Miracle-dz/UniFAR-FTUs huggingface dataset:
New shape analysis techniques for automatic parallelization of C codes

Automatic parallelization of codes with complex data structures is becoming very important. These complex, and often, recursive data structures are widely used in scientific computing. Shape analysis is one of the key steps in the automatic parallelization of such codes. In this paper we extend the Static Shape Graph (SSG) method to enable the successful and accurate detection of complex doubly linked structures. In addition, these techniques have been implemented in a compiler, which has been validated for several C codes. In particular, we present the results the compiler achieves for the C sparse LU factorization algorithm. The output SSG for this case study perfectly describes the complex data structure used during the LU code.
Ten microsatellite loci from Solms‐laubachia eurycarpa (Brassicaceae)

Solms‐laubachia eurycarpa is a medicinal herb endemic to the Hengduan Mountains region of south‐central China. We screened a partial genomic library enriched for microsatellites and characterized 10 polymorphic loci for S. eurycarpa. The number of alleles per locus ranged from five to 15, with an average of 9.6. The observed and expected heterozygosities ranged from 0.2 to 0.725 and from 0.585 to 0.871, respectively. Amplification in closely related taxa was successful for most loci. The results indicate significant potential for the utility of these markers in studying the population genetics of S. eurycarpa and related species.
The transcription factor Nerfin-1 prevents reversion of neurons into neural stem cells

Froldi et al. show that the Drosophila zinc finger transcription factor Nervous fingers 1 (Nerfin-1) locks neurons into differentiation, preventing their reversion into neuroblasts. The loss of Nerfin-1 causes reversion to multipotency and results in tumors in several neural lineages. RNA-seq and ChIP analysis show that Nerfin-1 administers its function by repression of self-renewing genes and activation of differentiation-specific genes.
Manufacturability evaluation: a CFD approach for Jominy hardenability test

ABSTRACT Heat treatment is an essential process in the manufacture of high-performance steel and other alloy components. Jominy end quench test is used to study the hardenability and microstructure growth in metal alloys when subjected to heat treatment. This is of significance to the manufacturing industry as it provides useful information, allowing selection of appropriate heat treatment procedures. An austenized cylinder is cooled with a fluid jet from one end, causing differential cooling, and resulting in varying properties along the length. This paper presents a manufacturability assessment tool for determination of hardenability of metal alloy components. Jominy test is simulated using a CFD approach as a conjugate heat transfer problem instead of conventional approaches that utilize estimations of the heat transfer rate. The model captures the problem physics by evaluating the temperature and velocity behavior. Cooling rate was found to be highly sensitive to jet velocity, increasing with increase in velocity. Oil was tried as a quenchant in the study and showed slower cooling rates as expected. This approach can be extended to achieve accurate results for cooling rates for complex parts and novel quenching procedures. This was demonstrated by studying the effect of inlet velocity and quenchant type on the cooling behavior.
Decoding Vigilance with NIRS

Sustained, long-term cognitive workload is associated with variations and decrements in performance. Such fluctuations in vigilance can be a risk factor especially during dangerous attention demanding activities. Functional MRI studies have shown that attentional performance is correlated with BOLD-signals, especially in parietal and prefrontal cortical regions. An interesting question is whether these BOLD-signals could be measured in real-world scenarios, say to warn in a dangerous workplace whenever a subjects' vigilance is low. Because fMRI lacks the mobility needed for such applications, we tested whether the monitoring of vigilance might be possible using Near-Infrared Spectroscopy (NIRS). NIRS is a highly mobile technique that measures hemodynamics in the surface of the brain. We demonstrate that non-invasive NIRS signals correlate with vigilance. These signals carry enough information to decode subjects' reaction times at a single trial level.
Automated Impedance Matching System for Robust Wireless Power Transfer via Magnetic Resonance Coupling

Recently, a highly efficient midrange wireless transfer technology using electromagnetic resonance coupling has been proposed and has received much attention due to its practical range and efficiency. The resonance frequency of the resonators changes as the gap between the resonators changes. However, when this technology is applied in the megahertz range, the usable frequency is bounded by the industrial, scientific, and medical (ISM) band. Therefore, to achieve maximum power transmission efficiency, the resonance frequency has to be fixed within the ISM band. In this paper, an automated impedance matching (IM) system is proposed to increase the efficiency by matching the resonance frequency of the resonator pair to that of the power source. The simulations and experiments verify that the IM circuits can change the resonance frequency to 13.56 MHz (in the ISM band) for different air gaps, improving the power transfer efficiency. Experiments also verified that automated IM can be easily achieved just by observing and minimizing the reflected wave at the transmitting side of the system.
Application of agent-based paradigm to model corrosion of steel in concrete environment

This paper explains developing an agent-based computer model to predict the corrosion of steel bars in the high alkaline environment in concrete. Modelling started with specific assumptions about ‘agents’, how they interact and how the population changes over time. A simulation approach, employing agent-based modelling, was formulated and corrosion of steel in the alkaline environment of concrete pore solution, with different chloride concentrations, was simulated. The electrochemical behaviour of steel in a chloride-contaminated concrete pore solution was also experimentally examined. Results of the simulations and the experiments were in good agreement.
FTY720 protects retinal ganglion cells in experimental glaucoma.

