Miracle-dz/UniFAR-FTUs huggingface dataset:
“Two channel” photocatalytic hydrogen peroxide production using g-C3N4 coated CuO nanorod heterojunction catalysts prepared via a novel molten salt-assisted microwave process

Photocatalytic H2O2 production has gained increasing attention as a sustainable and eco-friendly method to replace conventional anthraquinone-based and electrochemical production processes. In this work, we report a novel molten salt-assisted microwave process to synthesize g-C3N4 coated CuO nanorod heterojunction photocatalysts with outstanding H2O2 production ability. XRD, UV-Vis, N2 adsorption, SEM, TEM, XPS, EIS and PL were used to characterize the obtained catalysts. CN@CuO(4) with the largest heterojunction area displays the highest photocatalytic H2O2 production ability of 6.9 mmol L−1, which is 7.4 and 16.4 times higher than that of individual g-C3N4 and CuO. A “two channel pathway” is proposed for this reaction system which causes the remarkably promoted H2O2 production ability. In addition, compared with a traditional polycondensation method, the catalyst prepared via a molten salt-assisted microwave process exhibits a larger heterojunction area and more effective separation rate, causing higher photocatalytic H2O2 production ability and stability.
Numerical investigation of the influence of the inlet nozzle diameter on the degree of fluid exchange process in a hydraulic cylinder

Operation of heavy-duty machines leads to an observation that seals in hydraulic cylinders are frequently subjected to thermal degradation. The basis of this research is the previously proven thesis that the temperature of the liquid in the cylinder and the cylinder itself may significantly differ from the temperature of the liquid in the system and in the tank, thus enabling the temperature in the cylinders to rise above the operating temperature of the seals. This effect is a result of limited possibility of fluid exchange in the cylinder during operation. This paper presents simulation results of the liquid mixing phenomenon in the chamber of a hydraulic cylinder with a modified hydraulic power supply system. The article focuses on determining the influence of the diameter of the supply nozzle, and thus the change of the potential energy of the stream supplying the cylinder chamber, on the liquid exchange process, which was defined by the thermal balance of the system. The proposed method allows designing of hydraulic cylinders in order to maintain constant temperature conditions in each part of a cylinder and thus eliminate thermal degradation of sealing. The research was based on an experimentally verified CFD model of the cylinder chamber.
Role of Artificial Intelligence of Things (AIoT) to Combat Pandemic COVID-19

COVID-19 is caused by virus called SARS-CoV-2, which was declared by the WHO as global pandemic. Since the outbreak, there has been a rush to explore Artificial Intelligence (AI) and Internet of Things (IoT) for diagnosing, predicting, and treating infections. At present, individual technologies, AI and IoT, play important roles yet do not impact individually against the pandemic because of constraints like lack of historical data and the existence of biased, noisy, and outlier data. To overcome, balance among data privacy, public health, and human-AI-IoT interaction is must. Artificial Intelligence of Things (AIoT) appears to be a more efficient technological solution that can play a significant role to control COVID-19. IoT devices produce huge data which are gathered and mined for actionable effects in AI. AI converts data into useful results which are utilized by IoT devices. AIoT entails AI through machine learning and decision making to IoT and renovates IoT to add data exchange and analytics to AI. In this chapter, AIoT will serve as a potential analytical tool to fight against the pandemic.
Comprehensive Survey of Research on Emerging Communication Technologies from ICETC2020

SUMMARY The 2020 International Conference on Emerging Technologies for Communications (ICETC2020) was held online on December 2nd–4th, 2020, and 213 research papers were accepted and presented in each session. It is expected that the accepted papers will contribute to the development and extension of research in multiple research areas. In this survey paper, all accepted research papers are classiﬁed into four research areas: Physical & Fundamental, Communications, Network, and Information Technology & Application, and then research papers are classiﬁed into each research topic. For each research area and topic, this survey paper brieﬂy introduces the presented technologies and methods.
Wideband and High-Gain Wearable Antenna Array with Specific Absorption Rate Suppression

