nils-herrmann/clean_scirepeval huggingface dataset:
Activity-based sleep-wake identification: an empirical test of methodological issues.

The effects of actigraph placement and device sensitivity on actigraphic automatic sleep-wake scoring were assessed using concomitant polysomnographic and wrist actigraphic data from dominant and nondominant hands of 20 adults and 16 adolescents during 1 laboratory night. Although activity levels differed between dominant and nondominant wrists during periods of sleep (F = 4.57; p < 0.05) and wake (F = 15.5; p < 0.0005), resulting sleep-wake scoring algorithms were essentially the same and were equally explanatory (R2 = 0.64; p < 0.0001). When the sleep-wake scoring algorithm derived from the nondominant hand was used to score the nondominant data for sleep-wake, overall agreement rates with polysomnography scoring ranged between 91 and 93% for the calibration and validation samples. Results obtained with the same algorithm for the dominant-wrist data were within the same range. Agreement for sleep scoring was consistently higher than for wake scoring. Statistical manipulation of activity levels before applying the scoring algorithm indicated that this algorithm is quite robust toward moderate changes in activity level. Use of "twin-wrist actigraphy" enables identification of artifacts that may result from breathing-related motions.
A power-aware digital feedforward neural network platform with backpropagation driven approximate synapses

This paper proposes a power-aware digital feedforward neural network platform that utilizes the backpropagation algorithm during training to enable energy-quality trade-off. Given a quality constraint, the proposed approach identifies a set of synaptic weights for approximation in a neural network. The approach selects synapses with small impact on output error, estimated by the backpropagation algorithm, for approximation. The approximations are achieved by a coupled software (reduced bit-width) and hardware (approximate multiplication in the processing engine) based design approaches. The full-chip design in 130nm CMOS shows, compared to a baseline accurate design, the proposed approach reduces system power by ~38% with 0.4% lower recognition accuracy in a classification problem.
Ego, drives, and the dynamics of internal objects

This paper addresses the relationship between the ego, id, and internal objects. While ego psychology views the ego as autonomous of the drives, a less well-known alternative position views the ego as constituted by the drives. Based on Freud's ego-instinct account, this position has developed into a school of thought which postulates that the drives act as knowers. Given that there are multiple drives, this position proposes that personality is constituted by multiple knowers. Following on from Freud, the ego is viewed as a composite sub-set of the instinctual drives (ego-drives), whereas those drives cut off from expression form the id. The nature of the "self" is developed in terms of identification and the possibility of multiple personalities is also established. This account is then extended to object-relations and the explanatory value of the ego-drive account is discussed in terms of the addressing the nature of ego-structures and the dynamic nature of internal objects. Finally, the impact of psychological conflict and the significance of repression for understanding the nature of splits within the psyche are also discussed.
A Survey on Stock Market Prediction Techniques

Different techniques are available for the prediction of stock market. Very popular some of these are Neural Network, Data Mining, Hidden Markov Model(HMM) And Neuro-Fuzzy system. From these Neural Network and Neuro-Fuzzy Systems are the most leading machine learning techniques in stock market index prediction area. Other traditional methods do not cover all possible relation of stock price movements. Neural Network and Markov Model can be used exclusively in the financial markets and forecasting of stock price. Neural Networks discovers the non linear relationship in the input data set without knowing the relation between input and output. For the sample data which contain noisy information with least principle ANN can generalize and correctly infer the unseen part of data. Hence ANN suits well than any other models in the prediction of stock markets.
The effects of posture on forearm muscle loading during gripping.

The purpose of this study was to quantify the response of the forearm musculature to combinations of wrist and forearm posture and grip force. Ten healthy individuals performed five relative handgrip efforts (5%, 50%, 70% and 100% of maximum, and 50 N) for combinations of three wrist postures (flexed, neutral and extended) and three forearm postures (pronated, neutral and supinated). 'Baseline' extensor muscle activity (associated with holding the dynamometer without exerting grip force) was greatest with the forearm pronated and the wrist extended, while flexor activity was largest in supination when the wrist was flexed. Extensor activity was generally larger than that of flexors during low to mid-range target force levels, and was always greater when the forearm was pronated. Flexor activation only exceeded the extensor activation at the 70% and 100% target force levels in some postures. A flexed wrist reduced maximum grip force by 40-50%, but EMG amplitude remained elevated. Women produced 60-65% of the grip strength of men, and required 5-10% more of both relative force and extensor activation to produce a 50 N grip. However, this appeared to be due to strength rather than gender. Forearm rotation affected grip force generation only when the wrist was flexed, with force decreasing from supination to pronation (p < 0.005). The levels of extensor activation observed, especially during baseline and low level grip exertions, suggest a possible contributing mechanism to the development of lateral forearm muscle pain in the workplace.
Anxiety, Depression, and the Microbiome: A Role for Gut Peptides

