Miracle-dz/UniFAR-FTUs huggingface dataset:
A Parallel Quantum Computer Simulator

A Quantum Computer is a new type of computer which can efficiently solve complex problems such as prime factorization. A quantum computer threatens the security of public key encryption systems because these systems rely on the fact that prime factorization is computationally difficult. Errors limit the effectiveness of quantum computers. Because of the exponential nature of quantum com puters, simulating the effect of errors on them requires a vast amount of processing and memory resources. In this paper we describe a parallel simulator which accesses the feasibility of quantum computers. We also derive and validate an analytical model of execution time for the simulator, which shows that parallel quantum computer simulation is very scalable.
Optimization by Firefly with Predation for Ensemble Precipitation Estimation Using BRAMS

The precipitation is predicted by using a weighted combination of different convective parameterization schemes. The best prediction estimation is obtained by calculating the appropriated weights for the ensemble of parameterizations. The weight identification process is a type of inverse problem: parameter estimation. The inverse solution is computed by minimizing the functional of square difference between observations (measured precipitation) and precipitation calculated by a mathematical model (BRAMS model). The optimization problem is solved by a new meta-heuristic: Firefly algorithm with predation (FAP), where the best set for ensemble weights is found. The method is applied to the BRAMS (Brazilian developments on the Regional Atmospheric Model System) over South America during January 2006.
Ethnopharmacological study of flavonoid compounds in Magnolia champaca (L.) Baill. ex Pierre as anti-inflammatory agents by molecular docking

Context: Magnolia champaca (L.) Baill. ex Pierre has traditionally been used by the culture to prevent and cure the inflammatory disease. Aims: To determine the benefit of M. champaca in the local community, especially in the treatment of tuberculosis, and investigate the potency of the flavonoid content of M. champaca as an anti-inflammatory agent through in silico analysis. Methods: An ethnobotanical survey was conducted by structured interviews and responses in the Pamekasan district. The identification of flavonoid in selected plant was carried out from literature. Then, quercetin, (-)-epicatechin, and kaempferol were docked with protein targets including cyclooxygenase-2 (COX-2), mitogen-activated protein kinases (p38 MAPK), nuclear factor kappa B (NF-κB), and phosphoinositide 3 kinases (PI3k). The ability of complex compounds was considered dependent on energy binding and the ability to bind native ligand to proteins. Results: M. champaca exhibited the highest RFI value, indicated that this plant mainly used to treat tuberculosis symptoms in the local community. The compounds of quercetin and (-)-epicatechin can only be bound to a native ligand COX-2, NAG. The compounds quercetin, rutin, kaempferol, and (-)-epicatechin can then be bound to both the native proteins NF-κB and PI3K. Nevertheless, native ligand-protein p38 MAP-kinases cannot be bound by complex compounds like quercetin, rutin, kaempferol, and (-)-epicatechin. Conclusions: The research offers proof for considering the flavonoid compound in M. champaca as a beneficial ligand complex throughout the treatment and prevention of inflammatory diseases. Further in vitro and in vivo studies could prove its therapeutic potential.
Radiocarbon estimates of age and growth for a dominant Amazon palm species

We used destructive sampling to assess 14C‐calibrated age and growth of five individuals of Oenocarpus bataua from 7.35 to 21.6 m of total height. The largest individual was 59‐year‐old. Age decreased from the collar to the top of the aboveground stem and was positively correlated with number of leaf‐scars and height.
Effects of a Novel Gel Formulation of Dog Appeasing Pheromone (DAP) on Behavioral and Physiological Stress Responses in Dogs Undergoing Clinical Examination

