A Recipe for Success, Necessary Dimensions of Operations Management: A Case Study based on Walmart\u27s Triumphs

Purpose: This paper aims to analyse operations management\u27s importance in three main dimensions: having a successful strategy, an efficient supply chain management system, and continuous innovation. The case of Walmart is used to consolidate these three areas. The main methodology of this paper is quantitative. The previous empirical literature on operations management and Walmart will be utilized to assess three important areas of operations management that are relevant for business today. Findings: The results indicate that strategy, supply chain management, and innovation play an important role in effective operations management. Particularly, the research on Walmart has shown that a low-price policy (EDLP) strategy, streamlined supply chain management by constructing communication and relationship networks with suppliers, and routine innovation have enabled Walmart’s success. Limitations: The research only focuses on three operations management areas. Moreover, this research paper only studies Walmart’s case. Thus, its findings may not be generalizable unless more case studies can be analyzed

Influence of heterostructure on structure, electric and magnetic properties of Bi_0.5(Na_0.80,K_0.20)_0.5TiO₃/BaZrO₃ films prepared by the sol-gel method

This study reports on the structure, electric, and magnetic properties of Bi0.5(Na0.80,K0.20)0.5TiO3/BaZrO3 (BNKT/BZO) heterolayered films synthesized via chemical solution deposition on Pt/Ti/SiO2/Si substrates. The influence of different heterolayered configurations on the microstructure, electric and magnetic properties of the films was investigated thoroughly. The heterostructures are expected to generate strongly correlated electron systems in the BNKT and BZO layers that cause a magnetic interface effect in the BNKT/BZO conjunction layer. The BZO layer also prevents metal ion evaporation, resulting in a decline in oxygen vacancies and an enhancement in the electric and magnetic properties. The obtained results show that magnetic properties and leakage current density (J) of BNKT/BZO heterolayered films were greatly improved thanks to the heterolayered structure. Heterolayered 4BNKT/2BZO films (M42) yield the highest M s and M r values of 14.4 emu cm−3 and 1.7 emu cm−3, respectively, about three times higher than multilayered BNKT. Thanks to heterolayered structure, J decreases strongly from 16.0 × 10−4 A cm−2 for BNKT films to 1.4 × 10−4 A cm−2 for heterolayered M42 films. It has been verified that the leakage current in BNKT/BZO heterolayered films follows the Schottky barrier mechanism, with the barrier height fluctuating between 0.80 eV and 0.92 eV. The results of the study show that BNKT/BZO heterolayered films may be suitable for use in environmentally friendly multifunction devices.</p

Organic Fertilizer Production and Application in Vietnam

Crop production is an important subsector of Vietnam’s agriculture, has an impressive achievement in last 30 years and based on the intensive production with increasing use of chemical fertilizer and pesticide. Consequences are the negative effects on environment and human health and food safety. Organic agriculture has become a trend worldwide and is developing rapidly in the world. In Vietnam the certified organic farming area has expanded since 2012. Organic market revenue in Vietnam is estimated to be at $132.15 million a year. Most Vietnamese certified organic products are exported to international markets. Organic agriculture using organic fertilizer is one of Vietnam government’s priorities. Vietnam already produced organic fertilizer from different materials by using different production technologies, but the production capacity is small and does not meet the demand for organic agriculture. Vietnam government encourages, promotes the organic fertilizer production, application and has the policy to develop the organic fertilizer in Vietnam

UHPLC-UV method validation for simultaneous quantification of vitexin and isovitexin from Santalum album L. leaves

Santalum album L. is a precious medicinal herb with high economic value and has been extensively cultivated in Vietnam in recent years. Studies have revealed that the leaves contain two main active ingredients vitexin and isovitexin, which have demonstrated significant potential in treating diabetes, cancer, and inflammation. To contribute to the standardization of the title medicinal herb and its formula, a simple, fast precise and selective method for the simultaneous quantification of vitexin and isovitexin using ultra-performance liquid chromatography (UHPLC) has been developed and validated. The quantification procedure was performed on a Hypersil GOLD aQ Column (3 μm; 150 × 2.1 mm) at 35°C, with a mobile phase of acetonitrile (A) and and water with 0.1% formic acid (B), a flow rate of 0.3 mL/min, a detection wavelength of 336 nm, and an injection volume of 3 µL. The gradient program was set to 0.0-15.0 minutes, transitioning from 5% to 35% A, and 15.0-20.0 minutes, transitioning from 35% to 5% A. Validation of the quantification procedure, following ICH Q2 (R2) guidelines, demonstrated that the method achieved specificity, accuracy, precision, and linearity, with a high correlation between the peak area and the concentrations of vitexin and isovitexin (R2 values of 0.9998, respectively). Thus, the developed method can be utilized to determine the content of vitexin and isovitexin in Santalum album L. leaves, contributing to the standardization of medicinal herbs

