Miracle-dz/UniFAR-FTUs huggingface dataset:
DEGRADATION OF METHYLENE BLUE BY CATALYTIC WET AIR OXIDATION WITH FE AND CU CATALYST SUPPORTED ON MULTI-WALLED CARBON NANOTUBES

Oxidation catalysts, based on Fe and Cu on multi-walled carbon nanotubes were synthesized by incipient wetness impregnation. The metal content in the catalyst was 3 %. MWNT used was synthesized by chemical vapour deposition in our laboratories. The catalytic activity of the above mentioned formulations were tested in the oxidation reaction of 50 mg.L of Methylene Blue (MB) aqueous solutions, with 4, 5 and 6 g.L of catalyst and at 120-140 oC temperature range. Reaction tests were carried out in a batch reactor at 8.8 bar of partial pressure, and the non converted fractions were measured in UV-vis equipment. The parameters TOC, color and toxicity (EC50) were measured. In the tested temperature interval, no diminution in the MB concentration due to the non-catalyzed oxidation reaction was observed, practically. The catalyzed oxidation at 120 oC was low for two synthesized catalysts, however a significant activity at 140 oC. TOC and color removal was significantly affected by temperature. The catalyst concentration did not significantly affect to color and TOC removal. The Cu based catalyst showed the highest catalytic activity at 130 oC in color and TOC removal. At 150 min of reaction time, Cu/MWNT shows a decrease of EC50 to 68 %. A reaction mechanism has been proposed, based on experimental data obtained from GC-MS analysis.
Knowledge Distillation with Contrastive Inter-Class Relationship

Due to the high computational cost, the application of deep neural networks (DNNs) to the real-time tasks has been limited. A possible solution is to compress the size of the model so that the demand for computation resources can be decreased. A popular method is called knowledge distillation (KD). The basic philosophy behind KD is to transfer the information extracted from the larger teacher network to the smaller student network. The general knowledge transfer strategy is to match the one-to-one logit or intermedia layers between the teacher and student networks, correspondingly. This objective may neglect the informative relationship information between different samples. In this paper, we borrow the idea of metric learning to transfer the contrastive relationship information learned from teacher network to the student. Specifically, we use the well-known Triplet loss to regularize the training of the student network. By the modified negative selection strategy, our Contrastive Knowledge Distillation (CKD) method can efficiently improve the performance of the student network compared with the traditional KD methods. Empirical experiments on KD benchmarks and real-world datasets also demonstrate the superiority of CKD.
Driver activity recognition using spatial‐temporal graph convolutional LSTM networks with attention mechanism

Driver activity engagement while driving plays a vital role that leads to negative outcomes of driving safety. To reduce trafﬁc accidents and ensure driving safety, real-time driver activity recognition architecture is proposed in this study. Speciﬁcally, a total of eight kinds of common driving-related activities are identiﬁed, which include the normal driving, left or right checking, texting, answering the phone, using media, drinking, and picking up objects. Raw experiment videos are collected via onboard monocular cameras, which are used for the upper body skeleton information extraction of the driver. Then, the graph convolutional networks (GCN) are constructed for spatial structure feature reasoning in a single frame, which is consecutively followed by long short-term memory (LSTM) networks for temporal motion feature learning within the sequence. Moreover, the attention mechanism is further utilised to emphasise the keyframes to select discriminative sequential information. Finally, a large-scale driver activity dataset, consisting of both naturalistic driving data and simulative driving data, is collected for model training and evaluations. Experimental results show that the general recall ratio of those eight driving-related activities reaches up to 88.8% and the real-time recognition efﬁciency can reach up to
Raman Assisted Fiber Optical Parametric Amplifier for S-Band Multichannel Transmission System

In this paper we present results from the study of optical signal amplification using Raman assisted fiber optical parametric amplifier with considerable benefits for S-band telecommunication systems where the use of widely used erbium-doped fiber amplifier is limited. We have created detailed models and performed computer simulations of combined Raman and fiber optical parametric amplification in a 16-channel 40 Gbps/channel wavelength division multiplexed transmission system. Achieved gain bandwidth, as well as transmission system parameters—signal-to-noise ratio and bit-error-ratio—were analyzed by comparing the Raman assisted fiber optical parametric amplifier to the single pump fiber optical parametric amplifier. Results show that the 3 dB gain bandwidth in the case of combined amplification is up to 0.2 THz wider with 1.9 dB difference between the lowest and highest gain.
A Robust Method for Filling the Gaps in MODIS and VIIRS Land Surface Temperature Data

Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%–52.7% lower than those of RSDAST, 35.4%–38.7% lower than those of IMA, and 38.5%–46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.
Prediction of China’s Total Energy Consumption Based on Bayesian ARIMA-Nonlinear Regression Model

Energy management is the key to China’s economy prosperity and environment protection in the future. This paper makes predictions on whether China’s energy 13th Five-Year plan can be achieved and the trend of energy consumption in the next stage. Aiming at the shortcoming of insufficient extraction of nonlinear features in the energy system from existing energy consumption models, and low prediction accuracy due to single models. Based on the derivation of the joint posterior distribution density function of the stationary ARMA model parameters under the Normal-gamma prior distribution, this paper proposes a Bayesian ARIMA-nonlinear regression energy consumption prediction model. Bayesian parameter estimation makes full use of prior information and Markov Chain Monte Carlo (MCMC) technique gets rid of the tedious calculation of high-dimensional integrals. Compared with the existing model, the improved model has higher prediction accuracy. Its Root Mean Square Error and Mean Absolute Percentage Error are 3452.0294 and 0.4727%. In line with the development goals set out in the 13th Five-Year plan, an empirical study of the fluctuation law of China’s total energy consumption was carried out using data on China’s energy consumption from 1990 to 2019 and its factors. The results show that affected by the Corona Virus Disease 2019 epidemic, the growth rate of China’s energy demand will fluctuate slightly in 2020. By the end of the 13th Five-Year plan, the total energy consumption can reach the expected goal. After the epidemic is under control, energy demand for the whole society will usher in a rebound growth. In the next five years (2020∼2024), China’s energy consumption will continue to maintain a steady upward trend of slowing growth. In view of these situations, this paper suggests that, on the one hand, we should attach equal importance to energy conservation and technology development to improve energy efficiency; on the other hand, we should adjust industrial structure and establish a unified energy management system to promote sustainable development of the energy industry.
Digital forensic readiness in operational cloud leveraging ISO/IEC 27043 guidelines on security monitoring

An increase in the use of cloud computing technologies by organizations has led to cybercriminals targeting cloud environments to orchestrate malicious attacks. Conversely, this has led to the need for proactive approaches through the use of digital forensic readiness (DFR). Existing studies have attempted to develop proactive prototypes using diverse agent‐based solutions that are capable of extracting a forensically sound potential digital evidence. As a way to address this limitation and further evaluate the degree of PDE relevance in an operational platform, this study sought to develop a prototype in an operational cloud environment to achieve DFR in the cloud. The prototype is deployed and executed in cloud instances hosted on OpenStack: the operational cloud environment. The experiments performed in this study show that it is viable to attain DFR in an operational cloud platform. Further observations show that the prototype is capable of harvesting digital data from cloud instances and store the data in a forensic sound database. The prototype also prepares the operational cloud environment to be forensically ready for digital forensic investigations without alternating the functionality of the OpenStack cloud architecture by leveraging the ISO/IEC 27043 guidelines on security monitoring.
A distributed control for accurate active‐power sharing in islanded microgrids subject to clock drifts

Funding information Ministry of Science, Innovation, and Universities of Spain, Grant/Award Number: RTI2018-100732-BC22 Abstract Inverters-based islanded microgrids are operated by local digital signal processors, driven by their own local clocks. These local clocks have drifts affecting the clock signals which are used to control the inverters in real time. In this scenario, a steady-state deviation is observed in active power sharing, degrading the microgrid performance. This paper presents a new control scheme that improves the microgrid performance by cancelling the active power sharing error due to clock drifts. The study includes a theoretical stability analysis and a design procedure of the control parameters based on the dynamic characteristics. Finally, selected experimental results are obtained in a laboratory microgrid with four inverters. In this setup, each inverter was equipped with its digital signal processor with drifting local clock.
Data Disseminated Energy-Efficient Clustering Algorithm For Avoid Load and Bandwidth Consumption In WSN Integrated IoT Platform