In this paper, a wideband and high-gain antenna array with specific absorption rate suppression is presented. By adopting the wideband monopole antenna array and the uniplanar compact electromagnetic band gap (UC-EBG) structure, the proposed wearable antenna array can realize a high gain of 11.8–13.6 dBi within the operating band of 4.5–6.5 GHz. The sidelobe level of the proposed wearable antenna array is less than −12 dB, and the cross polarization in the main radiation direction is less than −35 dB. Benefiting from the UC-EBG design, the specific absorption rate is suppressed effectively, guaranteeing the safety of the proposed antenna array to the human body. The proposed antenna array is processed and tested, and the measurement results show good agreement with the simulation results.
Detecting abnormal fundus images by employing deep transfer learning

Background: To develop and validate a deep transfer learning (DTL) algorithm for detecting abnormalities in fundus images from nonmydriatic fundus photography examinations. Methods : A total of 1,295 fundus images from January 2017 to December 2018 at Yijishan Hospital of Wannan Medical College were collected for developing and validating the deep transfer learning algorithm in detecting abnormal fundus images. The DTL model was developed by using 929 (normal 254, abnormal 402) fundus images, including normal fundus images and abnormal fundus images, the latter including maculopathy, optic neuropathy, vascular lesion, choroidal lesions, vitreous disease, and cataracts. We tested our model using a subset of the publicly available Messidor dataset (using 366 images) and evaluated the testing performance of the DTL model for detecting abnormal fundus images. Results : In the internal validation dataset (n=273 images), the AUC, sensitivity, accuracy, and specificity of the DTL for correctly classified fundus images were 0.997, 97.41%, 97.07%, and 96.82%, respectively. For the test dataset (n=273 images), the AUC, sensitivity, accuracy, and specificity of the DTL for correctly classifying fundus images were 0.926, 88.17%, 87.18%, and 86.67%, respectively. Conclusion : In the evaluation, the DTL presented high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of the DTL in the community health care center. Key words : Fundus images; Deep transfer learning; Developing and validation; Artificial intelligence.
A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies we have applied multiple machine learning algorithms, modeling metrics and in some cases compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and comparison of our proprietary software Assay CentralTM with random forest, k-Nearest Neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (3 levels). Model performance was assessed using an array of five-fold cross-validation metrics including area-under-the-curve, F1 score, Cohen’s kappa and Matthews correlation coefficient. Based on ranked normalized scores for the metrics or datasets all methods appeared comparable while the distance from the top indicated Assay CentralTM and support vector classification were comparable. Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case where minimal tuning was performed of any of the methods. If anything, Assay CentralTM may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay CentralTMperformance, but support vector classification seems to be a strong competitor. We also apply Assay CentralTM to prospective predictions for PXR and hERG to further validate these models. This work currently appears to be the largest comparison of machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors and algorithms, as well as further refining methods for evaluating and comparing models.
Demand Forecasting for Liquified Natural Gas Bunkering by Country and Region Using Meta-Analysis and Artificial Intelligence

Ship exhaust emission is the main cause of coastal air pollution, leading to premature death from cardiovascular cancer and lung cancer. In light of public health and climate change concerns, the International Maritime Organization (IMO) and several governments are reinforcing policies to use clean ship fuels. In January 2020, the IMO reduced the acceptable sulfur content in ship fuel to 0.5% m/m (mass/mass) for sustainability. The use of liquified natural gas (LNG) as a ship fuel is currently the most likely measure to meet this regulation, and LNG bunkering infrastructure investment and network planning are underway worldwide. Therefore, the aim of this study is to predict the LNG bunkering demand for investment and planning. So far, however, there has been little quantitative analysis of LNG bunkering demand prediction. In this study, first, the global LNG bunkering demand was predicted using meta-regression analysis. Global demand for LNG bunkering is forecast to increase from 16.6 million tons in 2025 to 53.2 million tons in 2040. Second, LNG bunkering prediction by country and region was performed through analogy and artificial intelligence methods. The information and insights gained from this study may facilitate policy implementation and investments.
Effect of task-oriented exercise training program on the functional performance 
of children with cerebral palsy

