Leveraging Open-standard Interorganizational Information Systems for Process Adaptability and Alignment: An Empirical Analysis

PurposeThe purpose of this paper is to understand the value creation mechanisms of open-standard inter-organizational information system (OSIOS), which is a key technology to achieve Industry 4.0. Specifically, this study investigates how the internal assimilation and external diffusion of OSIOS help manufactures facilitate process adaptability and alignment in supply chain network.Design/methodology/approachA survey instrument was designed and administrated to collect data for this research. Using three-stage least squares estimation, the authors empirically tested a number of hypothesized relationships based on a sample of 308 manufacturing firms in China.FindingsThe results of the study show that OSIOS can perform as value creation mechanisms to enable process adaptability and alignment. In addition, the impact of OSIOS internal assimilation is inversely U-shaped where the positive effect on process adaptability will become negative after an extremum point is reached.Originality/valueThis study contributes to the existing literature by providing insights on how OSIOS can improve supply chain integration and thus promote the achievement of industry 4.0. By revealing a U-shaped relationship between OSIOS assimilation and process adaptability, this study fills previous research gap by advancing the understanding on the value creation mechanisms of information systems deployment

Evaluation of roughness, hardness, and strength of AA 6061 molds for manufacturing polymeric microdevices

In the manufacturing of polymeric microfluidic devices, micro-molds play a key role because they determine not only the manufacturing cost but also the quality of the molded parts. Recently, a high-quality aluminum alloy 6061 (AA6061) mold with fine features less than its grain size has been fabricated economically by a hot embossing technique. However, temperature cycling during hot embossing process in mold manufacturing reduces significantly the original tensile strength and hardness of the AA6061-T6 alloy substrate, which is not desirable. In this study, a tempering process is carried out to recover the tensile strength and hardness of the embossed mold. To evaluate the changes of these properties, surface roughness, tensile strength, and hardness values were measured in each stage: (1) before hot embossing, (2) after hot embossing, and (3) tempering to T4 and tempering to T6. The results obtained demonstrate that the original strengths and hardness can be fully recovered by a post-tempering process after hot embossing, but with an increase in surface roughness. Moreover, accelerated testing was carried out to evaluate the changes in hardness and roughness of AA6061-T4 and T6 molds under the typical hot embossing temperature cycles of manufacturing polymeric devices. The results obtained indicate that these temperature cycles have only a minor effect on the roughness of both T4 and T6 molds and will increase the hardness of T4 molds to T6 temper, and have negligible effect on the hardness of a T6 temper mold

Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation

Common measures of brain functional connectivity (FC) including covariance and correlation matrices are semi-positive definite (SPD) matrices residing on a cone-shape Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under an adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, e.g., brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.Comment: 10 pages, 4 figure

Assessing the performance of the simple noise chart method for construction noise prediction in earth-moving activity

Construction activity has long been associated with health problems caused by excessive noise exposure from the high noise emission machines. Indeed, predicting noise levels during the planning stages of a construction project can be challenging, particularly when considering complex and dynamic noise sources. This study aims to determine the accuracy and reliability of the simple prediction charts method in predicting construction noise. A case study of piling activity had been conducted at a construction site in Klang valley, Malaysia. The results showed that the average predicted noise levels were slightly higher than the actual measurements, but the highest absolute difference was only 0.9 dBA. The simple prediction charts can approximate the sound pressure level with high reliability with R2 values of 0.9959. These results show that the simple prediction charts can accurately and reliably predict construction noise levels, providing a useful tool for predicting the noise levels from earthmoving machines at any point of the construction site. With the help of these charts, construction noise practitioners can more easily anticipate and manage potential noise issues

Environmental noise impact assessment from mass rapid transit to the adjacent community

Due to the extension of the Mass Rapid Transit (MRT) network throughout the years, the noise generated from MRT had increased and this environmental noise tends to cause annoyance to the adjacent community. The aim of this research is to measure the environmental noise level generated by MRT at one of the MRT stations in Kuala Lumpur, Malaysia, compare the permissible limit and community annoyance response to the measured noise impact to the surrounding community. A total of 4 monitoring points were selected for environmental noise impact assessment. After that, a sound level meter was set up at these monitoring points to measure the sound level. For each monitoring point, the noise level was measured for 6 hours continuously from 7:00 am to 1:00 pm. Based on the results, the noise levels at critical monitoring points exceeded 9.23 % in Taman Midah MRT Station. Little community annoyance is caused in the study areas and this will affect the quality of life of the adjacent community. In conclusion, the environmental noise impact assessment was carried out to safeguard the noise level to be within the noise limit and to enhance the quality of life of the community alongside MRT

AI chatbots: Perspective from practitioners

In a recent advisory paper on the use of generative artificial intelligence (AI) in higher education (bit.ly/mqaai), the Malaysian Qualifications Agency (MQA) provided a comprehensive analysis of the potential benefits, drawbacks, and suggested approaches for higher education providers, academic staff, and students. The letter emphasizes the importance of maintaining academic integrity when utilizing generative AI in order to preserve the standard of education

