nils-herrmann/clean_scirepeval huggingface dataset:
Hepatoprotective herbal drug, silymarin from experimental pharmacology to clinical medicine.

Silymarin, a flavonolignan from 'milk thistle' (Silybum marianum) plant is used almost exclusively for hepatoprotection and amounts to 180 million US dollars business in Germany alone. In this review we discuss about its safety, efficacy and future uses in liver diseases. The use of silymarin may replace the polyherbal formulations and will avoid the major problems of standardization, quality control and contamination with heavy metals or bacterial toxins. Silymarin consists of four flavonolignan isomers namely--silybin, isosilybin, silydianin and silychristin. Among them, silybin being the most active and commonly used. Silymarin is orally absorbed and is excreted mainly through bile as sulphates and conjugates. Silymarin offers good protection in various toxic models of experimental liver diseases in laboratory animals. It acts by antioxidative, anti-lipid peroxidative, antifibrotic, anti-inflammatory, membrane stabilizing, immunomodulatory and liver regenerating mechanisms. Silymarin has clinical applications in alcoholic liver diseases, liver cirrhosis, Amanita mushroom poisoning, viral hepatitis, toxic and drug induced liver diseases and in diabetic patients. Though silymarin does not have antiviral properties against hepatitis virus, it promotes protein synthesis, helps in regenerating liver tissue, controls inflammation, enhances glucuronidation and protects against glutathione depletion. Silymarin may prove to be a useful drug for hepatoprotection in hepatobiliary diseases and in hepatotoxicity due to drugs. The non traditional use of silymarin may make a breakthrough as a new approach to protect other organs in addition to liver. As it is having a good safety profile, better patient tolerability and an effective drug at an affordable price, in near future new derivatives or new combinations of this drug may prove to be useful.
Freedom from Interference for AUTOSAR-based ECUs: a partitioned AUTOSAR stack

AUTOSAR is a standard for the development of software for embedded devices, primarily created for the automotive domain. It specifies a software architecture with more than 80 software modules that provide services to one or more software components. With the trend towards integrating safety-relevant systems into embedded devices, conformance with standards such as ISO 26262 [ISO11] or ISO/IEC 61508 [IEC10] becomes increasingly important. This article presents an approach to providing freedom from interference between software components by using the MPU available on many modern microcontrollers. Each software component gets its own dedicated memory area, a so-called memory partition. This concept is well known in other industries like the aerospace industry, where the IMA architecture is now well established. The memory partitioning mechanism is implemented by a microkernel, which integrates seamlessly into the architecture specified by AUTOSAR. The development has been performed as SEooC as described in ISO 26262, which is a new development approach. We describe the procedure for developing an SEooC.  AUTomotive Open System ARchitecture, see [ASR12].  Memory Protection Unit. 3 IMA: Integrated Modular Avionics, see [RTCA11]. 4 SEooC: Safety Element out of Context, see [ISO11].
Assessment of flora diversity in a minor river valley using ecological indicator values, Geographical Information Systems and Digital Elevation Models

Ellenberg indicator values (EIV) have been widely used to estimate habitat variables from floristic data and to predict vegetation composition based on habitat properties. Geographical Information Systems (GIS) and Digital Elevation Models (DEM) are valuable tools for studying the relationships between topographic and ecological characters of river systems. A 3-meter resolution DEM was derived for a. 3-km-long break section of the Szum River (SE Poland) from a 1:10,000 topographic map. Data on the diversity and ecological requirements of the local vascular flora were obtained while making floristic charts for 32 sections of the river valley (each 200 m long) and physical and chemical soil measurements; next, the data were translated into EIV. The correlations of the primary and secondary topographic attributes of the valley, species richness, and EIV (adapted for the Polish vascular flora) were assessed for all species recognized in each valley section. The total area and proportion of a flat area, mean slope, slope curvature, solar radiation (SRAD), and topographic wetness index (TWI) are the most important factors influencing local flora richness and diversity. The highest correlations were found for three ecological indicators, namely light, soil moisture, and soil organic content. The DEM seems to be useful in determination of correlations between topographic and ecological attributes along a minor river valley.
Fluorescent porous carbon nanocapsules for two-photon imaging, NIR/pH dual-responsive drug carrier, and photothermal therapy.