PURPOSE To investigate the neuroprotective effects of sphingosine-1-phosphate (S1P) analogue fingolimod (FTY720) in experimental glaucoma in rats. METHODS A unilateral chronic ocular hypertensive model was established by injections of microbeads into the anterior eye chamber of adult Sprague-Dawley rats. Fingolimod was administered to one group of rats intraperitoneally every week for 3 months. The scotopic threshold response (STR) was recorded to assess the function of the inner retina. Changes in cell density in the ganglion cell layer (GCL) were evaluated by hematoxylin and eosin staining on retinal sections and axonal count of the optic nerve was performed using Bielschowsky's silver staining. Effects of drug treatment on activation of Akt and Erk1/2 were evaluated using Western blotting by assessing phosphorylation levels of these proteins. The expression of S1P receptors in the optic nerve head region was also evaluated using Western blotting and immunohistochemistry. RESULTS Administration of FTY720 reduced the loss of STR amplitude in glaucomatous eyes (P < 0.05). Counting and plotting the cell numbers/axonal density showed significant neural preservation in the GCL and the optic nerve (P < 0.05). An increased phosphorylation level of Akt and Erk1/2 following FTY720 administration was observed. Both S1P1 and S1P5 receptors were found to be expressed in the retina and the expression of S1P1R was upregulated in experimentally-induced glaucoma. CONCLUSIONS This study demonstrates, for the first time, that FTY720 could act as a neuroprotective agent to protect retinal ganglion cells in experimental glaucoma. Administration of this drug significantly reduces the structural and functional loss of the inner retina elicited indicating that it may potentially be used to attenuate neuronal loss and optic nerve damage in glaucomatous patients.
Recent Progress in Biomedical Applications of Nanodiamonds

Carbon based nanomaterials have unique advantages in several areas due to its electronic, optical, thermal, and mechanical properties. In these terms, nanodiamonds have attracted considerable attention in recent years in different research areas such as biological sensing, medical therapy, fluorescent markers, enzyme immobilization and so on. Nanodiamond (ND) is a new member of carbon nanoparticle family with a truncated octahedral architecture that showed superior characteristics of diamond. This review is focus on synthesis methods, dispersion, functionalization, medical applications and toxicity of nanodiamonds.
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies

Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics.
SIMPLE: A Methodology for Programming High Performance Algorithms on Clusters of Symmetric Multiprocessors (SMPs)

Abstract We describe a methodology for developing high performance programs running on clusters of SMP nodes. The SMP cluster programming methodology is based on a small prototype kernel (S imple ) of collective communication primitives that make efficient use of the hybrid shared and message-passing environment. We illustrate the power of our methodology by presenting experimental results for sorting integers, two-dimensional fast Fourier transforms (FFT), and constraint-satisfied searching. Our testbed is a cluster of DEC AlphaServer 2100 4/275 nodes interconnected by an ATM switch.
Recent Developments on Wettability Treatment of Wool and Polyester Textiles

The present abstract is an overview of the advancements methods on wettability treatment of wool and polyester textiles. Article will emphasize the potential advantage of the use of these cutting edge strategies for treatment to reach to ideal treatment conditions and the best outcomes, particularly hydrophobicity, dampness substance and increment coloring handling while at the same time keeping up the physical and concoction properties of every material. Treatments divided into: chemical treatment, enzymatic treatment and physical treatment such as: alkoxides, plasma technology and microwave irradiation.
Optimal Operating Policies for Multiplant Stochastic Manufacturing Systems in a Changing Environment

