nils-herrmann/clean_scirepeval huggingface dataset:
Intracranial pressure and skull remodeling.

In this article we review bony changes resulting from alterations in intracranial pressure (ICP) and the implications for ophthalmologists and the patients for whom we care. Before addressing ophthalmic implications, we will begin with a brief overview of bone remodeling. Bony changes seen with chronic intracranial hypotension and hypertension will be discussed. The primary objective of this review was to bring attention to bony changes seen with chronic intracranial hypotension. Intracranial hypotension skull remodeling can result in enophthalmos. In advanced disease enophthalmos develops to a degree that is truly disfiguring. The most common finding for which subjects are referred is ocular surface disease, related to loss of contact between the eyelids and the cornea. Other abnormalities seen include abnormal ocular motility and optic atrophy. Recognition of such changes is important to allow for diagnosis and treatment prior to advanced clinical deterioration. Routine radiographic assessment of bony changes may allow for the identification of patient with abnormal ICP prior to the development of clinically significant disease.
The effects of probiotics on depressive symptoms in humans: a systematic review

Patients suffering from depression experience significant mood, anxiety, and cognitive symptoms. Currently, most antidepressants work by altering neurotransmitter activity in the brain to improve these symptoms. However, in the last decade, research has revealed an extensive bidirectional communication network between the gastrointestinal tract and the central nervous system, referred to as the "gut-brain axis." Advances in this field have linked psychiatric disorders to changes in the microbiome, making it a potential target for novel antidepressant treatments. The aim of this review is to analyze the current body of research assessing the effects of probiotics, on symptoms of depression in humans.



A systematic search of five databases was performed and study selection was completed using the preferred reporting items for systematic reviews and meta-analyses process.



Ten studies met criteria and were analyzed for effects on mood, anxiety, and cognition. Five studies assessed mood symptoms, seven studies assessed anxiety symptoms, and three studies assessed cognition. The majority of the studies found positive results on all measures of depressive symptoms; however, the strain of probiotic, the dosing, and duration of treatment varied widely and no studies assessed sleep.



The evidence for probiotics alleviating depressive symptoms is compelling but additional double-blind randomized control trials in clinical populations are warranted to further assess efficacy.
Micro-Doppler feature extraction using convolutional auto-encoders for low latency target classification

The radar is expected to go beyond the traditional functionality of range and speed estimation to target classification. The complementary use of radar and video is becoming increasingly popular for applications such as autonomous cars, smart home automation etc. Target classification based on radar depends on the characteristic motion patterns of target nonrigidities. The Micro-Doppler (MD) signal captures such motions that have been used to extract reliable distinguishing features for various classes of targets. Popular MD analysis techniques such as Cadence frequency estimation require long captures before reliably identifying the target. Such a latency has an impact on the response times especially in time critical systems such as autonomous cars. Although a finite latency is unavoidable, it is in the interest of the community to keep it as small as possible. In this paper, we use unsupervised learning, specifically auto-encoders for learning Micro-Doppler features. We use a particular fast learning algorithm which learns very quickly with little training data and deliver reliable classification.
Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models

Vehicle detection at night time is a challenging problem due to low visibility and light distortion caused by motion and illumination in urban environments. This paper presents a method based on the deformable object model for detecting and classifying vehicles by using monocular infra-red cameras. As some features of vehicles, such as headlight and taillights are more visible at night time, we propose a weighted version of the deformable part model. We define weights for different features in the deformable part model of the vehicle and try to learn the weights through an enormous number of positive and negative samples. Experimental results prove the effectiveness of the algorithm for detecting close and medium range vehicles in urban scenes at night time.
Class point approach for software effort estimation using soft computing techniques

The estimation of effort involved in developing a software product plays an important role in determining the success or failure of the product. Project managers require a reliable approach for software effort estimation. It is especially important during the early stage of the software development life cycle. An accurate software effort estimation is a major concern in current industries. In this paper, the main goal is to estimate the effort required to develop various software projects using class point approach. Then optimization of the effort parameters is achieved using adaptive regression based Multi-Layer Perceptron (ANN) technique to obtain better accuracy. Furthermore, a comparative analysis of software effort estimation using Multi-Layer Perceptron (ANN) and Radial Basis Function Network (RBFN) has been provided. By estimating the software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules.
Energy and Delay Efficient Routing Protocol (EDERP) for Threshold based Cluster Head Selection in Heterogeneous WSN

