Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Water Supply on Physiological Response and Phytonutrient Composition of Chili Peppers

Water supply is a primary contributor to the growth and phytonutrient composition in chili peppers. Several physiological stress factors can influence phytonutrients in chili peppers, resulting in their differential synthesis. Maintaining the right and exact amount of water through a drip system can promote an effective fruit set and crop quality. Four pepper cultivars (‘Hetényi Parázs’; HET, ‘Unikal’; UNIK, ‘Unijol’; UNIJ and ‘Habanero’; HAB) were investigated under different water supply treatments: RF or rain-fed, DI or deficit irrigation, and OWS or optimum water supply. The two-year experiment was carried out in May 2018 and 2019 under open field conditions. Physiological parameters (relative chlorophyll content, chlorophyll fluorescence, and canopy temperature) were measured during the growth stage and phytonutrients (vitamin C, capsaicinoids and carotenoids) analyzed using high-performance liquid chromatography (HPLC) at harvest in September. The study revealed that, due to higher precipitation and rainfall interruption, increased water supply affected physiological response and phytonutrients in the cultivars. HAB under OWS had a lower response during the growth period when compared to HET, UNIK, and UNIJ. As water supply increased, measured individual carotenoid concentration increased in some cultivars. On the other hand, as water supply decreased, vitamin C and capsaicinoids concentration increased. Even though cultivars responded to the water supply treatments differently, HET exhibited a more uniform and stable composition in all treatments.
Characterization of the Concentrations of Volatile Organic Compounds in the Romanian Littoral using General Regression Neural Networks: A Case Study

ABSTRACT In the last decade, preserving air quality in the northern Romanian Littoral has become a national priority. To monitor pollution, benzene, toluene, ortho-, meta-, and para-xylene, ethyl-benzene, and styrene were determined at six monitoring sites in three industrial and three urban areas. The sampling was done by an active method and the analysis by gas chromatography. The statistical analysis demonstrated that the concentrations of the volatile organic compounds were under the limits established by the European Union’s laws and that the pollutant distributions were skewed. Models were proposed for the concentrations of the volatile organic compounds, using the general regression neural network, with wind speed, air temperature, and humidity as regressors. The theoretical fit well with the experimental results, showing that the model is an effective alternative to conventional approaches.
Influence of oxygen content on the crystallinity of MgO layers in magnetic tunnel junctions

With RF sputtering process, Si/SiO2/Ta/Ru/Ta/CoFeB/MgO/CoFeB/Ta/Ru structure has been grown on Si (100) substrate. Attempting different targets and adjusting the oxygen dose, the crystallization quality of the MgO layer is studied. The X-ray diffraction measurements demonstrate that crystal structure and crystallization quality of MgO layers are related to the type of target and concentration of oxygen in sputtering process. With the method sputtering Mg in an ambient flow of oxygen, not only the crystallization quality of a normal MgO layer with lattice constant of 0.421 nm is improved, but also a new MgO crystal with lattice constant of 0.812 nm is formed and the perpendicular magnetic anisotropy of CoFeB is enhanced. Also it is found that crystallization quality for both the normal MgO and new MgO is more improved with MgO target and same oxygen dose, which means that this new method is helpful to form a new structure of MgO with lattice constant of 0.812 nm. All of the samples were annealed at 400 °C in vacuum.
Hijab Fashion Consciousness Among Young Muslim Women in Malaysia

In recent years, Muslim women have espoused Hijab fashion as a part of Islamic faith by mixing the contemporary fashion trend to carry out their act of veiling. In turn, this situation has led to the rapid growth of global Islamic clothing market and opened numerous money-spinning opportunities for the fashion industry players. For them, it has become imperative to understand the perception of their Muslim consumers to offer the right product. Therefore, this research attempts to study what considerations articulate together in Muslim women’s hijab fashion consciousness. The purpose of this study is to analyze the impact of self-congruity, modesty, conformity and need for uniqueness to the fashion consciousness. To find the effect of these four aspects on Hijab fashion consciousness, a self-administered survey with a structured questionnaire was conducted in Malaysia. Data were collected from 200 Muslim women in Malaysia who wear Hijab fashion and then, SmartPLS 3.2. 8 was used to analyze the hypothetical relationships. The findings of this study revealed that self-congruity and the need for uniqueness have a positive effect on the hijab fashion consciousness of Muslim women. It refers that Hijab fashion marketers must consider these two aspects while creating or designing a hijab fashion to satisfy consumer's expectations.
Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.
SLICACO: An automated novel hybrid approach for dermatoscopic melanocytic skin lesion segmentation