An efficient nanomedical platform that can combine two-photon cell imaging, near infrared (NIR) light and pH dual responsive drug delivery, and photothermal treatment was successfully developed based on fluorescent porous carbon-nanocapsules (FPC-NCs, size ∼100 nm) with carbon dots (CDs) embedded in the shell. The stable, excitation wavelength (λex)-tunable and upconverted fluorescence from the CDs embedded in the porous carbon shell enable the FPC-NCs to serve as an excellent confocal and two-photon imaging contrast agent under the excitation of laser with a broad range of wavelength from ultraviolet (UV) light (405 nm) to NIR light (900 nm). The FPC-NCs demonstrate a very high loading capacity (1335 mg g(-1)) toward doxorubicin drug benefited from the hollow cavity structure, porous carbon shell, as well as the supramolecular π stacking and electrostatic interactions between the doxorubicin molecules and carbon shell. In addition, a responsive release of doxorubicin from the FPC-NCs can be activated by lowering the pH to acidic (from 7.4 to 5.0) due to the presence of pH-sensitive carboxyl groups on the FPC-NCs and amino groups on doxorubicin molecules. Furthermore, the FPC-NCs can absorb and effectively convert the NIR light to heat, thus, manifest the ability of NIR-responsive drug release and combined photothermal/chemo-therapy for high therapeutic efficacy.
Accelerating Scientific Data Exploration via Visual Query Systems

The increasing availability of rich and complex data in a variety of scientific domains poses a pressing need for tools to enable scientists to rapidly make sense of and gather insights from data. One proposed solution is to design visual query systems (VQSs) that allow scientists to search for desired patterns in their datasets. While many existing VQSs promise to accelerate exploratory data analysis by facilitating this search, they are unfortunately not widely used in practice. Through a year-long collaboration with scientists in three distinct domains—astronomy, genetics, and material science— we study the impact of various features within VQSs that can aid rapid visual data analysis, and how VQSs fit into a scientists’ analysis workflow. Our findings offer design guidelines for improving the usability and adoption of next-generation VQSs, paving the way for VQSs to be applied to a variety of scientific domains.
KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning

In imbalanced learning, most standard classification algorithms usually fail to properly represent data distribution and provide unfavorable classification performance. More specifically, the decision rule of minority class is usually weaker than majority class, leading to many misclassification of expensive minority class data. Motivated by our previous work ADASYN , this paper presents a novel kernel based adaptive synthetic over-sampling approach, named KernelADASYN, for imbalanced data classification problems. The idea is to construct an adaptive over-sampling distribution to generate synthetic minority class data. The adaptive over-sampling distribution is first estimated with kernel density estimation methods and is further weighted by the difficulty level for different minority class data. The classification performance of our proposed adaptive over-sampling approach is evaluated on several real-life benchmarks, specifically on medical and healthcare applications. The experimental results show the competitive classification performance for many real-life imbalanced data classification problems.
Introducing compassion-focused therapy

ARTICLE The healing properties of compassion have been written about for centuries. The Dalai Lama often stresses that if you want others to be happy – focus on compassion; if you want to be happy yourself – focus on compassion (Dalai Lama 1995, 2001). Although all clinicians agree that compassion is central to the doctor–patient and therapist–client relationship, recently the components of com­ passion have been looked at through the lens of Western psychological science and research 2003a,b). Compassion can be thought of as a skill that one can train in, with in creasing evidence that focusing on and practising com passion can influence neurophysiological and immune systems (Davidson 2003; Lutz 2008). Compassion­focused therapy refers to the under pinning theory and process of applying a compassion model to psy­ chotherapy. Compassionate mind training refers to specific activities designed to develop compassion­ ate attributes and skills, particularly those that influence affect regula tion. Compassion­focused therapy adopts the philosophy that our under­ standing of psychological and neurophysiological processes is developing at such a rapid pace that we are now moving beyond 'schools of psychotherapy' towards a more integrated, biopsycho social science of psycho therapy (Gilbert 2009). Compassion­focused therapy and compassionate mind training arose from a number of observations. First, people with high levels of shame and self­ criticism can have enormous difficulty in being kind to themselves, feeling self­warmth or being self­compassionate. Second, it has long been known that problems of shame and self­criticism are often rooted in histories of abuse, bullying, high expressed emo­ tion in the family, neglect and/or lack of affection Individuals subjected to early experiences of this type can become highly sensitive to threats of rejection or criticism from the outside world and can quickly become self­attacking: they experience both their external and internal worlds as easily turning hostile. Third, it has been recognised that working with shame and self­criticism requires a thera peutic focus on memories of such early experiences And fourth, there are clients who engage with the cognitive and behavioural tasks of a therapy, and become skilled at generating (say) alternatives for their negative thoughts and beliefs, but who still do poorly in therapy (Rector 2000). They are likely to say, 'I understand the logic of my alterna­ tive thinking but it doesn't really help me feel much better' or 'I know I'm not to blame for the abuse but I still feel that I …
Service robot for the elderly