In recent era of ubiquitous computing the internet of things and sensor networks are researched widely. The deployment of the wireless sensor networks in the harsh environments ascends issues associated with delay clustering approaches, packet drop, delay, energy, link quality, mobility and coverage. Various research studies are proposing routing protocols clustering algorithm with research goal for reduction in terms of energy and delay. This paper focuses on delay and energy by introducing threshold based scheme. Furthermore energy and delay efficient routing protocol is proposed for cluster head selection in the heterogeneous wireless sensor networks. We have introduced delay and energy based adaptive threshold scheme in this paper to solve this problem. Furthermore this study presents new routing algorithm which contains energy and delay and velocity threshold based cluster-head election scheme. The cluster head is selected according to distance, velocity and energy where probability is set for the residual energy. The nodes are classified into normal, advanced and herculean levels. This paper presents new routing protocol named as energy and delay efficient routing protocol (EDERP). The MATLAB is used for simulation and comparison of the routing protocol with other protocols. The simulations results indicate that this protocol is effective regarding delay and energy.
Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification

The wide usage of Machine Learning (ML) has lead to research on the attack vectors and vulnerability of these systems. The defenses in this area are however still an open problem, and often lead to an arms race. We define a naive, secure classifier at test time and show that a Gaussian Process (GP) is an instance of this classifier given two assumptions: one concerns the distances in the training data, the other rejection at test time. Using these assumptions, we are able to show that a classifier is either secure, or generalizes and thus learns. Our analysis also points towards another factor influencing robustness, the curvature of the classifier. This connection is not unknown for linear models, but GP offer an ideal framework to study this relationship for nonlinear classifiers. We evaluate on five security and two computer vision datasets applying test and training time attacks and membership inference. We show that we only change which attacks are needed to succeed, instead of alleviating the threat. Only for membership inference, there is a setting in which attacks are unsuccessful (< 10% increase in accuracy over random guess). Given these results, we define a classification scheme based on voting, ParGP. This allows us to decide how many points vote and how large the agreement on a class has to be. This ensures a classification output only in cases when there is evidence for a decision, where evidence is parametrized. We evaluate this scheme and obtain promising results.
A survey of bio-inspired robotics hands implementation: New directions in dexterous manipulation

Recently, significant advances have been made in ROBOTICS, ARTIFICIAL INTELLIGENCE and other COGNITIVE related fields, allowing tomakemuch sophisticated biomimetic robotics systems. In addition, enormous number of robots have been designed and assembled, explicitly realize biological oriented behaviors. Towards much skill behaviors and adequate grasping abilities (i.e. ARTICULATION and DEXTEROUS MANIPULATION), a new phase of dexterous hands have been developed recently with biomimetically oriented and bio-inspired functionalities. In this respect, this manuscript brings a detailed survey of biomimetic based dexterous robotics multi-fingered hands. The aim of this survey, is to find out the state of the art on dexterous robotics end-effectors, known in literature as (ROBOTIC HANDS) or (DEXTEROUSMULTI-FINGERED) robot hands. Hence, this review finds such biomimetic approaches using a framework that permits for a common description of biological and technical based hand manipulation behavior. In particular, the manuscript focuses on a number of developments that have been taking place over the past two decades, and some recent developments related to this biomimetic field of research. In conclusions, the study found that, there are rich research efforts in terms of KINEMATICS, DYNAMICS, MODELING and CONTROL methodologies. The survey is also indicating that, the topic of biomimetic inspired robotics systems make significant contributions to robotics hand design, in four main directions for future research. First, they provide a genuine world test of models of biologically inspired hand designs and dexterous manipulation behaviors. Second, they provide novel manipulation articulations and mechanisms available for industrial and domestic uses, most notably in the field of human like hand design and real world applications. Third, this survey has also indicated that, there are quite large number of attempts to acquire biologically inspired hands. These attempts were almost successful, where they exposed more novel ideas for further developments. Such inspirations were directed towards a number of topics related (HAND MECHANICS AND DESIGN), (HAND TACTILE SENSING), (HAND FORCE SENSING), ( and (HANDCONFIGURATIONAND TOPOLOGY). FOURTH, in terms of employing AI related sciences and cognitive thinking, it was also found that, rare and exceptional research attempts were directed towards the employment of biologically inspired thinking, i.e. (AI, BRAIN AND COGNITIVE SCIENCES) for hand upper control and towards much sophisticated dexterous movements. Throughout the study, it has been found there are number of efforts in terms of mechanics and hand designs, tactical sensing, however, for hand soft actuation, it seems this area of research is still far away from having a realistic muscular type fingers and hand movements.
False-Positive Results and Contamination in Nucleic Acid Amplification Assays: Suggestions for a Prevent and Destroy Strategy

