Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of different antioxidants during in vitro establishment of allspice (Pimenta dioica L. Merrill): a recalcitrant species

Objective: To evaluate the effect of different antioxidant agents during in vitro establishment of allspice (Pimenta dioica L. Merrill). Design/methodology/approach: The effect of different antioxidant agents (Methylene blue, L-cysteine, and silver nanoparticles [AnNPs]) added to Murashige and Skoog culture medium at different concentrations were studied during axenic establishment of P. dioica. A completely randomized experimental design was used. All trials were performed in triplicate. The percentage of survival, oxidation, contamination was determined, the phenols content, antioxidant capacity and lipid peroxidation. Results: The highest survival occurred with the addition of L-cysteine. The lowest percentages oxidation were observed in explants treated with L-cysteine. Treatments with 100 and 200 mg L-1 AgNPs had the lowest contamination values. L-cysteine and 50 and 100 mg L-1 AgNPs resulted in an increase in the content of soluble phenols. The highest contents of cell wall-linked phenols were obtained in treatments with 200 mg L-1 methylene blue, L-cysteine, and 200 mg L-1 AgNPs. In this study, all treatments had a reaction of scavenging/reduction mechanisms free radicals. The highest content of malondialdehydes was observed in the control treatment and 200 mg L-1 methylene blue. The highest content of malondialdehydes was observed in the control treatment and 200 mg L-1 methylene blue. Limitations on study/implications: The highest percentage of oxidation was observed in the control treatments, 100 and 200 mg L-1 methylene blue, causing cell death. Findings/conclusions: The addition of L-cysteine to the culture medium is alternative to reduce oxidation during in vitro introduction of P. dioica.
Investment decisions in digital sukuk in the time of COVID-19: do tax incentives matter?

ABSTRACT Although the sukuk market has maintained remarkable growth momentum over the recent years, the optimism has been significantly moderated by the abrupt shock due to the pervasive COVID-19 pandemic. However, sukuk can be used as an effective financing option by governments to overcome a fiscal deficit and to support those adversely affected by the pandemic. Sukuk Prihatin (SP), the first-ever digital sukuk issued by the Government of Malaysia, has launched to engage citizens to contribute to the country's recovery efforts in the wake of COVID-19. Therefore, this study aims to probe the motivation that influences retail investors’ inclination to invest in such innovative sukuk. Based on an integrated model of planned behavior (TPB) and social cognitive theories (SCT) and data gathered from 279 retail investors, this research found that attitude towards SP investment (SPI), social norms, perceived control regarding SPI, sukuk features and digitization are significant determinants of investors’ willingness to invest in SP. It also revealed that tax incentives-moderated interactions of social norms, perceived control and sukuk features on SPI intention are significant.
25 kV AC Railways Overhead Contact System Protection - A Comparison Between the Traditional Approach and Alternative Low Cost Solution

In contrast to the early days of 25 kV AC railway electrification these days new electric traction systems are usually equipped with advanced supervisory control and data acquisition (SCADA) systems as well as intelligent protection devices. Those protection devices come with communication interfaces, permit data acquisition as well as remote parameter setting according to operational needs. By utilising the modern features of the protection and SCADA systems the number of circuit breakers and associated protection equipment may be reduced significantly without compromising safety and reliability of the traction power supply. This paper compares various aspects of overhead contact system protection in the traditional 25 kV AC electric traction system against an alternative solution. Pros and cons of each approach are discussed and potential future developments are proposed.
Mucormycosis with extensive cranial nerve involvement as the first presentation of diabetes mellitus: A case report

Mucormycosis, a rare fungal infection, mainly affects individuals with diabetes mellitus and those who were immunocompromised and has a high mortality rate. Its most common presentation is similar to that of acute bacterial sinusitis with symptoms of nasal congestion, headache, and fever. The involvement of multiple cranial nerves in mucormycosis was rarely reported in the literature and indicates severe disease. Herein, we report the case of a 56-year-old man who was referred to the ophthalmology outpatient clinic for facial nerve palsy. He was treated with systemic steroids for 10 days with no improvement. On examination, he had a loss of vision and a frozen orbit due to involvement of cranial nerves II, III, IV, V, VI, and VII. An extensive workup revealed a hemoglobin A1C of 10%. However, he was never diagnosed with diabetes mellitus previously and denied any of the classical symptoms of diabetes mellitus. He underwent ethmoidectomy, maxillectomy, and drainage of an intraorbital abscess after appropriate imaging studies. Histopathology confirmed the diagnosis of mucormycosis, and the patient was started on systemic amphotericin B. This case emphasizes the importance of screening for diabetes mellitus. Early recognition of underlying diabetes mellitus in this patient may have prevented the development of mucormycosis along with its devastating complications.
Personalisable in-vehicle systems, technology acceptance and product attachment

