Innovative Firm Performance Management Using a Recommendation System Based on Fuzzy Association Rules: The Case of Vietnam’s Apparel Small and Medium Enterprises

Purpose: This study aims to apply a classification algorithm based-on fuzzy association rules (FARs) to improve the effectiveness of firms' performance prediction problem. Particularly, this study investigates potential FARs exists between inputs and outputs of firms' performance management process. These extracted FARs could be used to help firm’s managers make better dicision to improve firm’s performance.   Theoretical framework: Private enterprise development has been identified as key to Vietnam's economy that was commonly depended on state enterprise. For that, understanding and improving firms' performance and productivity is one of the most important tasks, from both macro and micro perspectives. There have been many studies on Vietnam's firm performance, but mostly relying on econometric methods that limit the understanding with structural equations. This study, instead, attempts to utilize new achievements of Artificial Intelligence (AI) for this task. Among AI techniques, fuzzy association rule is able to address the relationship between input factors and firm performance indicators. For each company, the finding FARs can be used to predict its performance and then change the business plan or react to improve weekness of organization.   Design/Methodology/Approach: The proposal model is applied on data of small and medium-sized enterprises (SMEs) of the apparel industry in Vietnam in the period 2010-2015. The sample consist of a total of 23637 observation of  Vietnam firms in apparel and textile industry and contains 16 main criterias for those firms.   Finding: A recommendation system (RS) is constructed from disclosed FARs and is a key factor in a novel innovative firms' performance management process. The percentage of classified instances using the mining FARs is not quite high (about 82%), but it is not always the case. Vietnam’s apparel dataset includes rare classes of ROA, therefore applying only frequent FARs is not enough. This issue can be fixed by using both frequent and infrequent FARs.       Research, practical & social implications: The proposed model has a great opportunity to use not only in the small and medium-sized enterprises (SMEs) of the apparel industry but other industrial sectors. FARs support the well-understand of firm performance to firm’s manager and help them better to react. Besides, FARs could be used to create RSs that makes alerts about risk automatically.   Originality/Value: The fact, our current study is the first to inspect the ability of FARs on SMEs of the apparel industry in Vietnam. This study provides theoritical potential knowledge and empirical evidence in the application of FARs technology in innovative firm’s management

SIP-MBA: A secure IoT platform with brokerless and micro-service architecture

The Internet of Things is one of the most interesting technology trends today. Devices in the IoT network are often geared towards mobility and compact in size, thus having a rather weak hardware configuration. There are many light weight protocols, tailor-made suitable for limited processing power and low energy consumption, of which MQTT is the typical one. The current MQTT protocol supports three types of quality-of-service (QoS) and the user has to trade-off the security of the packet transmission by transmission rate, bandwidth and energy consumption. The MQTT protocol, however, does not support packet storage mechanisms which means that when the receiver is interrupted, the packet cannot be retrieved. In this paper, we present a broker-less SIP-MBA Platform, designed for micro-service and using gRPC protocol to transmit and receive messages. This design optimizes the transmission rate, power consumption and transmission bandwidth, while still meeting reliability when communicating. Besides, we implement users and things management mechanisms with the aim of improving security issues. Finally, we present the test results by implementing a collect data service via gRPC protocol and comparing it with streaming data by using the MQTT protocol.Web of Science12759358

IoHT-MBA: An Internet of Healthcare Things (IoHT) platform based on microservice and brokerless architecture

Internet of Thing (IoT), currently, is one of the technology trends that are most interested. IoT can be divided into five main areas including: Health-care, Environmental, Smart city, Commercial and Industrial. The IoHT-MBA Platform is considered the backbone of every IoT architecture, so the optimal design of the IoHT-MBA Platform is essential issue, which should be carefully considered in the different aspects. Although, IoT is applied in multiple domains, however, there are still three main features that are challenge to improve: i) data collection, ii) users, devices management, and iii) remote device control. Today's medical IoT systems, often too focused on the big data or access control aspects of participants, but not focused on collecting data accurately, quickly, and efficiently; power redundancy and system expansion. This is very important for the medical sector - which always prioritizes the availability of data for therapeutic purposes over other aspects. In this paper, we introduce the IoHT Platform for Healthcare environment which is designed by microservice and brokerless architecture, focusing strongly on the three aforementioned characteristics. In addition, our IoHT Platform considers the five other issues including (1) the limited processing capacity of the devices, (2) energy saving for the device, (3) speed and accurate of the data collection, (4) security mechanisms and (5) scalability of the system. Also, in order for the IoHT Platform to be suitable for the field of health monitoring, we also add realtime alerts for the medical team. In the evaluation section, moreover, we describe the evaluation to prove the effectiveness of the proposed IoHT Platform (i.e. the proof-of-concept) in the performance, non-error, and non affected by geographical distance. Finally, a complete code solution is publicized on the authors' GitHub repository to engage further reproducibility and improvement.Web of Science12760159

High cortisol and cortisone levels are associated with breast milk dioxin concentrations in Vietnamese women