Contamination of samples with DNA is still a major problem in microbiology laboratories, despite the wide acceptance of PCR and other amplification techniques for the detection of frequently low amounts of target DNA. This review focuses on the implications of contamination in the diagnosis and research of infectious diseases, possible sources of contaminants, strategies for prevention and destruction, and quality control. Contamination of samples in diagnostic PCR can have far-reaching consequences for patients, as illustrated by several examples in this review. Furthermore, it appears that the (sometimes very unexpected) sources of contaminants are diverse (including water, reagents, disposables, sample carry over, and amplicon), and contaminants can also be introduced by unrelated activities in neighboring laboratories. Therefore, lack of communication between researchers using the same laboratory space can be considered a risk factor. Only a very limited number of multicenter quality control studies have been published so far, but these showed false-positive rates of 9–57%. The overall conclusion is that although nucleic acid amplification assays are basically useful both in research and in the clinic, their accuracy depends on awareness of risk factors and the proper use of procedures for the prevention of nucleic acid contamination. The discussion of prevention and destruction strategies included in this review may serve as a guide to help improve laboratory practices and reduce the number of false-positive amplification results.
Green supply chain management implications for ‘ ‘ closing the loop ’ ’

In this paper we report on results from a cross-sectional survey with manufacturers in four typical Chinese industries, i.e., power generating, chemical/petroleum, electrical/electronic and automobile, to evaluate their perceived green supply chain management (GSCM) practices and relate them to closing the supply chain loop. Our findings provide insights into the capabilities of Chinese organizations on the adoption of GSCM practices in different industrial contexts and that these practices are not considered equitably across the four industries. Academic and managerial implications of our findings are discussed.
FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data

Spatial data have wide applications, e.g., location-based services, and geometric range queries (i.e., finding points inside geometric areas, e.g., circles or polygons) are one of the fundamental search functions over spatial data. The rising demand of outsourcing data is moving large-scale datasets, including large-scale spatial datasets, to public clouds. Meanwhile, due to the concern of insider attackers and hackers on public clouds, the privacy of spatial datasets should be cautiously preserved while querying them at the server side, especially for location-based and medical usage. In this paper, we formalize the concept of Geometrically Searchable Encryption, and propose an efficient scheme, named FastGeo, to protect the privacy of clients’ spatial datasets stored and queried at a public server. With FastGeo, which is a novel two-level search for encrypted spatial data, an honest-but-curious server can efficiently perform geometric range queries, and correctly return data points that are inside a geometric range to a client without learning sensitive data points or this private query. FastGeo supports arbitrary geometric areas, achieves sublinear search time, and enables dynamic updates over encrypted spatial datasets. Our scheme is provably secure, and our experimental results on real-world spatial datasets in cloud platform demonstrate that FastGeo can boost search time over 100 times.
Overhead transmission line sag, tension and length calculation using affine arithmetic

Transmission line sag, tension and conductor length varies with the variation of temperature due to thermal expansion and elastic elongation. Beside thermal effect, wind pressure and ice accumulation creates a horizontal and vertical loading on the conductor respectively. Such changes make the calculated data uncertain and require an uncertainty model. A novel affine arithmetic (AA) based transmission line sag, tension and conductor length calculation for parabolic curve is proposed and the proposed method is tested for different test cases. The results are compared with Monte Carlo (MC) and interval arithmetic (IA) methods. The AA based result gives a more conservative bound than MC and IA method in all the cases.
Developing an augmented reality racing game

Augmented reality (AR) makes it possible to create games in which virtual objects are overlaid on the real world, and real objects are tracked and used to control virtual ones. We describe the development of an AR racing game created by modifying an existing racing game, using an AR infrastructure that we developed for use with the XNA game development platform. In our game, the driver wears a tracked video see-through head-worn display, and controls the car with a passive tangible controller. Other players can participate by manipulating waypoints that the car must pass and obstacles with which the car can collide. We discuss our AR infrastructure, which supports the creation of AR applications and games in a managed code environment, the user interface we developed for the AR racing game, the game's software and hardware architecture, and feedback and observations from early demonstrations.
3-Dimensional Blow Torch-Molding of Fused Silica Microstructures

