Miracle-dz/UniFAR-FTUs huggingface dataset:
READ: Robustness-Oriented Edge Application Deployment in Edge Computing Environment

In recent years, edge computing has emerged as a prospective distributed computing paradigm that overcomes several limitations of cloud computing. In the edge computing environment, a service provider can deploy its application instances on edge servers at the edge of the network to serve its own users with low latency. Given a limited budget <inline-formula><tex-math notation="LaTeX">$\mathcal {K}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">K</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3015316.gif"/></alternatives></inline-formula> for deploying applications on the edge servers in a particular geographical area, a number of approaches have been proposed very recently to determine the optimal deployment strategy that achieves various optimization objectives, e.g., to maximize the servers’ coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider’s applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile edge computing environment. In this article, we make the first attempt to tackle this challenge. Specifically, we formulate this <italic>Robustness-oriented Edge Application Deployment</italic> (READ) problem as a constrained optimization problem and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3015316.gif"/></alternatives></inline-formula>-hardness. Then, we provide an integer programming based approach named READ-<inline-formula><tex-math notation="LaTeX">$\mathcal {O}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">O</mml:mi></mml:math><inline-graphic xlink:href="li-ieq3-3015316.gif"/></alternatives></inline-formula> for solving this problem precisely. We also provide an approximation algorithm, namely READ-<inline-formula><tex-math notation="LaTeX">$\mathcal {A}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">A</mml:mi></mml:math><inline-graphic xlink:href="li-ieq4-3015316.gif"/></alternatives></inline-formula>, for finding near-optimal solutions to large-scale READ problems efficiently. We prove its approximation ratio is not worse than <inline-formula><tex-math notation="LaTeX">$\mathcal {K}/2$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">K</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq5-3015316.gif"/></alternatives></inline-formula>, which is a constant regardless of the total number of edge servers. We evaluate our approaches experimentally on a widely-used real-world dataset against five representative approaches. The experiment results demonstrate that our approaches can solve the READ problem effectively and efficiently.
Increasing the Shelf Life of Milk by Metal Oxide Nanoparticles and Mild Heat

Background: The spread of pathogenic microorganisms in food and beverage and their resistance to antibiotics have raised major concerns for public health. The aim of this study was to investigate the antimicrobial activity of various metal oxide nanoparticles (NPs) including zinc oxide (ZnO), magnesium oxide (MgO), and iron oxide (Fe2O3) NPs against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Furthermore, the antimicrobial activity of these NPs in milk was studied along with mild heat. Methods: In this experimental study, the antibacterial activity of ZnO, MgO, and Fe2O3 NPs were initially evaluated by minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) methods. Later, the antimicrobial effect of these NPs was investigated in milk along with mild heating. To determine the morphological changes in S. aureus and E. coli, electron microscopy scanning was applied before and after the antimicrobial treatments. Results: The MBC and MIC values presented by Fe2O3, ZnO, and MgO NPs against pathogenic bacteria showed that MgO NPs were the most potent substances for inhibiting the growth of S. aureus and E. coli. The results also indicated that use of these NPs had synergistic effects in combination with the heating treatment. Electron microscopy scanning also revealed that treatment with MgO NPs could distort and impair the cell wall of the pathogenic bacteria, leading to the leakage of intracellular components and bacterial death. Conclusion: The results suggest that MgO, ZnO, and Fe2O3 NPs can be applied for industrial food processing as effective antimicrobial compounds to decrease the temperature required for pasteurizing milk.
Imaging with surface-related multiples to overcome large acquisition gaps

