A Comparative Study of LAMP and PCR in Relation to Time and Cost

Background: The Loop-mediated isothermal amplification (LAMP) represents a very sensitive, easy to use, and less time consuming diagnostic method. Aims: The aim was to establish a simple, cost-effective, molecular technique. Materials and methods: An analytical study was conducted using two hundred acute serum samples using two different molecular techniques; qPCR and LAMP to standardize a costeffective and less time-consuming technique. Results: The cost of in-house LAMP reagents was one-ninth of the cost of commercial qPCR. Consume cost was 23 times less than qPCR besides, lab setup cost was 92 times less than qPCR. More importantly, LAMP requires 5-6 times less time duration than qPCR. Conclusion: Due to its simple short-time operation with low cost, it would be a prevalent molecular technique globally, particularly in Bangladesh. J Shaheed Suhrawardy Med Coll 2020; 12(2): 72-75

Smart Pothole Detection Using Deep Learning Based on Dilated Convolution

Roads make a huge contribution to the economy and act as a platform for transportation. Potholes in roads are one of the major concerns in transportation infrastructure. A lot of research has proposed using computer vision techniques to automate pothole detection that include a wide range of image processing and object detection algorithms. There is a need to automate the pothole detection process with adequate accuracy and speed and implement the process easily and with low setup cost. In this paper, we have developed efficient deep learning convolution neural networks (CNNs) to detect potholes in real-time with adequate accuracy. To reduce the computational cost and improve the training results, this paper proposes a modified VGG16 (MVGG16) network by removing some convolution layers and using different dilation rates. Moreover, this paper uses the MVGG16 as a backbone network for the Faster R-CNN. In addition, this work compares the performance of YOLOv5 (Large (Yl), Medium (Ym), and Small (Ys)) models with ResNet101 backbone and Faster R-CNN with ResNet50(FPN), VGG16, MobileNetV2, InceptionV3, and MVGG16 backbones. The experimental results show that the Ys model is more applicable for real-time pothole detection because of its speed. In addition, using the MVGG16 network as the backbone of the Faster R-CNN provides better mean precision and shorter inference time than using VGG16, InceptionV3, or MobilNetV2 backbones. The proposed MVGG16 succeeds in balancing the pothole detection accuracy and speed.

An Order-Up-to Inventory Model with Sustainability Consideration

Along with growing interest in environmental concerns these days, significant academic efforts have been exerted to incorporate sustainability issues into the existing inventory models except for fixed-review interval (i.e., order-up-to models). In this study, we develop an order-up-to model considering environment-related costs and investigate the value of this new policy over the naïve one. Results of an extensive simulation study reveal that sustainability consideration reduces the total costs and that its value is higher when the mean demand is higher, when demand is more variable, when the costs of transshipment or inventory holding are lower, or when an ordering setup cost or an additional indirect cost of having inventory are higher. These findings fill the research gap in existing literature and contribute to managerial implications for periodic inventory control in practice.

Comparison of laser beam, oxygen and plasma arc cutting methods in terms of their advantages and disadvantages in cutting structural steels

The laser beam, plasma arc, and oxygen cutting methods are widely used in metal cutting processes. These methods are quite different from each other in terms of initial setup cost and cutting success. A powered laser beam is used in laser beam cutting, plasma is used in plasma arc cutting, flammable gas - oxygen mixture is used in the oxygen cutting method. In this study, the cutting success of these methods was investigated on tensile specimens. Microstructure, hardness (HV 0.1), surface roughness, and strengths were investigated after the cutting process. The tensile test implemented with tensile samples cut from the same material by these three methods, it was observed that the strength values of the samples changed by about 8% in tensile strength depending on the cutting process. The hardness of the cut surfaces in plasma arc cutting increased from 150 HV to 230 HV for S235JR material. For this reason, it is difficult to perform machining operations after plasma cutting. The hardness value reached after laser beam cutting is 185 HV. Plasma arc cutting is more cost-effective than laser beam cutting. 1-3° vertical inclination (conicity) occurs on the cut surface in plasma arc cutting, while this inclination almost does not occur in laser cutting. In plasma cutting benches, cutting is done with oxygen, and in cutting with oxygen, the taper is seen in a small amount.

Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm

Inventory cost control is an essential factor in supply chain management. If the supplier’s inventory is insufficient, then the chance to trade the product will be reduced. The manufacturer’s inadequate material inventory will have an effect in termination of production, delays, and a waste of resources and time. On the other hand, postponed transportation will certainly raise costs such as transportation costs and cancellation of orders. Therefore, the operation costs of enterprises will be more, which will lower profits. In conventional supply chains, inventory costs control is not feasible for the view of the entire supply chain. The main intent of this paper is to plan for intelligent inventory management using blockchain technology under the cloud sector. The inventory management of the supply chain includes “multiple suppliers, a manufacturer, and multiple distributors”. The proposed inventory management models consider some significant costs like “transaction cost, inventory holding cost, shortage cost, transportation cost, time cost, setup cost, backordering cost, and quality improvement cost”. This multi-objective cost function is minimized by a novel hybrid optimization algorithm; the concept of WOA is integrated to produce the new algorithm which is termed as Whale-based Multi Verse Optimization (W-MVO) algorithm. For securing the data of distributors, using blockchain technology in a cloud environment helps from the leakage of data to other unauthorized users. Once the cost is reduced in all aspects based on the proposed hybrid optimization algorithm, the distributer will store the concerning data in the blockchain under the cloud sector, where each distributer holds a hash function to store its data, which cannot be restored by the other distributers. The valuable performance analysis over the conventional optimization algorithms proves the effective and reliable performance of the proposed model over the conventional models.

Environmental Sustainability By Recycling of PET Plastic Bottles And Reusing Them In Textile Industry: A Game-Theoretic Approach

This study considers a sustainable supply chain including the collector, cleaner, and recycler for recycling of PET plastic bottles and reusing them in textile industry. In the market, some suppliers of textile industry purchase cleaned and non-fragmented bottles and then they fragment them, whereas others prefer to buy recycled materials (i.e., cleaned and fragmented bottles). The collector collects used plastic bottles. To meet demand of the recycled materials, the collector transfers a portion of the collected bottles to the recycler and then the recycler cleans and fragments them. Furthermore, the collector cleans another portion of the collected bottles himself or via a cleaner to meet demand of the cleaned and non-fragmented bottles. In this setting, two different structures are established for transferring the cleaned bottles to suppliers. Under the first structure, the collector cleans the collected bottles through the cleaner by giving a share of the profit to him, while he is equipped with the bottles cleaning technology by paying a setup cost under the second structure. Moreover, the game-theoretic models are developed including Nash, Stackelberg, and Centralized to make decisions under two considered structures.

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.