Deep Learning-Aided Multicarrier Systems

This paper proposes a deep learning (DL)-aided multicarrier (MC) system operating on fading channels, where both modulation and demodulation blocks are modeled by deep neural networks (DNNs), regarded as the encoder and decoder of an autoencoder (AE) architecture, respectively. Unlike existing AE-based systems, which incorporate domain knowledge of a channel equalizer to suppress the effects of wireless channels, the proposed scheme, termed as MC-AE, directly feeds the decoder with the channel state information and received signal, which are then processed in a fully data-driven manner. This new approach enables MC-AE to jointly learn the encoder and decoder to optimize the diversity and coding gains over fading channels. In particular, the block error rate of MC-AE is analyzed to show its higher performance gains than existing hand-crafted baselines, such as various recent index modulation-based MC schemes. We then extend MC-AE to multiuser scenarios, wherein the resultant system is termed as MU-MC-AE. Accordingly, two novel DNN structures for uplink and downlink MU-MC-AE transmissions are proposed, along with a novel cost function that ensures a fast training convergence and fairness among users. Finally, simulation results are provided to show the superiority of the proposed DL-based schemes over current baselines, in terms of both the error performance and receiver complexity

A Proposed CNN Model for Audio Recognition on Embedded Device

The audio detection system enables autonomous cars to recognize their surroundings based on the noise produced by moving vehicles. This paper proposes the utilization of a machine learning model based on convolutional neural networks (CNN) integrated into an embedded system supported by a microphone. The system includes a specialized microphone and a main processor. The microphone enables the transmission of an accurate analog signal to the main processor, which then analyzes the recorded signal and provides a prediction in return. While designing an adequate hardware system is a crucial task that directly impacts the predictive capability of the system, it is equally imperative to train a CNN model with high accuracy. To achieve this goal, a dataset containing over 3000 up-to-5-second WAV files for four classes was obtained from open-source research. The dataset is then divided into training, validation, and testing sets. The training data is converted into images using the spectrogram technique before training the CNN. Finally, the generated model is tested on the testing segment, resulting in a model accuracy of 77.54%

Some Limitations of ASEAN Organization in 55 Years of Operation

Association of Southeast Asian Nations (English : Association of South East Asian Nations, abbreviated as ASEAN) is a political , economic , cultural and social organization of countries in the Southeast region. Ah . ASEAN has a land area of 4.46 million km², accounting for 3% of the total land area of the Earth , and has a population of about 600 million people, accounting for 8.8% of the world's population . The waters of ASEAN are three times as large as land. Over 55 years of establishment and development, this is considered the most successful regional organization on earth. However, when it comes to ASEAN, people often talk about achievements, in the framework of this article, we would like to give some opinions on its limitations, as a basis for recommendations for leaders of the organization in this study. Keywords:ASEAN, limit, learn about, 55 years DOI: 10.7176/JESD/13-24-06 Publication date: December 31st 202

FPGA hardware acceleration framework for anomaly-based intrusion detection system in IoT

This study proposes a versatile framework for realtime Internet of Things (IoT) network intrusion detection using Artificial Neural Network (ANN) on heterogeneous hardware. With the increase in the volume of exchanged data, IoT networks' security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation ability to detect new attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. In this paper, a hierarchical decision-making approach for IDS is proposed and evaluated on the new IoT-23 dataset, with improved accuracy over the software-based methods. The inference engine is implemented on the Xilinx FPGA System on a Chip (SoC) hardware platform for high performance, high accuracy attack detection (more than 99.43%). For the resulting implemented design, the processing time of the ANN model on FPGA with an xc7z020clg400 device is 6.6 times and 40.5 times faster than GPU Quadro M2000 and CPU E5-2640 2.60GHz, respectively