To get the best result for seismic imaging using primary reflections, data with densely-spaced sources and receivers are ideally preferred. However, dense acquisition can sometimes be hindered by various obstacles, like platforms or complex topography. Such areas with large data gaps may deter exploration or monitoring, as conventional imaging strategies would either provide poor seismic images or turn out to be very expensive. Surface-related multiples travel along different paths compared to primaries, illuminating a wider subsurface area and hence making them valuable in case of data with large gaps. We propose different strategies of using surface-related multiples to get around the problem of imaging in the case of a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield in the forward-modelling process, which makes it still sensitive to missing data. We introduce a ‘non-linear’ inversion approach in which the surface multiples are modelled from the original source field. This makes the method less dependent on the receiver geometry, therefore, effectively exploiting the information from surface multiples in cases of limited illumination. However, such an approach is sensitive to the knowledge of the source properties. Therefore, we propose a ‘hybrid’ method that combines the non-linear imaging method with the conventional ‘linear’ multiple imaging method, which further improves our imaging result. We test the methods on numerical as well as field data. The results indicate substantial removal of artefacts in the image derived from linear imaging methods due to incomplete data, by exploiting the surface multiples to a maximum extent.
Iterative and Noniterative Splitting Methods of the Stochastic Burgers’ Equation: Theory and Application

In this paper, we discuss iterative and noniterative splitting methods, in theory and application, to solve stochastic Burgers’ equations in an inviscid form. We present the noniterative splitting methods, which are given as Lie–Trotter and Strang-splitting methods, and we then extend them to deterministic–stochastic splitting approaches. We also discuss the iterative splitting methods, which are based on Picard’s iterative schemes in deterministic–stochastic versions. The numerical approaches are discussed with respect to decomping deterministic and stochastic behaviours, and we describe the underlying numerical analysis. We present numerical experiments based on the nonlinearity of Burgers’ equation, and we show the benefits of the iterative splitting approaches as efficient and accurate solver methods.
SLEEP AND IMMUNITY

Abstract: Sleep has been reported in several studies as an important function of the organism, as well as its deprivation as harmful to the production of defense cells, which affects our immunity. It is also demonstrated in the literature that sleep is fundamental for the metabolism of free radicals and for the body to be able to restore cardiorespiratory functions. The weakened immune system implies an increase in the chances of contracting infectious diseases, in addition to having difficulty recovering from relatively simple illnesses such as colds and flu. This study discusses sleep and its implications for organismic immunity. The results highlighted the need to perform sleep hygiene, defined as the change from dysfunctional patterns to a new organization of habits and activities that allow better sleep induction and quality. Keywords: Sleep; Immunity; Quality of life.
Hydrogen and Methane Production from Styrofoam Waste Using an Atmospheric-pressure Microwave Plasma Reactor

ABSTRACT Polystyrene foam (PSF), has been used widely in oyster farming in Taiwan and is responsible for generate approximately 120,000 – 200,000 pieces floating PSF waste every year, this causes some issues related to its treatment, including, clogging during incineration, lack of economic benefits, and carcinogenic, and non-biodegradable components, and it also a menace to the seashore due to becoming debris. In this study, PSF waste was treated via an atmospheric-pressure microwave plasma reactor to obtain methane (CH4) and hydrogen (H2). The using of nitrogen as the carrier gas and higher microwave power (1200 W) show greater results as compared to argon in the production of H2 at concentrations of 4739 ppm, while the results for CH4 were less than 300 ppm. Higher amounts of PSF waste (0.2 g) indicate the production of CH4 and H2 at concentrations of 19,657 ppm and 440 ppm, respectively. The results of SEM-EDX and XRD testing confirmed the transformation of the PSF structure and a reduction of carbon (C) in the final residue. The application of atmospheric-pressure microwave plasma not only treats PSF waste but produces H2 and CH4 that recycles PSF waste as energy gases. This research contributed to an understanding of how plasma technology can be used to treat solid waste and produce valuable gases.
Behavior skills training for family caregivers of people with intellectual or developmental disabilities: a systematic review of literature

