Miracle-dz/UniFAR-FTUs huggingface dataset:
Metadata creation practices at the Lilongwe University of Agriculture and Natural Resources library's institutional repository

Purpose The purpose of this study was to investigate metadata creation practices in a functional academic institution repository in Malawi, with a specific focus on the Lilongwe University of Agriculture and Natural Resources (LUANAR) library. Design/methodology/approach The study used a qualitative approach with a case study design. The study adopted a case study strategy that focuses on the in-depth, holistic and in-context examination of one or more cases. The researcher used non-probability purposive sampling to include all three LUANAR Digital Repository (LDR) staff at LUANAR library because they were thought to be knowledgeable about the LDR metadata work. The three library staff members directly involved in repository metadata were investigated for the study. Data collection techniques used in a case study approach included semi-structuring face-to-face interviews and documentary analysis. Data from interviews and documentary reviews were manually analyzed and presented in thematic categories based on the study’s objectives. Findings Qualified Dublin Core (DC) was chosen by all participants as the only metadata structure scheme that they will use to create and implement metadata in the repository. DC application profile was the only scheme used to enforce uniform naming and capitalization conventions in the application of Qualified DC element definitions. The scheme, however, was discovered to be the Qualified DC default format in the DSpace system. All participants indicated that the Agricultural Organization of the United Nations Vocabulary is used. Participants highlighted that institutional repository system compatibility, the subject matter of the resources, resource types and staff expertise influenced the selection criteria for the metadata schemes. The repository policy had been developed but had yet to be adopted by the LUANAR management. Research limitations/implications The current study was limited to LUANAR library. A wider study across public and private universities in Malawi is needed to ascertain the role of metadata policy, technical knowledge and metadata specialist institutional repositories. Practical implications Metadata policy is to aid in the understanding of the data, ensuring that appropriate security measures are used to protect the data and for metadata harvesting purposes. Social implications Academic libraries should lobby for management support towards metadata policy for institutional repositories. Originality/value Very little is known about challenges affecting the growth of institutional repositories and standards adopted, including metadata harvesting. This paper bridges the gap in metadata standards for institutional repositories in developing countries.
Version 14, a Program for the Optimization of Lens Designs

The latest member of a long series of computer programs for the optimization of optical designs is described. The various features of this program and its advantages over earlier versions are listed, with more detailed accounts of the improved methods for the control of boundary conditions and the treatment of variable glasses. Applications of the program to the design of real systems and to research into general lens design problems are noted.
A study of the phase transition and magnetocaloric effect in multiferroic La2MnNiO6 single crystals

Magnetic and magnetocaloric properties of single crystal double perovskite La2MnNiO6 have been investigated in details. Its ordered phase with a high Curie temperature (TC = 280 K) exhibits a significant refrigerant capacity around room temperature. A model based on the mean field theory approximation has been used in order to quantify the magnetic and magnetocaloric properties in the ordered La2MnNiO6. The magnetization and entropy changes were satisfactorily simulated as a function of temperature and magnetic field. On the other hand, the presence of cationic disorder in La2MnNiO6 phases allows to shift the Curie point to low temperature without a significant change in the magnetocaloric performance.
Lightning and Severe Weather: A Comparison between Total and Cloud-to-Ground Lightning Trends

Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusiveresults.Conversely, it has been demonstrated that trendsin total lightning aremorerobustlycorrelatedtosevereweatheroccurrence,withrapidincreasesintotallightningobserved10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studieshaveexamined concurrent trendsin bothtotalandCG lightningwithinthesameseverethunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown thatthetotalflashraterapidlyincreasespriortotheonsetofsevereweather.Whatisuntestedwithinthesame framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utilitythattotallightningprovides overCGlightning,specifically withintheframeworkofdevelopingauseful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use oftotal lightningtrendswasmore effectivethanCG trends[probability ofdetection(POD),79% versus66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather ‘‘warning’’ was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence werehigherusingtotallightningascomparedwithCGlightning(20.65versus13.54 min). Theultimategoalof this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.
Reinforcement Learning Strategies for Compiler Optimization in High level Synthesis

