nils-herrmann/clean_scirepeval huggingface dataset:
Mutant p53 Gain of Function in Two Mouse Models of Li-Fraumeni Syndrome

The p53 tumor suppressor gene is commonly altered in human tumors, predominantly through missense mutations that result in accumulation of mutant p53 protein. These mutations may confer dominant-negative or gain-of-function properties to p53. To ascertain the physiological effects of p53 point mutation, the structural mutant p53R172H and the contact mutant p53R270H (codons 175 and 273 in humans) were engineered into the endogenous p53 locus in mice. p53R270H/+ and p53R172H/+ mice are models of Li-Fraumeni Syndrome; they developed allele-specific tumor spectra distinct from p53+/- mice. In addition, p53R270H/- and p53R172H/- mice developed novel tumors compared to p53-/- mice, including a variety of carcinomas and more frequent endothelial tumors. Dominant effects that varied by allele and function were observed in primary cells derived from p53R270H/+ and p53R172H/+ mice. These results demonstrate that point mutant p53 alleles expressed under physiological control have enhanced oncogenic potential beyond the simple loss of p53 function.
A Noise-Efficient 36 nV/ $\surd $ Hz Chopper Amplifier Using an Inverter-Based 0.2-V Supply Input Stage

This paper presents an analog front end (AFE) that achieves a high noise efficiency by using a chopper amplifier with a 0.2-V supply inverter-based input stage followed by a 0.8-V supply stage. The high input-stage current needed to reduce the input-referred noise is drawn from the 0.2-V supply, significantly reducing power consumption. The 0.8 V stage provides high gain and signal swing, improving linearity. Biasing and common-mode rejection techniques for the ultra-low-voltage stage are presented. The AFE is implemented in a 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and integrates the chopper low-noise instrumentation amplifier, a programmable-gain amplifier, and an antialiasing filter. The AFE consumes 0.79 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and achieves a competitive power efficiency factor (PEF) of 1.6 and an input noise of 0.94 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}_{\text {rms}}$ </tex-math></inline-formula> integrated from 0.5 to 670 Hz while maintaining a 36 nV/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz input noise density down to 0.5 Hz. The included 0.8/0.2-V buck converter may be used to provide the 0.2-V supply at 72%–74% efficiency without significantly increasing noise, yielding a PEF of 1.8.
Automatic 3D Indoor Scene Modeling from Single Panorama

We describe a system that automatically extracts 3D geometry of an indoor scene from a single 2D panorama. Our system recovers the spatial layout by finding the floor, walls, and ceiling; it also recovers shapes of typical indoor objects such as furniture. Using sampled perspective sub-views, we extract geometric cues (lines, vanishing points, orientation map, and surface normals) and semantic cues (saliency and object detection information). These cues are used for ground plane estimation and occlusion reasoning. The global spatial layout is inferred through a constraint graph on line segments and planar superpixels. The recovered layout is then used to guide shape estimation of the remaining objects using their normal information. Experiments on synthetic and real datasets show that our approach is state-of-the-art in both accuracy and efficiency. Our system can handle cluttered scenes with complex geometry that are challenging to existing techniques.
Crime Pattern Analysis , Visualization And Prediction Using Data Mining

Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analysing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyse crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyse, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various types of crimes against women taking place in different states and cities of India. Crime mapping will help the administration to plan strategies for prevention of crime, further using data mining technique data can be predicted and visualized in various form in order to provide better understanding of crime patterns. Keyword: K-Means , cluster, correlation .
INNOVATIVE BEHAVIOR IN THE WORKPLACE : THE ROLE OF PERFORMANCE AND IMAGE OUTCOME EXPECTATIONS

Why do employees engage in innovative behavior at their workplaces? We examine how employees’ innovative behavior is explained by expectations for such behavior to affect job performance (expected positive performance outcomes) and image inside their organizations (expected image risks and expected image gains). We found significant effects of all three outcome expectations on innovative behavior. These outcome expectations, as intermediate psychological processes, were shaped by contextual and individual difference factors, including perceived organization support for innovation, supervisor relationship quality, job requirement for innovativeness, employee reputation as innovative, and individual dissatisfaction with the status quo.
Degradation of Plastics Using Microorganisms

