Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Instructional Video in the Acquisition of Cognitive, Affective and Psychomotor Skills in Practical Sports Therapy Rehabilitation

The use of instructional multimedia within education is steadily increasing although the evidence-base regarding its usage typically only cites that it is equivalent to or as effective as live demonstration or traditional methods. This session will outline the findings of a longitudinal quasi-experimental study that sought to evaluate the efficacy of instructional videos when used as a method to complement the acquisition of cognitive, affective and psychomotor knowledge and skills for use within a practical setting. The session will cover underlying theory (Cognitive Load Theory, Multimedia Learning Theory and the Cognitive, Affective and Psychomotor Domains), outline guidelines for effective instructional video design and present the educationally significant findings. The session will also identify the growing use of effect size statistics in the analysis of research findings and the unexpectedly beneficial finding of a cross-over effect that typically would have a negative effect on the results of a study.
Lightweight Hardware Transactional Memory for GPU Scratchpad Memory

Graphics Processing Units (GPUs) have become the accelerator of choice for data-parallel applications, enabling the execution of thousands of threads in a Single Instruction - Multiple Thread (SIMT) fashion. Using OpenCL terminology, GPUs offer a global memory space shared by all the threads in the GPU, as well as a local memory space shared by only a subset of the threads. Programmers can use local memory as a scratchpad to improve the performance of their applications due to its lower latency as compared to global memory. In the SIMT execution model, data locking mechanisms used to protect shared data limit scalability. To take full advantage of the lower latency that local memory affords, and to provide an efficient synchronization mechanism, we propose GPU-LocalTM as a lightweight and efficient transactional memory (TM) for GPU local memory. To minimize the storage resources required for TM support, GPU-LocalTM allocates transactional metadata in the existing memory resources. Additionally, GPU-LocalTM implements different conflict detection mechanisms that can be used to match the characteristics of the application. For the workloads studied in our simulation-based evaluation, GPU-LocalTM provides from 1.1X up to 100X speedup over serialized critical sections.
Bioactivity of Viscum album extracts from Olive and Almond host plants in Palestine

Introduction:Viscum album is a semi-parasitic medicinal plant which has been used for many years as a remedy in traditional medicine. The plant is widely used in folk medicine in Palestine; mainly in the treatment of cancer, diabetes and heart disease. Since no previous reports on bioactivity of this plant in association with host plant specificity in Palestine, the current study aimed at evaluating bioactivity of almond and olive variants of this plant. Method: Methanolic extract of viscum album cultivated from almond and olive host plants were tested for antioxidant, antimicrobial . Prothrombin time (PT) and activated partial thromboplastin time (aPTT) were also used to assess anticoagulant activities of plant extracts. Result: The result demonstrated that Viscum album have an IC 50 of 25.34±3. 8µg/ml when hosted by olive while the IC 50 was 15.37±2. 2µg/ml when hosted by almond. Crude extracts of both Viscum album plants showed strong inhibition effects on the growth of the studied Staphylococcus aureus strains (ATCC 25923 and MRSA) with a pronounced effect when extracts of almond host was used. However, the effects of both host extracts were very limited or absent when tested against Gram-negative reference and clinical strains. Plant extracts of both host showed prolonged PT and PTT compared to phosphate buffered saline control solution. Conclusion: In conclusion, variations in the bioactivity of Viscum album is clearly influenced by host type and further studies required to illustrate such variations using other host plants.
Icons: Visual Representation to Enrich Requirements Engineering Work

Adapting icons in requirements engineering can support the multifaceted needs of stakeholders. Conventional approaches to RE are mainly highlighted in diagrams. This paper introduces icon-based information as a way to represent ideas and concepts in the requirements engineering domain. We report on icon artifacts that support requirements engineering work such as priority types, status states and stakeholder kinds. We evaluate how users interpret meanings of icons and the efficacy of icon prototypes shaped to represent those requirements attributes. Our hypothesis is whether practitioners can recognize the icons’ meaning in terms of their functional representation. According to the empirical data from 45 participants, the findings demonstrate the probability of providing users with icons and their intended functions that correspond to RE artifacts in a novel yet effective manner. Based on these findings, we suggest that icons could enrich stakeholders’ perception of the RE process as a whole; however, meaningful interpretation of an icon is subject to the user’s prior knowledge and experience.
A Search for Technosignatures from 14 Planetary Systems in the Kepler Field with the Green Bank Telescope at 1.15–1.73 GHz