Personalisable in-vehicle interfaces offer a flexible way to tailor content and appearance of the interface to suit the driver and bridge a number of functional and emotional issues. A questionnaire based on the technology acceptance model (TAM) was handed out to 137 respondents to study whether personalisable vehicle human-machine interfaces (HMIs) would be accepted for use in vehicles and also to investigate whether the emotionally-associated construct product attachment affects the behavioural intention. The main findings indicate that personalisable systems were fairly well accepted for use in vehicles and a system comprised of personalisable modes displayed the highest behavioural intention. Moreover, as personalisation offers a closer, more emotional and personal connection to the product, a person’s attachment to a product was shown to affect the intention to use the product.
Enhanced Trade-Off of Construction Projects: Finance-Resource-Profit

AbstractThe parameters of finance requirements, resource leveling, and anticipated profit have significant influence on many aspects of project management. These parameters interact and occasionally conflict with each other. Accordingly, achievement of a balance between these three parameters is crucial to ensure the accomplishment of project objectives. A multi-objective multimode scheduling optimization algorithm is proposed to establish the optimal trade-off between these three parameters. The strength Pareto evolutionary algorithm (SPEA) was implemented to obtain the solutions comprising the Pareto-optimal trade-off. The developed SPEA was validated by reproducing identical results of a time/cost trade-off problem in the literature. The developed SPEA was used to obtain the Pareto-optimal trade-off of a network of nine multimode activities that comprised fifty solutions. The trade-off of fifty solutions allows decision makers explore the impact of finance upon the efficiency of resource utilization an.
Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the inputs of others. How to offload these tasks to the network edge is a vital and challenging problem which aims to determine the placement of each running task in order to maximize the Quality-of-Service (QoS). Most of the existing studies either design heuristic algorithms that lack strong adaptivity or learning-based methods but without considering the intrinsic task dependency. Different from the existing work, we propose an intelligent task offloading scheme leveraging off-policy reinforcement learning empowered by a Sequence-to-Sequence (S2S) neural network, where the dependent tasks are represented by a Directed Acyclic Graph (DAG). To improve the training efficiency, we combine a specific off-policy policy gradient algorithm with a clipped surrogate objective. We then conduct extensive simulation experiments using heterogeneous applications modelled by synthetic DAGs. The results demonstrate that: 1) our method converges fast and steadily in training; 2) it outperforms the existing methods and approximates the optimal solution in latency and energy consumption under various scenarios.
Ignition and fire spread thresholds in gorse (Ulex europaeus)

Field experiments were carried out in stands of gorse (Ulex europaeus L.) in New Zealand to determine the conditions under which fires would both ignite and spread. Research and operational experience in shrub fuels suggest that there is a clear difference between conditions that support ignition only (fuel ignites but does not spread beyond a single bush or clump) and conditions that are conducive to fire spread (fuel ignites and develops into a spreading fire). It is important for fire management agencies to be equipped with knowledge of these thresholds, because the different conditions require different levels of preparedness and response. Results indicate that the major variable influencing both fire ignition and fire spread development in gorse is the moisture content of the elevated dead fine fuel layer. Fires were observed to spread successfully in this elevated fuel layer only, independently of the surface fuels and the near-surface fuels. Elevated dead fuels failed to ignite at a moisture content of greater than 36%, and ignition only resulted in a spreading fire at moisture contents below 19%. The results correlate well with field observations and fire practitioners’ experience in these fuels, and provide reliable guidelines for fire management planning.
2-Phase Synchronous Buck Controller Delivers Maximum Features in Minimum Footprint

The LTC3850’s two channels run out of phase, which reduces the input RMS current ripple and thus the input capacitance requirement. Switching frequency can be adjusted from 250kHz to 780kHz, either set with a voltage on the FREQ/PLLFLTR pin, or synchronized to a signal into the MODE/PLLIN pin using a phaselocked loop. During high frequency operation, the LTC3850 can operate normally at low duty cycles due to its short top switch minimum on-time. For example, a 20V to 1.5V converter operating at 700kHz requires a minimum on-time of less than
Magnetic Domain State and Anisotropy in Hematite (α‐Fe2O3) From First‐Order Reversal Curve Diagrams

