nils-herrmann/clean_scirepeval huggingface dataset:
Inserting needles into the body: a meta-analysis of brain activity associated with acupuncture needle stimulation.

Acupuncture is a therapeutic treatment that is defined as the insertion of needles into the body at specific points (ie, acupoints). Advances in functional neuroimaging have made it possible to study brain responses to acupuncture; however, previous studies have mainly concentrated on acupoint specificity. We wanted to focus on the functional brain responses that occur because of needle insertion into the body. An activation likelihood estimation meta-analysis was carried out to investigate common characteristics of brain responses to acupuncture needle stimulation compared to tactile stimulation. A total of 28 functional magnetic resonance imaging studies, which consisted of 51 acupuncture and 10 tactile stimulation experiments, were selected for the meta-analysis. Following acupuncture needle stimulation, activation in the sensorimotor cortical network, including the insula, thalamus, anterior cingulate cortex, and primary and secondary somatosensory cortices, and deactivation in the limbic-paralimbic neocortical network, including the medial prefrontal cortex, caudate, amygdala, posterior cingulate cortex, and parahippocampus, were detected and assessed. Following control tactile stimulation, weaker patterns of brain responses were detected in areas similar to those stated above. The activation and deactivation patterns following acupuncture stimulation suggest that the hemodynamic responses in the brain simultaneously reflect the sensory, cognitive, and affective dimensions of pain.



This article facilitates a better understanding of acupuncture needle stimulation and its effects on specific activity changes in different brain regions as well as its relationship to the multiple dimensions of pain. Future studies can build on this meta-analysis and will help to elucidate the clinically relevant therapeutic effects of acupuncture.
Factors affecting the Adoption Level of C-Commerce: An Empirical Study

The major objective of this paper is to examine the determinants of collaborative commerce (c-commerce) adoption with special emphasis on Electrical and Electronic organizations in Malaysia. Original research using a self-administered questionnaire was distributed to 400 Malaysian organizations. Out of the 400 questionnaires posted, 109 usable questionnaires were returned, yielding a response rate of 27.25%. Data were analysed by using correlation and multiple regression analysis. External environment, organization readiness and information sharing culture were found to be significant in affecting organ izations decision to adopt c-commerce. Information sharing culture factor was found to have the strongest influence on the adoption of c-commerce, followed by organization readiness and external environment. Contrary to other technology adoption studies, this research found that innovation attributes have no significant influence on the adoption of c-commerce. In terms of theoretical contributions, this study has extended previous researches conducted in western countries and provides great potential by advancing the understanding between the association of adoption factors and c-commerce adoption level. This research show that adoption studies could move beyond studying the factors based on traditional adoption models. Organizations planning to adopt c-commerce would also be able to applied strategies based on the findings from this research.
Self-Sorting Map: An Efficient Algorithm for Presenting Multimedia Data in Structured Layouts

This paper presents the Self-Sorting Map (SSM), a novel algorithm for organizing and presenting multimedia data. Given a set of data items and a dissimilarity measure between each pair of them, the SSM places each item into a unique cell of a structured layout, where the most related items are placed together and the unrelated ones are spread apart. The algorithm integrates ideas from dimension reduction, sorting, and data clustering algorithms. Instead of solving the continuous optimization problem that other dimension reduction approaches do, the SSM transforms it into a discrete labeling problem. As a result, it can organize a set of data into a structured layout without overlap, providing a simple and intuitive presentation. The algorithm is designed for sorting all data items in parallel, making it possible to arrange millions of items in seconds. Experiments on different types of data demonstrate the SSM's versatility in a variety of applications, ranging from positioning city names by proximities to presenting images according to visual similarities, to visualizing semantic relatedness between Wikipedia articles.
Toxic Behaviors in Esports Games: Player Perceptions and Coping Strategies

Toxicity in online environments is a complex and a systemic issue. Esports communities seem to be particularly suffering from toxic behaviors. Especially in competitive esports games, negative behavior, such as harassment, can create barriers to players achieving high performance and can reduce players' enjoyment which may cause them to leave the game. The aim of this study is to review design approaches in six major esports games to deal with toxic behaviors and to investigate how players perceive and deal with toxicity in those games. Our preliminary findings from an interview study with 17 participants (3 female) from a university esports club show that players define toxicity as behaviors disrupt their morale and team dynamics, and participants are inclined to normalize negative behaviors and rationalize it as part of the competitive game culture. If they choose to take an action against toxic players, they are likely to ostracize toxic players.
Privacy Preserving Data Mining