This paper addresses the horizontal coordination between production units located in different countries within a supply chain in a changing environment. The model incorporates (1) congestion and delay through uncertainties in demand and processing times, (2) a changing production cost environment through uncertainty in an environmental state (such as the exchange rate), and (3) allowing production to stock. The main contribution of the paper is to characterize the optimal production policy, assuming no switchover cost, as a barrier-type control policy for a series of models. For each plant, there is a barrier function defined on the environmental state that gives the total worldwide stock level barrier: This plant should be operated if and only if the stock level is below this barrier. The authors also show that, in a simple setting of two plants in different countries, the barrier functions are not always monotone in the exchange rate even when the production costs are. The authors present a condition under which the control barriers are shown to be monotone. Under this condition and with only two plants, it is optimal to follow a primary/secondary plant policy in which, given a fixed environmental state, it is optimal to operate both plants when stock is low, only the primary plant when stock is moderate, and neither when stock is high. Furthermore, the roles of the plants switch as the environmental state passes through the equal production cost state. In addition, the firm seeks to carry the most stock as a hedge against future environmental state moves, when one plant is significantly cheaper than the other. The firm seeks to carry the least stock when both plants are equally costly to operate.
On-Glass Optoelectronic Platform for On-Chip Detection of DNA

Lab-on-chip are analytical systems which, compared to traditional methods, offer significant reduction of sample, reagent, energy consumption and waste production. Within this framework, we report on the development and testing of an optoelectronic platform suitable for the on-chip detection of fluorescent molecules. The platform combines on a single glass substrate hydrogenated amorphous silicon photosensors and a long pass interferential filter. The design of the optoelectronic components has been carried out taking into account the spectral properties of the selected fluorescent molecule. We have chosen the [Ru(phen)2(dppz)]2+ which exhibits a high fluorescence when it is complexed with nucleic acids in double helix. The on-glass optoelectronic platform, coupled with a microfluidic network, has been tested in detection of double-stranded DNA (dsDNA) reaching a detection limit as low as 10 ng/µL.
A cross-border deforestation index to understand underlying drivers of deforestation

This paper considers a preliminary investigation involving the development of a Cross-Border Deforestation Index (CBDI), which is an attempt to quantify the differences in deforestation between two countries or potentially any two administrative units. In this study, the focus was on countries. For each pair of bordering countries, a 50km buffer zone was drawn and the average value of the Vegetation Continuous Field (VCF) was calculated for each country in the pair. The ratio of these two averages is the CBDI. Values of 1 indicate similar levels of forest cover but values greater than 1 point towards dissimilar land use policies within countries and/or sub-national administrative levels. This index was calculated for all pairs of bordering tropical countries in South and Central America, Asia and Africa. In addition, a visual analysis of the spatial variation of the VCF was undertaken to show how this can complement the CBDI. The results showed that countries such as Argentina, Brazil, Chile, El Salvador, Laos, Thailand and DR Congo, in combination with different neighbouring countries, all have CBDI values differing from 1. These areas are worth examining in greater detail in order to understand what types of drivers are behind these outlying CBDI values. These drivers could include land use policy, population pressure, accessibility, etc. Future work will i nclude the addition of environmental factors. By computing the CBDI for so called Homogeneous Response Units (HRU: areas of similar or identical environmental conditions in terms of soil, altitude and slope), we will be able to examine the changing effect on the CBDI. This work is still ongoing and will be expanded to consider HRU for all country pairs. In addition, regression of the CBDI with different drivers of deforestation will be attempted in order to help identify these underlying causes.
Statistical challenges to inductive inference in linked data

Many data sets can be represented naturally as collections of linked objects. For example, document collections can be represented as documents (nodes) connected by citations and hypertext references (links). Similarly, organizations can be represented as people (nodes) connected by reporting relationships, social relationships, and communication patterns (links). This type of data representation is facilitated by the growing availability of object-oriented databases and hypertext systems. The analysis of such data, often called link analysis, is becoming increasingly important in such diverse fields as law enforcement, fraud detection, epidemiology, and information retrieval. One important class of techniques for link analysis involves inductive inference — the construction of predictive relationships from linked data. For example, researchers in information retrieval might construct models to predict whether a particular WWW document is a homepage based on features of other documents to which it is connected (e.g., pages listing publications or family photos). Similarly, law enforcement agents might construct models to predict whether a person is a potential money-launderer based on bank deposits, international travel, business connections, and the arrest records of known associates. Constructing such predictive relationships is an essential part of link analysis. Despite growing data analysis opportunities, relatively little work examines the unique statistical challenges of inductive inference in linked data. This paper examines three such challenges: 1) statistical dependence caused by linked instances; 2) bias introduced by sampling density; and 3) multiple comparisons intensified by feature combinatorics. In general, current systems
PDMS/glass hybrid device with a reusable carbon electrode for on‐line monitoring of catecholamines using microdialysis sampling coupled to microchip electrophoresis with electrochemical detection