Hematite carries magnetic signals of interest in tectonic, paleoclimatic, paleomagnetic, and planetary studies. First‐order reversal curve (FORC) diagrams have become an important tool for assessing the domain state of, and magnetostatic interactions among, magnetic particles in such studies. We present here FORC diagrams for diverse hematite samples, which provide a catalog for comparison with other studies and explain key features observed for hematite. Ridge‐type signatures typical of uniaxial single‐domain particle assemblages and “kidney‐shaped” FORC signatures, and combinations of these responses, occur commonly in natural and synthetic hematite. Asymmetric features that arise from the triaxial basal plane anisotropy of hematite contribute to vertical spreading in kidney‐shaped FORC distributions and are intrinsic responses even for magnetostatically noninteracting particles. The dominant FORC distribution type in a sample (ridge, kidney‐shaped, or mixture) depends on the balance between uniaxial/triaxial switching. The identified signals explain magnetization switching and anisotropy features that are intrinsic to the magnetic properties of hematite and other materials with multiaxial magnetic anisotropy.
Study on Dynamics and Cluster Analysis between Carbon-based Nanoparticles and Amino Acids

We research the interaction between six representative carbon-based nanoparticles (CBNs) and 20 standard amino acids through all-atom molecular dynamics simulations. The six carbon-based nanoparticles are fullerene(C60), CNT55L3, CNT1010L3, CNT1515L3, CNT2020L3, and two-dimensional-graphene(Graphene33). Their curvatures decrease sequentially, and all of CNT are single-walled carbon nanotubes. We have observed that as the curvature of CBNs decreases, the adsorption effect of 20 amino acids with them has an increasing trend. In addition, we also used multi-dimensional clustering to analyze the adsorption effects of 20 amino acids on six carbon-based nanoparticles. We observed that the π-π interaction still plays an extremely important role in the adsorption of amino acids on carbon-based nanoparticles. Individual long-chain amino acids and “Benzene-like” Pro also have a strong adsorption effect with carbon-based nanoparticles.
4.4.3 It's the people that matter – applying a Systems Engineering approach to process improvement

This paper describes approaches taken within the UK Warship Support Agency (WSA) and BAE SYSTEMS to implement improved Systems Engineering processes throughout complex supply chains. The supply chains needed to develop and support complex products, such as large aircraft or warships, require large teams to collaborate across organisational and cultural boundaries.
Dwell Motion From Spatial Linkages

The potential of single-loop spatial linkages to produce an output dwell from a rotating input is broadly surveyed. Then three comparatively simple four-bar linkage arrangements are selected, and studied in detail. The dwell characteristics of all three are viewed via surface-surface or surface-curve “matching” at a spherical pair. Attention is mainly confined to symmetric output-input motions, and the accuracy of dwell is assessed.
AI Models for Green Communications Towards 6G

Green communications have always been a target for the information industry to alleviate energy overhead and reduce fossil fuel usage. In the current 5G and future 6G eras, there is no doubt that the volume of network infrastructure and the number of connected terminals will keep exponentially increasing, which results in the surging energy cost. It becomes growing important and urgent to drive the development of green communications. However, there is no doubt that 6G will have increasingly stringent and diversified requirements for Quality of Service (QoS), security, flexibility, and intelligence, all of which challenge the improvement of energy efficiency. Moreover, the dynamic energy harvesting process, which will be widely adopted in 6G, further complicates the power control and network management. To address these challenges and reduce human intervention, Artificial Intelligence (AI) has been extensively recognized and acknowledged as the only solution. Academia and industry have conducted extensive research to alleviate energy demand, improve energy efficiency, and manage energy harvesting in various communication scenarios. In this paper, we present main considerations for green communications and survey related research on AI-based green communications. We focus on how AI techniques are adopted to manage networks and improve energy efficiency towards the green era. We analyze how Machine Learning (ML) techniques including state-of-the-art Deep Learning (DL) can cooperate with conventional AI methods and mathematical models to reduce the algorithm complexity and improve the accuracy rate in 6G. Finally, we discuss the existing problems and envision the open research issues of AI models towards green 6G.
An 8-18-GHz YIG-Tuned FET Oscillator

We report here on the design and construction of a YIG- tuned FET oscillator tunable over the entire 8-18-GHz frequency range. The minimum output power from this device operating into a 50-omega load is about +6 dBm. The addition of a balanced buffer amplifier increases the power to about + 12-dBm minimum. When optimized for the 12-18-GHz band, the oscillator alone generates a minimum of + 10 dBm. The oscillator/ amplifier combination produces at least +15 dBm. We discuss a number of difficulties inherent in the design of broad-band oscillators, especially fixed frequency resonances, linearity, and power drop outs at the low end of the frequency range.
Comparison of Liu and two parameter principal component estimator to combat multicollinearity