There is a tremendous increase in the research of data mining. Data mining is the process of extraction of data from large database. Knowledge Discovery in database (KDD) is another name of data mining. Privacy protection has become a necessary requirement in many data mining applications due to emerging privacy legislation and regulations. One of the most important topics in research community is Privacy Preserving Data Mining (PPDM). Privacy preserving data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. The Success of Privacy Preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms etc. In this paper we will review on various privacy preserving techniques like Data perturbation, condensation etc.
Determinants of Big Data Adoption and Success

This research investigates the large hype surrounding big data (BD) and Analytics (BDA) in both academia and the business world. Initial insights pointed to large and complex amalgamations of different fields, techniques and tools. Above all, BD as a research field and as a business tool found to be under developing and is fraught with many challenges. The intention here in this research is to develop an adoption model of BD that could detect key success predictors. The research finds a great interest and optimism about BD value that fueled this current buzz behind this novel phenomenon. Like any disruptive innovation, its assimilation in organizations oppressed with many challenges at various contextual levels. BD would provide different advantages to organizations that would seriously consider all its perspectives alongside its lifecycle in the pre-adoption or adoption or implementation phases. The research attempts to delineate the different facets of BD as a technology and as a management tool highlighting different contributions, implications and recommendations. This is of great interest to researchers, professional and policy makers.
Factors Influencing Tourist Food Consumption *

This study attempts to identify the salient factors affecting tourist food consumption. By reviewing available studies in the hospitality and tourism literature and synthesising insights from food consumption and sociological research, five socio-cultural and psychological factors influencing tourist food consumption are identified: cultural/religious influences, socio-demographic factors, food-related personality traits, exposure effect/past experience, and motivational factors. The findings further suggest that the motivational factors can be categorised into five main dimensions: symbolic, obligatory, contrast, extension, and pleasure. Given the lack of research in examining tourist food consumption systematically, the multidisciplinary approach adopted in this study allows a comprehensive understanding of the phenomenon which forms the basis for further research and conceptual elaboration.
A 0.31–1 GHz Fast-Corrected Duty-Cycle Corrector With Successive Approximation Register for DDR DRAM Applications

This brief presents a duty cycle corrector (DCC) using a binary search algorithm with successive approximation register (SAR). The proposed DCC consists of a duty-cycle detector, a duty-cycle adjuster, its controller and an output buffer. In order to achieve fast duty-correction with a small die area, a SAR-controller is exploited as a duty-correction controller. The proposed DCC circuit has been implemented and fabricated in a 0.13-μm CMOS process and occupies 0.048 mm2. The measured duty-cycle error for the 50% duty-rate is below 1% (or 10 pS) within 320 pS external input duty-cycle error. The duty of output signal is corrected only with 14 cycles. This DCC operates from 312.5 MHz to 1 GHz and dissipates 3.2 mW at 1 GHz.
Comparison of Real Time Task Scheduling Algorithms

In a Real-Time System, the correctness of the system is not only depending on the logical result of the computation but also on the time at which result is produced is very important. In real time system, scheduling is effected using certain criteria that ensure processes complete their various tasks at a specific time of completion. The quality of real-time scheduling algorithm has a direct impact on real-time system's working. We studied popular scheduling algorithms mainly Earliest Deadline First, Rate Monotonic, Deadline Monotonic, Least laxity First, Group Earliest Deadline First and Group Priority Earliest Deadline First for periodic task. We observe that the choice of a scheduling algorithm is important in designing a real-time system. We conclude by discussing the results of the Real-Time scheduling algorithm survey.
Modified two-stage procedures for the treatment of gingival recession.

Unfavorable conditions at the soft tissues adjacent to a recession defect may preclude performing pedicle flaps (advanced or rotational) both as a root coverage procedure, and as a covering flap for a connective tissue graft. Free gingival grafts may not be recommended because of the low root coverage predictability and the poor esthetic outcome. The goal of the present case report is to suggest modifications of the two-stage surgical technique aimed at improving root coverage and esthetic outcomes, and reducing patient morbidity.