On‐line separations‐based sensors employing microdialysis (MD) coupled to microchip electrophoresis (ME) enable the continuous monitoring of multiple analytes simultaneously. Electrochemical detection (EC) is especially amenable to on‐animal systems employing MD‐ME due to its ease of miniaturization. However, one of the difficulties in fabricating MD‐ME‐EC systems is incorporating carbon working electrodes into the device. In this paper, a novel fabrication procedure is described for the production of a PDMS/glass hybrid device that is capable of integrating hydrodynamic MD flow with ME‐EC using a flow‐gated interface and a pyrolyzed photoresist film carbon electrode. This fabrication method enables the reuse of carbon electrodes on a glass substrate, while still maintaining a good seal between the PDMS and glass to allow for pressure‐driven MD flow. The on‐line MD‐ME‐EC device was characterized in vitro and in vivo for monitoring analytes in the dopamine metabolic pathway. The ultimate goal is to use this device and associated instrumentation to perform on‐animal, near‐real time in vivo monitoring of catecholamines.
Observer-Based Phase-Shift Fault Detection Using Adaptive Threshold for Rotor Position Sensor of Permanent-Magnet Synchronous Machine Drives in Electromechanical Brake

This paper designs an observer-based fault detection method with an adaptive threshold for a rotor-position-sensor fault of permanent-magnet synchronous motor drives in an electromechanical brake (EMB). The observer does not fully agree with the real process due to model uncertainties and parameter variations. To avoid missing or false alarms, a key design issue of the proposed detection method is the adaptive threshold, which is determined by analyzing position estimation errors of the observer. Experimental results using a test bench of an EMB show the effectiveness of the designed method.
Seizure relapse and development of drug resistance following long-term seizure remission.

OBJECTIVE To quantify and identify predictive risk factors for seizure relapse and development of drug resistance in patients who achieved long-term (>1 year) antiepileptic drug-induced seizure remission. DESIGN Prospective cohort study. SETTING Epilepsy Center, Rambam Medical Center. Patients Two hundred fifty-six consecutive patients who entered long-term (>1 year) antiepileptic drug-induced seizure remission were followed up prospectively for 2 years or more. MAIN OUTCOME MEASURES Seizure relapse and development of drug-resistant epilepsy. RESULTS Five years after entering seizure remission, 40.2% of patients experienced seizure relapse and 25.3% of patients developed drug-resistant epilepsy. The Kaplan-Meier curves could be fitted by monoexponential functions, with a maximal seizure relapse rate of 43.6%, maximal drug-resistance rate of 27.4%, and half-decay constant of 21.5 months for both curves. Treatment history served as a significant independent prognostic risk factor for both seizure relapse and development of drug resistance. The preremission seizure frequency and duration of epilepsy were also identified as significant prognostic risk factors in the univariant analysis but failed to reach statistical significance in the multivariate analysis. CONCLUSION Seizure relapse commonly occurs in patients following long-term seizure remission. Treatment history and duration of epilepsy are predictive risk factors for both seizure relapse and development of drug resistance.
Mini-ethnography: meaningful exploration made easy.

(Fam Med 2000;32(9):600-2. ) Editor’s Note: In this column, Jamie Weinstein, MSIV, of Oregon Health Sciences University, and William Ventres, MD, of Providence Southeast Family Medicine in Portland, Ore, present an interesting perspective on student community health projects. I welcome your comments about this feature, which is also published on the STFM Web site at www.stfm. org. I also encourage all predoctoral directors to make copies of this feature and distribute it to their preceptors (with the appropriate Family Medicine citation). Send your submissions to Paul Paulman, MD, University of Nebraska Medical Center, Department of Family Medicine, 983075 Nebraska Medical Center, Omaha, NE 68198-3075. 402-559-6818. Fax: 402-559-6501. E-mail: ppaulman@unmc.edu. Submissions should be no longer than 3–4 double-spaced pages. References can be used but are not required. Count each table or figure as one page of text.
Interaction management: The next (and necessary) step beyond knowledge management

Purveyors of knowledge management software have a disconcerting tendency to promote the myth that all problems may be solved by more powerful tools for the exchange of information in the workplace. This fallacy is based on the faulty assumption that knowledge management is about the management of knowledge (as if knowledge were a commodity that could be managed), as opposed to the management of people whose work depends critically on what they know. The origins of knowledge management are far more firmly rooted in the psychological legacy of organizational communication than they are in the technological legacy of information management systems. However, even organizational communication is an inadequate foundation, since various schools of thought in social theory, particularly the structuration theory of Anthony Giddens, inform us that interaction (in the workplace or in any other social setting) is not strictly limited to communication. Knowledge management thus requires moving beyond simplistic models of information exchange to more challenging problems of leveraging social interaction to the advantage of the enterprise. This paper focuses on the claim of structuration theory that the dimension of communication should be supplemented with additional dimensions of power and sanction. This perspective is then examined in light of a case study of crisis management practices, and the case study provides a basis for addressing implications for technological support.