Our aim is to develop the service robot based on a systematic software engineering method, particularly for real-time, embedded and distributed systems with UML. To do so, we applied the COMET method, which is a UML-based method for the development of concurrent applications, specifically distributed and real-time applications. We describe our experience of applying the COMET/UML method to developing the service robot for the elderly, T-Rot, which is under development at CIR. Here, our emphasis was on an autonomous navigation system for the service robot, which is one of the most challenging issues and is essential in developing service robots, especially mobile service robots to help elderly people. It includes hardware integration for various sensors and actuators as well as software development and integration of modules like a path planner and a localizer.
A new single current strategy for high-performance brushless DC motor drives

For closed-loop current control of brushless dc (BLDC) motors, instantaneous phase currents are measured using appropriate current sensors. Such sensors are often bulky, heavy, and expensive, making them appealing for elimination. Likewise, the use of two or more separated sensors can cause undesirable imbalances in the phase currents as well as pulsating torque due to mismatched in the current sensor sensitivities. To overcome these problems, a new single current strategy for high performance BLDC motor drives is proposed. It is based on estimation and regulation of phase currents, using two single sensors for dc-link voltage and current. In this method, the phase currents are reconstructed in a two-stage process including estimation and regulation. Estimation is based on dynamic motor model, while regulation relies on the inverter switches states and the measured dc-link current. Besides, to access better dynamic response characteristic of the motor speed, Particle Swarm Optimization (PSO) has been used to regulate the PID parameters of speed controller. The effectiveness of proposed system has been validated by comparative studies and simulation results.
Memory: brain systems and behavior

Certain questions about memory address a relatively global, structural level of analysis. Is there one kind of memory or many? What brain structures or systems are involved in memory and what jobs do they do? One useful approach to such questions has focused on studies of neurological patients with memory impair-merit and parallel studies with animal models. Memory impairment sometimes occurs as a circum-scribed disorder in the absence of other intellectual deficits 1-7. In such cases, the memory impairment occurs in the context of normal scores on conventional intelligence tests, normal immediate (digit span) memory, and intact memory for very remote events. The analysis of memory impairment can provide useful information about the organization of memory and about the function of the damaged neural structures. Clinically significant memory impairment, i.e. amnesia, can occur for a variety of reasons and is typically associated with bilateral damage to the medial temporal lobe or the diencephalic midline. The severity and purity of the amnesia can vary greatly depending on the extent and pattern of damage. Standard quantitative tests are available for the assessment of memory and other cognitive functions, so that the findings from different groups of study patients can be compared 8-1°. The deficit in amnesia is readily detectable in tests of paired-associate learning and delayed recall. Indeed, amnesic patients are deficient in most tests of new learning, especially when they try to acquire an amount of information that exceeds what can be kept in mind through active rehearsal or when they try to retain information across a delay. This deficit occurs regardless of the sensory modality in which information is presented and regardless whether memory is tested by recall or recognition techniques. Moreover, the memory impairment is not limited to artificial laboratory situations, where patients are instructed explicitly to learn material that occurs in a particular episode and then are later instructed explicitly to recall the material. For example, patients can be provided items of general information with no special instruction to learn (e.g. Angel Falls is located in Venezuela); and later they can simply be asked factual questions without any reference to a recent learning episode (e.g. Where is Angel Falls located?). In this case, amnesic patients are impaired both in tests of free recall as well as in tests of recognition memory, in which the correct answer is selected from among several alternatives 11. These aspects of amnesia show …
Artificial Intelligence Techniques in Software Engineering ( AITSE )

development process is a very complex process that, at present, is primarily a human activity. Programming, in software development, requires the use of different types of knowledge: about the problem domain and the programming domain. It also requires many different steps in combining these types of knowledge into one final solution. This paper intends to study the techniques developed in artificial intelligence (AI) from the standpoint of their application in software engineering. In particular, it focuses on techniques developed (or that are being developed) in artificial intelligence that can be deployed in solving problems associated with software engineering processes. This paper highlights a comparative study between the software development and expert system development. This paper also highlights absence of risk management strategies or risk management phase in AI based systems.
Macromastia and bilateralaxillary breast hypertrophy: a case report.

Breast hypertrophy presents at puberty or thereafter. It is a condition of abnormal enlargement of the breast tissue in excess of the normal proportion. Gland hypertrophy, excessive fatty tissue or a combination of both may cause this condition. Macromastia can be unilateral or bilateral.



