nils-herrmann/clean_scirepeval huggingface dataset:
Body fluid identification in forensics.

Determination of the type and origin of the body fluids found at a crime scene can give important insights into crime scene reconstruction by supporting a link between sample donors and actual criminal acts. For more than a century, numerous types of body fluid identification methods have been developed, such as chemical tests, immunological tests, protein catalytic activity tests, spectroscopic methods and microscopy. However, these conventional body fluid identification methods are mostly presumptive, and are carried out for only one body fluid at a time. Therefore, the use of a molecular genetics-based approach using RNA profiling or DNA methylation detection has been recently proposed to supplant conventional body fluid identification methods. Several RNA markers and tDMRs (tissue-specific differentially methylated regions) which are specific to forensically relevant body fluids have been identified, and their specificities and sensitivities have been tested using various samples. In this review, we provide an overview of the present knowledge and the most recent developments in forensic body fluid identification and discuss its possible practical application to forensic casework.
EXPLORING THE ASSOCIATION BETWEEN EMOTIONS AND EATING BEHAVIOR iii

Emotional eating is a change in consumption of food in response to emotional stimuli, and has been linked in negative physical and psychological outcomes. Observers have noticed over the years a correlation between emotions, mood and food choice, in ways that vary from strong and overt to subtle and subconscious. Specific moods such as anger, fear, sadness and joy have been found to affect eating responses and eating itself can play a role in influencing one’s emotions. With such an obvious link between emotions and eating behavior, the research over the years continues to delve further into the phenomenon. This includes investigating individuals of different weight categories, as well as children, adolescents and parenting styles. EXPLORING THE ASSOCIATION BETWEEN EMOTIONS AND EATING BEHAVIOR v
Effects of Cooking Conditions on the Lycopene Content in Tomatoes

Raw tomato contains a high level of lycopene, which has been reported to have many important health benefits. However, information on the changes of the lycopene content in tomato during cooking is limited. In this study, the lycopene content in raw and thermally processed (baked, microwaved, and fried) tomato slurries was investigated and analyzed using a high-performance liquid chromatography (HPLC) method. In the thermal stability study using a pure lycopene standard, 50% of lycopene was degraded at 100 ◦C after 60 min, 125 ◦C after 20 min, and 150 ◦C after less than 10 min. Only 64.1% and 51.5% lycopene was retained when the tomato slurry was baked at 177 ◦C and 218 ◦C for 15 min, respectively. At these temperatures, only 37.3% and 25.1% of lycopene was retained after baking for 45 min. In 1 min of the high power of microwave heating, 64.4% of lycopene still remained. However, more degradation of lycopene in the slurry was found in the frying study. Only 36.6% and 35.5% of lycopene was retained after frying at 145 and 165 ◦C for 1 min, respectively.
Face Synthesis for Eyeglass-Robust Face Recognition

In the application of face recognition, eyeglasses could significantly degrade the recognition accuracy. A feasible method is to collect large-scale face images with eyeglasses for training deep learning methods. However, it is difficult to collect the images with and without glasses of the same identity, so that it is difficult to optimize the intra-variations caused by eyeglasses. In this paper, we propose to address this problem in a virtual synthesis manner. The high-fidelity face images with eyeglasses are synthesized based on 3D face model and 3D eyeglasses. Models based on deep learning methods are then trained on the synthesized eyeglass face dataset, achieving better performance than previous ones. Experiments on the real face database validate the effectiveness of our synthesized data for improving eyeglass face recognition performance.
Teaching course: ion channelopathies in neurology

In 1990 the molecular basis for a hereditary disorder in humans, hyperkalemic periodic paralysis, was first genetically demonstrated to be impaired ion channel function. Since then over a dozen diseases, now termed as channelopathies, have been described. Most of the disorders affect excitable tissue such as muscle and nerve; however, kidney diseases have also been described. Basic research on structure-function relationships and physiology of excitation has benefited tremendously from the discovery of disease-causing mutations pointing to regions of special significance within the channel proteins. This course focuses mainly on the clinical and genetic features of neurological disturbances in humans caused by genetic defects in voltage-gated sodium, calcium, potassium, and chloride channels. Disorders of skeletal muscle are by far the most studied and therefore more detailed in this text than the neuronal channelopathies which have been discovered only very recently. Review literature may be found in the attached reference list [1–12]. Skeletal muscle sodium channelopathies
Mechanics of Crystalline Nanowires : An Experimental Perspective

