Miracle-dz/UniFAR-FTUs huggingface dataset:
THE EFFECT OF LEADERSHIP BEHAVIOUR AND REWARD SYSTEM ON THE EMPLOYEE PERFORMANCE MEDIATED BY ORGANIZATIONAL CITIZENSHIP BEHAVIOUR OF PT DANPAC PHARMA

The purpose of this research is to understand and explain the effect of Leadership Behavior and Reward System on Employee Performance is mediated by Organizational Citizenship Behavior for Employees PT Danpac Pharma. The method used is quantitative approach, where the population in this study were 305 employees of PT Danpac Pharma. Engineering sample selection is done by taking a sample saturated to permanent employees as much as 146 respondents. Questionnaire survey instrument used data collection techniques and have been submitted to the employees of PT Danpac Pharma. Data of this study, which analyzed using structural equation models (SEM) 3.2. 8 SmartPLS software program. This study reveaks that Leadership Behaviour and Reward System have a significant positive effect on Employee Performance mediated by Organizational Citizenship Behaviour both partially and simultaneously. In addition, the Leadership Behavior has a positive and significant effect on Organizational Citizenship Behavior and Reward System has a positive and significant effect on Organizational Citizenship Behavior. Leadership Behavior has a positive and significant effect on Employee Performance and Reward System has a positive and significant effect on Employee Performance.
Creep behaviour of a graphene-reinforced epoxy adhesively bonded joint: experimental and numerical investigation

ABSTRACT In this paper, the creep behaviour of aluminium to aluminium adhesively bonded joints reinforced with different amount of graphene nano-platelets (up to 0.5 wt %) was investigated experimentally and numerically. The mechanical properties of the neat and reinforced epoxy adhesive, as well as their creep behaviour were obtained by tensile tests and creep tests in three stress levels, respectively. The nonlinear Norton-Bailey creep model was used to obtain the creep behaviour of the neat adhesive. The effect of graphene content, up to 0.5 wt%, on the creep behaviour of the reinforced adhesive was also simulated by a new function, called graphene function. It was observed that the presence of graphene up to 0.5 wt% continuously reduced the creep strain of the epoxy adhesive. In the next step, the aluminium to aluminium single lap joints (SLJs) bonded by the neat and reinforced adhesive were subjected to simple tensiles and creep tests. It was observed that the optimum graphene content to increase the tensile strength and the creep resistance of the SLJs were 0.1 wt% and 0.25 wt%, respectively. Finally, the creep behaviour of the SLJs with various graphene content was analysed by finite element method (FEM) using the new creep model developed in this paper. The results show that the numerical creep displacement of the SLJs boned by the neat and graphene reinforced adhesive up to 0.25 wt% are in good agreement with experimental ones.
Optimization of microwave assisted hydrodistillation of essential oil from lemon (Citrus aurantifolia) leaves: Response surface methodology studies

In this paper, microwave-assisted hydro-distillation (MAHD) of essential oils from the lemon (Citrus aurantifolia) leaves was attempted and optimized. Optimization of MAHD was performed by using response surface methodology. In the optimization, selected parameters consisted of water and material ratio, microwave power and extraction time were. It is also indicated that quadratic polynomial model could be employed to optimize the microwave extraction of essential oil from Lemon (Citrus aurantifolia) leaves. The optimal extraction conditions included microwave power of 523.89W, water to material ratio of 3.27 mL/g, and extraction time of 84.47 minutes. The optimized yield (0.76%) approached predicted yield predicted by the model, implying that the model is suitable to predict behavior of the process.
Climate Change and Thermoregulatory Consequences of Activity Time in Mammals

