bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulatory MicroRNAs in T2DM and Breast Cancer
Authors: Durrani Ilhaam Ayaz , Bhatti Attya , John Peter
Published: May 2021

MicroRNAs orchestrate the tight regulation of numerous cellular processes and the deregulation in their activities has been implicated in many diseases, including diabetes and cancer. There is an increasing amount of epidemiological evidence associating diabetes, particularly type 2 diabetes mellitus, to an elevated risk of various cancer types, including breast cancer. However, little is yet known about the underlying molecular mechanisms and even less about the role miRNAs play in driving the tumorigenic potential of the cell signaling underlying diabetes pathogenesis. This article reviews the role of miRNA in bridging the diabetes–breast cancer association by discussing specific miRNAs that are implicated in diabetes and breast cancer and highlighting the overlap between the disease-specific regulatory miRNA networks to identify a 20-miRNA signature that is common to both diseases. Potential therapeutic targeting of these molecular players may help to alleviate the socioeconomic burden on public health that is imposed by the type 2 diabetes mellitus (T2DM)–breast cancer association.
Special Issue: Modeling and Simulation of Polymerization Processes
Authors: Vivaldo-Lima Eduardo , Mohammadi Yousef , Penlidis Alexander
Published: May 2021

This Special Issue (SI) of Processes on Modeling and Simulation of Polymerization Processes (MSPP), and the associated Special Issue reprint, contain papers that deal with this very important area of scientific investigation in polymer science and engineering, both in academic and particularly industrial environments [...]
A Coupled CFD-DEM Model for Resolved Simulation of Filter Cake Formation during Solid-Liquid Separation
Authors: Puderbach Vanessa , Schmidt Kilian , Antonyuk Sergiy
Published: May 2021

In cake filtration processes, where particles in a suspension are separated by forming a filter cake on the filter medium, the resistances of filter cake and filter medium cause a specific pressure drop which consequently defines the process energy effort. The micromechanics of the filter cake formation (interactions between particles, fluid, other particles and filter medium) must be considered to describe pore clogging, filter cake growth and consolidation correctly. A precise 3D modeling approach to describe these effects is the resolved coupling of the Computational Fluid Dynamics with the Discrete Element Method (CFD-DEM). This work focuses on the development and validation of a CFD-DEM model, which is capable to predict the filter cake formation during solid-liquid separation accurately. The model uses the Lattice-Boltzmann Method (LBM) to directly solve the flow equations in the CFD part of the coupling and the DEM for the calculation of particle interactions. The developed model enables the 4-way coupling to consider particle-fluid and particle-particle interactions. The results of this work are presented in two steps. First, the developed model is validated with an empirical model of the single particle settling velocity in the transition regime of the fluid-particle flow. The model is also enhanced with additional particles to determine the particle-particle influence. Second, the separation of silica glass particles from water in a pressurized housing at constant pressure is experimentally investigated. The measured filter cake, filter medium and interference resistances are in a good agreement with the results of the 3D simulations, demonstrating the applicability of the resolved CFD-DEM coupling for analyzing and optimizing cake filtration processes.
A Data-Driven Based Spatiotemporal Model Reduction for Microwave Heating Process with the Mixed Boundary Conditions
Authors: Zhong Jiaqi , Liang Shan
Published: May 2021

In this paper, a data-driven based spatiotemporal model reduction approach is proposed for predicting the temperature distribution and developing the computation speeds in the microwave heating process. Due to the mixed boundary conditions, it is difficult for the traditional spectral method to directly obtain the analytical eigenfunctions. Motivated by the time/space separation theory, we first propose a general framework of spatiotemporal model reduction, which can effectively develop the computation speeds in the numerical analysis of multi-physical fields. Subsequently, the empirical eigenfunctions are generated by applying the Karhunen–Loève theory to decompose the snapshots. Then, the partial differential Equation (PDE) model is discretized into a class of recursive equations and transformed as the reduced-order ordinary differential Equation (ODE) model. Finally, the effectiveness and superiority of the proposed approach is demonstrated by a comparison study with a traditional method on the microwave heating Debye medium.
Pool Boiling Heat Transfer Coefficients in Mixtures of Water and Glycerin
Authors: Vajc Viktor , Šulc Radek , Dostál Martin
Published: May 2021

