers. METHODS We conducted a case-cohort study nested within a cohort of 267,400 female textile workers in Shanghai, China. We compared the cumulative exposure histories of 628 case patients diagnosed with incident lung cancer from January 1, 1989, through December 31, 1998, with those of a lung cancer-free reference subcohort of 3184 workers who were frequency matched by 5-year age-groups to all cancer patients in the cohort. Cumulative endotoxin exposure for all participants was based on historic measurements and on additional measurements for this study. Relative risks and 95% confidence intervals (CIs) were estimated by hazard ratios (HRs) from Cox proportional hazards models. We conducted exposure-response trend analyses by use of cumulative exposures with lag times of 0, 5, 10, 15, or 20 years to account for disease latency. All analyses controlled for age and smoking status. All statistical tests were two-sided. RESULTS Cumulative exposure to endotoxin was strongly, statistically significantly, and inversely associated with lung cancer risk. The inverse trend was greatest with a 20-year lag time, for which highest endotoxin exposure was associated with a statistically significantly 40% less risk of lung cancer (HR = 0.60, 95% CI = 0.43 to 0.83; P(trend) across quintiles = . 002) than non-exposure. From a reported population rate of lung cancer among women in Shanghai of 19.1 per 100,000 for the year 2000 and the estimated reduction in risk of lung cancer observed for 20 years of endotoxin exposure in this population of workers, the incidence of lung cancer in this cohort was reduced by approximately 7.6 per 100,000 (range = 3.2-10.9 per 100,000). CONCLUSIONS Long-term and high-level exposure to endotoxin, compared with no exposure, appears to be associated with a reduced risk of lung cancer in this cohort.
Source Code Verification for Embedded Systems using Prolog

System relevant embedded software needs to be reliable and, therefore, well tested, especially for aerospace systems. A common technique to verify programs is the analysis of their abstract syntax tree (AST). Tree structures can be elegantly analyzed with the logic programming language Prolog. Moreover, Prolog offers further advantages for a thorough analysis: On the one hand, it natively provides versatile options to efficiently process tree or graph data structures. On the other hand, Prolog's non-determinism and backtracking eases tests of different variations of the program flow without big effort. A rule-based approach with Prolog allows to characterize the verification goals in a concise and declarative way. In this paper, we describe our approach to verify the source code of a flash file system with the help of Prolog. The flash file system is written in C++ and has been developed particularly for the use in satellites. We transform a given abstract syntax tree of C++ source code into Prolog facts and derive the call graph and the execution sequence (tree), which then are further tested against verification goals. The different program flow branching due to control structures is derived by backtracking as subtrees of the full execution sequence. Finally, these subtrees are verified in Prolog. We illustrate our approach with a case study, where we search for incorrect applications of semaphores in embedded software using the real-time operating system RODOS. We rely on computation tree logic (CTL) and have designed an embedded domain specific language (DSL) in Prolog to express the verification goals.
Prevalence of metabolic syndrome and its association with depression in patients with schizophrenia

Purpose To identify the point prevalence of metabolic syndrome in patients with schizophrenia and to evaluate the association between depressive symptoms and metabolic syndrome in patients with schizophrenia. Patients and methods Metabolic syndrome was assessed based on an updated definition derived from the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) and the International Diabetes Federation criteria. The 17-item Hamilton Depression Rating Scale (HDRS-17) was used to measure depressive symptoms in 80 patients with schizophrenia. Odds ratios and 95% confidence intervals were calculated using logistic regression for the association between each depressive symptom and metabolic syndrome. Results The point prevalence rates of metabolic syndrome according to the modified NCEP-ATP III and International Diabetes Federation criteria were 37% and 35%, respectively. The risk of having metabolic syndrome significantly increased in those who were widowed or separated, or had longer duration of illness. Central obesity was the metabolic feature with the highest odds ratios for metabolic syndrome at 19.3. Three out of 17 items of HDRS subscales were found to be significantly associated with metabolic syndrome, including depressed mood, middle insomnia, and retardation with the odds ratios of 3.0, 3.4, and 3.6, respectively. Conclusion This study showed that the prevalence of metabolic syndrome in patients with schizophrenia was higher than the overall rate but was slightly lower than in the general population in the USA. Central obesity, measured by waist circumference, was found to be highly correlated with metabolic syndrome. Depressed mood, middle insomnia, and retardation were significantly associated with metabolic syndrome in patients with schizophrenia. Waist circumference and screening for depression should be done at the clinics during patient follow-up.
ACTUALIZACION POR TEMAS VEGETAL MARIJUANA , ANIMAL MARIJUANA