In the current scenario, wireless sensor networks (WSNs) are embedded in the “Internet of Things (IoT) ” platform where sensor nodes automatically link and use the Internet to communicate and execute their activities. WSNs are well suited for the collection of long-term IoT representation environmental data. The WSNs includes wireless communication capabilities, computation process, and nodes with sensing capabilities. Data dissemination methods, power management, and many routing procedures have been mainly designed for WSNs integrated IoT platform. Also, we consider load and bandwidth consumption as an essential issue in our design. Hence, this paper introduces a data disseminated energy-efficient clustering algorithm using multiple parameter decision-making for selecting an optimal clustering algorithm. For the cluster head selection process, we consider different kinds of parameters such as Initial Energy, Average Energy of the Network, Energy Consumption Rate, and Residual Energy. By considering these factors, nodes are continually monitored, and the cluster header is selected according to the maximum energy value. The respective cluster members are chosen in the cluster coverage area using the swarming techniques. In other words, we used swarm techniques as a cluster head selection process to avoid load and bandwidth consumption. The excellence of the system is evaluated using simulation results which show that this introduced method is more effective in terms of preventing bandwidth and load consumption. In this context, we use network simulator 2 (NS2) to simulate different kinds of metrics such as a packet delivery ratio, network lifetime, and energy consumption.
Production And CharacTeristics of A Bioflocculant Produced By Pseudomonas SP. STRAIN 38A

Screening of microorganisms producing flocculating substances was achieved. A strain secreting a large amount of bioflocculant was isolated from wastewater samples collected from the inlet from Borg El-Arab industrial wastewater treatment plant. Based on the morphological properties and 16S rDNA sequence analysis, the isolate (designated 38A) was classified as pseudomonas sp. exhibiting the highest flocculating activity and a crude polymer yield of 3 g/L after 1 day cultivation. A bioflocculant produced by 38A was heat-stable and had strong flocculating activity in a wide range of pH with relatively low dosage requirement. By studying the flocculating activity of 38A, factors such as bioflocculant dosage, temperature and pH of the reaction solution were tested. The optimal conditions for the flocculating activity were 15 mg/l bioflocculant dosage and pH 7.0, at a temperature of 25˚C. Under these conditions, the flocculating rate of kaolin clay was 99.89%. Different cations were investigated, the best was the CaCl 2 . Also the optimal concentrations for the flocculating activity was 1ml CaCl 2 3%(w/v) which played the synergistic effect on kaolin flocculation. The bioflocculant of 38A was identified as a polysaccharide. Infrared spectra showed the presence of hydroxyl, carboxyl and methoxyl group in its molecules.
A chronological and palaeoenvironmental re‐evaluation of two loess‐palaeosol records in the northern Harz foreland, Germany, based on innovative modelling tools

The continuing development of analytical methods for investigating sedimentary records calls for iterative re‐examination of existing data sets obtained on loess‐palaeosol sequences (LPS) as archives of palaeoenvironmental change. Here, we re‐investigate two LPS (Hecklingen, Zilly) in the northern Harz foreland, Germany, being of interest due to their proximity to the Scandinavian Ice Sheet (SIS) and the position between oceanic climatic influence further west and continental influence towards the east. First, we established new quartz OSL and polymineral IRSL chronologies. Both methods show concordant ages in the upper part of the Hecklingen profile (~20–40 ka), but in the lower part IRSL underestimates OSL ages by up to ~15 ka for the period 40–60 ka. Interpretations hence refer to the OSL data set. Second, we applied Bayesian age‐depth modelling to data sets from Hecklingen to resolve inversions in the original ages, also reducing averaged 1σ uncertainty by ~19% (OSL) and ~12% (IRSL). Modelled chronologies point out phases of increased (MIS 2, early MIS 3) and reduced (middle and late MIS 3) sedimentation, but interpretation of numerical rates is problematic because of intense erosion and slope wash particularly during MIS 3. Finally, previously obtained grain‐size data were re‐investigated by end member modelling analyses. Three fundamental grain‐size distributions (loadings) explain the measured data sets and offer information on intensity and – combined with modelled OSL ages – timing of geomorphic processes. We interpret the loadings to represent (i) primary loess accumulation, (ii) postdepositional pedogenesis and/or input of aeolian fine fractions, and (iii) input of coarse aeolian material and/or slope wash. The applied modelling tools facilitate detailed understanding of site‐formation through time, allowing us to correlate a strong peak in mean grain size at ~26–24 ka to the maximum extent of the SIS and increased influence of easterly winds.
Effects of Intra-Set Rest on Morphological Variables, Maximal Strength and Jump Performance in University Students