The complex bidirectional communication between the gut and the brain is finely orchestrated by different systems, including the endocrine, immune, autonomic, and enteric nervous systems. Moreover, increasing evidence supports the role of the microbiome and microbiota-derived molecules in regulating such interactions; however, the mechanisms underpinning such effects are only beginning to be resolved. Microbiota–gut peptide interactions are poised to be of great significance in the regulation of gut–brain signaling. Given the emerging role of the gut–brain axis in a variety of brain disorders, such as anxiety and depression, it is important to understand the contribution of bidirectional interactions between peptide hormones released from the gut and intestinal bacteria in the context of this axis. Indeed, the gastrointestinal tract is the largest endocrine organ in mammals, secreting dozens of different signaling molecules, including peptides. Gut peptides in the systemic circulation can bind cognate receptors on immune cells and vagus nerve terminals thereby enabling indirect gut–brain communication. Gut peptide concentrations are not only modulated by enteric microbiota signals, but also vary according to the composition of the intestinal microbiota. In this review, we will discuss the gut microbiota as a regulator of anxiety and depression, and explore the role of gut-derived peptides as signaling molecules in microbiome–gut–brain communication. Here, we summarize the potential interactions of the microbiota with gut hormones and endocrine peptides, including neuropeptide Y, peptide YY, pancreatic polypeptide, cholecystokinin, glucagon-like peptide, corticotropin-releasing factor, oxytocin, and ghrelin in microbiome-to-brain signaling. Together, gut peptides are important regulators of microbiota–gut–brain signaling in health and stress-related psychiatric illnesses.
Camouflage of moderate Class III malocclusions with extraction of lower second molars and mandibular cervical headgear.

Orthodontic camouflage in patients with slight or moderate skeletal Class III malocclusions, can be obtained through different treatment alternatives. The purpose of this paper is to present a treatment that has not been described in the literature and which consists of the extraction of lower second molars and distal movement of the posterior segments by means of mandibular cervical headgear (MCH) and fixed appliances as a camouflage alternative. The force applied by the MCH was 250 gr per side (14hr/day). The total treatment time was 1 1/2 years.



the extraction of lower second molars along with the use of mandibular cervical headgear is a good treatment alternative for camouflage in moderate Class III patients in order to obtain good occlusal relationships without affecting facial esthetics or producing marked dental compensations.
On Adversarial Training and Loss Functions for Speech Enhancement

Generative adversarial networks (GANs) are becoming increasingly popular for image processing tasks. Researchers have started using GAN s for speech enhancement, but the advantage of using the GAN framework has not been established for speech enhancement. For example, a recent study reports encouraging enhancement results, but we find that the architecture of the generator used in the GAN gives better performance when it is trained alone using the $L_1$ loss. This work presents a new GAN for speech enhancement, and obtains performance improvement with the help of adversarial training. A deep neural network (DNN) is used for time-frequency mask estimation, and it is trained in two ways: regular training with the $L_1$ loss and training using the GAN framework with the help of an adversary discriminator. Experimental results suggest that the GAN framework improves speech enhancement performance. Further exploration of loss functions, for speech enhancement, suggests that the $L_1$ loss is consistently better than the $L_2$ loss for improving the perceptual quality of noisy speech.
Pulmonary arterial elastance for estimating right ventricular afterload in systolic heart failure.

Assessment of right ventricular afterload in systolic heart failure seems mandatory as it plays an important role in predicting outcome. The purpose of this study is to estimate pulmonary vascular elastance as a reliable surrogate for right ventricular afterload in systolic heart failure. Forty-two patients with systolic heart failure (ejection fraction <35%) were studied by right heart catheterization. Pulmonary arterial elastance was calculated with three methods: Ea(PV) = (end-systolic pulmonary arterial pressure)/stroke volume; Ea*(PV) = (mean pulmonary arterial pressure - pulmonary capillary wedge pressure)/stroke volume; and PPSV = pulmonary arterial pulse pressure (systolic - diastolic)/stroke volume. These measures were compared with pulmonary vascular resistance ([mean pulmonary arterial pressure - pulmonary capillary wedge pressure]/CO). All estimates of pulmonary vascular elastance were significantly correlated with pulmonary vascular resistance (r=0.772, 0.569, and 0.935 for Ea(PV), Ea*(PV), and PPSV, respectively; P <.001). Pulmonary vascular elastance can easily be estimated by routine right heart catheterization in systolic heart failure and seems promising in assessment of right ventricular afterload.
An Emotion Aware Task Automation Architecture Based on Semantic Technologies for Smart Offices