This paper presents a new and simple microfabrication process for creating various 3-D microstructures with high-aspect ratios . The key feature of this process is the use of a blow torch, which provides very intense localized heat for a short amount of time . The flame temperature can be up to 2500 °C for a propane-oxygen torch, above the melting temperatures of many high- Q materials. We demonstrate the fabrication of hemispherical and half-toroid (birdbath) shells from 100- μm-thick fused silica substrates. The structures have an rms surface roughness of 5.3 Å, which is crucial for achieving both high mechanical and optical Q. We create microbirdbath resonators by batch-level releasing the birdbath shells. We verify the resonance mode shapes of the degenerate n=2 wineglass modes using laser vibrometry and measure the frequency and Q of these modes using laser vibrometry and a capacitive measurement method. We demonstrate one of the best mechanical Q and smallest frequency split between the n=2 wineglass modes among existing micromechanical resonators. The birdbath resonator is promising for emerging applications such as the microrate-integrating gyroscope.
Students performance prediction using KNN and Naïve Bayesian

Data mining techniques is rapidly increasing in the research of educational domains. Educational data mining aims to discover hidden knowledge and patterns about student performance. This paper proposes a student performance prediction model by applying two classification algorithms: KNN and Naïve Bayes on educational data set of secondary schools, collected from the ministry of education in Gaza Strip for 2015 year. The main objective of such classification may help the ministry of education to improve the performance due to early prediction of student performance. Teachers also can take the proper evaluation to improve student learning. The experimental results show that Naïve Bayes is better than KNN by receiving the highest accuracy value of 93.6%.
Epidemic of charcoal burning suicide in Japan.

The charcoal burning suicide epidemics in both Hong Kong and Taiwan have been well documented. However, little is known about the situation in Japan.



To examine the impact of charcoal burning suicide on the overall and other method-specific suicide rates between 1998 and 2007 in Japan.



Using data obtained from the Vital Statistics of Japan, negative binomial regression analyses were performed to investigate the impact of the charcoal burning method.



In males and females aged 15-24 and 25-44 years, the charcoal burning epidemic led to a substantial increase in overall suicides, without a decrease in other methods. In all other age groups, no such trend was observed.



In young Japanese, the charcoal burning method may have appealed to individuals who might not have chosen other highly or relatively lethal methods, and consequently led to an increase in overall suicides.
Survey of existing fingerprint countermeasures

A number of fingerprint countermeasures have claimed that their countermeasures can prevent the user away from fingerprint tracking. The research attempts to prove their claims and requires to study the effectiveness of their fingerprint prevention and observe the results of side effects after defence in order to assist the attentive user to know the limitation of current approaches. Under investigation, all countermeasures will be installed on the web browser and visit the developed hybrid fingerprint website in order to know the efficiency of the fingerprint resistance of all types. The research shows that all fingerprint countermeasures nowadays are unable to obstruct all kinds of the fingerprint tracking and countermeasures that use the blocking technique appear more side effects to the web browser than other techniques. Also, the increasing number of fingerprint attributes are increasing cause of unusual combination inside the Internet browser.
Evaluation of a portable image overlay projector for the visualisation of surgical navigation data: phantom studies

Presenting visual feedback for image-guided surgery on a monitor requires the surgeon to perform time-consuming comparisons and diversion of sight and attention away from the patient. Deficiencies in previously developed augmented reality systems for image-guided surgery have, however, prevented the general acceptance of any one technique as a viable alternative to monitor displays. This work presents an evaluation of the feasibility and versatility of a novel augmented reality approach for the visualisation of surgical planning and navigation data. The approach, which utilises a portable image overlay device, was evaluated during integration into existing surgical navigation systems and during application within simulated navigated surgery scenarios. A range of anatomical models, surgical planning data and guidance information taken from liver surgery, cranio-maxillofacial surgery, orthopaedic surgery and biopsy were displayed on patient-specific phantoms, directly on to the patient’s skin and on to cadaver tissue. The feasibility of employing the proposed augmented reality visualisation approach in each of the four tested clinical applications was qualitatively assessed for usability, visibility, workspace, line of sight and obtrusiveness. The visualisation approach was found to assist in spatial understanding and reduced the need for sight diversion throughout the simulated surgical procedures. The approach enabled structures to be identified and targeted quickly and intuitively. All validated augmented reality scenes were easily visible and were implemented with minimal overhead. The device showed sufficient workspace for each of the presented applications, and the approach was minimally intrusiveness to the surgical scene. The presented visualisation approach proved to be versatile and applicable to a range of image-guided surgery applications, overcoming many of the deficiencies of previously described AR approaches. The approach presents an initial step towards a widely accepted alternative to monitor displays for the visualisation of surgical navigation data.