nils-herrmann/clean_scirepeval huggingface dataset:
Optimized design of parallel prefix Ling adder

Parallel-prefix computation provides a highly efficient solution to binary addition problem. This paper proposes an advanced design based on parallel-prefix Ling adder. In order to further improve the Ling adder's performance, the preprocessing block and carry propagation block are all optimized to reduce the delay path for sum generation. Experimental results indicate that the proposed adder has an improvement of 21 percent time delay and 25 percent area compared to the traditional Ling adders.
Cardioprotective actions of grape polyphenols.

The aim of this review is to discuss the accumulating evidence that suggests that grape extracts and purified grape polyphenols possess a diverse array of biological actions and may be beneficial in the prevention of some inflammatory-mediated diseases including cardiovascular disease. The active components from grape extracts, which include the grape seed, grape skin, and grape juice, that have been identified thus far include polyphenols such as resveratrol, phenolic acids, anthocyanins, and flavonoids. All possess potent antioxidant properties and have been shown to decrease low-density lipoprotein-cholesterol oxidation and platelet aggregation. These compounds also possess a range of additional cardioprotective and vasoprotective properties including antiatherosclerotic, antiarrhythmic, and vasorelaxation actions. Although not exclusive, antioxidant properties of grape polyphenols are likely to be central to their mechanism(s) of action, which also include cellular signaling mechanisms and interactions at the genomic level. This review discusses some of the evidence favoring the consumption of grape extracts rich in polyphenols in the prevention of cardiovascular disease. Consumption of grape and grape extracts and/or grape products such as red wine may be beneficial in preventing the development of chronic degenerative diseases such as cardiovascular disease.
IncCond MPPT control using flyback converter

MPPT is widely used in the application of photovoltaic system. This paper shows the circuit which improves efficiency of photovoltaic system using soft switching flyback and decreases a loss in elements at switching to control the IncCond, one of the MPPT. To test validity of ways shown in this paper, this paper includes the simulation using both Matlab and Psim, and is an indepth analysis of simulation result.
Genotype-phenotype analysis of the branchio-oculo-facial syndrome.

Branchio-oculo-facial syndrome (BOFS; OMIM#113620) is a rare autosomal dominant craniofacial disorder with variable expression. Major features include cutaneous and ocular abnormalities, characteristic facies, renal, ectodermal, and temporal bone anomalies. Having determined that mutations involving TFAP2A result in BOFS, we studied a total of 30 families (41 affected individuals); 26/30 (87%) fulfilled our cardinal diagnostic criteria. The original family with the 3.2 Mb deletion including the TFAP2A gene remains the only BOFS family without the typical CL/P and the only family with a deletion. We have identified a hotspot region in the highly conserved exons 4 and 5 of TFAP2A that harbors missense mutations in 27/30 (90%) families. Several of these mutations are recurrent. Mosaicism was detected in one family. To date, genetic heterogeneity has not been observed. Although the cardinal criteria for BOFS have been based on the presence of each of the core defects, an affected family member or thymic remnant, we documented TFAP2A mutations in three (10%) probands in our series without a classic cervical cutaneous defect or ectopic thymus. Temporal bone anomalies were identified in 3/5 patients investigated. The occurrence of CL/P, premature graying, coloboma, heterochromia irides, and ectopic thymus, are evidence for BOFS as a neurocristopathy. Intrafamilial clinical variability can be marked. Although there does not appear to be mutation-specific genotype-phenotype correlations at this time, more patients need to be studied. Clinical testing for TFAP2A mutations is now available and will assist geneticists in confirming the typical cases or excluding the diagnosis in atypical cases.
The Application of Cloud Computing and the Internet of Things in Agriculture and Forestry

Cloud Computing and The Internet of Things are the two hot points in the Internet field. The application of the two new technologies is in hot discussion and research, but quite less on the field of agriculture and forestry. Thus, in this paper, we analyze the study and application of Cloud Computing and The Internet of Things on agriculture and forestry. Then we put forward an idea that making a combination of the two techniques and analyze the feasibility, applications and future prospect of the combination.
Effects of a 60 Hz magnetic field on photosynthetic CO2 uptake and early growth of radish seedlings.