Activity times structure the thermal environments experienced by organisms. In mammals, species shift from being nocturnal to diurnal and vice versa, but the thermal consequences of variable activity patterns remain largely unexplored. Here we used theoretical thermoregulatory polygons bounded by estimates of basal metabolic rates (BMR), maximum metabolic rates (MMR), and thermal conductance (C) in small mammals to explore the metabolic consequences of exposure to global-scale daytime and nighttime temperatures. Model predictions indicated higher metabolic scope for activity for nocturnal species at low latitudes and that reduced minimum C and larger body size increased the geographic range in which nocturnality was advantageous. Consistent with predictions, within rodents nocturnal species have low C. However, nocturnal mammals tend to be smaller than diurnal species, likely reflecting the importance of additional factors driving body size. Projections of warming impacts on small mammals suggest that diurnal species could lose habitable space globally. Conversely, warming could lift cool temperature constraints on nocturnal species and increase habitable space, suggesting that a shift toward nocturnal niches might be favored in a warming world. Taken together, these findings demonstrate the importance of energetic considerations for endotherms in managing global change impacts on nocturnal and diurnal species.
Reformulation of Productivity Improvement Model for Public Sector Automobile Rebuild Organizations

Public sector automobile rebuild industry contributes significantly towards national economy in developing countries. Nonetheless, productivity and output of public sector rebuild organizations remains very low despite huge budgetary disbursements. On the contrary, private sector organizations achieve high productivity and output as these organizations work to maximize their profit. This paper attempts to identify reasons for low productivity and reduced output of public sector automobile rebuild organizations of Pakistan. Input in this research was sought primarily from top, middle and lower management of public sector automobile rebuilt organizations. Data from a total of two hundred and sixty-two respondents representing twenty-one different automobile rebuild organizations was compiled through quantitative survey and analyzed statistically through SPSS. Results implied that public sector automobile rebuild organizations due to certain peculiar characteristics possess low productivity and essentially require a distinct productivity improvement model. Based on the shortcomings/inadequacies identified through literature review, qualitative and quantitative analysis, an inclusive productivity enhancement model for public sector automobile rebuild organizations was reformulated. Proposed model when implemented holistically in rebuild organizations can produce positive results whereas implementation of proposed practices in piecemeal/isolation may not accrue true and optimal outcomes. The findings of this research will facilitate top and middle management to enhance productivity and output of public sector automobile rebuild organizations of developing countries in general and Pakistan in particular.
Use of Neural Models of Proliferation and Neurite Outgrowth to Screen Environmental Chemicals in the ToxCast Phase I Library

Abstract The U.S. Environmental Protection Agency's ToxCast program aims to develop rapid and cost-effective toxicity testing approaches. As part of this effort, we screened the ToxCast Phase I library of 309 chemicals using previously developed and optimized high-content imaging and analysis assays for proliferation in human ReNcell CX neuroprogenitor cells and neurite outgrowth in PC12 cells. Cytotoxicity was determined concurrently in both models. For the initial screening, cells were exposed in triplicate to chemicals at 40 μM for 24 (proliferation) or 96 hr (neurite outgrowth). Chemicals were deemed active if effects on any endpoint were >3×the standard deviation of control means. A total of 130/309 chemicals (42.1%) altered at least one of the four endpoints examined. In ReNcell CX cells, 126 (41%) of chemicals were active, and 46 (15%) were active in PC12 cells. In ReNcell CX cells, 63 chemicals selectively inhibited proliferation. In PC12 cells, only four chemicals (methyl isothiocyanate, phosalon.
CORONAVIRUS DISEASE 2019 PANDEMIC: EPIDEMIOLOGY, PATHOGENESIS AND CHALLENGES

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered SARS-CoV-2 virus. The ongoing pandemic of Coronavirus disease 2019 has rigorous affects worldwide. It is caused by severe acute respiratory syndrome corona virus 2. Till date, more than 190 countries have been affected. Approximately, half million people are suffering from the disease worldwide at the moment. Over 25000 deaths have been reported due to this pandemic. Although the death rate is about 4.5%, yet immunocompromised patients and people incubating other infections are more vulnerable and prone to COVID-19. Treatment and vaccine against this virus are in development phase. Different regimes of treatment have been tried with varying degree of success under different circumstances. Health awareness, hygiene practices, strict precautionary measures and quarantine are needed to hamper the spread of disease.
ANALIZA GENETSKE RAZNOLIKOSTI "LOVRANSKOG MARUNA" ( Castanea sativa Mill.) KORIŠTENJEM MIKROSATELITNIH BILJEGA

