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%.

     

Investigation of gear performance of MLNGPs as an additive on polyamide 6 spur gear

Molded plastic gears have long provided an alternative to metal gears in lightly loaded drives. They transmit power quietly and often without lubrication in numerous applications, furthermore decrease the quantity of parts and oppose chemicals in numerous applications. Previously, plastic gears were restricted to to 0.25 hp because of varieties in their properties and uncertainties about how they react to natural conditions such as moisture, temperature and chemical. Today, better molding controls combined with design practices that more accurately encompass environmental factors have boosted plastic gear drive capacity to 1.5 hp. Using reinforcement this is standout amongst the most practices to enhance the gear performance.

This study estimated the effects of multilayer graphene nanoplatelets (MLNGPs) as an additive on polyamide 6 (PA6) spur gear performance. These include strength, elastic modulus, thermal stability, dynamic mechanical analysis, moisture absorption, and wear characteristics.The nanocomposite gear was made by melt mixing method and injection moulded into thick flanges. The flanges were machined using CNC milling machine to produce spur gear. The wear experiments were performed at a running speed of 1400 rpm and at torques of 13 and 16 Nm with different concentration 0, 0.1, 0.3 and 0.5 wt% MLNGPs using test rig. The result showed that 0.3% of MLGNPs is the optimum concentration. Young's modulus increased up to 40%, Vickers microhardness value increased up to 25%, storage modulus E’ is increased up to 37% and glass transition temperature is increased up to 14%. On the other hand TGA result shows that the Tonest increased up to 7.5% and Td increased up to 2%, and wear decreased by 35% at 16 Nm and 54% at 13 Nm.     

Analyzing cytogenetic chromosomal aberrations on fibrolamellar hepatocellular carcinoma detected by single-nucleotide polymorphs array

Fibrolamellar hepatocellular carcinoma is a unique malignant liver tumor type which arises in young adults and children. It is uncommon variation subtype of hepatocellular carcinoma which remains ineffectively recorded. Learning of cytogenetic changes in fibrolamellar hepatocellular carcinoma has lagged behind the information obtained from alternate entities of hepatocellular carcinoma lately. Gene expression profiling may prompt new biomarkers that may help develop diagnostic precision for distinguishing fibrolamellar hepatocellular carcinoma. The subatomic cytogenetic approach permits positional identification of gains, amplification, and deletion of DNA sequences of the whole tumor genome, to search for recurrent and particular cytogenetic changes in human fibrolamellar hepatocellular carcinoma. In this work, 13 cell lines of fibrolamellar carcinomas and 30 hepatocellular carcinoma samples examined by a single-nucleotide polymorphs array using two techniques to give more accuracy of the results. The majority of the abnormalities found in the fibrolamellar hepatocellular carcinoma positive cases seen as gain in 1q, 4q, 6q, 7p, 8q, 17q, 20q and loss in 1p, 4p-q, 8p, 11p, 13q, 17p, 18q, 19p, and 22q. The ultimate successive were central amplification at 1q (in 54% of 13 samples), 4q (in 54% of 13 samples), 7p (in 46% of 13 samples), and deletions at 19p13 (in 28% of 13 samples). The study revealed 3 distinct structural variations highlights-related genes MDM4, PRDM5, and WHSC1, and these genes are a novel target signature that can help to predict survival of patients with detecting fibrolamellar hepatocellular carcinoma.

     

An Ultra‐Shapeable,Smart Sensing Platform Based on a Multimodal Ferrofluid‐Infused Surface

The development of wearable, all‐in‐one sensors that can simultaneously monitor several hazard conditions in a real‐time fashion imposes the emergent requirement for a smart and stretchable hazard avoidance sensing platform that is stretchable and skin‐like. Multifunctional sensors with these features are problematic and challenging to accomplish. In this context, a multimodal ferrofluid‐based triboelectric nanogenerator (FO‐TENG), featuring sensing capabilities to a variety of hazard stimulus such as a strong magnetic field, noise level, and falling or drowning is reported. The FO‐TENG consists of a deformable elastomer tube filled with a ferrofluid, as a triboelectric layer, surrounded by a patterned copper wire, as an electrode, endowing the FO‐TENG with excellent waterproof ability, conformability, and stretchability (up to 300%). In addition, The FO‐TENG is highly flexible and sustains structural integrity and detection capability under repetitive deformations, including bending and twisting. This FO‐TENG represents a smart multifaceted sensing platform that has a unique capacity in diverse applications including hazard preventive wearables, and remote healthcare monitoring.     

Effect of the melt‐mixing condition on the physical property of poly(l‐lactic acid)/poly(d‐lactic acid) blends

The effect of the mixing condition in a mill‐type mixer on the thermal property and the crystal formation of the poly(l ‐lactide)/poly(d ‐lactide) blends is investigated. The blends melt‐mixed at 200 and 210 °C under application of a high shear flow tend to show a single melting peak of the stereocomplex crystal (SC) in the differential scanning calorimetry first and second heating processes without indicating the trace of the melting of homo‐chiral crystal. The mixing at an elevated temperature causes a serious thermal degradation. Further kneading of the blends at an elevated temperature higher than T_m of SC causes the transesterification between the same enatiomeric chains forming block copolymers of l ‐ and d ‐chains. This block copolymer acts as a nucleating agent of SC and the compatibilizing agent between poly(l ‐lactide) and poly(d ‐lactide) and promotes the formation of SC. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45489.     

Remarks on the Surface Area and Equality Conditions in Regular Forms Part II: Quadratic Prisms

Following the same methodology and rules that were previously applied in Part I of this work, this part presents the remarks of the mathematical analysis for the regular quadratic right prisms. These include the rectangular and isosceles trapezoidal rooms. The first remark examines the effect of θ (or θ and β) on S. The second remark calculates the minimum total surface area (S_Min) in two cases, case of constant θ (or θ and β) and case of variable θ (or θ and/or β). The third remark calculates the two ratios R_W and R _Wo . The last remark studies the required conditions for the numerical equality between (Per–Ar), and (S–V).     

InSb Based Microstrip Patch Antenna Temperature Sensor for Terahertz Applications

Reconfigurable sensing antennas (RSA) play a significant role in modern internet-of-things (IOT) applications. The RSAs are capable of transmiting and receiving electromagnetic waves besides sensing different enviroment parameters. This paper introduces a reconfigurable sensing microstrip patch antenna desinged to sense high temperature variations in harsh environment. The indium antimonide (InSb) semiconductor material is a temperature sensitive material employed in RSA designs in the Terahertz (THz) frequency band. An investigation of the temperature dependency of the electrical properties of the InSb-material is introduced. The proposed sensing antenna introduces high sensitivity of 1.588 GHz shift in resonance frequency per unit change in temperature (Kelvin). The resonance frequency of the InSb sensor antenna is changed according to the surrounding environment temperature from 264 GHz to 502.2 GHz with a broadband tuning range of 90.2%. The InSb patch sensor have temperature sensing range of 150 K starting from 250 up-to 400 K. At 300 K the InSb sensor antenna proposes a peak gain of 6.4 dBi with impedance matching bandwidth of 9.57%. An equivalent circuit consists of five lumped elements is estimated for the InSb sensor antenna using particle swarm optimization (PSO) technique at different temperatures. At T?=?250 K the maximum radiation efficiency is 2.5% and is increased up to 86.3% at T?=?400 K.