Analysis of Kepler mission data suggests that the Milky Way includes billions of Earth-sized planets in the habitable zone of their host stars. Current technology enables the detection of technosignatures emitted from a large fraction of the Galaxy. We describe a search for technosignatures that is sensitive to Arecibo-class transmitters located within ∼420 ly of Earth and transmitters that are 1000 times more effective than Arecibo within ∼13000 ly of Earth. Our observations focused on 14 planetary systems in the Kepler field and used the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. Each source was observed for a total integration time of 5 minutes. We obtained power spectra at a frequency resolution of 3 Hz and examined narrowband signals with Doppler drift rates between ±9 Hz s−1. We flagged any detection with a signal-to-noise ratio in excess of 10 as a candidate signal and identified approximately 850,000 candidates. Most (99%) of these candidate signals were automatically classified as human-generated radio-frequency interference (RFI). A large fraction (>99%) of the remaining candidate signals were also flagged as anthropogenic RFI because they have frequencies that overlap those used by global navigation satellite systems, satellite downlinks, or other interferers detected in heavily polluted regions of the spectrum. All 19 remaining candidate signals were scrutinized and none were attributable to an extraterrestrial source.
Freestanding, Fiber‐Based, Wearable Temperature Sensor with Tunable Thermal Index for Healthcare Monitoring

Fiber‐based sensors integrated on textiles or clothing systems are required for the next generation of wearable electronic platforms. Fiber‐based physical sensors are developed, but the development of fiber‐based temperature sensors is still limited. Herein, a new approach to develop wearable temperature sensors that use freestanding single reduction graphene oxide (rGO) fiber is proposed. A freestanding and wearable temperature‐responsive rGO fiber with tunable thermal index is obtained using simple wet spinning and a controlled graphene oxide reduction time. The freestanding fiber‐based temperature sensor shows high responsivity, fast response time (7 s), and good recovery time (20 s) to temperature. It also maintains its response under an applied mechanical deformation. The fiber device fabricated by means of a simple process is easily integrated into fabric such as socks or undershirts and can be worn by a person to monitor the temperature of the environment and skin temperature without interference during movement and various activities. These results demonstrate that the freestanding fiber‐based temperature sensor has great potential for fiber‐based wearable electronic platforms. It is also promising for applications in healthcare and biomedical monitoring.
Intracellular Hyperthermia for Cancer Using Magnetite Cationic Liposomes: Ex vivo Study

Heating properties of magnetite cationic liposomes (MCL) were investigated in ex vivo experiments using implanted cell pellets. The cell pellets, which consisted of rat glioma T9 cells into which MCL had been incorporated in a petri dish, were implanted subcntaneously in the left femoral region of female F344 rats. The rats were placed in a magnetic field generating coil and irradiated with an alternating magnetic field (384 Oe, 118 kHz) for 60 min. The cell pellets were heated to over 43°C by MCL in the magnetic field, hut other body parts of the rats were not heated. After 3 cycles of magnetic heating, all glioma cells were killed and no tumor take was observed.
Local Electronic and Chemical Structure of Oligo-acetylene Derivatives Formed Through Radical Cyclizations at a Surface