Plastics have become an important part of modern life and are used in different sectors of applications like packaging, building materials, consumer products and much more. Each year about 100 million tons of plastics are produced worldwide. Demand for plastics in India reached about 4.3 million tons in the year 2001-02 and would increase to about 8 million tons in the year 2006-07. Degradation is defined as reduction in the molecular weight of the polymer. The Degradation types are (a).Chain end degradation/de-polymerization (b).Random degradation/reverse of the poly condensation process. Biodegradation is defined as reduction in the molecular weight by naturally occurring microorganisms such as bacteria, fungi, and actinomycetes. That is involved in the degradation of both natural and synthetic plastics. Examples of Standard Testing for Polymer Biodegradability in Various Environments. ASTM D5338: Standard Test Method for Determining the Aerobic Biodegradation of Plastic Materials under Controlled Composting Conditions, ASTM D5210: Standard Test Method for Determining the Anaerobic Biodegradation of Plastic Materials in the Presence of Municipal Sewage Sludge, ASTM D5526: Standard Test Method for Determining Anaerobic Biodegradation of Plastic Materials under Accelerated Landfill Conditions, ASTM D5437: Standard Practice for Weathering of Plastics under Marine Floating Exposure. Plastics are biodegraded, (1).In wild nature by aerobic conditions CO2, water are produced,(2).In sediments & landfills by anaerobic conditions CO2, water, methane are produced, (3).In composts and soil by partial aerobic & anaerobic conditions. This review looks at the technological advancement made in the development of more easily biodegradable plastics and the biodegradation of conventional plastics by microorganisms. Additives, such as pro-oxidants and starch, are applied in synthetic materials to modify and make plastics biodegradable. Reviewing published and ongoing studies on plastic biodegradation, this paper attempts to make conclusions on potentially viable methods to reduce impacts of plastic waste on the
Monitoring of Civil Infrastructures by Interferometric Radar: A Review

Ground-based radar interferometry is an increasingly popular technique for monitoring civil infrastructures. Many research groups, professionals, and companies have tested it in different operative scenarios, so it is time for a first systematic survey of the case studies reported in the literature. This review is addressed especially to the engineers and scientists interested to consider the applicability of the technique to their practice, so it is focused on the issues of the practical cases rather than on theory and principles, which are now well consolidated.
A Random Forest Guided Tour

The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is versatile enough to be applied to large-scale problems, is easily adapted to various ad-hoc learning tasks, and returns measures of variable importance. The present article reviews the most recent theoretical and methodological developments for random forests. Emphasis is placed on the mathematical forces driving the algorithm, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures. This review is intended to provide non-experts easy access to the main ideas.
OPTIMIZATION OF A WAVE CANCELLATION MULTIHULL SHIP USING CFD TOOLS

A simple CFD tool, coupled to a discrete surface representation and a gradient-based optimization procedure, is applied to the design of optimal hull forms and optimal arrangement of hulls for a wave cancellation multihull ship. The CFD tool, which is used to estimate the wave drag, is based on the zeroth-order slender ship approximation. The hull surface is represented by a triangulation, and almost every grid point on the surface can be used as a design variable. A smooth surface is obtained via a simplified pseudo-shell problem. The optimal design process consists of two steps. The optimal center and outer hull forms are determined independently in the first step, where each hull keeps the same displacement as the original design while the wave drag is minimized. The optimal outer-hull arrangement is determined in the second step for the optimal center and outer hull forms obtained in the first step. Results indicate that the new design can achieve a large wave drag reduction in comparison to the original design configuration.
Glucagon and regulation of glucose metabolism.

As a counterregulatory hormone for insulin, glucagon plays a critical role in maintaining glucose homeostasis in vivo in both animals and humans. To increase blood glucose, glucagon promotes hepatic glucose output by increasing glycogenolysis and gluconeogenesis and by decreasing glycogenesis and glycolysis in a concerted fashion via multiple mechanisms. Compared with healthy subjects, diabetic patients and animals have abnormal secretion of not only insulin but also glucagon. Hyperglucagonemia and altered insulin-to-glucagon ratios play important roles in initiating and maintaining pathological hyperglycemic states. Not surprisingly, glucagon and glucagon receptor have been pursued extensively in recent years as potential targets for the therapeutic treatment of diabetes.
Recent progress on the genetics and molecular breeding of brown planthopper resistance in rice