The appropriate dosage of resistance training could promote physical and physiological adaptations and decrease injuries. The aim of the study was to analyze the effects of the different intra-set rest after eight weeks of resistance train ing on morphological variables, maximal strength, and jump performance in physically active university students. Twenty-five students (15 men and 10 women) were randomized by sex and distributed in Control Group (CG) (n=8) with rest only at the end of the series; Experimental Group 1 (E 1) (n=9) with an intra-set rest of 30 s, and Experimental Group 2 (EG2) (n=8) with four intra-set rest of 10 s. Morphological variables [b ody weight, bipedal height, body mass index (BMI), fat mass and muscle mass], maximum upper body strength (bench press and military press), lower body strength [parallel squat (45 °) and deadlift], as well as countermovement jump (CMJ) were measured. All three groups obtained a significant increase (p<0.01) in body weight and BMI, as well as an essential reduction (p<0.01) of fat mass. Muscle mass increased significantly (p<0.01) for both CG and EG1. Maximum upperand lower-body strength increased considerably (p<0.05) ac ross all three groups and for all exercises, while the CMJ notably increased for CG and EG1. There are no significant changes betwe en the different intra-set rest, when using the same volume, intensity, and total recovery time during the exercise series (p>0.05); t hereby, there is an equivalent inc rease in muscle mass, maximal strength, jump performance, and a fat mass reduction.
An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset

Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform: - Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.
Sentiment analysis of movie reviews based on deep learning

In recent years, with the rapid development of NLP (Natural Language Processing) and deep learning, public opinion and public opinion on the Internet have decreased a lot compared to the past. Many Internet users have changed from mere "bystanders" to disseminators of Internet information. Movie review sentiment analysis technology is an emerging category in the field of information mining. More and more people have joined the "review army". The quality of a movie is closely related to movie reviews! The manual screening method not only consumes a lot of manpower and material resources, but also is inefficient. Therefore, the use of deep learning-based sentiment analysis has become the current general trend. Based on the principle of word mosaic (word vector) and deep learning, this paper proposes a movie review sentiment analysis technology based on deep learning and machine learning word mosaic. Experiments show that the method used in this article has reached the correct rate of emotional classification of movie reviews. 83.13%, the experimental results prove the practicability and scalability of this method and the effectiveness of this method.
Poly(1,5-Diaminonaphthalene)-Modified Screen-Printed Device for Electrochemical Lead Ion Sensing

Poly(1,5-diaminonaphthalene) has been electropolymerized on the screen-printed device with a three-electrode configuration. The modified electrodes have been developed as the new electrode for electrochemical determination of trace levels of lead ions (Pb2+). The poly(1,5-diaminonaphthalene) film prevents the deposition of Pb2+ into the surface defects of the bare carbon screen-printed electrode and possesses sensitivity to heavy metal ions thanks to amine and secondary amino groups on the polymer chain. The square wave anodic stripping voltammetry was applied to detect Pb2+ ions, showing a sharp stripping peak with the linear range from 0.5 μg·L-1 to 5.0 μg·L-1 ( R 2 = 0.9929 ). The limit of detection was found to be 0.30 μg·L-1. The sensors were applied to the analysis of Pb2+ in the tap water sample matrix with satisfactory results.
RELATIONSHIP BETWEEN DISABILITY AND RADIOGRAPHIC FINDINGS OF LUMBAR DISK DEGENERATION IN SAMPLE OF IRAQI PATIENTS WITH MECHANICAL LOW BACKACHE

Objectives: Disability related to chronic low back pain (LBP) is a complex and multidimensional phenomenon all over the world. The prevalence of backache in middle age and elderly is up to 84%. This study aims to evaluate the associations of X-ray features of lumbar disk degeneration with severity of disability among patients with mechanical LBP. Patients and Methods: A cross-sectional study was conducted on a total of 300 patients with chronic mechanical LBP. Severity of disability was measured using Modified Oswestry Disability Index and intensity of backache was assessed using numeric rating scale (0–10). X-ray features of lumbar disc degeneration according to Lane classification and spondylolisthesis were assessed in lateral recumbent lumbar X-rays. Results: The mean age of our sample was 52.45±7.87 and 71.7% of involved patients were women. Most patients were recorded as overweight or obese. The findings of disk space narrowing were mild in 65.7%, moderate in 28.7%, and severe in 5.6%, where the presence of osteophytes were small in 76.9%, moderate in 20.5%, and large in 2.6%. Regarding disability, two-third of cases were focused on minimal disability, followed by moderate, severe, and crippled as (26%), (6%), and (2%), respectively. There was highly significant association between women and pain radiation to legs (p=0.004). Obesity and overweight had meaningless effects on all markers. Conclusions: The severity of disability was significantly more in women, high intensity of lower back pain, presence of pain radiating to legs, moderate/severe disk space narrowing on X-ray, and disk degenerative disease score on X-ray, while age, presence of osteophytes and spondylolisthesis, body mass index, and pain duration were not associated with severity of disability.