Miracle-dz/UniFAR-FTUs huggingface dataset:
Macro and micronutrients deficiency symptoms of hybrid corn plants BRS 1010.

The nutritional deficiencies on corn culture contribute significantly to the fall in productivity and, consequently, of the farmer gain. It is important to identify the nutritional deficiencies at the beginning of the plant cycle so that providences can be taken in at time to save the harvest. Thus, this objective of this study was to identify the deficiency symptoms of the nutrients nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, boron, manganese, zinc, molybdenum, and copper in hybrid corn BRS 1010. The experimental design was entirely randomized with thirteen treatments and three repetitions, consisting of a total of thirty nine experimental parcels. Each parcel was represented by a Leonard vase contained two plants. The ‘complete’ treatment was represented by plants that received, through of nutrition solution, everybody the essential nutrients to growth and development. The other treatments were formed by subtraction of every one of the essential nutrients, with the others supplied normally. The deficiency symptoms of each macro and micronutrients were recorded during all periods of the experiment. The diagnosis method by subtraction showed to be very efficient to visually evaluate the symptoms of nutritional deficiency in corn.
Exploring Stress and Recovery Among High-Achieving Career Development Event Teams: A Mixed Methods Study

School Based Agricultural Education (SBAE) teachers can experience physiological stress through their various roles and responsibilities, including preparing Career Development Event (CDE) teams. Through CDE participation, students can encounter numerous benefits, but these academic competitions could be catalysts for stress. The purpose of this exploratory case study was to contextualize the experiences of high-achieving SBAE teachers and CDE participants through the lens of stress and resilience. Using a concurrent triangulated mixed methods design, physiological, quantitative data were collected through ambulatory heart rate monitors, and qualitative data were collected through teacher and CDE participant daily journals. We found that teachers encountered stress reactions the majority of the time with little to no recovery, where students experienced a more extensive range of stress reactions and recovery within the quantitative analysis. Both CDE contextspecific and role-specific stress catalysts existed within the qualitative and convergent analysis. SBAE teachers and CDE participants should utilize strategies that address both (a) sleep deprivation and (b) anticipatory and in-action stress to allow for increased cognitive performance and resilience.
A new species of the Gnamptogenys Mordax subgroup (Hymenoptera: Formicidae), with an Identification key to the species within the subgroup

T We describe, Gnamptogenys rugimala n. sp , a very distinct new species of the ectatommine genus Gnamptogenys, from specimens collected in forests of southeastern Brazil. We provide some data about G. rugimala n. sp occurrence and ecology as well as a species identification key for the mordax
Multi-View Consensus Proximity Learning for Clustering

Most proximity-based multi-view clustering methods are sensitive to the initial proximity matrix, where the clustering performance is quite unstable when using different initial proximity matrixes. This problem is defined as the initial value sensitivity problem. Since clustering is an unsupervised learning task, it is unrealistic to tune the initial proximity matrix. Thus, how to overcome the initial value sensitivity problem is a significant but unsolved issue in the proximity-based multi-view clustering. To this end, this paper proposes a novel multi-view proximity learning method, named multi-view consensus proximity learning (MCPL). On the one hand, by integrating the information of all views in a self-weighted manner and giving a rank constraint on the Laplacian matrix, the MCPL method learns the consensus proximity matrix to directly reflect the clustering result. On the other hand, different from most multi-view proximity learning methods, in the proposed MCPL method, the data representatives rather than the original data objects are adopted to learn the consensus proximity matrix. The data representatives will be updated in the process of the proximity learning so as to weaken the impact of the initial value on the clustering performance. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
EFFECT OF OMEGA 3 AND REGULAR EXERCISE ON THE MUSCLE PERFORMANCE: SPECIAL PREVALENCE OF HISTAMINE AND NITRIC OXIDE PRODUCTION