Photosynthetic CO2 uptake rate and early growth parameters of radish Raphanus sativus L. seedlings exposed to an extremely low frequency magnetic field (ELF MF) were investigated. Radish seedlings were exposed to a 60 Hz, 50 microT(rms) (root mean square) sinusoidal magnetic field (MF) and a parallel 48 microT static MF for 6 or 15 d immediately after germination. Control seedlings were exposed to the ambient MF but not the ELF MF. The CO2 uptake rate of ELF MF exposed seedlings on day 5 and later was lower than that of the control seedlings. The dry weight and the cotyledon area of ELF MF exposed seedlings on day 6 and the fresh weight, the dry weight and the leaf area of ELF MF exposed seedlings on day 15 were significantly lower than those of the control seedlings, respectively. In another experiment, radish seedlings were grown without ELF MF exposure for 14 d immediately after germination, and then exposed to the ELF MF for about 2 h, and the photosynthetic CO2 uptake rate was measured during the short-term ELF MF exposure. The CO2 uptake rate of the same seedlings was subsequently measured in the ambient MF (control) without the ELF MF. There was no difference in the CO2 uptake rate of seedlings exposed to the ELF MF or the ambient MF. These results indicate that continuous exposure to 60 Hz, 50 microT(rms) sinusoidal MF with a parallel 48 microT static MF affects the early growth of radish seedlings, but the effect is not so severe that modification of photosynthetic CO2 uptake can observed during short-term MF exposure.
Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network

In order to automate routine fecal examination for parasitic diseases, the authors propose in this study a computer processing algorithm using digital image processing techniques and an artificial neural network (ANN) classifier The morphometric characteristics of eggs of human parasites in fecal specimens were extracted from microscopic images through digital image processing. An ANN then identified the parasite species based on those characteristics. The authors selected four morphometric features based on three morphological characteristics representing shape, shell smoothness, and size. A total of 82 microscopic images containing seven common human helminth eggs were used. The first stage (ANN-1) of the proposed ANN classification system isolated eggs from confusing artifacts. The second stage (ANN-2) classified eggs by species. The performance of ANN was evaluated by the tenfold cross-validation method to obviate the dependency on the selection of training samples. Cross-validation results showed 86.1% average correct classification ratio for ANN-1 and 90.3% for ANN-2 with small variances of 46.0 and 39.0, respectively. The algorithm developed will be an essential part of a completely automated fecal examination system.
Ontology for malware behavior: A core model proposal

The ubiquity of Internet-connected devices motivates attackers to create malicious programs (malware) to exploit users and their systems. Malware detection requires a deep understanding of their possible behaviors, one that is detailed enough to tell apart suspicious programs from benign, legitimate ones. A step to effectively address the malware problem leans toward the development of an ontology. Current efforts are based on an obsolete hierarchy of malware classes that defines a malware family by one single prevalent behavior (e.g., viruses infect other files, worms spread and exploit remote systems autonomously, Trojan horses disguise themselves as benign programs, and so on). In order to address the detection of modern, complex malware families whose infections involve sets of multiple exploit methods, we need an ontology broader enough to deal with these suspicious activities performed on the victim's system. In this paper, we propose a core model for a novel malware ontology that is based on their exhibited behavior, filling a gap in the field.
Cryptorchidism and hypospadias in sons of gardeners and farmers.