Marijuana is the illicit drug most commonly used worldwide. The mechanisms by which this drug affects the brain have been studied exhaustively during the last 40 years, and better understood in the last decade. For example, the discovery of receptors to which marijuana binds, has been a major achievement in neurosciences and in the study of drug addiction. Moreover, the description of the endogenous ligands, the endocannabinoids, has shed light to the physiology of the brain regulating several behaviors: from pain to pleasure and from sex to thinking. For all these exciting effects, endocannabinoids are an important target for therapeutic endeavors.
Direct Lyapunov design - a synthesis procedure for motor schema using a second-order Lyapunov stability theorem

In this paper we propose a new procedure for construction of motor schema typically used in behaviour-based robotics. The procedure reverses the standard stability analysis approach by searching for a control function to fit a pre-defined Lyapunov function. In order to improve the applicability of this procedure, a new second-order extension to Lyapunov's second method is proposed, allowing a stable schema to be defined directly in terms of actuator force demands. We propose a synthesis procedure called direct Lyapunov design, which searches for motor schema maps whose set points satisfy the second-order theorem. The procedure has been applied to a simple subsumption architecture controller for an inverted pendulum simulation, yielding stable behaviour.
Multiple LDPC decoder of very low bit-error rate

The error correcting capability of LDPC based systems at low noise levels is often dominated by the so-called error floor, a region in the BER vs. noise level plot, where BER reduction slows down as the noise level decreases. The error floor behavior is commonly attributed to the sub-optimality of iterative decoding algorithms on graphs with cycles, which become trapped to local minimum solutions. Trapping of the decoder depends on several factors, including the decoding algorithm. A particular received word that is not decoded by a certain algorithm may be decoded successfully by a different algorithm. The proposed Multiple Decoder exploits this diverse behavior, by decoding a particular received word with N different algorithms, composing an LDPC decoder that achieves very low BER in the error floor region of operation, less iterations and higher throughput than the equivalent single decoder system.
Hybrid Mobile Wireless Sensor Network Cooperative Localization

Although there has been much effort to improve localization in both wireless sensor networks and robotics, few have combined the localization techniques of both worlds so that they can cooperatively and mutually benefit each other. Therefore, we seek to design a localization algorithm based on statistical framework that can combine the localization methodology of both autonomous robotics and sensor networks, such that a two-way cooperative localization can be accomplished instead of the usual one-way only cooperation. To achieve this, we need to integrate three important research areas: wireless sensor networks, robotics and statistical filtering. In this paper, we will present the key elements of our approach and simulation results to validate the scheme. We prove that mobile robots and static sensor nodes can cooperatively help each other in their localization by first localizing the mobile robots with the static nodes, after which the mobile robots will provide feedback to the static nodes so that the static nodes can then refine their own location estimates.
‘ Women ’ s Education and Social Transformation ’ with Th Special Reference to the Role of Christian Missionaries in Kerala