Background: Skeletal muscle fatigue due to unaccustomed hard and/or prolonged physical exercise may be caused by increase of lactic acid, inflammatory mediators and oxidative stress. Stimulation of H1 receptors by histamine and the ensuing production of nitric oxide trigger the vasodilating effect of histamine on arterioles in exercise to supply O2 and nutrients and removal of CO2 and waste products. Objective: Investigating the effect of omega 3 alone or with regular exercise on improving muscle performance and delaying muscle fatigue. Subjects and methods: Forty apparently healthy subjects were studied. Depending on their previous physical conditioning status, subjects were divided into four equal groups: Two sedentary groups (group 1a without omega 3 therapy, and group 1b with omega 3), and two athletes groups (group 2a without omega 3 therapy, and group 2b with omega 3). Exercise tolerance was evaluated for each subject by using a free swimming competition (100 meters). Pulmonary ventilation, vital capacity, respiratory rate and heart rate were estimated before and after competition. Total antioxidant, lipid peroxides, total nitrate and nitrites, lactic acid and histamine levels were determined Results: Omega-treated and/or regular exercise volunteers showed significantly increase of pulmonary ventilation, vital capacity, total antioxidant, nitric oxide (NO) and histamine levels, and significantly decrease of qualifying time competition, respiratory, resting and maximal heart rates, plasma lipid peroxides, and lactic acid compared with corresponding values of volunteers without Omega 3 or regular exercise. The combination of omega 3 plus regular exercise was more effective than each one alone. Conclusions: The results obtained in the present study provided an evidence that daily administration of omega 3 and/or regular exercise improved muscle performance and delayed fatigue through release of histamine and NO during exercise.
Hanno: experimental investigation of Gyoki impacting on the human heart rate variability and the autonomic nervous system

This paper reports on an analysis of body response processes to empty minded gyoki skin contacting by applying methods from experimental physics. To that aim, measuring techniques are being evaluated testing their applicability to eventually enable an in situ, in process monitoring of human body’s response processes to gyoki touch, causing self-healing by bio intelligence. Heart Rate Variability (HRV) testing method of the autonomic nervous system has been tested as a measure of body’s stress level and its ability to recover. It was demonstrated that HRV testing is capable of measuring differences in body response to gyoki and non-gyoki touch.
Generalized Risk-Sensitive Optimal Control and Hamilton–Jacobi–Bellman Equation

In this article, we consider the generalized risk-sensitive optimal control problem, where the objective functional is defined by the controlled backward stochastic differential equation (BSDE) with quadratic growth coefficient. We extend the earlier results of the risk-sensitive optimal control problem to the case of the objective functional given by the controlled BSDE. Note that the risk-neutral stochastic optimal control problem corresponds to the BSDE objective functional with linear growth coefficient, which can be viewed as a special case of the article. We obtain the generalized risk-sensitive dynamic programming principle for the value function via the backward semigroup associated with the BSDE. Then we show that the corresponding value function is a viscosity solution to the Hamilton–Jacobi–Bellman equation. Under an additional parameter condition, the viscosity solution is unique, which implies that the solution characterizes the value function. We apply the theoretical results to the risk-sensitive European option pricing problem.
Phenotypic plasticity of ecotypes across habitats

Phenotypic plasticity is the ability of a genotype to produce multiple phenotypes depending on the environmental conditions and it can allow persistence of populations in heterogeneous habitats or under climate change. Therefore, phenotypic plasticity can play a major role in the divergence of populations across habitats. Trade-offs in plant performance in various habitats can give rise to the evolution of specialized ecotypes which are locally adapted (specialized) populations of the same species in distinct environments. According to the specialization hypothesis, specialization of ecotypes to either relatively favorable or unfavorable habitats results in increased or decreased phenotypic plasticity, respectively. The presence of phenotypic plasticity differences among ecotypes can be easily detected by examining their performances at home (native) versus foreign (alien) environments in reciprocal field experiments. In this meta-analysis, I compared phenotypic plasticity of ecotypes specialized in favorable and unfavorable habitats to test the specialization hypothesis by extracting data from 47 empirical studies. Log response ratio (LRR) and plasticity index ( PI v ) were used as effect sizes to detect and quantify significant differences in phenotypic plasticity of ecotypes across habitats. The overall result indicated that it was failed to find an effect of habitat origin on phenotypic plasticity expression of ecotypes. Specialization to either favorable or unfavorable habitats may not alter phenotypic plasticity expression in ecotypes. The interplay between phenotypic plasticity and specialization is quite complex and results of this study may shed light into these two important evolutionary mechanisms in plant ecology which have implications for biodiversity conservation, environmental management, agricultural industry, and ecosystem services.
CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques

Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.
The Evolution of Technological Space and Firms’ Workforce Composition in a Manufacturing Region

The development of the technological space of a manufacturing region relates to its human capital. However, the dynamic relation between local firms’ workforce composition and their adoption of Industry 4.0 enabling technologies over time is still under investigated. The paper contributes to filling this gap analysing the relation over 10 years between technology adoption and the occupational choices of 1800 firms from one of the most industrialized regions of Italy: the Veneto Region. The results from descriptive as well as inferential analysis show that such relational dynamics are a multifaceted phenomenon, presenting a series of counterintuitive features.
Understanding Citizens’ Emotional Pulse in a Smart City Using Artificial Intelligence

Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens' emotions and perceptions, which could be considered to provide the means to feel the “emotional pulse” of a city. We propose an automated AI-based observation framework to detect the emergence of public emotions and negativity in conversations. We evaluated the applicability of the framework using 29 928 social media conversations toward the much-debated topic of self-driving vehicles which will become increasingly relevant to smart cities. The patterns and transitions of citizens’ collective emotions were modeled using the Natural Language Processing and Markov models while the negativity (toxicity) in conversations was evaluated using a deep learning based classifier. The framework could be adopted by industry leaders and government officials for smart observation of citizen opinions to improve security, communication, and policymaking.
A 0.7–5.7 GHz Reconfigurable MIMO Receiver Architecture for Analog Spatial Notch Filtering Using Orthogonal Beamforming

A highly reconfigurable direct-conversion software-defined multiple-input multiple-output (MIMO) receiver with four RF inputs and four I/Q baseband outputs is proposed. It allows for digital MIMO but also analog interference rejection by spatial notch filtering through four flexible and simultaneous orthogonal beams. A segmented constant-Gm vector modulator (VM) with improved interference tolerance and wide RF frequency range targeting the sub-6-GHz bands is proposed. It exploits current-domain beamforming before $I$ – $V$ conversion by transimpedance amplifiers. A 0.7–5.7-GHz 22-nm fully depleted silicon-on-insulator (FD-SOI) prototype chip achieves >29 dB spatial filtering for a single notch and an ultrawideband 20-dB notch suppression bandwidth of 2.3 GHz at broadside excitation at an local oscillator (LO) frequency of 2.5 GHz. In the notches, an IIP3 of +16 dBm and B1dB of −11.5 dBm at a 41-dB gain is achieved, improving IIP3 and B1dB by 35 and 27 dB, respectively, by spatial filtering. A single-element noise figure (NF) of 5.5–7 dB is achieved on the VM constellation corners, degrading about 2 dB on the points nearby the biggest circle fitting into a square constellation. However, sub-3-dB system NF is potentially achievable, taking into account up to 6-dB improvement by the four-element beamforming. Given both gain and phase control provided by the VM, spatial patterns with up to three independent nulls can be synthesized with the four-element antenna array. The chip of 0.52 mm2 active area consumes 77–139 mW at an LO-frequency of 0.7–5.7 GHz from a 0.8-V supply.
Investigation of the transport of aflatoxin M1 by the transporter ABCG2 in bovine mammary epithelial cells

Aflatoxin M1 (AFM1), a hydrogenated metabolite of aflatoxin B1 (AFB1), is one of the most common and threatening aflatoxins found in milk and dairy products. The ATP-binding cassette G2 efflux transporter (ABCG2) plays an important role in the mammary transport of drugs and toxins in animals, but whether ABCG2 could affect the transport of AFM1 in bovine mammary epithelial cells (BMECs) has not been clarified. Therefore, this study aimed to investigate the effects of AFM1 on tight junctions (TJs), lactation and cells proliferation in BMECs, and explored the regulatory role of ABCG2 in AFM1 transport in vitro in BMECs. The results showed that the integrity of the TJs of BMECs was not permanently compromised after exposure to AFM1. AFM1 exposure had no obvious effects on lactation or cell proliferation in BMECs. Gene function study revealed that ABCG2 was a positive regulator of AFM1 transport. These results demonstrate that it has little effect on TJs, lactation or cell proliferation in BMECs exposed to a small dose of AFM1, and ABCG2 is a critical regulator for AFM1 transport in BMECs.
Heterogeneously Integrated RF Circuits Using Highly Scaled off-the-Shelf GaN HEMT Chiplets