People with intellectual or developmental disabilities (IDDs) face lifelong challenges in areas such as self-care, learning and socializing abilities. Having such individuals at home brings family caregivers extra stress, especially when support is insufficient. Because of the inadequacy of professional support available globally, the need to increase the ability of the caregivers to provide effective support is evident. Behavior skills training (BST), an approach consisting of instruction, modelling, rehearsal, and feedback, has been used in numerous studies to train caregivers in necessary skills to help their family members with IDDs. This study conducted a systematic review of past literatures, wherein BST was used to train family caregivers of individuals with IDDs. Seventeen relevant studies were summarized according to their countries, participant characteristics, training focus, intervention details, and outcomes. Outcomes revealed that BST had been used to train caregivers of varying demographics, various target skills (e.g. discrete trial training, incidental training, etc.), order of delivery, and sometimes the introduction of novel components. Most studies showed an improvement for both caregivers and the individuals with IDDs. However, some studies did not present desired outcomes in relation to the individuals. Possible reasons and future measures were also discussed in this review.
Thermoase-hydrolysed pigeon pea protein and its membrane fractions possess in vitro bioactive properties (antioxidative, antihypertensive, and antidiabetic).

Enzymatic hydrolysis can liberate bioactive peptides from protein materials, thus, pigeon pea was hydrolysed using thermoase. Crude hydrolysate (PPHT) was subjected to ultrafiltration using different molecular weight cutoffs to collect <1, 1-3, 3-5, 5-10, and >10 kDa peptide fractions. Fractions were analysed for in vitro antioxidative, antihypertensive, and antidiabetic properties. The peptide fractions had stronger DPPH• scavenging and renin inhibition when compared to PPHT. In contrast, ACE inhibition was stronger for the PPHT and <1 kDa peptide fraction while activity decreased as peptide size increased. The <1 kDa peptide also showed significantly stronger ferric reducing antioxidant power, OH• scavenging and inhibition of linoleic acid oxidation when compared to PPHT. α-amylase and α-glucosidase were inhibited by all the peptide fractions, though the 3-5 and >10 kDa had higher values. We conclude that the PPHT and peptide fractions could serve as potential ingredients to formulate antihypertensive and antidiabetic functional foods and nutraceuticals. PRACTICAL APPLICATIONS: Oxidative stress promotes the generation of free radicals, which have a significant impact in the pathogenesis of human chronic diseases such as cardiovascular impairment, cancer, and diabetes. Peptides generated from enzymatic hydrolysis of proteins have been identified to impart beneficial health effects. In this work, we showed that a thermoase digest of pigeon pea protein as well as the fractionated peptides had strong antioxidant properties in addition to exhibiting inhibitory activities against renin and angiotensin converting enzyme, the main therapeutic targets for antihypertensive agents. The peptide products also inhibited α-amylase and α-glucosidase activities, providing potential ingredients that can be used to formulate antidiabetic functional foods.
Perpetual Bluetooth Communications for the IoT

Battery-powered beacon devices introduce high maintenance costs due to the finite operation time dictated by the fixed capacity of their batteries. To tackle this problem we propose FreeBLE: an indoor beacon system aimed at operating perpetually without batteries. We propose three methods to increase the utilization efficiency of harvested Radio Frequency (RF) energy in the beacon system, by which the energy consumption level becomes low enough to fit within the energy harvesting budget. We implement FreeBLE using off-the-shelf Bluetooth Low Energy (BLE) and RF energy harvesting devices, and test FreeBLE in a laboratory environment. Our results show that FreeBLE enables perpetual operation in an indoor deployment of RF-powered BLE beacon devices.
Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network

Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.
Demystifying Loops in Smart Contracts

This paper aims to shed light on how loops are used in smart contracts. Towards this goal, we study various syntactic and semantic characteristics of loops used in over 20,000 Solidity contracts deployed on the Ethereum blockchain, with the goal of informing future research on program analysis for smart contracts. Based on our findings, we propose a small domain-specific language (DSL) that can be used to summarize common looping patterns in Solidity. To evaluate what percentage of smart contract loops can be expressed in our proposed DSL, we also design and implement a program synthesis toolchain called Solis that can synthesize loop summaries in our DSL. Our evaluation shows that at least 56% of the analyzed loops can be summarized in our DSL, and 81% of these summaries are exactly equivalent to the original loop.
Research on the Current Status and Policy Evolution of International Education Industry in China