In the first case report, a Miller class II gingival recession, associated with a deep buccal probing depth, affecting a lower central incisor was treated. In the first step of the surgery an epithelized graft with an apical-coronal dimension equal to the keratinized tissue height of the adjacent teeth was sutured apical to the bone dehiscence. Four months later, a coronally advanced flap was performed to cover the root exposure. In the second case report, a Miller class III gingival recession, complicated with a deep buccal probing depth affecting the mesial root of the first lower molar was treated. In the first step of the surgery, a free gingival graft was positioned mesially to the root exposure to create keratinized tissue lateral to the recession defect. This was adequate to perform the laterally moved, coronally advanced flap that was used as a second-step root coverage surgical procedure.



In the first case report complete root coverage, an increase (4 mm) in keratinized tissue height and realignment of the mucogingival line were achieved 1 year after the surgery. The reduced dimension of the graft permitted to minimize patient's discomfort and to obtain good esthetics of mucogingival tissues. These successful outcomes were well maintained for 5 years. In the second case report successful root coverage, increase (3 mm) in keratinized tissue height and good harmony of mucogingival tissues were achieved 1 year after the surgery. These outcomes were well maintained 5 years after the surgery.



The present study suggested that modifications of the two-stage procedure by minimizing the dimension of the graft and by standardizing the surgical techniques allowed successful results to be achieved in the treatment of gingival recessions characterized by local conditions that otherwise preclude the use of one-step root coverage surgical techniques.
Rotorigami: A rotary origami protective system for robotic rotorcraft

Applications of aerial robots are progressively expanding into complex urban and natural environments. Despite remarkable advancements in the field, robotic rotorcraft is still drastically limited by the environment in which they operate. Obstacle detection and avoidance systems have functionality limitations and substantially add to the computational complexity of the onboard equipment of flying vehicles. Furthermore, they often cannot identify difficult-to-detect obstacles such as windows and wires. Robustness to physical contact with the environment is essential to mitigate these limitations and continue mission completion. However, many current mechanical impact protection concepts are either not sufficiently effective or too heavy and cumbersome, severely limiting the flight time and the capability of flying in constrained and narrow spaces. Therefore, novel impact protection systems are needed to enable flying robots to navigate in confined or heavily cluttered environments easily, safely, and efficiently while minimizing the performance penalty caused by the protection method. Here, we report the development of a protection system for robotic rotorcraft consisting of a free-to-spin circular protector that is able to decouple impact yawing moments from the vehicle, combined with a cyclic origami impact cushion capable of reducing the peak impact force experienced by the vehicle. Experimental results using a sensor-equipped miniature quadrotor demonstrated the impact resilience effectiveness of the Rotary Origami Protective System (Rotorigami) for a variety of collision scenarios. We anticipate this work to be a starting point for the exploitation of origami structures in the passive or active impact protection of robotic vehicles.
The effect of cognitive strategies of association and dissociation on central nervous activation : A controlled trial with long distance runners

The purpose of the present study was to experimentally assess the effect of cognitive strategies of association and dissociation while running on central nervous activation. A total of 30 long distance runners volunteered for the study. The study protocol consisted on three sessions (scheduled in three different days): (1) maximal incremental treadmill test, (2) associative task session, and (3) dissociative task session. The order of sessions 2 and 3 was counterbalanced. During sessions 2 and 3, participants performed a 55 min treadmill run at moderate intensity. Both, associative and dissociative tasks responses were monitoring and recording in real time through dynamic measure tools. Consequently, was possible to have an objective control of the attentional. Results showed a positive session (exercise+attentional task) effect for central nervous activation. The benefi ts of aerobic exercise at moderate intensity for the performance of self-regulation cognitive tasks are highlighted. The used methodology is proposed as a valid and dynamic option to study cognitions while running in order to overcome the retrospective approach.
Image Steganography using LSB, LSB+Huffman Code, and LSB+Arithmetic Code