The evolution of the Internet of Things leads to new opportunities for the contemporary notion of smart offices, where employees can benefit from automation to maximize their productivity and performance. However, although extensive research has been dedicated to analyze the impact of workers’ emotions on their job performance, there is still a lack of pervasive environments that take into account emotional behaviour. In addition, integrating new components in smart environments is not straightforward. To face these challenges, this article proposes an architecture for emotion aware automation platforms based on semantic event-driven rules to automate the adaptation of the workplace to the employee’s needs. The main contributions of this paper are: (i) the design of an emotion aware automation platform architecture for smart offices; (ii) the semantic modelling of the system; and (iii) the implementation and evaluation of the proposed architecture in a real scenario.
Retrieval practice: the lack of transfer to deductive inferences.

Retrieval practice has been shown to enhance later recall of information reviewed through testing, whereas final-test measures involving making inferences from the learned information have produced mixed results. In four experiments, we examined whether the benefits of retrieval practice could transfer to deductive inferences. Participants studied a set of related premises and then reviewed these premises either by rereading or by taking fill-in-the-blank tests. As was expected, the testing condition produced better final-test recall of the premises. However, performance on multiple-choice inference questions showed no enhancement from retrieval practice.
Rectangular Waveguide Filters With Meandered Topology

In this paper, a new topology for rectangular waveguide bandpass and low-pass filters is presented. A simple, accurate, and robust design technique for these novel meandered waveguide filters is provided. The proposed filters employ a concatenation of ±90°  $E$ -plane mitered bends (±90° EMBs) with different heights and lengths, whose dimensions are consecutively and independently calculated. Each ±90° EMB satisfies a local target reflection coefficient along the device so that they can be calculated separately. The novel structures allow drastically reduce the total length of the filters and embed bends if desired, or even to provide routing capabilities. Furthermore, the new meandered topology allows the introduction of transmission zeros above the passband of the low-pass filter, which can be controlled by the free parameters of the ±90° EMBs. A bandpass and a low-pass filter with meandered topology have been designed following the proposed novel technique. Measurements of the manufactured prototypes are also included to validate the novel topology and design technique, achieving excellent agreement with the simulation results.
Reconstructive Management of Devastating Electrical Injuries to the Face.

Devastating fourth-degree electrical injuries to the face and head pose significant reconstructive challenges. To date, there have been few peer-reviewed articles in the literature that describe those reconstructive challenges. The authors present the largest case series to date that describes the management of these injuries, including the incorporation of face transplantation.



A retrospective case series was conducted of patients with devastating electrical injuries to the face who were managed at two level-1 trauma centers between 2007 and 2011. Data describing patient injuries, initial management, and reconstructive procedures were collected.



Five patients with devastating electrical injuries to the face were reviewed. After initial stabilization and treatment of life-threatening injuries, all five underwent burn excision and microsurgical reconstruction using distant flaps. Two of the patients eventually underwent face transplantation. The authors describe differences in management between the two trauma centers, one of which had the availability for composite tissue allotransplantation; the other did not. Also described is how initial attempts at traditional reconstruction affected the eventual face transplantation.



The care of patients with complex electrical burns must be conducted in a multidisciplinary fashion. As with all other trauma, the initial priority should be management of the airway, breathing, and circulation. Additional considerations include cardiac arrhythmias and renal impairment attributable to myoglobinuria. Before embarking on aggressive reconstruction attempts, it is advisable to determine early whether the patient is a candidate for face transplantation in order to avoid antigen sensitization, loss of a reconstructive "lifeboat," surgical plane disruption, and sacrifice of potential recipient vessels.



Therapeutic, V.
Compact active antenna for mobile devices supporting 4G LTE

The advent of 4G LTE has ushered in a growing demand for embedded antennas that can cover a wide range of frequency bands from 698 MHz to 2.69 GHz. A novel active antenna design is presented in this paper that is capable of covering a wide range of LTE bands while being constrained to a 1.8 cm3 volume. The antenna structure utilizes Ethertronics EtherChip 2.0 to add tunability to the antenna structure. The paper details the motivation behind developing the antenna and further discusses the fabrication of the active antenna architecture on an evaluation board and presents the measured results.
Universal product design involving elderly users: a participatory design model.

Recent studies have shown that people prefer to age in their familiar environments, thus guiding designers to provide a safe and functionally appropriate environment for ageing people, regardless of their physical conditions or limitations. Therefore, a participatory design model is proposed where human beings can improve their quality of life by promoting independence, as well as safety, useability and attractiveness of the residence. Brainstorming, scenario building, unstructured interviews, sketching and videotaping are used as techniques in the participatory design sessions. Quality deployment matrices are employed to find the relationships between the elderly user's requirements and design specifications. A case study was devised to apply and test the conceptual model phase of the proposed model.
Job demands-resources predicting burnout and work engagement among Belgian home health care nurses: A cross-sectional study.