The paper analyzed the current status and policy evolution of international education industry in China based on statistical data of the education market scale, source countries, education levels and study subjects. It is found that the international education in China has undergone three major stages, including the stage of “unified national management dominated by politics”, the stage of “exploration of the model for self-financed study abroad”, and the stage of “all-round open development”. The achievements of international education industry are closely related to the key policies in the 3 stages. The research shows that the scale of international education in China is basically the same as that in the developed countries but there exists a big import and export deficit since China has slightly smaller international market share. Asian and African countries are the main sending countries of international students, and students from these countries prefer to receive academic education in China than those from Europe and America. In terms of major subjects, Chinese language and literature are still the main subjects, but engineering, economics and management are becoming more popular. Finally, suggestions are made on how to further expand the international education market and optimize the international education structure in China.
Assessment of technology strategies for sustainable tourism planning

Purpose An essential requirement for tourism planning and policymaking is to review past strategies. The development of future roadmaps depends on a robust strategy validation process. The research analyses the technology strategies in Goa from 1962–2017 and highlights the impact of technology on tourism in Goa. Design/methodology/approach The paper presents a unique methodology that integrates tourism area life cycle (TALC) and Mintzberg’s strategy analysis framework to evaluate strategies at a tourism destination. Findings The findings show that most technological advancements have taken place during the period 1989–2008. Both deliberate and emergent strategies were identified. Development of technology altered the type of tourism practiced in Goa. The use of internet facilitated the growth of tourism. Research limitations/implications This study clarifies the styles of public planning for tourism. It provides the foundation for strategic planning process. Practical implications Effective and efficient use of policy through technology road mapping can aid in co-creating a sustainable tourism destination for the future. Social implications The strategic evaluation framework can be used in any tourism destinations to identify past strategies and assess its impact on the future. Originality/value The paper presents a unique methodology that integrates TALC and Mintzberg’s strategy analysis framework to evaluate strategies at a tourism destination. The research proposes a strategic evaluation process that recognizes past strategies to validate the strategic analysis process.
Room semantics inference using random forest and relational graph convolutional networks: A case study of research building

Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79).
Weight-gaining medications used by patients followed up at the bariatric and metabolic surgery service of a university hospital

Objective: To verify an association between weight and use of medications that lead to weight gain (MLGP) in a population of obese patients monitored in the Unified Health System. Methods: This is a retrospective descriptive sectional study with data from medical records of all patients. patients treated by the University Hospital Multiprofessional Service for obese patients who are candidates for bariatric surgery. The collection took place from January 2014 to March 2016. A weight-dependent variable, used in the body mass index (BMI) and degree of obesity and as independent variables of sex, age, education, income, number of diseases, number of drugs and number of MLGP. The association was calculated using the Ro Spearman correlation and the chi-square test. Results: 102 patients were studied, mostly women (87%), with a mean BMI of 45.9 kg / m2 (min 32.56 - maximum 70.98 kg / m2 SD = 6.43). The main comorbidities were systemic arterial hypertension (65.7%) and osteoarticular disorders (39.2%). The average number of drugs per patient was 4.3 and the most used class was for the renin-angiotensin system (67.3%). MLGP corresponds to 10.4% of the drugs used and 27.4% of the patients use them. MLGP patients used an average of 5.8 medications and the most prevalent classes were betablocking agents (60.6%), followed by medications used in diabetes (24.2%). Atenolol and a glibenclamide were the most used MLGP. The mean BMI of patients who used more than one MLGP was 51 kg / m2. Conclusion: There was no correlation between weight and the use of MLGP in this population. However, patients who used the MLGP combination had a higher BMI.