A wide variety of crystalline nanowires (NWs) with outstanding mechanical properties have recently emerged. Measuring their mechanical properties and understanding their deformation mechanisms are of important relevance to many of their device applications. On the other hand, such crystalline NWs can provide an unprecedented platform for probing mechanics at the nanoscale. While challenging, the field of experimental mechanics of crystalline nanowires has emerged and seen exciting progress in the past decade. This review summarizes recent advances in this field, focusing on major experimental methods using atomic force microscope (AFM) and electron microscopes and key results on mechanics of crystalline nanowires learned from such experimental studies. Advances in several selected topics are discussed including elasticity, fracture, plasticity, and anelasticity. Finally, this review surveys some applications of crystalline nanowires such as flexible and stretchable electronics, nanocomposites, nanoelectromechanical systems (NEMS), energy harvesting and storage, and strain engineering, where mechanics plays a key role.
Refractory dissecting Cellulitis of the Scalp Successfully controlled with Adalimumab.

Dissecting cellulitis of the scalp (DCS) is an uncommon inflammatory disease that often results in scarring alopecia. Numerous therapies have either proved ineffective or only temporarily effective in the management of this condition. Recent reports show adequate responses to tumor necrosis factor (TNF) inhibitors in cases of DCS. We report a case of severe recalcitrant DCS successfully treated with adalimumab.
Soft Actor-Critic Algorithms and Applications

Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods typically suffer from two major challenges: high sample complexity and brittleness to hyperparameters. Both of these challenges limit the applicability of such methods to real-world domains. In this paper, we describe Soft Actor-Critic (SAC), our recently introduced off-policy actor-critic algorithm based on the maximum entropy RL framework. In this framework, the actor aims to simultaneously maximize expected return and entropy. That is, to succeed at the task while acting as randomly as possible. We extend SAC to incorporate a number of modifications that accelerate training and improve stability with respect to the hyperparameters, including a constrained formulation that automatically tunes the temperature hyperparameter. We systematically evaluate SAC on a range of benchmark tasks, as well as real-world challenging tasks such as locomotion for a quadrupedal robot and robotic manipulation with a dexterous hand. With these improvements, SAC achieves state-of-the-art performance, outperforming prior on-policy and off-policy methods in sample-efficiency and asymptotic performance. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving similar performance across different random seeds. These results suggest that SAC is a promising candidate for learning in real-world robotics tasks.
THE 1966 LAW CONCERNING PROHIBITION OF ABORTION IN ROMANIA AND ITS CONSEQUENCES. THE FATE OF ONE GENERATION

From the ancient times, abortion appeared as a method of controlling the fertility. But starting with XIX century, modern states used the abortion as a mechanism of demographic policy, the state intending to adjust the fertility of population by this. It is worth underlining the ethical and moral aspects of abortion, which determined strong debates on the political scene and further at the level of civil society and mass media. So, in the last decades there have been questioned not only ethic and moral implications of abortion, but also practical outsets. A responsible demographic policy should take into account the whole set of socio-economic and cultural factors that condition a society.
Urbanisation and infectious diseases in a globalised world.

The world is becoming urban. The UN predicts that the world's urban population will almost double from 3·3 billion in 2007 to 6·3 billion in 2050. Most of this increase will be in developing countries. Exponential urban growth is having a profound effect on global health. Because of international travel and migration, cities are becoming important hubs for the transmission of infectious diseases, as shown by recent pandemics. Physicians in urban environments in developing and developed countries need to be aware of the changes in infectious diseases associated with urbanisation. Furthermore, health should be a major consideration in town planning to ensure urbanisation works to reduce the burden of infectious diseases in the future.
Belief revision in structured probabilistic argumentation