Biased estimation methods like ridge regression, Liu‐type regression, two‐parameter regression and principal component regression have become very popular in the analysis of applied researches for health, economics, chemometrics, and social sciences in recent years. A dataset in such applied fields tends to be characterized by many independent variables on relatively fewer observations. In addition, there is a high degree of near collinearity among the explanatory variables. It is common knowledge that under these conditions, ordinary least squares estimations of regression coefficients may be very unstable, leading to very poor prediction accuracy. The aim of this article is to examine the performance of the combination of principal components regression and some biased regression estimators such as ridge, Liu and two‐parameter estimators. For this reason, a real‐life application is presented in which different selection methods of the biasing parameters are employed.
Measuring Daphnia life history in the wild: The efficacy of individual field cages

Abstract Life‐history studies are often conducted in a laboratory environment where it is easy to assay individual animals. However, factors such as temperature, photoperiod, and nutrition vary greatly between laboratory and field environments, making it difficult to compare results. Consequently, there is a need to study individual life histories in the field, but this is currently difficult in systems such as Daphnia where it is not possible to mark and track individual animals. Here, we present a proof of principle study showing that field cages are a reliable method for collecting individual‐level life‐history data in Daphnia magna. As a first step, we compared the life history of paired animals reared outside and inside cages to test the hypothesis that cages allow free flow of algal food resources. We then used a seminatural mesocosm setting to compare the performance of individual field cages versus glass jars refilled with mesocosm water each day. We found that cages did not inhibit food flow and that differences in life histories between three clones detected in the jar assays were also detectable using the much less labor‐intensive field cages. We conclude that field cages are a feasible approach for collecting individual‐level life‐history data in systems such as Daphnia where individual animals cannot be marked and tracked.
A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.
Remote sensing and the UN Ocean Decade: high expectations, big opportunities

Remote sensing and the UN Ocean Decade: high expectations, big opportunities Vincent Lecours, Mathias Disney, Kate He, Nathalie Pettorelli, J. Marcus Rowcliffe, Temuulen Sankey & Kylie Scales School of Forest, Fisheries, & Geomatics Sciences, University of Florida, 7922 NW 71st St., Gainesville Florida, 32653, USA Department of Geography, University College London, Gower Street, London WC1E 6BT, UK Department of Biological Sciences, Murray State University, Murray Kentucky, 42071, USA Zoological Society of London, Institute of Zoology, Regent’s Park, London NW1 4RY, UK School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff Arizona, 86011, USA Global-Change Ecology Research Group, University of the Sunshine Coast, Maroochydore Queensland 4556, Australia
Using Carbon Dioxide Concentration to Assess Indoor Air Quality in Offices

Carbon dioxide (CO2) concentration is commonly used as a surrogate indicator for assessing indoor air quality (IAQ) and ventilation efficiency. The measurement and analysis of average indoor CO2 concentration is useful to understand the performance of a ventilation system. However, uncertainties due to the ventilation rate effectiveness as well as the initial state, build-up, fluctuation, and decay of indoor CO2 concentration from occupant load variation have not been addressed in many measurements; considerable measurement efforts are obligatory. This study, taking the long-term CO2 measurement and occupant profile of a typical open-plan office building as reference, has developed a database to examine the probable errors of an alternative sampling scheme for assessing indoor CO2 level in which the average pollutant concentration is obtained from two short sampling periods in two sampling sessions when the building is occupied. The results show that the assessed CO2 level deviates from the long-term average and the probable errors correlate with the required measurement time and occupant load profile. At certain confidence levels, the potential reductions in measurement time of the alternative scheme, as compared with an 8-h continuous one, will be up to 30%, some 20% less than the reduction of measurement time for assessing indoor radon level.
EFFECTS OF 7% MALTODEXTRIN SUPLEMENTS INGESTION DURING AEROBIC ACTIVITY ON MALE HIGH SCHOOL ATHLETE STUDENTS ANAEROBIC INDICIES

The main purpose of this study was to investigate the effects of Maltodextrin 7 % ingestion during an aerobic exercise on the anaerobic indexes in male student athletes. The participants of this study were 30 athlete students that selected randomly. Participant dividend in two experimental groups (Maltodextrin, 15 athletes) and control group (H2o, 15 athletes). In this study Digital scale Seca 767 model, Treadmill Tecno Gym, Calipers for measuring subcutaneous fat layer were used. Descriptive analysis was used for data description, mean and standard deviation. Kolmogorov- Smirnov test was used to determine distribution of data and Levin test was used to homogeneity of variance. Independent t-test was used for data analysis. The results indicate that ingestion of 7% Maltodextrin solution during 70% maximal heart rate (MHR) exercise failed to improve anaerobic performance and power output of the high school students compared to a placebo drink (0.05).