To present a case of massive bilateral gigantomastia with huge bilateral hypertrophy of the axillary breasts.



Review of the prentation, clinical and investigative findings aswell as the outcome of surgical intervention of a young Nigerian woman with bilateral severe breast hypertrophy and severe hypertrophy of axillary breasts.



The patient was a 26-year-old woman who presented with massive swelling of her breasts and bilateral axillary swellings, both of six years duration.. In addition to the breast pathology, she also suffered significant psychological problems. The breast ultrasonography confirmed only diffuse swellings, with no visible lumps or areas of calcifiCation. She had total bilateral excision of the hypertrophied axillary breasts, and bilateral breast amputation with composite nipple-areola complex graft of the normally located breasts.The total weight of the breast tissues removed was 44.8 kilogram.



Macromastia of this size is very rare. This case to date is probably the largest in the world literature. Surgical treatment of the condition gives a satisfactory outcome.
Investigating Pilot Performance Using Mixed-Modality Simulated Data Link

General aviation (GA) pilot performance utilizing a mixed-modality simulated data link was objectively evaluated based on the time required in accessing, understanding, and executing data link commands. Additional subjective data were gathered on workload, situation awareness (SA), and preference.



Research exploring mixed-modality data link integration to the single-pilot GA cockpit is lacking, especially with respect to potential effects on safety.



Sixteen visual flight rules (VFR)-rated pilots participated in an experiment using a flight simulator equipped with a mixed-modality data link. Data link modalities were text display, synthesized speech, digitized speech, and synthesized speech/text combination. Flight conditions included VFR (unlimited ceiling and visibility) or marginal VFR flight conditions (clouds 2,800 ft above ground level, 3-mile visibility).



Statistically significant differences were found in pilot performance, mental workload, and SA across the data link modalities. Textual data link resulted in increased time and workload as compared with the three speech-type data link conditions, which did not differ. SA measures indicated higher performance with textual and digitized speech data link conditions.



Textual data link can be significantly enhanced for single-pilot GA operations by the addition of a speech component.



Potential applications include operational safety in future GA systems that incorporate data link for use by a single pilot and guidance in the development of flight performance objectives for these systems.
Islanding Detection in Distributed Generation By Neuro-fuzzy Approach

The islanding methods are classified in terms of the islanding principle and the distributed Generation (DG). Islanding detection techniques are mainly divided into three types for distribution systems: Remote, local and communication based techniques. Passive methods are based on the information available on the DG site at the point of common coupling (PCC) with the utility grid. A new approach in passive techniques is the use of data-mining to classify the system parameters. Passive techniques are fast and they don’t introduce disturbance in the system but they have a large non detectable zone (NDZ) where they fail to detect the islanding condition. In this paper, considerable indices of a distribution system are collected by using MATLAB simulation. These indices are change in power, change in voltage, rate of change of power, rate of change of voltage, total harmonic distortion (THD) current, voltage, and change in power factor. Adaptive Neuro-Fuzzy inference System (ANFIS) in MATLAB used to classifying for these indices and define the boundaries. The results show the use of ANFIS in reducing the NDZ of passive islanding detection systems. KeywordsNon detectable zone (NDZ), Adaptive NeuroFuzzy inference System (ANFIS), Distributed generation (DG), islanding detection.
Design of a Q-learning-based client quality selection algorithm for HTTP adaptive video streaming

Over the past decades, the importance of multimedia services such as video streaming has increased considerably. HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive video streaming services. In HAS, a video is split into multiple segments and encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network and device conditions. Current HAS client heuristics are however hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, an adaptive Q-Learning-based HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behavior corresponding to the current network environment. Considering multiple aspects of video quality, a tunable reward function has been constructed, giving the opportunity to focus on different aspects of the Quality of Experience, the quality as perceived by the end-user. The proposed HAS client has been thoroughly evaluated using a network-based simulator, investigating multiple reward configurations and Reinforcement Learning specific settings. The evaluations show that the proposed client can outperform standard HAS in the evaluated networking environments.
Temperature control for a pasteurization miniplant using multiloop and cascade controllers

In order to obtain a temperature range of a pasteurization process, a good controller that can reject the unidentified disturbance which may occur at any time is needed. In this paper, control structure of both multi-loop and cascade controllers are designed for a pasteurization mini plant Armfied PCT23 MKIL The control algorithm uses proportional-integral-derivative (PID) controller. Some tuning methods are simulated to obtain the best controller performance. The two controllers are simulated and tested on real plant and their performances are compared. From experiments, it is found that the multiloop controller has a superior set point tracking performance whereas the cascade controller is better for disturbance rejection.