High Level Synthesis (HLS) offers a possible programmability solution for FPGAs by automatically compiling CPU codes to custom hardware configurations, but currently delivers far lower hardware quality than circuits written using Hardware Description Languages (HDLs). One reason is because the standard set of code optimizations used by CPU compilers, such as LLVM, are not well suited for a FPGA back end. Code performance is impacted largely by the order in which passes are applied. Similarly, it is also imperative to find a reasonable number of passes to apply and the optimum pass parameter values. In order to bridge the gap between hand tuned and automatically generated hardware, it is thus important to determine the optimal sequence of passes for HLS compilations, which could vary substantially across different workloads. Machine learning (ML) offers one popular approach to automate finding optimal compiler passes but requires selecting the right method. Supervised ML is not ideal since it requires labeled data mapping workload to optimal (or close to optimal) sequence of passes, which is computationally prohibitive. Unsupervised ML techniques don’t take into account the requirement that a quantity representing performance needs to be maximized. Reinforcement learning, which represents the problem of maximizing longterm rewards without requiring labeled data has been used for such planning problems before. While much work has been done along these lines for compilers in general, that directed towards HLS has been limited and conservative. In this paper, we address these limitations by expanding both the number of learning strategies for HLS compiler tuning and the metrics used to evaluate their impact. Our results show improvements over state-of-art for each standard benchmark evaluated and learning quality metric investigated. Choosing just the right strategy can give an improvement of $23 \times$ in learning speed, $4 \times$ in performance potential, $3 \times$ in speedup over -O3, and has the potential to largely eliminate the fluctuation band from the final results. This work provides basis for an efficient recommender system enabling developers to choose the best possible reinforcement learning training options based on their target goals.
Permutation tests for multi-factorial analysis of variance

Several permutation strategies are often possible for tests of individual terms in analysis-of-variance (ANOVA) designs. These include restricted permutations, permutation of whole groups of units, permutation of some form of residuals or some combination of these. It is unclear, especially for complex designs involving random factors, mixed models or nested hierarchies, just which permutation strategy should be used for any particular test. The purpose of this paper is two-fold: (i) we provide a guideline for constructing an exact permutation strategy, where possible, for any individual term in any ANOVA design; and (ii) we provide results of Monte Carlo simulations to compare the level accuracy and power of different permutation strategies in two-way ANOVA, including random and mixed models, nested hierarchies and tests of interaction terms. Simulation results showed that permutation of residuals under a reduced model generally had greater power than the exact test or alternative approximate permutation methods (such as permutation of raw data). In several cases, restricted permutations, in particular, suffered more than other procedures, in terms of loss of power, challenging the conventional wisdom of using this approach. Our simulations also demonstrated that the choice of correct exchangeable units under the null hypothesis, in accordance with the guideline we provide, is essential for any permutation test, whether it be an exact test or an approximate test. For reference, we also provide appropriate permutation strategies for individual terms in any two-way or three-way ANOVA for the exact test (where possible) and for the approximate test using permutation of residuals.
AN ECLIPSING BLUE STRAGGLER IN THE GLOBULAR CLUSTER ω CENTAURI

ω Centauri is the largest globular cluster in the Milky Way and hence contains the largest number of variable stars within a single cluster. The results of photometric solutions are presented for the EA-type binary V239 in this cluster. According to our analysis, V239 is a typical Algol-type binary. We obtain M = 1.20 ± 0.10 M☉, R = 1.21 ± 0.03 R☉, and L = 13.68 ± 0.63 L☉ for the primary component. The secondary component has M = 0.07 ± 0.02 M☉, R = 0.90 ± 0.03 R☉, and L = 2.17 ± 0.14 L☉. The binary system is located in the blue straggler region on the color–magnitude diagram of ω Centauri and the mass of the primary component exceeds the mass of a turnoff star. Therefore, we think that V239 is a blue straggler and that V239 was formed by mass transfer from the present secondary component to the present primary.
Variable frequency matching to a radiofrequency source immersed in vacuum