Semiconducting π-conjugated polymers have attracted significant interest for applications in light-emitting diodes, field-effect transistors, photovoltaics, and nonlinear optoelectronic devices. Central to the success of these functional organic materials is the facile tunability of their electrical, optical, and magnetic properties along with easy processability and the outstanding mechanical properties associated with polymeric structures. In this work we characterize the chemical and electronic structure of individual chains of oligo-(E)-1,1′-bi(indenylidene), a polyacetylene derivative that we have obtained through cooperative C1–C5 thermal enediyne cyclizations on Au(111) surfaces followed by a step-growth polymerization of the (E)-1,1′-bi(indenylidene) diradical intermediates. We have determined the combined structural and electronic properties of this class of oligomers by characterizing the atomically precise chemical structure of individual monomer building blocks and oligomer chains (via noncontact atomic force microscopy (nc-AFM)), as well as by imaging their localized and extended molecular orbitals (via scanning tunneling microscopy and spectroscopy (STM/STS)). Our combined structural and electronic measurements reveal that the energy associated with extended π-conjugated states in these oligomers is significantly lower than the energy of the corresponding localized monomer orbitals, consistent with theoretical predictions.
Market value and patent citations

We explore the usefulness of patent citations as a measure of the "importance" of a firm's patents, as indicated by the stock market valuation of the firm's intangible stock of knowledge. Using patents and citations for 1963--1995, we estimate Tobin's q equations on the ratios of R&D to assets stocks, patents to R&D, and citations to patents. We find that each ratio significantly affects market value, with an extra citation per patent boosting market value by 3%. Further findings indicate that "unpredictable" citations have a stronger effect than the predictable portion, and that self-citations are more valuable than external citations.
An Hypervolume based constraint handling technique for multi-objective optimization problems

Formulation of structural optimization problems usually leads to the individuation of one or more objective functions to be minimized under different constraints. Many multi-objective evolutionary algorithms are approached by a Pareto-compliant ranking method, where no a priori information on the problem is needed and the concept of non-dominated solutions is used. In this paper a constraint handling technique based on the concept of hypervolume indicator is presented. Initially proposed to compare different multi-objective algorithms hypervolume indicator is the only single set quality measure to reflects the dominance of solution’s sets. The constraint handling technique proposed use an extension of stochastic ranking approach for single-objective optimization problem to multi-objective ones. The extension proposed use the hypervolume indicator to compares different solutions and is tested on a structural constrained multi-objective problems. Results show the suitability of the proposed approach.
Multifrequency subsurface sensing in the presence of a moderately rough air–soil interface via quasi‐ray Gaussian beams

An adaptive framework is presented for frequency‐stepped ground‐penetrating radar (GPR) imaging of low‐contrast buried objects in the presence of a moderately rough air–soil interface, with potential applications intended in the area of humanitarian demining. The proposed approach, so far restricted to two‐dimeansional (2‐D) geometries, works with sparse data and relies on recently developed problem‐matched narrow‐waisted Gaussian beam (GB) algorithms as fast forward scattering predictive models to estimate and compensate for the effects of the coarse‐scale roughness profile. Possible targets are subsequently imaged by inverting the Born‐linearized subsurface scattering model via object‐based curve evolution (CE) techniques. This frequency domain (FD) strategy implements a further step in our planned sequential approach toward a physics based, robust, and numerically efficient framework for rough surface underground imaging in both FD and time domain (TD). Numerical experiments indicate that the proposed framework is attractive from both computational and robustness viewpoints. The results in this paper could also be used for synthesis of TD illumination (in a previous study [ Galdi et al., 2001b ], we have dealt with wideband illumination directly in the TD).
Retrievals of a size parameter for phytoplankton and spectral light absorption by colored detrital matter from water‐leaving radiances at SeaWiFS channels in a continental shelf region off Brazil