In real-world applications, knowledge bases consisting of all the available information for a specific domain, along with the current state of affairs, will typically contain contradictory data, coming from different sources, as well as data with varying degrees of uncertainty attached. An important aspect of the effort associated with maintaining such knowledge bases is deciding what information is no longer useful; pieces of information may be outdated; may come from sources that have recently been discovered to be of low quality; or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing these basic issues. The formalism is capable of handling contradictory and uncertain data, and we study non-prioritized belief revision over probabilistic PreDeLP programs that can help with knowledge-base maintenance. For belief revision, we propose a set of rationality postulates — based on well-known ones developed for classical knowledge bases — that characterize how these belief revision operations should behave, and study classes of operators along with theoretical relationships with the proposed postulates, including representation theorems stating the equivalence between classes of operators and their associated postulates. We then demonstrate how our framework can be used to address the attribution problem in cyber security/cyber warfare.
Mining the impact of object oriented metrics for change prediction using Machine Learning and Search-based techniques

Change in a software is crucial to incorporate defect correction and continuous evolution of requirements and technology. Thus, development of quality models to predict the change proneness attribute of a software is important to effectively utilize and plan the finite resources during maintenance and testing phase of a software. In the current scenario, a variety of techniques like the statistical techniques, the Machine Learning (ML) techniques and the Search-based techniques (SBT) are available to develop models to predict software quality attributes. In this work, we assess the performance of ten machine learning and search-based techniques using data collected from three open source software. We first develop a change prediction model using one data set and then we perform inter-project validation using two other data sets in order to obtain unbiased and generalized results. The results of the study indicate comparable performance of SBT with other employed statistical and ML techniques. This study also supports inter project validation as we successfully applied the model created using the training data of one project on other similar projects and yield good results.
A 32 Gb/s Backplane Transceiver With On-Chip AC-Coupling and Low Latency CDR in 32 nm SOI CMOS Technology

This paper describes key design features of a 32 Gb/s 4-tap FFE/15-tap DFE transceiver in 32 nm SOI CMOS which mitigate major sources of degradation in transceiver performance. The transceiver employs a passive feed-forward restore (FFR) scheme in an on-chip AC-coupling network to prevent pattern-dependent baseline wander, a low-latency clock and data recovery (CDR) to improve high-frequency jitter tolerance, and a token-based power management scheme to reduce supply ripple. At 32 Gb/s, the transceiver can equalize a channel with 30 dB of loss at a bit-error rate below 10-12 while consuming 21 mW/Gbps at 1 V supply and an area of 0.7 mm2.
Deterministic Annealing for Clustering , Compression , Classification , Regression , and Related Optimization Problems

The deterministic annealing approach to clustering and its extensions has demonstrated substantial performance improvement over standard supervised and unsupervised learning methods in a variety of important applications including compression, estimation, pattern recognition and classification, and statistical regression. The method offers three important features: 1) the ability to avoid many poor local optima; 2) applicability to many different structures/architectures; and 3) the ability to minimize the right cost function even when its gradients vanish almost everywhere, as in the case of the empirical classification error. It is derived within a probabilistic framework from basic information theoretic principles (e.g., maximum entropy and random coding). The application-specific cost is minimized subject to a constraint on the randomness (Shannon entropy) of the solution, which is gradually lowered. We emphasize intuition gained from analogy to statistical physics, where this is an annealing process that avoids many shallow local minima of the specified cost and, at the limit of zero “temperature,” produces a nonrandom (hard) solution. Alternatively, the method is derived within rate-distortion theory, where the annealing process is equivalent to computation of Shannon’s rate-distortion function, and the annealing temperature is inversely proportional to the slope of the curve. This provides new insights into the method and its performance, as well as new insights into rate-distortion theory itself. The basic algorithm is extended by incorporating structural constraints to allow optimization of numerous popular structures including vector quantizers, decision trees, multilayer perceptrons, radial basis functions, and mixtures of experts. Experimental results show considerable performance gains over standard structure-specific and application-specific training methods. The paper concludes with a brief discussion of extensions of the method that are currently under investigation.
Health-care seeking behaviour among persons with diabetes in Uganda: an interview study

Healthcare-seeking behaviour in patients with diabetes mellitus (DM) has been investigated to a limited extent, and not in developing countries. Switches between different health sectors may interrupt glycaemic control, affecting health. The aim of the study was to explore healthcare-seeking behaviour, including use of complementary alternative medicine (CAM) and traditional healers, in Ugandans diagnosed with DM. Further, to study whether gender influenced healthcare-seeking behaviour.