Simple Summary Safeguarding the health of dogs presupposes undergoing regular veterinary visits (VVs). However, the VV can be emotionally challenging, thus impairing the welfare of the patient, also reducing dog and owner compliance. The fear and anxiety experienced by the dogs evokes behavioral and physiological stress responses for adaptive purposes. It is therefore desirable to lower the stress from the visit. With this intent, we tested a gel formulation of a synthetic analogue of the appeasing pheromone secreted by bitches, the dog appeasing pheromone (DAP), as a situational support to improve the perception of the stay in the waiting room and the physical examination. In the waiting room, the dogs exposed to DAP exhibited changes in their behavior, namely significant decrease in lip licking, increase in panting, and nearly significant reduction of low body postures. On the examination table, neither behavioral nor physiological differences were found. DAP did not alter markedly the stress behavior and physiology of dogs during a VV, maybe due to a stress level exceeding the potential efficacy of the product. However, the change of a few stress-associated behaviors suggests that DAP could contribute to improving the welfare of dogs staying in the waiting room before the physical examination. Abstract The veterinary visit is necessary for safeguarding the health of dogs, but it can be stressful and threaten both the welfare of the patient and the accuracy of the examination. This randomized, triple-blind, placebo-controlled, crossover study aims at evaluating how dog appeasing pheromone (DAP) in a novel gel formulation influences the behavioral and physiological stress responses of 28 dogs undergoing a standardized clinical examination, while staying in the waiting room (WR) and visited in the examination room (ER). Behavioral responses were studied through behavioral categories and subjective scales (WR and ER). Autonomic response considered heart rate (WR and ER), blood pressure (WR and ER), respiratory rate (ER), and rectal temperature (ER). Neuroendocrine response considered salivary cortisol (WR and ER). In the waiting room, the use of DAP was associated with a significant reduction of lip licking (p = 0.0189), an increase in panting (p = 0.0276), and a reduction close to significance (p = 0.0584) of low body postures. No significant differences were observed within the physiological responses. In the examination room, neither behavioral nor physiological differences were found.
Adaptive Codebook-Based Channel Estimation in OFDM-Aided Hybrid Beamforming mmWave Systems

In order to reduce the hardware complexity and cost of mmWave transceivers, hybrid beamforming techniques have been developed, which rely on the channel state information (CSI) available to the receiver and/or transmitter. In mmWave channel estimation, the compressed sensing (CS)-based algorithms like orthogonal matching pursuit (OMP) have been widely studied to take the advantages of the sparse characteristics of mmWave channels. Specifically, the OMP-assisted adaptive codebook channel estimation has the merit of reduced implementation complexity, but it performs undesirably in low signal to noise ratio (SNR) scenarios. To circumvent this problem, in this paper, we develop an improved adaptive codebook channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) mmWave systems, which enhances the estimation performance by exploiting the multi-carrier signals for joint decision making. Our studies show that the proposed channel estimation is capable of significantly improving the estimation accuracy at low SNR, while enjoying a low complexity for implementation.
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale

Abstract. In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multilayer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in large-scale hydrological models. The evaluation of the accuracy of these model outputs is challenging because (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions: the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater–soil and groundwater–river exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional-scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100–250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that grid resolution is the main factor that affects water table depth bias because it smooths river incision, while pumping affects time fluctuations. Finally, we use the model to assess the impact of groundwater-based irrigation pumping on evapotranspiration, groundwater recharge, and water table observations from boreholes.
Analysis of an existing experiment on the interaction of acoustic waves with a laminar boundary layer

The hot-wire anemometer amplitude data contained in the 1977 report of P. J. Shapiro entitled, ""The Influence of Sound Upon Laminar Boundary'' were reevaluated. Because the low-Reynolds number boundary layer disturbance data were misinterpreted, an effort was made to improve the corresponding disturbance growth rate curves. The data are modeled as the sum of upstream and downstream propagating acoustic waves and a wave representing the Tollmien-Schlichting (TS) wave. The amplitude and phase velocity of the latter wave were then adjusted so that the total signal reasonably matched the amplitude and phase angle hot-wire data along the plate laminar boundary layer. The revised rates show growth occurring further upstream than Shapiro found. It appears that the premature growth is due to the adverse pressure gradient created by the shape of the plate. Basic elements of sound propagation in ducts and the experimental and theoretical acoustic-stability literature are reviewed.
Multiwavelength-emitting InGaN quantum wells on convex-lens-shaped GaN microstructures

We fabricated InGaN quantum wells on GaN microlens structures by employing a thermal reflow method. The peak emission wavelengths shift from ∼490 nm on the top of the microstructure to ∼400 nm on the bottom. This variation is attributed to the In composition distribution caused by the continuously changing off-angles from the (0001) plane. The obtained results suggest that not only stable planes exposed in microstructures by selective area growth technique but also unstable planes can be useful for InGaN-based microstructures with multiwavelength emission properties.
Disproof of the no-Communication Theorem by Decoherence Theory