Cryptorchidism and hypospadias have been related to prenatal estrogen exposure in animal models. Some chemicals used in farming and gardening have been shown to possess estrogenic and other hormone-disrupting effects. Earlier studies have indicated increased risks of urogenital malformations in the sons of pesticide appliers. In the present study, parental occupation in the farming and gardening industry among 6,177 cases of cryptorchidism, 1,345 cases of hypospadias, and 23,273 controls, born live from 1983 to 1992 in Denmark, was investigated in a register-based case-control study. A significantly increased risk of cryptorchidism but not hypospadias was found in sons of women working in gardening (adjusted odds ratio = 1.67; 95% confidence interval, 1.14-2.47). The risks were not increased in sons of men working in farming or gardening. The increased risk of cryptorchidism among sons of female gardeners could suggest an association with prenatal exposure to occupationally related chemicals.
Correlation between bullying and clinical depression in adolescent patients

A literature review of the associations between involvement in bullying and depression is presented. Many studies have demonstrated a concurrent association between involvement in bullying and depression in adolescent population samples. Not only victims but also bullies display increased risk of depression, although not all studies have confirmed this for the bullies. Retrospective studies among adults support the notion that victimization is followed by depression. Prospective follow-up studies have suggested both that victimization from bullying may be a risk factor for depression and that depression may predispose adolescents to bullying. Research among clinically referred adolescents is scarce but suggests that correlations between victimization from bullying and depression are likely to be similar in clinical and population samples. Adolescents who bully present with elevated numbers of psychiatric symptoms and psychiatric and social welfare treatment contacts.
Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system

The volume of traveling websites is rapidly increasing. This makes relevant information extraction more challenging. Several fuzzy ontology-based systems have been proposed to decrease the manual work of a full-text query search engine and opinion mining. However, most search engines are keyword-based, and available full-text search engine systems are still imperfect at extracting precise information using different types of user queries. In opinion mining, travelers do not declare their hotel opinions entirely but express individual feature opinions in reviews. Hotel reviews have numerous uncertainties, and most featured opinions are based on complex linguistic wording (small, big, very good and very bad). Available ontology-based systems cannot extract blurred information from reviews to provide better solutions. To solve these problems, this paper proposes a new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE. The system reformulates the user’s full-text query to extract the user requirement and convert it into the format of a proper classical full-text search engine query. The proposed system retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology. The fuzzy domain ontology, user information and hotel information are integrated to form a type-2 fuzzy merged ontology for the retrieving of feature polarity and individual hotel polarity. The Protégé OWL-2 (Ontology Web Language) tool is used to develop the type-2 fuzzy ontology. A series of experiments were designed and demonstrated that T2FOBOMIE performance is highly productive for analyzing reviews and accurate opinion mining.
Simultaneous co-clustering and learning to address the cold start problem in recommender systems

Recommender Systems (RSs) are powerful and popular tools for e-commerce. To build their recommendations, RSs make use of varied data sources, which capture the characteristics of items, users, and their transactions. Despite recent advances in RS, the cold start problem is still a relevant issue that deserves further attention, and arises due to the lack of prior information about new users and new items. To minimize system degradation, a hybrid approach is presented that combines collaborative filtering recommendations with demographic information. The approach is based on an existing algorithm, SCOAL (Simultaneous Co-Clustering and Learning), and provides a hybrid recommendation approach that can address the (pure) cold start problem, where no collaborative information (ratings) is available for new users. Better predictions are produced from this relaxation of assumptions to replace the lack of information for the new user. Experiments using real-world datasets show the effectiveness of the
Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks

Effective energy management in heterogeneous wireless sensor networks (WSNs) is a more challenging issue compared to homogeneous WSNs. Much of the existing research has often assumed homogeneous sensor nodes in the networks. The energy preservation schemes for the homogeneous WSNs do not perform efficiently when applied to heterogeneous WSNs. In this paper, we have presented an energy-efficient multi-hop communication routing (MCR) protocol in order to address the traditional problem of load balancing, lifetime, stability and energy efficiency in the WSNs. MCR protocol is based on dividing the network into dynamic clusters. The cluster-heads election is based on weighted probability. The cluster’s nodes communicate with an elected cluster head node by using single hop communication approach, and then the cluster heads communicate the information to the base station via multi-hop communication approach. Performance studies indicate that MCR effectively solves the problem of load balancing across the network, extends the network lifetime, stability and is more energy efficient in comparison to multi-hop low-energy adaptive clustering hierarchy (M-LEACH) and multilayer energy efficient cluster head communication protocol (MEECHCP).
A 0.13/spl mu/m CMOS T/R switch design for ultrawideband wireless applications