Social Transformation is the process of change or the formation of value system in the fullest sence. Globilization has presented new challenges in the realization of the goal of transforming women and now women transformation has become the sloganand motto of many social reformers, governmental agencies and voluntary organization. There is a long cherished wish among all the women to have better avenues in life in order to lead the life in a more fruitful way. The paper deeply goes through the historical trends on women’s education in Kerala and analyzes how we can transform women to applying education. The study tries to explore the theoretical approach of women’s education and social transformation and assess the role of missionaries in women’s education. The prime aim of this review is to provide a historical framework of women’s education and social transformation and to generate new approach about education on the histoirical point of view. The analytical and theoretical techniques are used for preparing this work. The review throws light in analyzing the concept ‘Social Transformation’through education by incorporating all divergent views with suffient logical reasoning. It also explains the problems faced and the possibilities ahead on achieving women’s transformation.
A two-chip interface for a MEMS accelerometer

A proposed third-order noise-shaping accelerometer interface circuit enhances the signal-to-noise ratio, compared with previously presented interface circuits. The solution for the two-chip implementation is described and a novel cross-coupled correlated double sampling integrator is proposed. This scheme functions even with large parasitic capacitances between the sensor and the interface circuit. The op-amp noise is first-order shaped. Dithering circuit is also implemented on the chip, fabricated in an 1.6-/spl mu/m CMOS process.
Identification and Molecular Characterization of NKp 30 , a Novel Triggering Receptor Involved in Natural Cytotoxicity Mediated by Human Natural Killer Cells

Two major receptors involved in human natural cytotoxicity, NKp46 and NKp44, have recently been identified. However, experimental evidence suggested the existence of additional such receptor(s). In this study, by the generation of monoclonal antibodies (mAbs), we identified NKp30, a novel 30-kD triggering receptor selectively expressed by all resting and activated human natural killer (NK) cells. Although mAb-mediated cross-linking of NKp30 induces strong NK cell activation, mAb-mediated masking inhibits the NK cytotoxicity against normal or tumor target cells. NKp30 cooperates with NKp46 and/or NKp44 in the induction of NK-mediated cytotoxicity against the majority of target cells, whereas it represents the major triggering receptor in the killing of certain tumors. This novel receptor is associated with CD3 z chains that become tyrosine phosphorylated upon sodium pervanadate treatment of NK cells. Molecular cloning of NKp30 cDNA revealed a member of the immunoglobulin superfamily, characterized by a single V-type domain and a charged residue in the transmembrane portion. Moreover, we show that NKp30 is encoded by the previously identified 1C7 gene, for which the function and the cellular distribution of the putative product were not identified in previous studies.
Analysis of aging drivers' behaviors navigating with in-vehicle visual display systems

A study was conducted to analyze the behavioral and visual strategies of drivers observed through videotape recordings which took into account subject gaze movements and field of view. Driver performance was evaluated in terms of the total time from starting point to destination and the number of navigational errors. Analysis showed that aging drivers modified their visual strategies under guidance. The duration and frequency of glances toward the navigational system were higher for the elderly in comparison with young adults, indicating a noticeable decrease in the time spent looking at the road. Compared with a paper map, the guidance system was found to lead to fewer navigational errors, less loss of time for environmental landmark search, and shorter route completion time. <<ETX>>
Iterative Computation of Camera Paths in an Image Based Rendering Application

This thesis presents a novel algorithm to iteratively compute camera paths of long image sequences. Scale Invariant Features are first extracted from the ordered set of images. These images are then matched pair-wise sequentially and correspondences are computed. An initial geometric path is found after by applying a bundle adjustment algorithm on these correspondences. Distances between cameras are computed from this initial estimation. The iteration process starts by grouping nearby cameras and then bundle adjusting the groups, and ends by merging the groups. This process is repeated until the reprojection errors fall into the preset tolerance. The key point in this algorithm is to take advantage of loopbacks in the image sequences. We have obtained excellent results for two camera paths, namely the spiral path and the snake like path. Our algorithm achieves both precise and stable results.
A Low Cost 3D Foot Scanner for Custom-Made Sports Shoes