Heterogeneous integration of IC chiplets into systems on a chip (SoC) is further revolutionizing the semiconductor industry. This letter introduces a metal embedded chiplet assembly for microwave integrated circuit (MECAMIC) technology that uses high-scaled GaN HEMT chiplets. MECAMIC relies on the cointegration of transistor chiplets with passive interposers using an embedding backside metal process. We report the development of standardized 40-nm AlGaN/GaN HEMT chiplets to rapidly prototype GaN circuits up to <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band by combining advances in <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula>-beam gate processes and massively parallel singulation techniques. GaN chiplets with <inline-formula> <tex-math notation="LaTeX">$f_{\mathrm {T}} > 110$ </tex-math></inline-formula> GHz and <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm {max}} > 1.3$ </tex-math></inline-formula> A/mm were fabricated in yielded quantities exceeding 50000 per <inline-formula> <tex-math notation="LaTeX">$4\prime\prime$ </tex-math></inline-formula> wafer. Simulation and experimental results of single-stage MECAMIC circuits with <inline-formula> <tex-math notation="LaTeX">$4\,\,\mu \text{m}\,\,\times 37.5\,\,\mu \text{m}$ </tex-math></inline-formula> device chiplets demonstrated 6.8-dB gain at 77 GHz. The MECAMIC technology not only optimizes the GaN wafer utilization but also provides the ability to incorporate other technologies, offering unique design flexibility at millimeter wave.
Data-Driven Nonlinear Control Designs for Constrained Systems

Systems with nonlinear dynamics are theoretically constrained to the realm of nonlinear analysis and design, while explicit constraints are expressed as equalities or inequalities of state, input, and output vectors of differential equations. Few control designs exist for systems with such explicit constraints, and no generalized solution has been provided. This dissertation presents general techniques to design stabilizing controls for a specific class of nonlinear systems with constraints on input and output, and verifies that such designs are straightforward to implement in selected applications. Additionally, a closed-form technique for an open-loop problem with unsolvable dynamic equations is developed. Typical optimal control methods cannot be readily applied to nonlinear systems without heavy modification. However, by embedding a novel control framework based on barrier functions and feedback linearization, well-established optimal control techniques become applicable when constraints are imposed by the design in real-time. Applications in power systems and aircraft control often have safety, performance, and hardware restrictions that are combinations of input and output constraints, while cryogenic memory applications have design restrictions and unknown analytic solutions. Most applications fall into a broad class of systems known as passivity-short, in which certain properties are utilized to form a structural framework for system interconnection with existing general stabilizing control techniques. Previous theoretical contributions are extended to include constraints, which can be readily applied to the development of scalable system networks in practical systems, even in the presence of unknown dynamics. In cases such as these, model identification techniques are used to obtain estimated system models which are guaranteed to be at least passivity-short. With numerous analytic tools accessible, a data-driven nonlinear control design framework is developed using model identification resulting in passivity-short systems which handles input and output saturations. Simulations are presented that prove to effectively control and stabilize example practical systems.
The Education model of principles on the relationship (PRIOR) for native youth organizations of sekaa teruna teruni improving reproductive health behavior of teenagers in Denpasar city

Submitted: 27 January 2020 Revised: 18 February 2020 Accepted: 09 March 2020 Reproductive health problems in teenagers become an important issue in Indonesia. A program has been done in national scale, but that’s program have many obstacles, the service time is during working hours, so there are difficult to access, they also have limited resources, Only 12% of teenagers know about that’s a program and only 2% access that services. This is a community trial, involve 241 teenagers. The results showed that there is a significant effect in knowledge 95% CI (14,627-21,952), attitudes 95% CI (6,000-9,001) and practices 95% CI (0.380-2.46), while in negotiation skills there is no effect (P>0.05). Conclusion: The educational intervention model of PRIOR has been proven to improve knowledge, attitudes, and practices. Research suggestions: It is necessary to provide a negotiation strategy topic in an education program for reproductive health to improve the achievement of knowledge, attitudes, and practices into the negotiation skills.
Phase-Shifted Model Predictive Control to Achieve Power Balance of CHB Converters for Large-Scale Photovoltaic Integration

Cascaded H-bridge (CHB) converters are attractive candidates for next generation photovoltaic (PV) inverters. CHB converters present a reduced voltage stress per power switch and a high modularity. Therefore, the plant can be divided in several PV strings that can be connected to each H-bridge cell. However, due to variability on solar irradiance conditions, each PV string may present different maximum available power levels, which difficult the overall converter operation. To address this issue, this article presents a model predictive control (MPC) strategy, which works along with a phase-shifted pulsewidth modulation (PS-PWM) stage; hence, its name phase-shifted MPC (PS-MPC). The novelty of this proposal is the way both interbridge and interphase power imbalance are directly considered into the optimal control problem by a suitable system reference design. Thus, the interphase imbalance power is tackled by enforcing the converter to operate with a proper zero-sequence voltage component. Then, by exploiting the PS-PWM working principle, PS-MPC is able to handle each H-bridge cell independently. This allows the predictive controller to also deal with an interbridge power imbalance using the same control structure. Experimental results on a 3-kW prototype are provided to verify the effectiveness of the proposed PS-MPC strategy.