A CMOS transmit-receive (T/R) switch design for ultrawideband (UWB) wireless applications is presented. A shunt inductor is adopted in the T/R switch signal path to improve the insertion loss (IL) and work as electrostatic discharge (ESD) protection device. Two bypassing switches at the gate of the main switching transistors provide additional isolation. Control voltage as low as 1.2V is applied to handle the T/R switch operation. Implemented in UMC 0.13mum MMRF CMOS technology, the measured results show an IL of 0.78-0.99dB in the receive (RX) mode and an isolation from TX port to RX port higher than 30.8dB in the transmit (TX) mode in the 3.1-4.8GHz frequency range. The measured ESD rating is 3kV HBM. The T/R switch is designed for monolithic integration with UWB RF front-end building blocks. The on-chip inductor eliminates the need of an off-chip one for the wideband matching network
Mining concept-drifting data streams using ensemble classifiers

Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two challenges, the overwhelming volume of the streaming data, and the concept drifts. In this paper, we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Beyesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models.
Online Misinformation: Challenges and Future Directions

Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view. We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications.
Benchmark data and method for real-time people counting in cluttered scenes using depth sensors

Vision-based automatic counting of people has widespread applications in intelligent transportation systems, security, and logistics. However, there is currently no large-scale public dataset for benchmarking approaches on this problem. This work fills this gap by introducing the first real-world RGBD People Counting DataSet (PCDS) containing over 4, 500 videos recorded at the entrance doors of buses in normal and cluttered conditions. It also proposes an efficient method for counting people in real-world cluttered scenes related to public transportations using depth videos. The proposed method computes a point cloud from the depth video frame and re-projects it onto the ground plane to normalize the depth information. The resulting depth image is analyzed for identifying potential human heads. The human head proposals are meticulously refined using a 3D human model. The proposals in each frame of the continuous video stream are tracked to trace their trajectories. The trajectories are again refined to ascertain reliable counting. People are eventually counted by accumulating the head trajectories leaving the scene. To enable effective head and trajectory identification, we also propose two different compound features. A thorough evaluation on PCDS demonstrates that our technique is able to count people in cluttered scenes with high accuracy at 45 fps on a 1.7 GHz processor, and hence it can be deployed for effective real-time people counting for intelligent transportation systems.
T-cell integrins: more than just sticking points.

T cells use integrins in essentially all of their functions. They use integrins to migrate in and out of lymph nodes and, following infection, to migrate into other tissues. At the beginning of an immune response, integrins also participate in the immunological synapse formed between T cells and antigen-presenting cells. Because the ligands for integrins are widely expressed, integrin activity on T cells must be tightly controlled. Integrins become active following signalling through other membrane receptors, which cause both affinity alteration and an increase in integrin clustering. Lipid raft localization may increase integrin activity. Signalling pathways involving ADAP, Vav-1 and SKAP-55, as well as Rap1 and RAPL, cause clustering of leukocyte function-associated antigen-1 (LFA-1; integrin alphaLbeta2). T-cell integrins can also signal, and the pathways dedicated to the migratory activity of T cells have been the most investigated so far. Active LFA-1 causes T-cell attachment and lamellipodial movement induced by myosin light chain kinase at the leading edge, whereas RhoA and ROCK cause T-cell detachment at the trailing edge. Another important signalling pathway acts through CasL/Crk, which might regulate the activity of the GTPases Rac and Rap1 that have important roles in T-cell migration.
A foundation for capturing and querying complex multidimensional data

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead. r 2001