Conventional methods to obtain foot anthropometry for custom made sports shoes using anthropometer, callipers and measuring tapes are inaccurate due to the complex anatomy and curvature of the instep, foot arc and related joints. They lead to poor repeatability and large variances, particularly when measurements are taken of different people. Measurements from 3D model have been claimed as a perfect tool to obtain anthropometric data. However a commercial 3D foot scanner to create a 3D foot model can be very costly. In this paper we propose a low cost 3D foot scanner system by integrating available image capture technology such as the Kinect®, appropriate 3D scanning software and a foot scanner rig. An experiment was conducted to compare the anthropometry data taken using conventional method and from the 3D model. The differences recorded for all regions were found to be less than 5%, suggesting that the 3D model produced by this method is accurate. The use of 3D scanner has also decreased the measurement duration, thus increasing the repeatability whilst decreasing human errors that normally occur during the measurement process.
Taxonomically Clustering Organisms Based on the Profiles of Gene Sequences Using PCA

The biological implications of bioinformatics can already be seen in various implementations. Biological taxonomy may seem like a simple science in which the biologists merely observe similarities among organisms and construct classifications according to those similarities[1], but it is not so simple. By applying data mining techniques on gene sequence database we can cluster the data to find interesting similarities in the gene expression data. One of the applications of such kind of clustering is taxonomically clustering the organisms based on their gene sequential expressions. In this study we outlined a method for taxonomical clustering of species of the organisms based on the genetic profile using Principal Component Analysis and Self Organizing Neural Networks. We have implemented the idea using Matlab and tried to cluster the gene sequences taken from PAUP version of the ML5/ML6 database. The taxa used for some of the basidiomycetous fungi form the database. To study the scalability issues another large gene sequence database was used. The proposed method clustered the species of organisms correctly in almost all the cases. The obtained were more significant and promising. The proposed method clustered the species of organisms correctly in almost all the cases. The obtained results were more significant and promising.
User-driven relational models for entity-relation search and extraction

The ability to extract new knowledge from large datasets is one of the most significant challenges facing society. The problem spans across domains from intelligence analysis and scientific research to basic web search. Current information extraction and retrieval tools either lack the flexibility to adapt to evolving information needs or require users to sift through search results and piece together relevant information. With so much data compounded by the criticality of finding relevant information, new tools and methods are needed to discover and relate relevant pieces of information in ever expanding repositories of data. We posit that user-driven relational models are needed to collectively learn and discover fine-grained entities and relations that are relevant to a user's information need. To meet this need, we present a ranked retrieval and extraction framework for collectively learning and integrating evidence of entities and relational dependencies to predict at query time, a ranking of sentences containing the most relevant entities and relational dependencies. By using a relational model, evidence can be leveraged across entity and relation instances. By performing joint inference at query time, NLP pipeline errors are minimized, and more adaptive and discriminative models that meet the specific knowledge discovery needs of the user can be developed. Our goal is to develop user-driven relational models of entities and their relational dependencies, and a search system based on these models that allow users to search for known entities and relations, discover new relations from known entities, and discover new entities from known relations. Preliminary qualitative and quantitative evaluations demonstrate the efficacy and potential of the proposed relational modeling approach.
A SVM Classifier Research Based on RS Reducts

Support vector machine (SVM) is a promising method of machine learning based on the structural risk minimization principle, which is characteristic of good generalization performance; Rough set (RS) is an effective tool to decrease data dimension in dealing with vagueness and uncertainty information. A SVM classifier based on RS reducts is researched in order to enhance the predicting performance in this paper. We firstly get all the attribute reducts; Then, every element is chosen from the set of all reducts and relevant SVM Classifiers are constructed respectively; Finally an enhanced SVM classifier based on RS reducts is obtained from them, which make great use of the advantages of RS in eliminating redundant information and take full advantage of SVM to train and test the data. Experiment results explain the validity and feasibility of our proposed algorithm.