nils-herrmann/clean_scirepeval huggingface dataset:
Web-based Assessment and Test Analyses (WATA) system: development and evaluation

This study introduces the development of a Web-based assessment system, the Web-based Assessment and Test Analyses (WATA) system, and examines its impacts on teacher education. The WATA system is a follow-on system, which applies the Triple-A Model (assembling, administering, and appraising). Its functions include (1) an engine for teachers to administer and manage testing, (2) an engine for students to apply tests, and (3) an engine for generating test results and analyses for teachers. Two studies were undertaken to assess the usefulness and potential benefits of the WATA system for teacher education. In the first study, 47 in-service teachers were asked to assess the functions of the WATA system. The results indicated that they were satisfied with the Triple-A Model of the WATA system. In the second study, 30 pre-service teachers were required to use the WATA system during the teacher-training program. After 4 months of experience in using the WATA system, the preservice teachers’ perspectives of assessment have been changed significantly. The findings of these two studies might provide some guidance to help those who are interested in the development of Web-based assessment and intend to infuse information technology into teacher education.
Learning to Create Jazz Melodies Using Deep Belief Nets

We describe an unsupervised learning technique to facilitate automated creation of jazz melodic improvisation over chord sequences. Specifically we demonstrate training an artificial improvisation algorithm based on unsupervised learning using deep belief nets, a form of probabilistic neural network based on restricted Boltzmann machines. We present a musical encoding scheme and specifics of a learning and creational method. Our approach creates novel jazz licks, albeit not yet in real-time. The present work should be regarded as a feasibility study to determine whether such networks could be used at all. We do not claim superiority of this approach for pragmatically creating jazz.
Morphology of the infective larval stage of the equid parasite Habronema muscae (Spirurida: Habronematidae), from houseflies (Musca domestica)

The infective larva of the spirurid nematode Habronema muscae, a parasite of houseflies, was measured and specimens fixed in Karnovsky's fluid were examined by scanning electron microscopy. The oral opening contains six teeth and is surrounded by large bilobed dorsal and ventral lips and smaller lateral lips. A pair of amphids lie behind the lateral lips. There are two rows of four cephalic papillae. The body is deeply ridged, both transversely and longitudinally. The caudal end of the worm is studded by small papillae. The position of the anal opening is somewhat ambiguous. These larval morphological features are discussed, as well as the changes which must have occurred in the metamorphosis of the infective larva to the adult in the stomach of horses.
Argus: Low-Cost, Comprehensive Error Detection in Simple Cores

We have developed Argus, a novel approach for pro- viding low-cost, comprehensive error detection for simple cores. The key to Argus is that the operation of a von Neumann core consists of four fundamental tasks--control flow, dataflow, computation, and mem- ory access--that can be checked separately. We prove that Argus can detect any error by observing whether any of these tasks are performed incorrectly. We describe a prototype implementation, Argus-1, based on a single-issue, 4-stage, in-order processor to illus- trate the potential of our approach. Experiments show that Argus-1 detects transient and permanent errors in simple cores with much lower impact on performance (4% average overhead) and chip area (17% over- head) than previous techniques.
Systematic literature review and improved model for mitigating bullwhip effect in low shelf life food supply chain

Bullwhip effect in the supply chain distribution network is a phenomenon that is highly avoided because it can lead to high operational costs. It drew the attention of researchers to examine ways to minimize the bullwhip effect. Bullwhip effect occurs because of incorrect company planning in pursuit of customer demand. Bullwhip effect occurs due to increased amplitude of demand variance towards upper supply chain level. If the product handled is a perishable product it will make the bullwhip effect more sensitive. The purpose of this systematic literature review is to map out some of the variables used in constructing mathematical models to minimize the bullwhip effect on food supply chains that have perishable product characteristics. The result of this systematic literature review is that the authors propose an appropriate optimization model that will be applied in the food supply chain sales on the train in Indonesian railways in the next research.
An ADAS Design based on IoT V2X Communications to Improve Safety - Case Study and IoT Architecture Reference Model

Several technologies are used today to improve safety in transportation systems. The development of a system for drivability based on both V2V and V2I communication is considered an important task for the future. V2X communication will be a next step for the transportation safety in the nearest time. A lot of different structures, architectures and communication technologies for V2I based systems are under development. Recently a global paradigm shift known as the Internet-of-Things (IoT) appeared and its integration with V2I communication could increase the safety of future transportation systems. This paper brushes up on the state-of-the-art of systems based on V2X communications and proposes an approach for system architecture design of a safe intelligent driver assistant system using IoT communication. In particular, the paper presents the design process of the system architecture using IDEF modeling methodology and data flows investigations. The proposed approach shows the system design based on IoT architecture reference model.
Socio-demographic correlates of six indicators of alcohol consumption: survey findings of students across seven universities in England, Wales and Northern Ireland

This study assessed the prevalence of six alcohol consumption indicators in a sample of university students. We also examined whether students' sociodemographic and educational characteristics were associated with any of the six alcohol consumption indicators; and whether associations between students' sociodemographic and educational characteristics and the six alcohol consumption indicators differed by gender.