Many efforts are currently oriented toward extracting more information from ocean color than the chlorophyll a concentration. Among biological parameters potentially accessible from space, estimates of phytoplankton cell size and light absorption by colored detrital matter (CDM) would lead to an indirect assessment of major components of the organic carbon pool in the ocean, which would benefit oceanic carbon budget models. We present here 2 procedures to retrieve simultaneously from ocean color measurements in a limited number of bands, magnitudes, and spectral shapes for both light absorption by CDM and phytoplankton, along with a size parameter for phytoplankton. The performance of the 2 procedures was evaluated using different data sets that correspond to increasing uncertainties: (1) measured absorption coefficients of phytoplankton, particulate detritus, and colored dissolved organic matter (CDOM) and measured chlorophyll a concentrations and (2) SeaWiFS upwelling radiance measurements and chlorophyll a concentrations estimated from global algorithms. In situ data were acquired during 3 cruises, differing by their relative proportions in CDM and phytoplankton, over a continental shelf off Brazil. No local information was introduced in either procedure, to make them more generally applicable. Over the study area, the absorption coefficient of CDM at 443 nm was retrieved from SeaWiFS radiances with a relative root mean square error (RMSE) of 33%, and phytoplankton light absorption coefficients in SeaWiFS bands (from 412 to 510 nm) were retrieved with RMSEs between 28% and 33%. These results are comparable to or better than those obtained by 3 published models. In addition, a size parameter of phytoplankton and the spectral slope of CDM absorption were retrieved with RMSEs of 17% and 22%, respectively. If these methods are applied at a regional scale, the performances could be substantially improved by locally tuning some empirical relationships.
Mirovia: A Benchmarking Suite for Modern Heterogeneous Computing

This paper presents Mirovia, a benchmark suite developed for modern day heterogeneous computing. Previous benchmark suites such as Rodinia and SHOC are well written and have many desirable features. However, these tools were developed years ago when hardware was less powerful and software had fewer features. For example, unified memory was introduced in CUDA 6 as a new programming model and wasn't available when Rodinia was released. Meanwhile, the increasing demand for graphics processing units (GPUs) due to the recent rise in popularity of deep neural networks (DNNs) has opened doors for many new research problems. It is essential to consider DNNs as first-class citizens in a comprehensive benchmark suite. However, the main focus is usually limited to inference and model performance evaluation, which is not desirable for hardware architects studying for emerging platforms. Drawing inspiration from Rodinia and SHOC, Mirovia is a benchmark suite that is designed to take advantage of modern GPU architectures, while also representing a diverse set of application domains. By adopting applications from Rodinia and SHOC, and including newly written applications with special focus on DNNs, Mirovia better characterizes modern heterogeneous systems.
Automatically diagnosing and repairing error handling bugs in C

Correct error handling is essential for building reliable and secure systems. Unfortunately, low-level languages like C often do not support any error handling primitives and leave it up to the developers to create their own mechanisms for error propagation and handling. However, in practice, the developers often make mistakes while writing the repetitive and tedious error handling code and inadvertently introduce bugs. Such error handling bugs often have severe consequences undermining the security and reliability of the affected systems. Fixing these bugs is also tiring-they are repetitive and cumbersome to implement. Therefore, it is crucial to develop tool supports for automatically detecting and fixing error handling bugs. To understand the nature of error handling bugs that occur in widely used C programs, we conduct a comprehensive study of real world error handling bugs and their fixes. Leveraging the knowledge, we then design, implement, and evaluate ErrDoc, a tool that not only detects and characterizes different types of error handling bugs but also automatically fixes them. Our evaluation on five open-source projects shows that ErrDoc can detect error handling bugs with 100% to 84% precision and around 95% recall, and categorize them with 83% to 96% precision and above 90% recall. Thus, ErrDoc improves precision up to 5 percentage points, and recall up to 44 percentage points w.r. t. the state-of-the-art. We also demonstrate that ErrDoc can fix the bugs with high accuracy.
A Cross-Domain Recommender System Based on Common-Sense Knowledge Bases