Brown planthopper (BPH) is the most devastating pest of rice. Host-plant resistance is the most desirable and economic strategy in the management of BPH. To date, 29 major BPH resistance genes have been identified from indica cultivars and wild rice species, and more than ten genes have been fine mapped to chromosome regions of less than 200 kb. Four genes (Bph14, Bph26, Bph17 and bph29) have been cloned. The increasing number of fine-mapped and cloned genes provide a solid foundation for development of functional markers for use in breeding. Several BPH resistant introgression lines (ILs), near-isogenic lines (NILs) and pyramided lines (PLs) carrying single or multiple resistance genes were developed by marker assisted backcross breeding (MABC). Here we review recent progress on the genetics and molecular breeding of BPH resistance in rice. Prospect for developing cultivars with durable, broad-spectrum BPH resistance are discussed.
Flourishing on facebook: virtue friendship & new social media

The widespread and growing use of new social media, especially social networking sites such as Facebook and Twitter, invites sustained ethical reflection on emerging forms of online friendship. Social scientists and psychologists are gathering a wealth of empirical data on these trends, yet philosophical analysis of their ethical implications remains comparatively impoverished. In particular, there have been few attempts to explore how traditional ethical theories might be brought to bear upon these developments, or what insights they might offer, if any. In attempting to address this lacuna in applied ethical research, this paper investigates the ethical significance of online friendship by means of an Aristotelian theory of the good life, which holds that human flourishing is chiefly realized through ‘complete’ friendships of virtue. Here, four key dimensions of ‘virtue friendship’ are examined in relation to online social media: reciprocity, empathy, self-knowledge and the shared life. Online social media support and strengthen friendship in ways that mirror these four dimensions, particularly when used to supplement rather than substitute for face-to-face interactions. However, deeper reflection on the meaning of the shared life (suzên) for Aristotle raises important and troubling questions about the capacity of online social media to support complete friendships of virtue in the contemporary world, along with significant concerns about the enduring relevance of this Aristotelian ideal for the good life in the 21st century.
Reinforcement learning for robot soccer

Batch reinforcement learning methods provide a powerful framework for learning efficiently and effectively in autonomous robots. The paper reviews some recent work of the authors aiming at the successful application of reinforcement learning in a challenging and complex domain. It discusses several variants of the general batch learning framework, particularly tailored to the use of multilayer perceptrons to approximate value functions over continuous state spaces. The batch learning framework is successfully used to learn crucial skills in our soccer-playing robots participating in the RoboCup competitions. This is demonstrated on three different case studies.
An Energy-Aware Trust Derivation Scheme With Game Theoretic Approach in Wireless Sensor Networks for IoT Applications

Trust evaluation plays an important role in securing wireless sensor networks (WSNs), which is one of the most popular network technologies for the Internet of Things (IoT). The efficiency of the trust evaluation process is largely governed by the trust derivation, as it dominates the overhead in the process, and performance of WSNs is particularly sensitive to overhead due to the limited bandwidth and power. This paper proposes an energy-aware trust derivation scheme using game theoretic approach, which manages overhead while maintaining adequate security of WSNs. A risk strategy model is first presented to stimulate WSN nodes' cooperation. Then, a game theoretic approach is applied to the trust derivation process to reduce the overhead of the process. We show with the help of simulations that our trust derivation scheme can achieve both intended security and high efficiency suitable for WSN-based IoT networks.
Staying engaged during the week: the effect of off-job activities on next day work engagement.

Although studies on employee recovery accumulate at a stunning pace, the commonly used theory (Effort-Recovery model) that explains how recovery occurs has not been explicitly tested. We aimed to unravel the recovery process by examining whether off-job activities enhance next morning vigor to the extent that they enable employees to relax and detach from work. In addition, we investigated whether adequate recovery also helps employees to work with more enthusiasm and vigor on the next workday. On five consecutive days, a total of 74 employees (356 data points) reported the hours they spent on various off-job activities, their feelings of psychological detachment, and feelings of relaxation before going to sleep. Feelings of vigor were reported on the next morning, and day-levels of work engagement were reported after work. As predicted, leisure activities (social, low-effort, and physical activities) increased next morning vigor through enhanced psychological detachment and relaxation. High-duty off-job activities (work and household tasks) reduced vigor because these activities diminished psychological detachment and relaxation. Moreover, off-job activities significantly affected next day work engagement. Our results support the assumption that recovery occurs when employees engage in off-job activities that allow for relaxation and psychological detachment. The findings also underscore the significance of recovery after work: Adequate recovery not only enhances vigor in the morning, but also helps employees to stay engaged during the next workday.
The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models

