Effect of load,processing time and due date variation on the effectiveness of scheduling rules

In real manufacturing environments, variations in production factors (i.e. processing time, demand, due-dates) are inevitable facts. All these dynamic changes, together with random disturbances (e.g. machine breakdowns) can seriously affect the system performance. In this paper we focus on load, processing time and due date variation and analyse their impacts on a scheduling system. Specifically, we investigate the impact of variation on dispatching policies in a job shop environment via simulation. The statistical analysis of the results leads to two major conclusions: first, the relative performance of rules is not threatened much by PV (processing time variation), LV (load variation) or DDV (due date variation) -- a result that can be a consolation for practitioners in the field. Secondly, the performance of the rules deteriorates, in particular at high levels of PV, LV and DDV -- a result that can provide new insights into the problem and produces useful information for researchers in their continuous effort to develop better dispatching rules.     

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation‐based selection method and as the output, the best features are selected. These selected features are validated through feed‐forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.     

A Review on Preparation,Properties and Applications of Polymeric Nanoparticle-Based Materials

Polymeric nanoparticles (PNPs)-based materials have gained much deliberation due to advancement in polymer science and technology. PNPs are playing an eccentric role in broad spectrum of applications. The importance of these materials lies in idiosyncratic features associated with PNPs. Due to the impact of the materials on nanotechnology; a comprehensive review has been presented here. We covered not only different techniques for the fabrication of PNPs, but also highlighted the properties and parameters involved and enhanced in PNP-based materials. Moreover the worth of PNP-based materials in applications regarding nanocomposites, drug delivery for cancer treatment, and photovoltaic relevance has been discussed.     

A Review on Properties and Fabrication Techniques of Polymer/Carbon Nanotube Composites and Polymer Intercalated Buckypapers

This review is a comprehensive source for synthesis, functionalization, and physical properties of polymer/carbon nanotube nanocomposites. The effectiveness of processing methods for carbon nanotube reinforcement in matrix for proper dispersion and appropriate interfacial adhesion is discussed. The novelty of polymer/carbon nanotube buckypaper fabrication with preformed networks through microfiltration of nanotube suspension has also been discussed. Moreover, preparation, properties, and manufacturing proficiencies of buckypaper are reviewed. Different approaches of intertwining buckypaper through infiltration, compression, soaking, and dry transfer have been analyzed. The polymer/carbon nanotube buckypaper obtained by vacuum infiltration has micron-scale bicontinuous morphology and improved thermal properties due to effectual heat transfer within nanotube rich phase.     

Design of carbon/glass/epoxy‐based radar absorbing composites: Microwaves attenuation properties

Due to high specific strength, fatigue resistance, and stiffness, reinforced plastic composites have great deal of relevance in aerospace applications such as electromagnetic interference shielding, radar absorption, etc. These radar absorption structures offer good mechanical properties, absorption characteristics, and less interference with external profile making them viable for aerospace structure design. Present venture trades the investigation concerning the attenuation of electrical component (electromagnetic waves) through the inclusion of different carbon‐based absorbers in glass/epoxy. Short carbon fibers like carbon black and multi‐walled carbon nanotubes were utilized as the lossy components. The samples were manufactured via vacuum assisted resin infusion technique which gave uniform thickness and minimum void content. Particles were mixed in resin and infused in the glass preform. The effects of changing the absorber materials, number of layers of glass fiber mat and glass fiber itself were analyzed. Sandwiched absorbing structures with aramid honeycomb were also fabricated. The samples were tested via free space measurement technique. Two charts of results were obtained i.e. one with dielectric parameters and other with the reflection loss. Short carbon fibers showed the best fallouts in this study. Moreover, thickness with 16 layers of glass fiber was also evidenced as optimum for the structure. POLYM. ENG. SCI., 54:2508–2514, 2014. © 2013 Society of Plastics Engineers     

Self‐assembled nanoblends of functional polystyrene and a reactive aramid: Morphological and thermomechanical profile