A low-weight (0.12 kg) low-volume fixed ceramic capacitor impedance matching system is developed for frequency agile tuning of a radiofrequency (rf) Helicon plasma thruster. Three fixed groups of capacitors are directly mounted onto a two loop rf antenna with the thruster immersed in a vacuum chamber. Optimum plasma tuning at the resonance frequency is demonstrated via measurements of the load impedance, power transfer efficiency and plasma density versus driving frequency in the 12.882–14.238 MHz range. The resonance frequency with the plasma on is higher than the resonance frequency in vacuum. The minimum rf power necessary for ignition decreases when the ignition frequency is shifted downwards from the resonance frequency. This development has direct applications in space qualification and space use of rf plasma thrusters.
Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study

The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants' mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.
CFD based simulations of flutter characteristics of ideal thin plates with and without central slot

In this paper, the airflow around an ideal thin plate (hereafter referred to as ITP) with various ratios of central slot is simulated by using the finite-difference-method (FDM)-based Arbitrary-Lagrangian-Eulerian descriptions for the rigid oscillating body. The numerical procedure employs the second-order projection scheme to decouple the governing equations, and the multigrid algorithm with three levels to improve the computational efficiency in evaluating of the pressure equation. The present CFD method is validated through comparing the computed flutter derivatives of the ITP without slot to Theodorsen analytical solutions. Then, the unsteady aerodynamics of the ITP with and without central slot is investigated. It is found that even a smaller ratio of central slot of the ITP has notable effects on pressure distributions of the downstream section, and the pressure distributions on the downstream section will further be significantly affected by the slot ratio and the reduced wind speeds. Continuous increase of with the increase of central slot may be the key feature of the slotted ITP. Finally, flutter analyses based on the flutter derivatives of the slotted ITP are performed, and moreover, flutter instabilities of a scaled sectional model of a twin-deck bridge with various ratios of deck slot are investigated. The results confirm that the central slot is effective to improve bridge flutter stabilities, and that the flutter critical wind speeds increase with the increase of slot ratio.
Stress effects on diffusional interface sharpening in ideal binary alloys

As an extension of our previous paper [Phys. Rev. Lett. 89, 168901 (2002)], we demonstrate, using computer simulations based on a general continuum model, that the diffusional interface sharpening in ideal binary alloys (found recently from deterministic kinetic equations in discrete atomic approach) is still present even if the stress development and relaxations during interdiffusion or if the ``built-in'' stresses (caused by lattice mismatch or thermal expansion) are also taken into account. Thus we conclude that this diffusional sharpening, in systems with large diffusion asymmetry, is really a quite general phenomenon.
WEB‐spline based mesh‐free finite element analysis for the heat equation and the time‐dependent Navier–Stokes equation: A survey

In this article, we give some numerical techniques and error estimates using web‐spline based mesh‐free finite element method for the heat equation and the time‐dependent Navier–Stokes equations on bounded domains. The web‐spline method uses weighted extended B‐splines on a regular grid as basis functions and does not require any grid generation. We demonstrate the method by providing numerical results for the Poisson's and stationary Stokes equation. © 2012 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2013
Evaluation of the Small Heat Shock Protein Family Members HSPB2 and HSPB3 in Bladder Cancer Prognosis and Progression