Maruni (maroni) su sorte europskog pitomog kestena (Castanea sativa Mill.) dobivene selekcijom, koje se od davnina uzgajaju radi proizvodnje krupnih i kvalitetnih plodova. Maruni su u Hrvatskoj sađeni na privatnim posjedima istocnih padina Ucke, u okolici Lovrana i poznati su pod nazivom "lovranski marun". Do sada nije bilo znanstvenih istraživanja lovranskog maruna te nije poznato s kojim su biljnim materijalom nasadi podignuti, odnosno koliko je razlicitih genotipova zastupljeno. Ta saznanja kljucna su za sve daljnje korake koje treba poduzeti kako bi se ocuvali postojeci genetski izvori. Cilj ovoga istraživanja bila je analiza genetske raznolikosti stabala lovranskog maruna u postojecim nasadima, koristenjem mikrosatelitnih biljega. Istraživanje je rađeno na uzorku od 72 stabla, koristenjem 5 mikrosatelitnih biljega. Analiza je pokazala prisutnost 11 multilokusnih genotipova, sto govori u prilog raznovrsnosti i bogatstvu svojti pitomog kestena na lovranskom podrucju, koje jos uvijek nisu taksonomski određene, a vode se pod kolektivnim nazivom "lovranski marun". Vecina uzorkovanih stabala, 58, pripada istom genotipu, sto se može tumaciti staticnoscu u smislu introdukcije novih svojti na istraživano podrucje i forsiranjem, tj. ekstenzivnim uzgojem.
Nonemptiness Problems of Wang Cubes with Two Colors

This investigation studies the nonemptiness problems of Wang cubes with two colors. Wang cubes are unit cubes with colored faces, which are generalized from Wang tiles. For a set B of Wang cubs, Σ(B) is the set of all global patterns on Z that can be constructed by the cubes in B. The nonemptiness problem is to determine whether Σ(B) 6= ∅ or not. Denote by P(B) the set of all periodic patterns on Z that can be constructed by the cubes in B. For Wang cubes, the corresponding Wang’s conjecture is that if Σ(B) 6= ∅, then P(B) 6= ∅. We introduce the transition matrices and trace operators to determine whether Σ(B) 6= ∅ and P(B) 6= ∅ or not, respectively. A basic set B is called a minimal cycle generator if P(B) 6= ∅ but P(B′) = ∅ for all B′ $ B. By computer computation, there exist 86 equivalence classes of minimal cycle generators with two colors. By verifying that the basic sets B that contains no minimal cycle generators satisfy Σ(B) = ∅, we prove that the Wang’s conjecture for Wang cubes with two colors is true.
Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations

We consider the problem of automatically learning the dynamics of physical processes evolving in space and time from incomplete observations. This is a central problem in many fields that remains complicated for large observation spaces and complex dynamics. We propose a data-driven framework, where the system’s dynamics are modeled by an unknown time-varying differential equation and the evolution term for the state is estimated from the partially observed data only, using a deep convolutional neural network. Our method yields improvements w.r. t. state-of-the-art recurrent deep network models for the forecast of observations corresponding to complex fluid dynamics. We analyze the latent state representations learned by this model and propose two settings that help interpret the learned states. The model is evaluated on the incompressible Navier Stokes equations.
A Hip Active Assisted Exoskeleton That Assists the Semi-Squat Lifting