Various blends composed of pure polystyrene (PS) and modified PS [amino‐functionalized polystyrene (PS–NH₂)] were prepared with a new aromatic polyamide obtained through the polycondensation of 1,5‐diaminonaphthalene and 1,4‐phenylenediamine with isophthaloyl chloride. The variation in the morphological and thermophysical profiles in the two blend systems with aramid loading were investigated. The amine functionality introduced to PS enhanced its compatibility with the polyamide because of the formation of an aramid‐graft‐PS copolymer. The grafting, hydrogen bonding, and phenylene‐ring (π–π) stacking between the chains of the two components finally nurtured self‐assembled nanostructured blends. A strong compatibilizing effect was observed for the 50, 60, and 70 wt % aramid blends, where an exceptionally inimitable cocontinuous self‐assembled morphology was formed by PS–NH₂/aramid. Significant developments in the morphology along with thermal and mechanical stability were observed for the reactive PS/aramid system. The most favorable mechanical and thermal data supported by the finest nanostructure were observed with 70 wt % polyamide addition. Future prospects may involve the formation of nanotemplates and nanostructured membranes. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 39954.     

A study on high-performance poly(azo-pyridine-benzophenone-imide) nanocomposites via self-reinforcement of electrospun nanofibers

In this study, initially high molecular weight poly(azo-pyridine-benzophenone-imide) (PAPBI) has been fabricated using facile approach. Uniformly aligned electrospun PAPBI and PAPBI/multi-walled carbon nanotube (MWCNT) nanofibers were then produced via electrospinning of desired solutions. Self-reinforcement technique was used to fabricate PAPBI-based nanofiber reinforced films. Uniform dispersion, orientation and adhesion between carbon nanotubes and polymer improved the physical properties of resulting nanocomposites. Fourier transform infrared spectroscopy was used to identify the structures of polymer and self-reinforced nanocomposite films. Scanning and transmission electron microscopy showed that the electrospun PAPBI/MWCNT nanofibers were uniformly aligned and free of defects. Moreover, polyimide matrix was evenly coated on the surface of electrospun nanofibers, thus, preventing the fibers from bundling together. Samples of 1–3 wt% of as-prepared electrospun nanofibers were self-reinforced to enhance the tensile strength of the films. Films of 3 wt% PAPBI/MWCNT nanofiber-based nanocomposite showed higher value in tensile strength (417 MPa) relative to 3 wt% PAPBI nanofibers (361 MPa) reinforced film. Tensile modulus of the PAPBI/MWCNT system was also significantly improved (19.9–22.1 GPa) compared with PAPBI system (13.9–16.2 GPa). Thermal stability of PAPBI/MWCNT nanofibers reinforced polyimide was also superior having 10 % gravimetric loss at 600–634 °C and glass transition temperature 272–292 °C relative to the neat polymer (T ₁₀ 545 °C, T _g 262 °C) and PAPBI nanofiber-based system (T ₁₀ 559–578 °C, T _g 264–269 °C). New high-performance self-reinforced polyimide nanocomposites may act as potential contenders for light-weight aerospace materials.     

Auditory habituation in young and older adults: The verbal transformation effect.

In 3 experiments, auditory massed repetition was used to examine age-related differences in habituation by means of the verbal transformation paradigm. Participants heard 10 words (5 high frequency and 5 low frequency), each presented 180 times, and they reported perceived changes in the repeated words (verbal transformations). In these experiments, older adults reported fewer illusory percepts than young adults. Older adults' loss of auditory acuity and slowing of processing, stimulus degradation (in young adults), and instructions biasing the report of these illusory percepts did not account for the fewer illusory percepts reported by the older adults. These findings suggest that older adults' reduced susceptibility to habituation arises from centrally located declines in the transmission of information within the word recognition pathway. The discussion focuses on the implications that these age-related declines may have on word identification during on-line speech perception. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Deep Learning Based Face Detection and Identification of Criminal Suspects

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.     

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.