Objective: Dioxin (polychlorinated dibenzodioxins Cpolychlorinated dibenzofurans) is one of the most toxic chemical substances known. Although it is suspected to cause endocrine disruption, very few epidemiological studies have been carried out on its effects on human steroid hormones. The aim of this study was to elucidate the association of dioxin exposure with steroid hormone levels in the saliva and serum of Vietnamese women. Study design : Two areas, namely Phu Cat (hot spot) and Kim Bang (nonexposed area), were selected for the study. The study subjects consisted of 51 and 58 women respectively. Saliva, blood, and breast milk samples were collected from the subjects in both the areas. Methods: Cortisol, cortisone, DHEA, androstenedione, estrone, and estradiol levels in serum and saliva were determined by liquid chromatography-tandem mass spectrometry; dioxin concentrations in breast milk were measured by gas chromatography-mass spectrometry. Results: Dioxin concentrations in the breast milk of women from the dioxin hot spot were three to four times higher than those in the breast milk of women from the nonexposed area. Good correlations were found between the levels of six steroid hormones in saliva and those in serum respectively. Salivary and serum cortisol and cortisone levels in women from the dioxin hot spot were significantly higher than those in women from the nonexposed area (P<0.001) and those in all the subjects were positively associated with dioxin concentrations in Vietnamese women (P<0.01). Conclusion: These results suggest that dioxin influences steroidogenesis in humans. Saliva samples can be used for hormone analysis and are therefore excellent specimens in epidemiological studies. © 2014 European Society of Endocrinology

Levels of polychlorinated dibenzodioxins and polychlorinated dibenzofurans in breast milk samples from three dioxin-contaminated hotspots of Vietnam

We determined polychlorinated dibenzodioxin (PCDD) and polychlorinated dibenzofuran (PCDF) levels in breast milk of 143 primiparae living around the three most dioxin-contaminated areas of Vietnam. The women sampled lived in the vicinity of former U.S. air bases at Bien Hoa (n. =. 51), Phu Cat (n. =. 23), and Da Nang (n. =. 69), which are known as dioxin hotspots. Breast milk samples from Bien Hoa City, where residents live very close to the air base, showed high levels of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), with 18% of the samples containing >. 5. pg. TCDD/g lipid. However, Phu Cat residents lived far from the air base and their samples showed lower TCDD levels, with none containing >. 5. pg. TCDD/g lipid. In Da Nang, TCDD levels in mothers from Thanh Khe (close to the air base, n. =. 43) were significantly higher than those in mothers from Son Tra (far from the air base, n. =. 26), but not other PCDD and PCDF (PCDD/F) congeners. Although TCDD levels in Bien Hoa were the highest among these hotspots, levels of other PCDD/F congeners as well as the geometric mean concentration of total PCDD/F level in Bien Hoa (9.3. pg toxic equivalents [TEQ]/g lipid) were significantly lower than the level observed in Phu Cat (14.1. pg. TEQ/g lipid), Thanh Khe (14.3. pg. TEQ/g lipid), and Son Tra (13.9. pg. TEQ/g lipid). Our findings indicated that residents living close to former U.S. air bases were exposed to elevated levels of TCDD, but not of other PCDD/F congeners

Spatiotemporal evolution of SARS-CoV-2 Alpha and Delta variants during large nationwide outbreak of COVID-19, Vietnam, 2021

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions

Top-quark mass measurement in the all-hadronic tt¯ decay channel at √s=8 TeV with the ATLAS detector

The top-quark mass is measured in the all-hadronic top-antitop quark decay channel using proton-proton collisions at a centre-of-mass energy of √s=8 TeV with the ATLAS detector at the CERN Large Hadron Collider. The data set used in the analysis corresponds to an integrated luminosity of 20.2 fb−1. The large multi-jet background is modelled using a data-driven method. The top-quark mass is obtained from template fits to the ratio of the three-jet to the dijet mass. The three-jet mass is obtained from the three jets assigned to the top quark decay. From these three jets the dijet mass is obtained using the two jets assigned to the W boson decay. The top-quark mass is measured to be 173.72 ± 0.55 (stat.) ± 1.01 (syst.) GeV

Measurement of the double-differential high-mass Drell-Yan cross section in pp collisions at √s = 8 TeV with the ATLAS detector

This paper presents a measurement of the double-differential cross section for the Drell-Yan Z/γ∗ → ℓ+ℓ− and photon-induced γγ → ℓ+ℓ− processes where ℓ is an electron or muon. The measurement is performed for invariant masses of the lepton pairs, mℓℓ, between 116 GeV and 1500 GeV using a sample of 20.3 fb−1 of pp collisions data at centre-of-mass energy of √s = 8 TeV collected by the ATLAS detector at the LHC in 2012. The data are presented double differentially in invariant mass and absolute dilepton rapidity as well as in invariant mass and absolute pseudorapidity separation of the lepton pair. The single-differential cross section as a function of mℓℓ is also reported. The electron and muon channel measurements are combined and a total experimental precision of better than 1% is achieved at low mℓℓ. A comparison to next-to-next-to-leading order perturbative QCD predictions using several recent parton distribution functions and including next-to-leading order electroweak effects indicates the potential of the data to constrain parton distribution functions. In particular, a large impact of the data on the photon PDF is demonstrated

Measurements of inclusive and differential fiducial cross-sections of tt production with additional heavy-flavour jets in proton-proton collisions at √ s= 13 TeV with the ATLAS detector

This paper presents measurements of tt production in association with additional b-jets in pp collisions at the LHC at a centre-of-mass energy of 13 TeV. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1 fb−1. Fiducial cross-section measurements are performed in the dilepton and lepton-plus-jets tt decay channels. Results are presented at particle level in the form of inclusive cross-sections of tt final states with three and four b-jets as well as differential cross-sections as a function of global event properties and properties of b-jet pairs. The measured inclusive fiducial cross-sections generally exceed the ttbb predictions from various next-to-leading-order matrix element calculations matched to a parton shower but are compatible within the total uncertainties. The experimental uncertainties are smaller than the uncertainties in the predictions. Comparisons of state-of-the-art theoretical predictions with the differential measurements are shown and good agreement with data is found for most of them