(1) Background: In the case of quick picking and heavy lifting, the carrying action results in a much more active myoelectric signal in the lower back than in an upright stationary one, and there is a high risk of back muscle injury without proper handling skills and equipment. (2) Methods: To reduce the risk of LBP during manual handing tasks, a hip active exoskeleton is designed to assist human manual lifting. A power control method is introduced into the control loop in the process of assisting human transportation. The power curve imitates the semi-squat movement of the human body as the output power of the hip joint. (3) Results: According to the test, the torque can be output according to the wearer’s movement. During the semi-squat lifting process, the EMG (electromyogram) signal of the vertical spine at L5/S1 was reduced by 30–48% and the metabolic cost of energy was reduced by 18% compared the situation of without EXO. (4) Conclusion: The exoskeleton joint output torque can change in an adaptive manner according to the angular velocity of the wearer’s joint. The exoskeleton can assist the waist muscles and the hip joint in the case of the reciprocating semi-squat lifting movement.
Mechatronic robot arm with active vibration absorbers

Serial robots are typically able to cover large workspace, but their mass/stiffness ratio does not allow combining high accuracy and high dynamic of the end effector operations. Widely spread usage of serial robots, even for tasks such as drilling, leads to high accuracy demands through its workspace. Absolute measurement of the end point for position feedback can be challenging due to objects or even a workpiece in the workspace. Moreover, inbuilt motors of the serial robot cannot response in the frequency range high enough as vibration of the end point. Instead, an additional spring–mass system is attached to the robot to suppress vibrations. The narrow frequency range of a passive dynamic absorber can be extended with active elements between the robot and absorber. An active approach is also necessary because of robots eigenfrequencies and eigenmodes variability. The study deals with a planar flexible robot equipped with a three-degree-of-freedom planar active absorber. The absorber is tuned passively to one value of multiple eigenfrequency. The linear-quadratic regulator control with a state observer has been designed as an active absorber control algorithm. Feedback inputs are absorber body acceleration, end effector acceleration, and relative motions in three absorber actuators realized by voice coils. The end effector vibration suppression along the robot trajectory is achieved using gain scheduling of local controller’s outputs.
GLAD Magnetron Sputtered Ultra-Thin Copper Oxide Films for Gas-Sensing Application

Copper oxide (CuO) ultra-thin films were obtained using magnetron sputtering technology with glancing angle deposition technique (GLAD) in a reactive mode by sputtering copper target in pure argon. The substrate tilt angle varied from 45 to 85° and 0°, and the sample rotation at a speed of 20 rpm was stabilized by the GLAD manipulator. After deposition, the films were annealed at 400 °C/4 h in air. The CuO ultra-thin film structure, morphology, and optical properties were assessed by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), X-ray reflectivity (XRR), and optical spectroscopy. The thickness of the films was measured post-process using a profilometer. The obtained copper oxide structures were also investigated as gas-sensitive materials after exposure to acetone in the sub-ppm range. After deposition, gas-sensing measurements were performed at 300, 350, and 400 °C and 50% relative humidity (RH) level. We found that the sensitivity of the device is related to the thickness of CuO thin films, whereas the best results are obtained with an 8 nm thick sample.
Multi-Target Adaptive Reconfigurable Acceleration for Low-Power IoT Processing

Low-power processors for the Internet-of-Things (IoT) demand a high degree of adaptability to efficiently execute applications with different resource requirements under varying scenarios. Current single-ISA heterogeneous Chip Multiprocessors (CMPs), such as ARM's big.LITTLE, provide multiple cores and voltage/frequency levels to address this challenge. However, finding the best possible type of core and the corresponding voltage/frequency level for all the execution scenarios, which involve different applications and phases, remains far from being reached. In this article, we propose extending such a single-ISA heterogeneous CMP with a Coarse-Grained Reconfigurable Array (CGRA) and a hardware-based dynamic binary translation (DBT) module that transparently maps application code onto the CGRA for acceleration. To achieve low-energy levels and efficiently manage the power consumption of the CGRA, we introduce an additional voltage rail that enables operation in the Near-Threshold Voltage (NTV) regime when needed, leveraging key features of the CGRA's structure to address the implementation challenges of NTV computing. For less than 35 percent area overhead to the baseline CMP, performance and energy consumption are improved as follows. Compared to: (a) power-efficient execution in the LITTLE core, MuTARe achieves 29 percent reduction in energy consumption, and <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math><alternatives> <mml:math> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> <inline-graphic xlink:href="brandalero-ieq1-2984736.gif"/></alternatives></inline-formula> speedup; (b) performance-efficient execution in the big core, a speedup of <inline-formula><tex-math notation="LaTeX">$1.6\times$</tex-math><alternatives> <mml:math> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>. </mml:mo> <mml:mn>6</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> <inline-graphic xlink:href="brandalero-ieq2-2984736.gif"/></alternatives></inline-formula> with an energy reduction of 41 percent is achieved.
An FPGA-Based Digital Real-Time Simulator for Hardware-in-the-Loop Testing of Traveling-Wave Relays