The digital world is growing day by day; many new risks have emerged during the exchange of information around the world; and many ways have evolved to protect the information. In this paper, this paper will conceal information into an image by using three methods that concentrate on the compression of the date before hiding it into the image and then compare the results using Peak Signal to Noise Ratio (PSNR). The three methods that will be used are Least Significant Bit (LSB), Huffman Code, and Arithmetic Coding and then the result will be compared.
Assessment of maturity models for smart cities supported by maturity model design principles

The concept of smart cities has gained relevance over the years. City leaders plan investments with the aim of evolving the city towards a smart city. Several models and frameworks, of which maturity models, provide directions or support such investment decisions. Nevertheless, it is not always clear whether the maturity models developed so far are able to fulfil their proposed objectives. This paper identifies smart city maturity models and assesses them, taking into account an approach based on the design principles framework proposed by Pöppelbuß & Röglinger . The main objective of this paper is to infer on the relevance of current maturity models for smart cities, taking into account their purpose. Furthermore, it aims at creating awareness towards the need for completeness when developing a maturity model.
Learning Contextual Inquiry and Distributed Cognition: a case study on technology use in anaesthesia

There have been few studies on how analysts learn or use frameworks to support gathering and analysis of field data. Distributed Cognition for Teamwork (DiCoT) is a framework that has been developed to facilitate the learning of Distributed Cognition (DCog), focusing on analysing small team interactions. DiCoT, in turn, exploits representations from Contextual Inquiry (CI). The present study is a reflective account of the experience of learning first CI and then DiCoT for studying the use of infusion devices in operating theatres. We report on how each framework supported a novice analyst (the first author) in structuring his data gathering and analysis, and the challenges that he faced. There are three contributions of this work: (1) an example of learning CI and DCog in a semi-structured way; (2) an account of the process and outcomes of learning and using CI and DiCoT in a complex setting; and (3) an outline account of information flow in anaesthesia. While CI was easier to learn and consequently gave better initial support to the novice analyst entering a complex work setting, DiCoT gave added value through its focus on information propagation and transformation as well as the roles of people and artefacts in supporting communication and situation awareness. This study makes visible many of the challenges of learning to apply a framework that are commonly encountered but rarely reported.
Compact Modeling Source-to-Drain Tunneling in Sub-10-nm GAA FinFET With Industry Standard Model

We present a compact model for source-to-drain tunneling current in sub-10-nm gate-all-around FinFET, where tunneling current becomes nonnegligible. Wentzel–Kramers–Brillouin method with a quadratic potential energy profile is used to analytically capture the dependence on biases in the tunneling probability expression and simplify the equation. The calculated tunneling probability increases with smaller effective mass and with increasing bias. We at first use the Gaussian quadrature method to integrate Landauer’s equation for tunneling current computation without further approximations. To boost simulation speed, some approximations are made. The simplified equation shows a good accuracy and has more flexibility for compact model purpose. The model is implemented into industry standard Berkeley Short-channel IGFET Model-common multi-gate model for future technology node, and is validated by the full-band atomistic quantum transport simulation data.
Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models

In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e., (i) a total of nine commonly used condition monitoring methods of induction motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM-RF ensemble (or FMM-RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM-RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The signals are processed to produce a set of harmonic-based features for classification using the FMM-RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM-RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM-RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments.
STI stress aware placement optimization based on geometric programming

Shallow trench isolation(STI) is the mainstream CMOS isolation technology for advanced integrated circuits. While STI process gives the isolation benefits due to its scalable characteristics, exploiting the compressive stress exerted by STI wells on device active regions to improve performance of devices has been one of the major industry focuses. However, in the present research of VLSI physical design, there has no yet a global optimization methodology on the whole chip layout to control the size of the STI wells, which affects the stress magnitude along with the size of active region of transistors. In this paper, we present a novel methodology that is capable of determining globally the optimal STI well width following the chip placement stage. The methodology is based on the observation that both of the terms in charge of chip width minimization and transistor channel mobility optimization in the objective function can be modeled as posynomials of the design variables, that is, the width of STI wells. Then, this stress aware placement optimization problem could be solved efficiently as a convex geometric programming (GP) problem. Finally, by a MOSEK GP problem solver, we do our STI width aware placement optimization on the given placements of some GSRC and IBM-PLACE benchmarks. Experiment results demonstrated that our methodology can obtain decent results with an acceptable runtime when satisfy the necessary location constraints from DRC specifications.