The No-communication Theorem has been seen as the bar to communication by quantum state collapse. The essence of this theory is the procedure of taking the partial trace on an entangled, hence inseparable multi-particle system. This mathematical procedure applied unthinkingly, strikes out the off-diagonal elements from the ensemble density matrix and renders the reduced trace matrix representative of a mixed state. Decoherence theory is able to justify this mathematical procedure and we review it to show: the partial trace results for both unitary and non-unitary processes (hence measurement) on one, several or all particles of the ensemble; and that a unitary process keeps interference terms in the trace reduced matrix.
Assessment of the Effects of Chitosan, Chitooligosaccharides and Their Derivatives on Lemna minor

Chitosan, chitooligosaccharides and their derivatives’ production and use in many fields may result in their release to the environment, possibly affecting aquatic organisms. Both an experimental and a computational approach were considered for evaluating the effects of these compounds on Lemna minor. Based on the determined EC50 values against L. minor, only D-glucosamine hydrochloride (EC50 = 11.55 mg/L) was considered as “slightly toxic” for aquatic environments, while all the other investigated compounds, having EC50 > 100 mg/L, were considered as “practically non-toxic”. The results obtained in the experimental approach were in good agreement with the predictions obtained using the admetSAR2.0 computational tool, revealing that the investigated compounds were not considered toxic for crustacean, fish and Tetrahymena pyriformis aquatic microorganisms. The ADMETLab2.0 computational tool predicted the values of IGC50 for Tetrahymena pyriformis and the LC50 for fathead minnow and Daphnia magna, with the lowest values of these parameters being revealed by totally acetylated chitooligosaccharides in correlation with their lowest solubility. The effects of the chitooligosaccharides and chitosan on L. minor decreased with increased molecular weight, increased with the degree of deacetylation and were reliant on acetylation patterns. Furthermore, the solubility mainly influenced the effects on the aqueous environment, with a higher solubility conducted to lower toxicity.
Ageism and employee silence: the serial mediating roles of work alienation and organizational commitment

ABSTRACT Ageism is a common phenomenon in the workplace, despite being unethical. Although previous studies have explored the many negative effects of ageism on employees, employee silence has rarely been empirically tested as a negative outcome. Therefore, we explored the positive relationship between ageism and employee silence and its underlying mechanism. A total of 416 working adults completed two time-lagged surveys, with items measuring ageism, work alienation, organizational commitment, and employee silence, administered four weeks apart. The results indicate that work alienation and organizational commitment have serial mediating effects on the relationship between ageism and employee silence. Ageism led to employees experiencing more work alienation, leading to harmful consequences for their organizational commitment and to employee silence. We discuss the theoretical and practical implications of our findings in this paper.
Pengambilan Keputusan Dalam Pemilihan Mesin Filler Syrup Dengan Metode AHP-TOPSIS Pada PT X

PT X is a pharmaceutical company and one of its products is cough syrup. The company wants to install a new filler machine to support their business development. There are 4 machine brands will be chosen by the company which are ITM, 3CR, IMA, MCS. This research focuses on filler machine selection for the production line. Research objectives are to determine the priority criteria and the best alternative filler machine. Using AHP-TOPSIS method where AHP method is used for weighting the assessment criteria while TOPSIS method is to sort alternative machines. The company decides there are 5 assessment criteria which are machine price, filling accuracy, filling speed, lead time availability and maintenance team; where the highest weight on maintenance team with 0,34. Based on TOPSIS method, the best alternative machine comes from the IMA brand with preference value of 0,9084. Sensitivity analysis shows machine price and filling speed could affect the selected machine. This research conclusion is AHP-TOPSIS method can be used as a decision-making framework in machine selection and the IMA brand is decided to be installed on the production line.
Mass and Heat Transfer at the Bottom of Cylindrical Unbaffled Stirred Tank Reactor and Possible Applications