Introduction. To evaluate the effects of task-oriented exercise training program (ToET) on the functional performance (mobility and walking efficiency) of children with cerebral palsy (CP). Methods. overall, 46 children with CP were recruited from outpatient units of the physiotherapy clinics of Aminu Kano Teaching Hospital, Murtala Mohammed Specialist Hospital, and a school for children with disabilities. They were randomly assigned to the ToET group ( n = 23) and the control group (CG) ( n = 23). Children in CG practised conventional physiotherapy. Each group trained twice weekly for 12 weeks. Mobility and walking efficiency were assessed with a mobility questionnaire and 10-meter walk test, respectively, at baseline, after 6th and 12th weeks, and after 6 weeks of follow-up. data were analysed with repeated measures ANoVA, t -test, Mann-Whitney U test, and Friedman’s test, with < 0.05. Results. A total of 39 children with CP completed the study, 19 in CG and 20 in ToET group. The between-group scores of mobility (ToET: 69.15 ± 17.32; CG: 67.03 ± 10.49) and walking efficiency (ToET: 0.63 ± 0.2 m/s; CG: 0.63 ± 0.19 m/s) were not different at baseline and at the 6 th week ( p > 0.05). At the 12 th week, there were significant between-group differences in mobility (ToET: 86.16 ± 7.20; CG: 71.09 ± 13.58) and walking efficiency (ToET: 1.00 ± 0.29 m/s; CG: 0.78 ± 0.28 m/s) ( p < 0.05). The within-group scores of walking efficiency and mobility increased in both ToET and CG ( p < 0.005 for both comparisons). Conclusions. ToET is an effective intervention to improve functional performance of children with CP.
Design concept evaluation technique via functional link matrix and fuzzy VIKOR based on left and right scores

ABSTRACT This article presents a new technique for determining the weights of design features by searching for functional links between their sub-features. The method further applies fuzzy VIKOR based on left and right scores to determine the optimal design concept from a decision matrix obtained from three experts view. The application of this technique to the design of a Reconfigurable Assembly Fixture (RAF) shows that it is a viable method for determining the weights of design features and identifying the optimal design concept from a set of alternative designs. In order to support the viability of this technique, the results obtained from its application to the design of a RAF was compared with the results of other Multi-Criteria Decision-Making (MCDM) methods when applied to the design of the RAF. The comparison shows that the technique is feasible and can be employed to determine the weights of design features in order to identify optimal design at the conceptual phase.
A Melting Pot

Abstract:Leo Breiman's article "Statistical Modeling: The two cultures" was timely and provocative. He advocated for Statisticians to learn about and appreciate a different "culture": an algorithmic approach, as distinct from the familiar, stochastic, data modeling approach to Statistics. While we have appreciated and contributed to the algorithmic approach, we have always had a foot in both camps. Here we advocate for a "melting pot", arguing that both approaches have their virtues, sometimes on the same problem.
Blockchain-Based Auditable Privacy-Preserving Data Classification for Internet of Things

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification (PPDC) scheme for IoT. We put forth a new controllably linkable group signature (CL-GS) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance of PPDC.
An in vitro study of Antioxidant effect of Santalum album, the Indian sandalwood

The antioxidant potential of Indian sandalwood, Santalum album, was investigated in this study, by DPPH scavenging, FRAP and hydroxyl radical scavenging in vitro assays. Aqueous extract of sandalwood callus, at concentrations of 25, 50, 75 and 100 micrograms/ml was used. The in vitro studies demonstrated significant antioxidant ability of sandalwood, which was concentration-dependent. At a concentration of 100 micrograms/ml, sandalwood extract showed 48.7% DPPH scavenging, 1 micromoles of ferrous iron/ml (by FRAP assay) and 53.2: scavenging of hydroxyl radicals. Further studies with multiple concentrations and comparison with standard antioxidants are required.
Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images

HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.
AccelSDP: A Reconfigurable Accelerator for Software Data Plane Based on FPGA SmartNIC

Software-defined networking (SDN) has attracted much attention since it was proposed. The architecture of the SDN data plane is also evolving. To support the flexibility of the data plane, the software implementation approach is adopted. The software data plane of SDN is commonly implemented on a commercial off-the-shelf (COTS) server, executing an entire processing logic on a commodity CPU. With sharp increases in network capacity, CPU-based packet processing is overwhelmed. However, completely implementing the data plane on hardware weakens the flexibility. Therefore, hybrid implementation where a hardware device is adopted as the accelerator is proposed to balance the performance and flexibility. We propose an FPGA SmartNIC-based reconfigurable accelerator to offload some of the operation-intensive packet processing functions from the software data plane to reconfigurable hardware, thus improving the overall data plane performance while retaining flexibility. The accelerated software data plane has a powerful line-rate packet processing capability and flexible programmability at 100 Gbps and higher throughput. We offloaded a cached-rule table to the proposed accelerator and tested its performance with 100 GbE traffic. Compared with the software implementation, the evaluation result shows that the throughput can achieve a 600% improvement when processing small packets and a 100% increase in large packet processing, and the latency can be reduced by about 20× and 100×, respectively, when processing small packets and large packets.
Unraveling Structural Details in Ga-Pd SCALMS Systems Using Correlative Nano-CT, 360° Electron Tomography and Analytical TEM

We present a comprehensive structural and analytical characterization of the highly promising supported catalytically active liquid metal solutions (SCALMS) system. This novel catalyst shows excellent performance for alkane dehydrogenation, especially in terms of resistance to coking. SCALMS consists of a porous support containing catalytically active low-melting alloy particles (e.g., Ga-Pd) featuring a complex structure, which are liquid at reaction temperature. High-resolution 3D characterization at various length scales is required to reveal the complex pore morphology and catalytically active sites’ location. Nano X-ray computed tomography (nano-CT) in combination with electron tomography (ET) enables nondestructive and scale-bridging 3D materials research. We developed and applied a correlative approach using nano-CT, 360°-ET and analytical transmission electron microscopy (TEM) to decipher the morphology, distribution and chemical composition of the Ga-Pd droplets of the SCALMS system over several length scales. Utilizing ET-based segmentations of nano-CT reconstructions, we are able to reliably reveal the homogenous porous support network with embedded Ga-Pd droplets featuring a nonhomogenous elemental distribution of Ga and Pd. In contrast, large Ga-Pd droplets with a high Ga/Pd ratio are located on the surface of SCALMS primary particles, whereas the droplet size and the Ga/Pd ratio decreases while advancing into the porous volume. Our studies reveal new findings about the complex structure of SCALMS which are required to understand its superior catalytic performance. Furthermore, advancements in lab-based nano-CT imaging are presented by extending the field of view (FOV) of a single experiment via a multiple region-of-interest (ROI) stitching approach.
Does Foreign Direct Investment Improve Inclusive Green Growth? Empirical Evidence from China

The main aim of the study was based on whether the foreign direct investment improves inclusive green growth in case of empirical evidence from China. In order to empirically analyse the association of FDI with the green inclusive growth, the researcher of the study has used secondary quantitative data. The data has been taken from 1981-2019 from World Bank. The IGG has been computed using Principal Component Analysis (PCA) which has been later used as the independent variable. The data has been considered of the last 30 years with annual frequency. The findings of the study revealed that in long-run, there is no relation between FDI and inclusive green growth. However, based on the short-run results, it can be evaluated that FDI and IGG are related. The paper still has some of the limitation because only China was used for the testing the variable.