This is a descriptive study with a snowball sample from a community in Uganda. Semi-structured interviews were held with 16 women and 8 men, aged 25-70. Data were analysed by qualitative content analysis.



Healthcare was mainly sought among doctors and nurses in the professional sector because of severe symptoms related to DM and/or glycaemic control. Females more often focused on follow-up of DM and chronic pain in joints, while males described fewer problems. Among those who felt that healthcare had failed, most had turned to traditional healers in the folk sector for prescription of herbs or food supplements, more so in women than men. Males more often turned to private for-profit clinics while females more often used free governmental institutions.



Healthcare was mainly sought from nurses and physicians in the professional sector and females used more free-of-charge governmental institutions. Perceived failure in health care to manage DM or related complications led many, particularly women, to seek alternative treatment from CAM practitioners in the folk sector. Living conditions, including healthcare organisation and gender, seemed to influence healthcare seeking, but further studies are needed.
Cranioplasty: indications and advances.

Cranioplasty has been long practiced, and the reconstructive techniques continue to evolve. With a variety of options available for filling cranial defects, a review of the current practices in cranioplasty allows for reporting the most advanced techniques and specific indications.



Overwhelming support remains for the use of autologous bone grafts in filling the cranial defects. Alloplastic alternatives have relative advantages and disadvantages depending on the patient population and specific indications. Application of imaging technology has allowed for the utilization of custom-made alloplastic implants when autologous bone grafts are not feasible.



Autologous bone grafts remain the best option for adult and pediatric patients with viable donor sites and small-to-medium defects. Large defects in the adult population can be reconstructed with titanium mesh and polymethylmethacrylate overlay with or without the use of computer-assisted design and manufacturing customization. In pediatric patients, exchange cranioplasty offers a viable technique for using an autologous bone graft, while simultaneously filling the donor site with particulate bone graft. Advances in alloplastic materials and custom manufacturing of implants will have an important influence on cranioplasty techniques in the years to come.
Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r 2 and root mean square error (RMSE), performed on training as well as checking datasets. The direct contact between cutting tool, workpiece material, and the chips during machining operation imposes extreme thermal and mechanical stresses on the cutting tool. As a result, changes to the geometry, volume loss, and sharpness of the cutting tool, can occur either gradually or abruptly. These changes, which are known as tool wear, normally take place at the rates dependent upon machining conditions, workpiece material, as well as the cutting tool material or geometry. As discussed in earlier research study , abrasive wear on the flank face of the cutting tool has been the dominant wear mechanism that influences the tool sharpness during machining of glass fibre-reinforced polymer (GFRP) composites. On the basis of this, reduction of tool sharpness puts constraint on the dimensional accuracies and surface qualities of the composites product. Often, in-service or mechanical performance of poorly machined GFRPs degrades and under the worst circumstances, causes them rejected prior to the end applications. Similar to the case of metallic materials and their metal matrix composite counterparts, it is essential to develop accurate tool wear predictive models as monitoring its condition during machining can extend its useful life. There exists a significant body …
Nitrate Pollution and Preliminary Source Identification of Surface Water in a Semi-Arid River Basin , Using Isotopic and Hydrochemical Approaches

Nitrate contamination in rivers has raised widespread concern in the world, particularly in arid/semi-arid river basins lacking qualified water. Understanding the nitrate pollution levels and sources is critical to control the nitrogen input and promote a more sustainable water management in those basins. Water samples were collected from a typical semi-arid river basin, the Weihe River watershed, China, in October 2014. Hydrochemical assessment and nitrogen isotopic measurement were used to determine the level of nitrogen compounds and identify the sources of nitrate contamination. Approximately 32.4% of the water samples exceeded the World Health Organization (WHO) drinking water standard for NO3 ́-N. Nitrate pollution in the main stream of the Weihe River was obviously much more serious than in the tributaries. The δ15N-NO3 ́ of water samples ranged from +8.3h to +27.0h. No significant effect of denitrification on the shift in nitrogen isotopic values in surface water was observed by high dissolved oxygen (DO) values and linear relationship diagram between NO3 ́-N and δ15N-NO3 ́, except in the Weihe River in Huayin County and Shitou River. Analyses of hydrochemistry and isotopic compositions indicate that domestic sewage and agricultural activities are the main sources of nitrate in the river.