A better knowledge of the job aspects that may predict home health care nurses' burnout and work engagement is important in view of stress prevention and health promotion. The Job Demands-Resources model predicts that job demands and resources relate to burnout and work engagement but has not previously been tested in the specific context of home health care nursing.



The present study offers a comprehensive test of the Job-Demands Resources model in home health care nursing. We investigate the main and interaction effects of distinctive job demands (workload, emotional demands and aggression) and resources (autonomy, social support and learning opportunities) on burnout and work engagement.



Analyses were conducted using cross-sectional data from 675 Belgian home health care nurses, who participated in a voluntary and anonymous survey.



The results show that workload and emotional demands were positively associated with burnout, whereas aggression was unrelated to burnout. All job resources were associated with higher levels of work engagement and lower levels of burnout. In addition, social support buffered the positive relationship between workload and burnout.



Home health care organizations should invest in dealing with workload and emotional demands and stimulating the job resources under study to reduce the risk of burnout and increase their nurses' work engagement.
Dermoscopic findings in psoriasis and seborrheic dermatitis on the scalp and correlation with disease severity

Dermoscopy has frequently been used for hair and scalp disorders. Dermoscopic examinations of psoriasis and seborrheic dermatitis lesions yielded different morphologies. This study aimed to investigate the usefulness of dermoscopy in the clinical differentiation of psoriasis and seborrheic dermatitis on the scalp and additionally to evaluate its capacity in determining disease severity. The study included 46 psoriasis and 50 seborrheic dermatitis patients who were clinically diagnosed and had scalp lesions. Dermoscopic images taken from lesional scalp were then reviewed for microvascular patterns. In addition, we assessed the severity of scalp disease and looked for a correlation with these patterns. The frequency of twisted loops, glomerular vessels, red dots/globules, and polymorphous beaded lines/circles were statistically higher in the psoriasis group, whereas the frequencies of simple loops and arborizing vessels were statistically higher in the seborrheic dermatitis group (p<0.001). There was no significant difference in the frequency of featureless areas between two groups (p=0.579). There was no correlation with disease severity and dermoscopic findings. In conclusion, dermoscopy is a valuable and easy-to-use tool for differentiating psoriasis and seborrheic dermatitis on the scalp even if it doesn’t have enough strength to determine disease severity. In addition, there is some diversity and ambiguity concerning terminology, which can be resolved with future studies and the establishment of conventional terms.
An Image Processing Approach to Solve Labyrinth Discovery Robotics Problem

Maze solving using multiple algorithms is one of the important problems in the last years, maze solving problem is to determine the path of a mobile robot from its source position to a destination position through a workspace populated with obstacles, in addition to finding the shortest path among the solutions. Autonomous robotics is a field with wide-reaching applications, from bomb-sniffing robots to autonomous devices for finding humans in wreckage to home automation; many people are interested in low-power, high speed, reliable solutions. we will introduce an algorithm to solve mazes and avoid robots to go through the long process, this algorithm is based on image processing and shortest path algorithm, the algorithm will work efficiently because of the preprocessing on maze's image data rather than going through the maze cell by cell. Therefore, it focuses on the entire maze rather than the current part that an agent is in it. This approach will give the robot preplanning time before falling in mistakes like loops and being trapped in a local minimum of dead ends of labyrinth besides saving time, because the entire maze map will be known before the navigation starts.
Spectral Ensemble Clustering via Weighted K-Means: Theoretical and Practical Evidence

As a promising way for heterogeneous data analytics, consensus clustering has attracted increasing attention in recent decades. Among various excellent solutions, the co-association matrix based methods form a landmark, which redefines consensus clustering as a graph partition problem. Nevertheless, the relatively high time and space complexities preclude it from wide real-life applications. We, therefore, propose Spectral Ensemble Clustering (SEC) to leverage the advantages of co-association matrix in information integration but run more efficiently. We disclose the theoretical equivalence between SEC and weighted K-means clustering, which dramatically reduces the algorithmic complexity. We also derive the latent consensus function of SEC, which to our best knowledge is the first to bridge co-association matrix based methods to the methods with explicit global objective functions. Further, we prove in theory that SEC holds the robustness, generalizability, and convergence properties. We finally extend SEC to meet the challenge arising from incomplete basic partitions, based on which a row-segmentation scheme for big data clustering is proposed. Experiments on various real-world data sets in both ensemble and multi-view clustering scenarios demonstrate the superiority of SEC to some state-of-the-art methods. In particular, SEC seems to be a promising candidate for big data clustering.