Liquid-solid mass and heat (by analogy) transfer at the bottom of a cylindrical stirred vessel were studied by using the well-known electrochemical technique. Variables studied included the impeller geometry and its rotational speed. Results revealed that the radial flow impeller is preferable over the axial flow one in terms of mass transfer enhancement, additionally, increasing the impeller rotational speed led to an increase in the mass transfer coefficient. Results were correlated by dimensionless equations. Application of these equations in the design and operation of an improved catalytic biochemical reactor suitable for conducting diffusion-controlled immobilized cell (or enzyme) reactions was highlighted. These equations can serve in predicting the corrosion rate and corrosion allowance required to calculate the bottom thickness of the agitated vessel in its design stage. Furthermore, these equations can be used in estimating the heat lost from the tank bottom, accordingly, design an effective cooling jacket surrounding the agitated vessel bottom in case of exothermic biochemical reactions to avoid thermal degradation of the biomass.
On the singular limit of solutions to the CIR interest rate model with stochastic volatility

In this paper we are interested in term structure models for pricing zero coupon bonds under rapidly oscillating stochastic volatility. We analyze solutions to the generalized Cox-Ingersoll-Ross two factors model describing clustering of interest rate volatilities. The main goal is to derive an asymptotic expansion of the bond price with respect to a singular parameter representing the fast scale for the stochastic volatility process. We derive the second order asymptotic expansion of a solution to the two factors generalized CIR model and we show that the first two terms in the expansion are independent of the variable representing stochastic volatility.
EEG Correlation Coefficient Change with Motor Task Activation Can Be a Predictor of Functional Recovery after Hemiparetic Stroke

Background: Recently, it was reported that the extent of cortico-cortical functional connections can be estimated by the correlation coefficient based on electroencephalography (EEG) monitoring. We aimed to investigate whether the EEG correlation coefficient change with motor task activation can predict the functional outcomes of hemiparetic stroke patients. Methods: Sixteen post-stroke hemiparetic patients admitted to our rehabilitation ward were studied. On admission, EEG recording to calculate the correlation coefficient was performed at rest and during motor task activation. For the analysis of the EEG data, the program software FOCUS (NIHON KOHDEN, Japan) was used. The motor function of paretic limbs was evaluated with the Fugl–Meyer Assessment (FMA) on admission and 4 weeks after admission. Results: Significant increases in the correlation coefficient with motor task activation were noted in C3-F3 or C4-F4, C3-F7 or C4-F8, and F3-F7 or F4-F8 of the lesional hemisphere. Among them, the rate of the correlation coefficient change in F3-F7 or F4-F8 in the lesional hemisphere was significantly correlated with the rate of the upper-limb FMA score change. Conclusion: The extent of the EEG correlation coefficient change with motor task activation in F3-F7 or F4-F8 of the lesional hemisphere may help predict the motor functional outcomes of hemiparetic upper limbs after stroke.
Ex2: Monte Carlo Tree Search‐based test inputs prioritization for fuzzing deep neural networks

Fuzzing is considered to be an essential approach to guarantee the reliability of deep neural networks (DNNs) based systems. The DNN fuzzing leverages various inputs prioritization methods to guide the testing process. The current research mainly focus on constructing testing metrics that symbolize the logical representation of the DNN to guide the generation of test cases, which neglects the potential performance brought by implementing heuristic algorithm. Moreover, the straightforward implementation of queue structure can not represent the metamorphic relationships between generated inputs in DNN fuzzing. Therefore, developing the appropriate heuristic algorithm‐based inputs prioritization method is critical to improve the performance of DNN fuzzers. In this paper, we propose a Monte Carlo Tree Search (MCTS) based inputs prioritization method called E x 2 $E{x}^{2}$ (Exploration and Exploitation) that formulates DNN testing exploration as the sequential decision process. The technique introduces an innovative tree‐structure design that schedules inputs from the statistical perspective. Different from traditional DNN testing, the batch pool is maintained in the form of nodes in MCTS. The links between nodes precisely represent the metamorphic relationship between input batches, which indicates the potential value for in‐depth search. Furthermore, a novel simulation mechanism is implemented to adapt MCTS in DNN testing, which attain better coverage feedback. The effectiveness of our method is comprehensively investigated on six popular deep learning models from LeNet and VGG families. The comparison experiments are conducted between DeepHunter, TensorFuzz, and DeepSmartFuzzer to demonstrate efficacy on various testing metrics. The experimental results show that the E x 2 $E{x}^{2}$ significantly enhance the coverage gain of DNN fuzzing up to 30% against the best performance in comparison groups.
A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning Within a Test

The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test.