Physical properties and stability evaluation of fish oil-in-water emulsions stabilized using thiol-modified β-lactoglobulin fibrils-chitosan complex

Fish oil-in-water emulsions containing fish oil, thiol-modified β-lactoglobulin (β-LG) fibrils, chitosan and maltodextrin were fabricated using a high-energy method. The results showed that chitosan coating induced charge reversal; denoting successful biopolymers complexation. A significantly (p < 0.05) larger droplet size and lower polydispersity index value, attributed to the thicker chitosan coating at the oil-water interface, were observed. At high chitosan concentrations, the cationic nature of chitosan strengthened the electrostatic repulsion between the droplets, thus conferring high oxidative stability and low turbidity loss rate to the emulsions. The apparent viscosity of emulsions stabilized using thiol-modified β-LG fibrils-chitosan complex was higher than those stabilized using β-LG fibrils alone, resulting in the former's higher creaming stability. Under thermal treatments (63 °C and 100 °C), emulsions stabilized using thiol-modified β-LG fibrils-chitosan complex possessed higher heat stability as indicated by the consistent droplet sizes observed. Chitosan provided a thicker protective layer that protected the oil droplets against high temperature. Bridging flocculation occurred at low chitosan concentration (0.1%, w/w), as revealed through microscopic observations which indicated the presence of large flocs. All in all, this work provided us with a better understanding of the application of protein fibrils-polysaccharide complex to produce stable emulsion

Characterisation of alkaline treatment and fiber content on the physical, thermal and mechanical properties of ground coffee waste/oxo-biodegradable HDPE Biocomposites

Effect of alkali treatment on ground coffee waste/oxo-biodegradable HDPE (GCW/oxoHDPE) composites was evaluated using 5%, 10%, 15% and 20% volume fraction of GCW. The composites were characterize using structurally (fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM)), thermally (thermogravimetry analysis (TGA) and differential scanning calorimetry (DSC)), mechanically (tensile and impact test), and water absorption. FTIR spectrum indicated the eradication of lipids, hemicellulose, lignin and impurities after the treatments the lead to an improvement of the filler/matrix interface adhesion. This is confirmed by SEM results. Degree of crystallinity index was increased by 5% after the treatment. Thermal stability for both untreated and treated GCW composites were alike. Optimum tensile result achieved when using 10% volume fraction with enhancement of 25% for tensile strength and 24% for tensile modulus compared to untreated. Specific tensile strength and modulus had improved as the composite has lower density. The highest impact properties was achieved when using 15% volume fraction that lead to an improvement of 6%. Treated GCW composited show a better water resistance with 57% improvement compared to untreated. This light weight and ecofriendly biocomposite has the potential in packaging, internal automotive parts, lightweight furniture and other composite engineering applications

Physicochemical and sensory analyses of high fibre bread incorporated with corncob powder

The primary objectives of the present work were to produce corncob powder (CCP) from corncobs and incorporate the CCP into bread formulation in order to develop high fibre bread, and to investigate the physicochemical and sensory properties of the produced high fibre bread (HFB). The corncobs were collected and washed before they underwent the grinding and drying processes. The obtained CCP was incorporated into the bread formulation in three different proportions (5, 10 and 20%) to partially substitute bread flour in the formulation. All three bread samples and the control (0% CCP in the formulation) were analysed to obtain their physicochemical and sensory properties. The incorporation of CCP significantly affected the texture, colour and volume attributes of the produced breads. Increasing the content of CCP in the formulation was found to be responsible for firmer, smaller and darker bread loaves as compared to the composite bread samples. The bread formulation incorporated with 10% CCP had the highest mean scores (7.00) of overall acceptability among all the other formulations, and it was comparable to the commercial breads in the current market

Production, safety, health effects and applications of diacylglycerol functional oil in food systems: a review

Diacylglycerol (DAG) is a world leading anti-obesity functional cooking oil synthesized via structural modification of conventional fats and oils. DAG exits in three stereoisomers namely sn-1,2-DAG, sn-1,3-DAG, and sn-2,3-DAG. DAG particularly sn-1,3-DAG demonstrated to have the potential in suppressing body fat accumulation and lowering postprandial serum triacylglycerol, cholesterol and glucose level. DAG also showed to improve bone health. This is attributed to DAG structure itself that caused it to absorb and digest via different metabolic pathway than conventional fats and oils. With its purported health benefits, many studies attempt to enzymatically or chemically synthesis DAG through various routes. DAG has also received wide attention as low calorie fat substitute and has been incorporated into various food matrixes. Despite being claimed as healthy cooking oil the safety of DAG still remained uncertain. DAG was banned from sale as it was found to contain probable carcinogen glycidol fatty acid esters. The article aims to provide a comprehensive and latest review of DAG emphasizing on its structure and properties, safety and regulation, process developments, metabolism and beneficial health attributes as well as its applications in the food industry