A cross-sectional study of 3706 students enrolled at 7 universities in England, Wales and Northern Ireland. A self-administered questionnaire assessed six alcohol consumption measures: length of time of last (most recent) drinking occasion; amount consumed during last drinking occasion; frequency of alcohol consumption; heavy episodic drinking (≥ 5 drinks in a row); problem drinking; and possible alcohol dependence as measured by CAGE. The questionnaire also collected information on seven relevant student sociodemographic characteristics (age, gender, academic year of study, current living circumstances - accommodation with parents, whether student was in intimate relationship, socioeconomic status of parents - parental education, income sufficiency) and two academic achievement variables (importance of achieving good grades at university, and one's academic performance in comparison with one's peers).



The majority of students (65% of females, 76% of males) reported heavy episodic drinking at least once within the last 2 weeks, and problem drinking was prevalent in 20% of females and 29% of males. Factors consistently positively associated with all six indicators of alcohol consumption were male gender and perceived insufficient income. Other factors such as living away from home, being in 1st or 2nd year of studies, having no intimate partner, and lower academic achievement were associated with some, but not all indicators of alcohol consumption.



The high level of alcohol consumption calls for regular/periodic monitoring of student use of alcohol, and for urgent preventive actions and intervention programmes at the universities in the UK.
Comprehensive proteome analysis of Actinoplanes sp. SE50/110 highlighting the location of proteins encoded by the acarbose and the pyochelin biosynthesis gene cluster.

Acarbose is an α-glucosidase inhibitor produced by Actinoplanes sp. SE50/110 that is medically important due to its application in the treatment of type2 diabetes. In this work, a comprehensive proteome analysis of Actinoplanes sp. SE50/110 was carried out to determine the location of proteins of the acarbose (acb) and the putative pyochelin (pch) biosynthesis gene cluster. Therefore, a comprehensive state-of-the-art proteomics approach combining subcellular fractionation, shotgun proteomics and spectral counting to assess the relative abundance of proteins within fractions was applied. The analysis of four different proteome fractions (cytosolic, enriched membrane, membrane shaving and extracellular fraction) resulted in the identification of 1582 of the 8270 predicted proteins. All 22 Acb-proteins and 21 of the 23 Pch-proteins were detected. Predicted membrane-associated, integral membrane or extracellular proteins of the pch and the acb gene cluster were found among the most abundant proteins in corresponding fractions. Intracellular biosynthetic proteins of both gene clusters were not only detected in the cytosolic, but also in the enriched membrane fraction, indicating that the biosynthesis of acarbose and putative pyochelin metabolites takes place at the inner membrane.



Actinoplanes sp. SE50/110 is a natural producer of the α-glucosidase inhibitor acarbose, a bacterial secondary metabolite that is used as a drug for the treatment of type 2 diabetes, a disease which is a global pandemic that currently affects 387 million people and accounts for 11% of worldwide healthcare expenditures (www.idf.org). The work presented here is the first comprehensive investigation of protein localization and abundance in Actinoplanes sp. SE50/110 and provides an extensive source of information for the selection of genes for future mutational analysis and other hypothesis driven experiments. The conclusion that acarbose or pyochelin family siderophores are synthesized at the inner side of the cytoplasmic membrane determined from this work, indicates that studying corresponding intermediates will be challenging. In addition to previous studies on the genome and transcriptome, the work presented here demonstrates that the next omic level, the proteome, is now accessible for detailed physiological analysis of Actinoplanes sp. SE50/110, as well as mutants derived from this and related species.
Connectivism: A Learning Theory for the Digital Age

Behaviorism, cognitivism, and constructivism are the three broad learning theories most often utilized in the creation of instructional environments. These theories, however, were developed in a time when learning was not impacted through technology. Over the last twenty years, technology has reorganized how we live, how we communicate, and how we learn. Learning needs and theories that describe learning principles and processes, should be reflective of underlying social environments. Vaill emphasizes that “learning must be a way of being – an ongoing set of attitudes and actions by individuals and groups that they employ to try to keep abreast o the surprising, novel, messy, obtrusive, recurring events...” (1996, p.42).
Nonsteroidal anti-inflammatory drugs as anticancer agents: mechanistic, pharmacologic, and clinical issues.