In the event of sensor failure, it is necessary for autonomous vehicles to safely execute emergency maneuvers while avoiding other vehicles on the road. In order to accomplish this, the sensor-failed vehicle must predict the future semantic behaviors of other drivers, such as lane changes, as well as their future trajectories given a small window of past sensor observations. We address the first issue of semantic behavior prediction in this paper, by introducing a prediction framework that leverages the power of recurrent neural networks (RNNs) and graphical models. Our prediction goal is to predict the future categorical driving intent, for lane changes, of neighboring vehicles up to three seconds into the future given as little as a one-second window of past LIDAR, GPS, inertial, and map data. We collect real-world data containing over 500,000 samples of highway driving using an autonomous Toyota vehicle. We propose a pair of models that leverage RNNs: first, a monolithic RNN model that tries to directly map inputs to future behavior through a long-short-term-memory network. Second, we propose a composite RNN model by adopting the methodology of Structural Recurrent Neural Networks (RNNs) to learn factor functions and take advantage of both the highlevel structure of graphical models and the sequence modeling power of RNNs, which we expect to afford more transparent modeling and activity than the monolithic RNN. To demonstrate our approach, we validate our models using authentic interstate highway driving to predict the future lane change maneuvers of other vehicles neighboring our autonomous vehicle. We find that both RNN models outperform baselines, and they outperform each other in certain conditions.
Challenges and opportunities of Mobile Cloud Computing

Cloud Computing (CC) is fast becoming well known in the computing world as the latest technology. CC enables users to use resources as and when they are required. Mobile Cloud Computing (MCC) is an integration of the concept of cloud computing within a mobile environment, which removes barriers linked to the mobile devices' performance. Nevertheless, these new benefits are not problem-free entirely. Several common problems encountered by MCC are privacy, personal data management, identity authentication, and potential attacks. The security issues are a major hindrance in the mobile cloud computing's adaptability. This study begins by presenting the background of MCC including the various definitions, infrastructures, and applications. In addition, the current challenges and opportunities will be presented including the different approaches that have been adapted in studying MCC.
The effects of health worker motivation and job satisfaction on turnover intention in Ghana: a cross-sectional study

Motivation and job satisfaction have been identified as key factors for health worker retention and turnover in low- and middle-income countries. District health managers in decentralized health systems usually have a broadened 'decision space' that enables them to positively influence health worker motivation and job satisfaction, which in turn impacts on retention and performance at district-level. The study explored the effects of motivation and job satisfaction on turnover intention and how motivation and satisfaction can be improved by district health managers in order to increase retention of health workers.



We conducted a cross-sectional survey in three districts of the Eastern Region in Ghana and interviewed 256 health workers from several staff categories (doctors, nursing professionals, allied health workers and pharmacists) on their intentions to leave their current health facilities as well as their perceptions on various aspects of motivation and job satisfaction. The effects of motivation and job satisfaction on turnover intention were explored through logistic regression analysis.



Overall, 69% of the respondents reported to have turnover intentions.  0.60 to 0.92) and job satisfaction (OR = 0.74, 95% CI: 0.57 to 0.96) were significantly associated with turnover intention and higher levels of both reduced the risk of health workers having this intention. The dimensions of motivation and job satisfaction significantly associated with turnover intention included career development (OR = 0.56, 95% CI: 0.36 to 0.86), workload (OR = 0.58, 95% CI: 0.34 to 0.99), management (OR = 0.51. 95% CI: 0.30 to 0.84), organizational commitment (OR = 0.36, 95% CI: 0.19 to 0.66), and burnout (OR = 0.59, 95% CI: 0.39 to 0.91).



Our findings indicate that effective human resource management practices at district level influence health worker motivation and job satisfaction, thereby reducing the likelihood for turnover. Therefore, it is worth strengthening human resource management skills at district level and supporting district health managers to implement retention strategies.