Low contrast images and blurriness pose challenge in the over‐segmentation of image, which increases model complexities. In this work, a novel hybrid dermoscopic skin‐lesion segmentation method, namely SLICACO, is proposed incorporating the simple linear iterative clustering (SLIC) and ant colony optimization (ACO) algorithms. The working of proposed method is multifold. First, over‐segmentation of preprocessed image is generated using SLIC super‐pixel technique. Second, clusters of super‐pixels generated by SLIC are used by ACO with the pixels of similar intensity for edge detection and seek for the optimum pathway in a strained zone. Third, lesion area is segmented using the Convex Hull and Thresholding. Fourth, Erosion Filtering is used to obtain the final segmented image. The performance of SLICACO is assessed on five benchmark dermatoscopic datasets and compared with deep learning models to test its generalizing behavior. Promising results are obtained on the PH2 archive data set with an accuracy of 95.9%.
Un-dainty fat Jewish daughter: Jewish mothers’ racialized disgust, and embodied recognition across racial difference

ABSTRACT This analytical and exo-autoethnography begins with a depiction of how my Jewish family wished to control my fat body to fit into whiteness. I close with a narrative about an interracial and transnational relationship in which I experienced embodied recognition across racial difference. The purpose is to illustrate the broader intersections between Jewish women’s fatness, associations with blackness, internalized antisemitism, assimilation into whiteness, and the links between the African and Jewish diasporas. Jewish mothers frequently critique their daughters’ bodies to try to assimilate into white femininity. Internalized gendered antisemitism creates disgust for one’s own body that is passed down to daughters. My mom wanted me to have a private pride in being Jewish that she could not access because of her gender, but did not want my Jewishness to physically mark me. Historically Jewish women have been associated with fatness, blackness, vulgarity, and lack of femininity. Collective recognition and acceptance of fat bodies across racial difference is possible through the connections between the Jewish and African diasporas and the challenge to white-centric beauty norms within Black communities.
Seasonal Prediction of Bottom Temperature on the Northeast U.S. Continental Shelf

The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model-based seasonal-to-interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy-resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to months in the Mid-Atlantic Bight and up to in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large-scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES. this study, we have developed a collection of statistical models that produce seasonal predictions of NES bottom temperature with 1–12 months lead time. Variables considered in these prediction models include local persistence of bottom temperature from prior months, bottom temperature from an upstream or nearby region, and large-scale atmospheric and oceanic indices representing the North Atlantic Oscillation or position of the Gulf Stream (GS). Only considering local persistence provides significant skill for lead times up to ∼ 5 months in the Mid-Atlantic Bight and up to ∼ 10 months in the Gulf of Maine, although the skill varies by season. Using upstream or nearby bottom temperature and the GS index both generally improve the prediction skill. However, the GS index does not provide higher prediction skill than those upstream or nearby bottom temperatures. A simplified statistical model has been developed, which can be tailored to fisheries management on the NES.
Cholesteric layers with tangential-conical surface anchoring for an electrically controlled polarization rotator

The voltage dependences of polarization characteristics of light passed through the cholesteric layers with tangential-conical boundary conditions have been investigated. It has been shown that such layers allow turning the polarization azimuth more than $70^\circ$ because of the unique untwisting effect of the cholesteric helix, due to free azimuthal rotation of the director on the substrate with conical anchoring. Optical cells under study have some advantages (low control voltage, smooth variation of polarization azimuth, simplicity of design) and can be used as an electrically controlled polarization rotator of white light.
Microbiological evaluation of the meat from young collared peccaries (Pecari tajacu Linnaeus, 1758)