This paper presents an FPGA-based Digital Real-Time Simulator (DRTS) platform for Traveling-Wave Relay Testing (TWRT). Traveling-Wave Relays (TWRs) have relatively high sampling rate, as compared to traditional relays, and operate based on the high-frequency response of the power system. Therefore, performing Hardware-In-the-Loop (HIL) tests for a TWR requires i) high resolution, i.e., small simulation time-step and ii) accurate transmission line model to ensure proper replication of traveling waves subsequent to faults. These requirements introduce challenges for DRTS platforms. The envisioned platform in this work comprises an FPGA-based DRTS hosted by a CPU-based platform where the small time-step calculations are conducted by the former. A new state-space realization of the Frequency-Dependent Phase-Domain (FDPD) line model is introduced which enables a fixed hardware design, capable of accommodating its real and imaginary poles. The adopted solution approach enables the parallel solution of the FDPD model and network equations to maximize parallelism and facilitates the use of small time-steps. Performance and accuracy of the proposed DRTS are evaluated and verified against off-line simulation results. The platform is employed for closed-loop HIL testing of a commercial TWR and the results are reported.
Action threshold development in cabbage pest management using synthetic and botanical insecticides

25 As synthetic insecticides can have environmentally detrimental side effects, it is desirable to 27 limit their use while still achieving good marketable yield. One approach is to apply 28 pesticides only when needed, as determined by an action threshold (AT), defined as the 29 number of pests per crop plant or damage intensity at which application is recommended. 30 Another approach is to adopt alternative pesticides, which can also be applied according to 31 ATs. Here, ATs are developed in cabbage pest management using both approaches against the 32 moths Plutella xylostella (L.), Helicoverpa armigera (Hübner) and Spodoptera litura (F.) and 33 the aphid Brevicoryne brassicae (L.). Action thresholds were derived using fixed spraying 34 regimes for the synthetic insecticides (imidacloprid and Voliam Flexi) and for azadirachtin, a 35 neem-derived botanical. Botanical insecticide was as effective as synthetics in suppressing 36 pests and protecting yield. For synthetics, derived ATs are 40 individuals per plant for B. 37 brassicae , 0.3 larvae for P. xylostella and 0.2 medium-sized larvae for H. armigera and for S. 38 litura . For H. armigera and S. litura , negative relationships between marketable yield and 39 pest were found when larvae were medium or large sized, but not when larvae were small. 40 Compared to synthetics, benefits of using neem formulations include higher action thresholds 41 against P. xylostella (0.6/plant) and H. armigera (0.4/plant) and an oviposition deterrent effect 42 against S. litura . Although regional limits may apply to the accuracy of any ATs derived, the 43 approach used towards their establishment is simple and transferable to other agricultural 44 regions and crops. 45
Inferential Reasoning for Heterogeneous Multi-Agent Mission

 Abstract — We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.
Structural Change in the Trend and Cycle in Korea

On the basis of regression of consumption on output, we develop a new approach to identify a structural break in business cycles. Using this approach, we find that a structural change in the trend and cycle in Korea occurred in 1993 and that the business cycles in Korea started to show the regularities of emerging market business cycles beginning in 1993. To explain these, we estimate the parameters of the productivity process in Korea, using data on Solow residuals. Our estimates show that the relative importance of trend growth shocks in the post-break period increased by a factor of 2.56 from that in the pre-break period.