Amination of biochar surface from watermelon peel for toxic chromium removal enhancement

Watermelon peel residues were used to produce a new biochar by dehydration method. The new biochar has undergone two methods of chemical modification and the effect of this chemical modification on its ability to adsorb Cr(VI) ions from aqueous solution has been investigated. Three biochars, Melon-B, Melon-BO-NH_2 and Melon-BO-TETA, were made from watermelon peel via dehydration with 50% sulfuric acid to give Melon-B followed by oxidation with ozone and amination using ammonium hydroxide to give Melon-BO-NH_2 or Triethylenetetramine(TETA) to give Melon-BO-TETA. The prepared biochars were characterized by BET, BJH,SEM, FT-IR, TGA, DSC and EDAX analyses. The highest removal percentage of Cr(VI) ions was 69% for Melon-B,98% for Melon-BO-NH_2 and 99% for Melon-BO-TETA biochars of 100 mg·L~(-1) Cr(VI) ions initial concentration and 1.0 g·L~(-1) adsorbents dose. The unmodified biochar(Melon-B) and modified biochars(Melon-BO-NH_2 and Melon-BO-TETA) had maximum adsorption capacities(Qm) of 72.46, 123.46, and 333.33 mg·g~(-1), respectively.The amination of biochar reduced the pore size of modified biochar, whereas the surface area was enhanced.The obtained data of isotherm models were tested using different error function equations. The Freundlich,Tempkin and Langmuir isotherm models were best fitted to the experimental data of Melon-B, Melon-BO-NH_2 and Melon-BO-TETA, respectively. The adsorption rate was primarily controlled by pseudo-second–order rate model. Conclusively, the functional groups interactions are important for adsorption mechanisms and expected to control the adsorption process. The adsorption for the Melon-B, Melon-BO-NH_2 and Melon-BO-TETA could be explained for acid–base interaction and hydrogen bonding interaction.     

Ultrasonic Assisted Nano-structures of Novel Organotin Supramolecular Coordination Polymers as Potent Antitumor Agents

Journal of Inorganic and Organometallic Polymers and Materials - The reaction of potassium tetracyanocupprate(I) with triethyltin bromide in presence of phenanthroline (Phen) and quinoxaline (Qox)...     

An accelerated algorithm for full band electron–phonon scattering rate computation

Computing scattering rates of electrons and phonons stands at the core of studies of electron transport properties. In the high field regime, the interactions between all electron bands with all phonon bands need to be considered. This full band interaction implies a huge computational burden in calculating scattering rates. In this study, a new accelerated algorithm is presented for this task, which speeds up the computation by two orders of magnitude (100 times) and dramatically simplifies the coding. At the same time, it visually demonstrates the physical process of scattering more clearly.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Intrusion detection alarms reduction using root cause analysis and clustering

As soon as the Intrusion Detection System (IDS) detects any suspicious activity, it will generate several alarms referring to as security breaches. Unfortunately, the triggered alarms usually are accompanied with huge number of false positives. In this paper, we use root cause analysis to discover the root causes making the IDS triggers these false alarms; most of these root causes are not attacks. Removing the root causes enhances alarms quality in the future. The root cause instigates the IDS to trigger alarms that almost always have similar features. These similar alarms can be clustered together; consequently, we have designed a new clustering technique to group IDS alarms and to produce clusters. Then, each cluster is modeled by a generalized alarm. The generalized alarms related to root causes are converted (by the security analyst) to filters in order to reduce future alarms’ load. The suggested system is a semi-automated system helping the security analyst in specifying the root causes behind these false alarms and in writing accurate filtering rules. The proposed clustering method was verified with three different datasets, and the averaged reduction ratio was about 74% of the total alarms. Application of the new technique to alarms log greatly helps the security analyst in identifying the root causes; and then reduces the alarm load in the future.     

Rate-dependent transient extrusion

Plane strain slip line field solutions are developed for deformation at the edge of a cylindrical billet when this is backward extruded to form a thin-walled cup. The extrusion pressure for a perfectly plastic material is then derived assuming homogeneous compression in the centre of the billet. A geometric factor, derived from the slip line field, is identified which permits the extrusion pressure to be determined for a nonlinear viscous material.Theoretical results agree well with recent experimental data on back extrusion of highly rate-sensitive superplastic alloys. It is shown that an apparent steady state exists in the process and that extrusion pressure increases with ram speed with a rate index approximately equal to that of the material. The punch profile shape has a greater effect on extrusion pressure for viscous materials than for perfectly plastic ones. The method of analysis appears to have a generality beyond the particular process considered and may be used to optimize extrusion equipment designs.     

Anticonvulsant Profiles of Certain New 6-Aryl-9-substituted-6,9-diazaspiro-[4.5]decane-8,10-diones and 1-Aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones

Synthesis and anticonvulsant potential of certain new 6-aryl-9-substituted-6,9-diazaspiro[4.5]decane-8,10-diones (6a–l) and 1-aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones (6m–x) are reported. The intermediates 1-[(aryl)(cyanomethyl)amino]cycloalkanecarboxamides (3a–f) were prepared via adopting Strecker synthesis on the proper cycloalkanone followed by partial hydrolysis of the obtained nitrile functionality and subsequent N-cyanomethylation. Compounds 3a–f were subjected to complete nitrile hydrolysis to give the respective carboxylic acid derivatives 4a–f which were cyclized under mild conditions to give the spiro compounds 5a–f. Ultimately, compounds 5a–f were alkylated or aralkylated to give the target compounds 6a–i and 6m–u. On the other hand, compounds 6j–l and 6v–x were synthesized from the intermediates 5a–f through alkylation, dehydration and finally tetrazole ring formation. Anticonvulsant screening of the target compounds 6a–x revealed that compound 6g showed an ED₅₀ of 0.0043 mmol/kg in the scPTZ screen, being about 14 and 214 fold more potent than the reference drugs, Phenobarbital (ED₅₀ = 0.06 mmol/kg) and Ethosuximide (ED₅₀ = 0.92 mmol/kg), respectively. Compound 6e exhibited an ED₅₀ of 0.019 mmol/kg, being about 1.8 fold more potent than that of the reference drug, Diphenylhydantoin (ED₅₀ = 0.034 mmol/kg) in the MES screen. Interestingly, all the test compounds 6a–x did not show any minimal motor impairment at the maximum administered dose in the neurotoxicity screen.