Bladder cancer (BlCa) represents the sixth most commonly diagnosed type of male malignancy. Due to the clinical heterogeneity of BlCa, novel markers would optimize treatment efficacy and improve prognosis. The small heat shock proteins (sHSP) family is one of the major groups of molecular chaperones responsible for the maintenance of proteome functionality and stability. However, the role of sHSPs in BlCa remains largely unknown. The present study aimed to examine the association between HSPB2 and HSPB3 expression and BlCa progression in patients, and to investigate their role in BlCa cells. For this purpose, a series of experiments including reverse transcription-quantitative PCR, Western blotting, MTT assay and flow cytometry were performed. Initial analyses revealed increased vs. human transitional carcinoma cells, expression levels of the HSPB2 and HSPB3 genes and proteins in high grade BlCa cell lines. Therefore, we then evaluated the clinical significance of the HSPB2 and HSPB3 genes expression levels in bladder tumor samples and matched adjusted normal bladder specimens. Total RNA from 100 bladder tumor samples and 49 paired non-cancerous bladder specimens were isolated, and an accurate SYBR-Green based real-time quantitative polymerase chain reaction (qPCR) protocol was developed to quantify HSPB2 and HSPB3 mRNA levels in the two cohorts of specimens. A significant downregulation of the HSPB2 and HSPB3 genes expression was observed in bladder tumors as compared to matched normal urothelium; yet, increased HSPB2 and HSPB3 levels were noted in muscle-invasive (T2–T4) vs. superficial tumors (TaT1), as well as in high-grade vs. low-grade tumors. Survival analyses highlighted the significantly higher risk for post-treatment disease relapse in TaT1 patients poorly expressing HSPB2 and HSPB3 genes; this effect tended to be inverted in advanced disease stages (muscle-invasive tumors) indicating the biphasic impact of HSPB2, HSPB3 genes in BlCa progression. The pro-survival role of HSPB2 and HSPB3 in advanced tumor cells was also evident by our finding that HSPB2, HSPB3 genes expression silencing in high grade BlCa cells enhanced doxorubicin toxicity. These findings indicate that the HSPB2, HSPB3 chaperone genes have a likely pro-survival role in advanced BlCa; thus, they can be targeted as novel molecular markers to optimize treatment efficacy in BlCa and to limit unnecessary interventions.
Accumulation effect of active species in atmospheric-pressure plasma jet driven by nanosecond high-voltage pulses with MHz pulse repetition rate

With respect to successful applications of pulsed power in gas discharges, the enhanced generation of desired active species and control of plasma parameters as required are always decisive issues. In this study, a bipolar nanosecond high-voltage pulse generator with a maximum pulse repetition rate (PRR) of up to 1 MHz (i.e. a minimum pulse interval of 1 µs) in burst mode is developed, based on the principle of full-bridge converter and pulse transformer. This pulse source is used to generate an atmospheric-pressure plasma jet in Ar + 1%CH4 gas flow, and the influence of pulse intervals (from 1–10 µs) is explored. It is found that the pulse interval can strongly modulate the active species, i.e. a short pulse interval enhances the generation of C2 radial and H atom due to the accumulation effect, when the pulse interval is comparable with their lifetime, while it slightly suppresses the generation of Ar excited states and the energy fraction into electronic excitation. Reduced pulse intervals also prominently increase the energy fraction of vibrational excitation. This study demonstrates how the PRR effectively modulates active species and energy branching and enhances the generation of certain active species in atmospheric-pressure plasma driven by pulsed power.
A Study on the Performance of AlGaN/GaN HEMTs Regrown on Mg-Implanted GaN Layers With Low Channel Thickness

In this work, a comprehensive study on the performance of AlGaN/GaN high-electron-mobility transistors (HEMTs) regrown on Mg-implanted layers is shown. A comparably sharp doping profile into regrown AlGaN/GaN-stacks was verified by secondary-ion mass spectrometry (SIMS) even at standard metal–organic chemical vapor deposition (MOCVD) temperatures above 1000 °C. Static and dynamic characterization by a full 100-mm wafer map exhibited neither an impact on the threshold voltage, transconductance nor on the saturation current even for channel thicknesses as low as 150 nm. Slight current collapse was observed at high OFF-state conditions with large recovery times above 5 s indicating rather slow traps from the nonconnected p-GaN. Within the leakage current, three different mechanisms were identified across the vertical epi-stack. While variable-range hopping (VRH) dominates below <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {DS}}}$ </tex-math></inline-formula> = 35 V, the Pool–Frenkel emission (PFE) was identified for <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {DS}}} >35$ </tex-math></inline-formula> V. At high electric fields (<inline-formula> <tex-math notation="LaTeX">${E} >$ </tex-math></inline-formula> 1 MV/cm), the devices revealed either a direct change from PFE to drain-induced barrier lowering (DIBL) or a change from PFE to space-charge-limited currents (SCLCs) to DIBL was observed. The device results demonstrate the feasibility of the demonstrated process for reproducible device fabrication on large-scale wafers with low channel thicknesses for future device developments of CAVETs, SJ-HEMTs, p-GaN back gates, and intrinsic body diodes.