The increased demand for meat from wild animals for human consumption, as an alternative protein source, brings with it the need for research that demonstrates the quality of such product. The technical information about the meat from collared peccaries still needs studies and aiming to collaborate with the already existing knowledge, this research had as its objective to provide subsidies for evaluation of the sanitary quality of the meat from collared peccaries. Research, Society and Development, v. 10, n. 5, e22010514772, 2021 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi. org/10.33448/rsd-v10i5.14772 2 Twelve animals were used, six males and six females, aged four to five months, from the breeding stock of the Federal Rural University of the Semi-Arid and, after euthanasia by a slaughter technique used in goats, the meat samples were collected through sterile swabs at the locations of the hind leg, loin, belly and axilla of each animal, totaling 48 samples. The maximum mesophilic count found was 1.9 x 10 CFU/ml, the total coliform and thermotolerant coliform counts were below the one determined in the legislation for meat from domestic animals, however, through biochemical tests, the presence of E. coli from the biotype 1 and biotype 2 was found, in seven and six samples, respectively. Search results for Salmonella spp. were negative in all samples and, as for Staphylococcus spp. , only one sample had the count above the level allowed by law. The contamination load related to the bacteria E. coli proved to be high, making 13 samples unfit for consumption. Regarding the other data, the meat from young collared peccaries showed a low contamination load.
Bruton’s tyrosine kinase (BTK) regulates myeloid cell recruitment during acute inflammation

Bruton’s tyrosine kinase (BTK) is a non-receptor kinase best known for its role in B lymphocyte development that is critical for proliferation, and survival of leukaemia cells in B cell malignancies. However, BTK is expressed in myeloid cells, particularly monocytes and macrophages where its inhibition has been reported to exhibit anti-inflammatory properties. Therefore, we explored the role of BTK on the migration of myeloid cells in vitro and in vivo. Using the zymosan induced peritonitis model of sterile inflammation we demonstrated that acute (1 h prior to zymosan) inhibition of BTK using a wide range of FDA (Ibrutinib and Acalabrutinib) and non-FDA approved inhibitors (ONO-4059, CNX-774, Olumatinib and LFM-A13) reduced neutrophil and monocyte recruitment. XID mice, which have a point mutation in the Btk gene had reduced neutrophil and monocyte recruitment to the peritoneum following zymosan challenge. To better understand the role of BTK in myeloid cell recruitment we investigated both chemotaxis and chemokine production in monocytes and macrophages. Pharmacological or genetic inhibition of BTK signalling substantially reduced human monocyte and murine macrophage chemotaxis to a range of chemoattractants (complement C5a and CCL2). We also demonstrated that inhibition of BTK in tissue resident macrophages significantly decreases chemokine secretion by reducing NF-kB activity and Akt signalling. Our work has identified a new role of BTK in regulating myeloid cell recruitment via two mechanisms, 1) reducing monocyte/macrophages’ ability to undergo chemotaxis, and 2) reducing chemokine secretion, via reduced NF-kB activity in tissue resident macrophages.
A 3.6 TOPS/W Hybrid FP-FXP Deep Learning Processor with Outlier Compensation for Image-to-Image Application

A Hybrid floating-point (FP) and fixed-point (FXP) deep learning processor with an outlier-aware channel splitting algorithm is proposed for image-to-image applications on mobile devices. Since the high quality of the reconstructed image through deep learning based image-to-image application requires high bit-precision (> FP16), the mobile processor suffers from the high computation power and large external memory access (EMA). In this work, the proposed algorithm reduces 16-bit FP data to 8-bit FXP data, and only few outliers (< 10%) are computed in 16-bit FP while maintaining the image reconstruction quality. Therefore, it reduces EMA by 45.5% Moreover, the hierarchical processor accelerates these dense 8bit FXP data and sparse 16-bit FP data, and the functional L2 memory aggregates the convolution output of them by forming the pipeline, which reduces 98% of latency. The proposed system is simulated in 28nm COMS technology, and it occupies 4.16mm2. The hierarchical processor successfully demonstrates the x 4 scale Full-HD super-resolution generation achieving 76 frames-per-second (fps) with 133.3 mW power-consumption at 0.9 V supply and 3.6 TOPS/W of energy-efficiency which is × 3.27 higher than the previous 16-bit FXP processor.
RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users’ viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.
Relay selection, clustering, and data aggregation routing in wireless body area networks