Numerous experimental, epidemiologic, and clinical studies suggest that nonsteroidal anti-inflammatory drugs (NSAIDs), particularly the highly selective cyclooxygenase (COX)-2 inhibitors, have promise as anticancer agents. NSAIDs restore normal apoptosis in human adenomatous colorectal polyps and in various cancer cell lines that have lost adenomatous polyposis coli gene function. NSAIDs also inhibit angiogenesis in cell culture and rodent models of angiogenesis. Many epidemiologic studies have found that long-term use of NSAIDs is associated with a lower risk of colorectal cancer, adenomatous polyps, and, to some extent, other cancers. Two NSAIDs, sulindac and celecoxib, have been found to inhibit the growth of adenomatous polyps and cause regression of existing polyps in randomized trials of patients with familial adenomatous polyposis (FAP). However, unresolved questions about the safety, efficacy, optimal treatment regimen, and mechanism of action of NSAIDs currently limit their clinical application to the prevention of polyposis in FAP patients. Moreover, the development of safe and effective drugs for chemoprevention is complicated by the potential of even rare, serious toxicity to offset the benefit of treatment, particularly when the drug is administered to healthy people who have low annual risk of developing the disease for which treatment is intended. This review considers generic approaches to improve the balance between benefits and risks associated with the use of NSAIDs in chemoprevention. We critically examine the published experimental, clinical, and epidemiologic literature on NSAIDs and cancer, especially that regarding colorectal cancer, and identify strategies to overcome the various logistic and scientific barriers that impede clinical trials of NSAIDs for cancer prevention. Finally, we suggest research opportunities that may help to accelerate the future clinical application of NSAIDs for cancer prevention or treatment.
Recommendations using linked data

Linked Data offers new opportunities for Semantic Web-based application development by connecting structured information from various domains. These technologies allow machines and software agents to automatically interpret and consume Linked Data and provide users with intelligent query answering services. In order to enable advanced and innovative semantic applications of Linked Data such as recommendation, social network analysis, and information clustering, a fundamental requirement is systematic metrics that allow comparison between resources. In this research, we develop a hybrid similarity metric based on the characteristics of Linked Data. In particular, we develop and demonstrate metrics for providing recommendations of closely related resources. The results of our preliminary experiments and future directions are also presented.
3D tree reconstruction from laser range data

We present a method for reconstructing 3D models of tree branch structure from laser range data. Our approach is probabilistic, and uses general knowledge of tree structure to guide an iterative reconstruction process. Our goal is to recover parameters such as branch locations, angles, radii, and lengths, as well as connectivity information between branches. These parameters can then be fed into functional-structural plant models to study the relationships between the structure of a plant, its environment, and its internal biology. In this paper we present an algorithm for finding these parameters, and results on both simulated and real datasets.
Internet of things (IOT) for smart solar energy: A case study of the smart farm at Maejo University

This research aims to prototype a small smart off-grid solar cell system. o provide an alternative electrical supply for a smart Lingzhi mushroom farm. This research applied the use of IOT with voltage and current sensors to measure and monitor the voltage of solar cell charging, amp (ampere) of current charging from the solar panel into a battery and (ampere) of current loading from a battery to the irrigation systems (fog and sprinkler pumps). Voltage and current data processed through Blynk was developed as an IOT cloud service. Data was stored into the Blynk and displayed on mobile devices in real time. The functional status (switching on and off for periods of time, current consumption) pushes notifications through Blynk IOT cloud service on the Blynk application. The equipment and tools used in this research were NodeMCU, voltage and current sensors, relay modules, DC sprinkler, and fog pumps. A small off-grid solar cell was applied. The solar cell system consists of a charger, battery, and solar panels. The IOT cloud used is Blynk. The results of the research shows that using IOT with voltage and current sensors enhances the prototype of a smart farming as an alternative and green electrical resource.
Antifungal activity of the clove essential oil from Syzygium aromaticum on Candida, Aspergillus and dermatophyte species.