A system able to extract and recommend technical terms from various domains is proposed in this paper. The motivation is to provide keywords that users may not be familiar with in the beginning but will be interested in after studying. To acquire domain knowledge, we collect documents from various sources, and the words in the documents are then represented as semantic word vectors. Given queries from users, the system first extracts important terms from given documents and computes the semantic similarity between those terms. Next, we utilize third party common-sense knowledge bases such as ConceptNet and Wikipedia to connect the queries to those extracted keywords through the network structures. Finally, the system will collect all keywords traversed and recommend the top-n of them. We propose and compare four models for the recommendation, and the differences between using ConceptNet and Wikipedia for discovering related knowledge are also investigated in this work.
RESONANCE BETWEEN PLANAR COILS VS RING COILS EXCITED BY SQUARE WAVES

This paper presents a basic analysis about experiment results using planar coils inner ring coils, deflning a transformer, when this system is excited by square wave voltage. In this study phenomena of response to step voltage due to parasitic capacitances is analyzed, and a sum of responses when the input square wave frequency increases is given too. These phenomena are uncommon in literature, which can be applied to several areas of researches in electrical engineering, pulse transformers and power electronics.
Isolation and molecular characterization of novel glucarpidases: Enzymes to improve the antibody directed enzyme pro-drug therapy for cancer treatment

Repeated cycles of antibody-directed enzyme pro-drug therapy (ADEPT) and the use of glucarpidase in the detoxification of cytotoxic methotrexate (MTX) are highly desirable during cancer therapy but are hampered by the induced human antibody response to glucarpidase. Novel variants of glucarpidase (formal name: carboxypeptidase G2, CPG2) with epitopes not recognized by the immune system are likely to allow repeated cycles of ADEPT for effective cancer therapy. Towards this aim, over two thousand soil samples were collected and screened for folate hydrolyzing bacteria using folate as the sole carbon source. The work led to the isolation and the characterization of three new glucarpidase producing strains, which were designated as: Pseudomonas lubricans strain SF168, Stenotrophomonas sp SA and Xenophilus azovorans SN213. The CPG2 genes of Xenophilus azovorans SN213 (named Xen CPG2) and Stenotrophomonas sp SA (named Sten CPG2) were cloned and molecularly characterized. Both Xen CPG2 and Sten CPG2 share very close amino acid sequences (99%); we therefore, focused on the study of Xen CPG2. Finally, we demonstrated that a polyclonal antibody raised against our new CPG2, Xen CPG2, does not react with the CPG2 from Pseudomonas sp. strain RS-16 (Ps CPG2) that are currently in clinical use. The two enzymes, therefore could potentially be used consecutively in the ADEPT protocol to minimize the effect of the human antibody response that hampers current treatment with Ps CPG2. The identified novel CPG2 in this study will, therefore, pave the way for safer antibody directed enzyme pro-drug therapy for cancer treatment.
Risk factors for ruptured intracranial aneurysms

Background & objectives: The treatment of unruptured intracranial aneurysms (IAs) remains controversial; the ability to predict the risk of rupture for an aneurysm would be of clinical value. The aim of this study was to determine and evaluate the predictive value of the risk factors of IA rupture. Methods: This retrospective study involved 379 consecutive patients with 441 aneurysms between August 2011 and July 2014. Based on clinical data and computed tomography angiography findings, the potential of risk factors to predict the aneurysmal rupture was assessed using statistical methods. Results: Age, hypertension, heart disease, diabetes mellitus, cerebral atherosclerosis, aneurysms located at the internal carotid artery (ICA) and neck width (N) correlated negatively with rupture risk. Aneurysms located at the anterior communicating artery, bifurcation, irregularity, with a daughter sac, aneurysm height, maximum size, aspect ratio (AR), height-to-width ratio and bottleneck factor were significantly and positively correlated with rupture risk. The multivariate logistic regression model revealed that bifurcation aneurysm, irregular aneurysm and high AR increased the rupture risk, while cerebral atherosclerosis, aneurysm located on the ICA and greater N decreased the risk. Receiver operating characteristic analysis of AR curve values differed according to circumstances. Interpretation & conclusions: Cerebral atherosclerosis, location in the ICA and larger N were the protective factors against aneurysm rupture, and IAs located at bifurcations, irregular shape and increased AR indicated a greater rupture risk.