This paper addresses the problems of relay selection, clustering, and routing to extend the lifetime of wireless body area networks. We first propose an efficient algorithm called “Energy‐aware Relay Selection and Cluster‐based Routing (ERSCR)” to develop a hybrid data aggregation tree in the network. ERSCR has three phases, including the relay selection, clustering and Cluster Head (CH) selection, and data transmission. It divides the biosensors into several clusters and selects an appropriate CH for each cluster. Each biosensor transmits data to its CH or relay node. The aggregated data are then routed to the sink through an energy‐balanced routing tree. The proposed scheme considers both residual energy and distance for routing data of biosensors. It not only reduces the energy consumption in the network but also balances the energy consumed by different biosensors. Subsequently, we improve the ERSCR algorithm and introduce the “Joint Relay Selection, Clustering, and Routing (JRSCR)” algorithm to achieve a better network performance. JRSCR benefits from the advantages of the ERSCR algorithm. Moreover, it reduces the number of transmissions with the direct use of the relay nodes as CH. As another advantage, both ERSCR and JRSCR algorithms are compatible with the natural physical states of the human body. Simulation results demonstrate the efficiency of the proposed algorithms in terms of energy consumption, network lifetime, and maximum hop count.
Experimental validation for the employment of shifting strategies optimized via i-AWGA in a gear shift indicator system for manual transmission vehicles

Abstract In the present work, a computational routine was developed in order to optimize the shifting strategies implemented in a Gear Shift Indicator to assist the driver of manual transmission vehicles according to a desired driving style. In order to achieve this goal, a simulation algorithm was developed to estimate the fuel consumption and acceleration performance of a vehicle equipped with a 5-speed manual gearbox propelled by a 1.4 L engine when submitted to the FTP-72 driving cycle. A multi-objective Genetic Algorithm was then employed to solve the optimization problem for both fuel consumption and performance. Among the set of optimized solutions, 3 shifting strategies were selected for experimental evaluation on a chassis dynamometer. The experiments showed that the strategy with focus on fuel economy was able to save 10.7% of fuel, being the best performance strategy responsible for enhancing the vehicle acceleration in 8.33%, when compared to the standard strategy provided by the vehicle manufacturer. Finally, the best-compromised solution presented 6.4% fuel-savings with an acceleration performance similar to the best performance strategy.
Intelligent Wavelet Based Pre-Filtering Method for Ultrasonic Sensor Fusion Inverse Algorithm Performance Optimization

Certain obstacle mapping applications require the live evaluation of the measured data to prevent collision with obstacles. The fusion of different or similar sensors usually has a high calculation demand, which increases significantly with the area to be evaluated and the number of sensors. In the present considerations, we propose a wavelet-based adaptive optimization method, which can greatly decrease the number of grid points to be evaluated, and thus the necessary computation time. The basis of the method is to use the fact that the areas to be evaluated mostly face a rather small number of obstacles, which cover a smaller percentage of the whole environment. The first step in a pre-filtering process is the determination of the zones where no obstacles are present. This step can already result in a considerable decrease in the computation time, however with the transformation to polar coordinates, the method will not only be more fitted to the problem to be solved, but the area of the evaluation can also be increased with the same number of grid points. As a last step, we applied wavelet transformation to identify the regions of interest, where the application of a refined raster is necessary, and thus further decreasing the number of grid points where the calculation has to be carried out. We used our previously developed probability-based ultrasonic sensor fusion inverse algorithm to demonstrate the efficiency of the proposed method.