The composition and antifungal activity of clove essential oil (EO), obtained from Syzygium aromaticum, were studied. Clove oil was obtained commercially and analysed by GC and GC-MS. The EO analysed showed a high content of eugenol (85.3 %). MICs, determined according to Clinical and Laboratory Standards Institute protocols, and minimum fungicidal concentration were used to evaluate the antifungal activity of the clove oil and its main component, eugenol, against Candida, Aspergillus and dermatophyte clinical and American Type Culture Collection strains. The EO and eugenol showed inhibitory activity against all the tested strains. To clarify its mechanism of action on yeasts and filamentous fungi, flow cytometric and inhibition of ergosterol synthesis studies were performed. Propidium iodide rapidly penetrated the majority of the yeast cells when the cells were treated with concentrations just over the MICs, meaning that the fungicidal effect resulted from an extensive lesion of the cell membrane. Clove oil and eugenol also caused a considerable reduction in the quantity of ergosterol, a specific fungal cell membrane component. Germ tube formation by Candida albicans was completely or almost completely inhibited by oil and eugenol concentrations below the MIC values. The present study indicates that clove oil and eugenol have considerable antifungal activity against clinically relevant fungi, including fluconazole-resistant strains, deserving further investigation for clinical application in the treatment of fungal infections.
On the development of procedural knowledge.

Amnesic patients demonstrate by their performance on a serial reaction time task that they learned a repeating spatial sequence despite their lack of awareness of the repetition (Nissen & Bullemer, 1987). In the experiments reported here, we investigated this form of procedural learning in normal subjects. A subgroup of subjects showed substantial procedural learning of the sequence in the absence of explicit declarative knowledge of it. Their ability to generate the sequence was effectively at chance and showed no savings in learning. Additional amounts of training increased both procedural and declarative knowledge of the sequence. Development of knowledge in one system seems not to depend on knowledge in the other. Procedural learning in this situation is neither solely perceptual nor solely motor. The learning shows minimal transfer to a situation employing the same motor sequence.
Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy.

Harmful cyanobacterial blooms, reflecting advanced eutrophication, are spreading globally and threaten the sustainability of freshwater ecosystems. Increasingly, non-nitrogen (N(2))-fixing cyanobacteria (e.g., Microcystis) dominate such blooms, indicating that both excessive nitrogen (N) and phosphorus (P) loads may be responsible for their proliferation. Traditionally, watershed nutrient management efforts to control these blooms have focused on reducing P inputs. However, N loading has increased dramatically in many watersheds, promoting blooms of non-N(2) fixers, and altering lake nutrient budgets and cycling characteristics. We examined this proliferating water quality problem in Lake Taihu, China's 3rd largest freshwater lake. This shallow, hyper-eutrophic lake has changed from bloom-free to bloom-plagued conditions over the past 3 decades. Toxic Microcystis spp. blooms threaten the use of the lake for drinking water, fisheries and recreational purposes. Nutrient addition bioassays indicated that the lake shifts from P limitation in winter-spring to N limitation in cyanobacteria-dominated summer and fall months. Combined N and P additions led to maximum stimulation of growth. Despite summer N limitation and P availability, non-N(2) fixing blooms prevailed. Nitrogen cycling studies, combined with N input estimates, indicate that Microcystis thrives on both newly supplied and previously-loaded N sources to maintain its dominance. Denitrification did not relieve the lake of excessive N inputs. Results point to the need to reduce both N and P inputs for long-term eutrophication and cyanobacterial bloom control in this hyper-eutrophic system.
An Economical Class of Digital Filters for Decimation and Interpolation

Abstruct-A class of digital h e a r phase fiiite impulse response (FIR) filters for decimation (sampling rate decrease) and interpolation (sampling rate increase) are presented. They require no multipliers and use limited storage making them an economical alternative to conventional implementations for certain applications. A digital fiiter in this class consists of cascaded ideal integrator stages operating at a high sampling rate and an equal number of comb stages operating at a low sampling rate. Together, a single integrator-comb pair produces a uniform FIR. The number of cascaded integrator-comb pairs is chosen to meet design requirements for aliasing or imaging error. Design procedures and examples are given for both decimation and interpolation filters with the emphasis on frequency response and register width.
Next-term student grade prediction

An enduring issue in higher education is student retention to successful graduation. To further this goal, we develop a system for the task of predicting students' course grades for the next enrollment term in a traditional university setting. Each term, students enroll in a limited number of courses and earn grades in the range A-F for each course. Given historical grade data, our task is to predict the grades for each student in the courses they will enroll in during the next term. With this problem formulation, the next-term student grade prediction problem becomes quite similar to a rating prediction or next-basket recommendation problem. The factorization machine (FM), a general-purpose matrix factorization (MF) algorithm suitable for this task, is leveraged as the state-of-the-art method and compared to a variety of other methods. Our experiments show that FMs achieve the lowest prediction error. Results for both cold-start and non-cold-start prediction demonstrate that FMs can be used to accurately predict in both settings. Finally, we identify limitations observed in FMs and the other models tested and discuss directions for future work. To our knowledge, this is the first study that applies state-of-the-art collaborative filtering algorithms to solve the next-term student grade prediction problem.