Heat transfer coefficients were investigated for saturated nucleate pool boiling of binary mixtures of water and glycerin at atmospheric pressure in a wide range of concentrations and heat fluxes. Mixtures with water mass fractions from 100% to 40% were boiled on a horizontal flat copper surface at heat fluxes from about 25 up to 270kWm−2. Experiments were carried out by static and dynamic method of measurement. Results of the static method show that the impact of mixture effects on heat transfer coefficient cannot be neglected and ideal heat transfer coefficient has to be corrected for all investigated concentrations and heat fluxes. Experimental data are correlated with the empirical correlation α=0.59q0.714+0.130ωw with mean relative error of 6%. Taking mixture effects into account, data are also successfully correlated with the combination of Stephan and Abdelsalam (1980) and Schlünder (1982) correlations with mean relative error of about 15%. Recommended coefficients of Schlünder correlation C0=1 and βL=2×10−4ms−1 were found to be acceptable for all investigated mixtures. The dynamic method was developed for fast measurement of heat transfer coefficients at continuous change of composition of boiling mixture. The dynamic method was tested for water–glycerin mixtures with water mass fractions from 70% down to 35%. Results of the dynamic method were found to be comparable with the static method. For water–glycerin mixtures with higher water mass fractions, precise temperature measurements are needed.
HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
Authors: Khan Muhammad Ashfaq
Published: May 2021

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.
Introduction from the Guest Editor of Special Issue “Modern Problems of Scientometric Assessment of Publication Activity”
Authors: Mikhailov Oleg V.
Published: May 2021

In any branch of intellectual activity that claims to be called the word “science”, there are two approaches to describe the phenomena and objects associated with it; namely, qualitative analysis and quantitative analysis [...]
The Representation of Indigenous Languages of Oceania in Academic Publications
Authors: Koller Eve , Thompson Malayah
Published: May 2021

Of the estimated 7117 languages in the world, approximately 1500 (21%) are indigenous to the Pacific. Despite composing approximately one-fourth of the world’s linguistic diversity, the representation of these languages in academic publication is scant, even in periodicals focused on Pacific Island studies. We investigated 34 periodicals that focus on research in Oceania. We report on (1) journal names; (2) how many are currently in circulation; (3) how many accept submissions in Indigenous Pacific languages; (4) what percent of the most recent articles were actually in Indigenous languages of the Pacific and (5) which languages those were. Five of the 34 journals allowed submissions written in Indigenous Pacific languages. Three of the five journals specified Hawaiian as an accepted language of publication; one Sāmoan and one Tahitian and any other Indigenous language of Polynesia. We were able to collect data on four of the five journals, which averaged 11% of recent publications in an Indigenous language. None accepted submissions in Indigenous languages from the Pacific outside of Polynesia.
Factors Affecting Mobile Waste Recycling through RSCM: A Literature Review
Authors: Ben Yahya Taher , Jamal Noriza Mohd , Sundarakani Balan , Omain Siti Zaleha
Published: May 2021

Mobile phone consumers have been motivated by the rapid growth of technology and encouraged to update their devices regularly to keep up with new innovations, architectures, and capabilities. Consequently, mobile/cell phone waste has risen significantly in the last decade. Due to their small size, it is convenient for users to keep outdated or unused mobile phones at home or the office, rather than recycling them appropriately. A reverse supply chain (RSC) is one possible method of mitigating the questionable e-waste activity present in the ecosystem. RSC has been significant for the mobile phone industry, user states, analysts, and scholars. This paper reviews the available literature in the RSC management (RSCM) domain, along with its processes and strategies. The paper reviews 223 published scholarly papers in the domain of mobile phone waste recycling and investigates 22 papers related to factors influencing consumers’ intention with regard to electronic recycling, focusing on mobile devices, which contribute significantly to electronic waste management practices.
Valorization of Agri-Food Waste from Pistachio Hard Shells: Extraction of Polyphenols as Natural Antioxidants
Authors: Cardullo Nunzio , Leanza Melania , Muccilli Vera , Tringali Corrado
Published: May 2021

The agricultural processing industry usually generates a remarkable amount of by-products rich in bioactive compounds, which can be exploited for agri-food or nutraceutical applications. Pistachio’s hard shell is one of the major by-products from pistachio industrial processing. The aim of this work was the evaluation of pistachio shells as a potential source of natural antioxidants. We evaluated different extraction procedures by measuring total phenolic content, total flavonoid content and antioxidative activity (DPPH•, TEAC and ORAC). The microwave-assisted ethanol extract turned out to be the most promising and was fractionated by XAD-16 column chromatography, affording six fractions analyzed through HPLC/ESI-MS/MS and 1H-NMR to identify the main antioxidative constituents. Fractions Fr4–Fr6 demonstrated the highest antioxidant activity. Gallic acid and a monogalloylglusose isomer are the main phenolic constituents of Fr4. Both simple and complex phenolics, such as flavonoids and hydrolysable tannins, were identified in fractions Fr5 and Fr6; pentagalloylglucose and kaempferol, well-known for their antioxidant activity, are the most abundant constituents. The results highlighted that the proposed methodology can be an effective way to recover bioactive phenolic compounds from pistachio hard shell, making this by-product a promising source of compounds with potential applications in food and healthcare sectors.
Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions
Authors: Orlando Giuseppe , Bufalo Michele
Published: May 2021

The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling frequency. We examine returns of swaps, equity and bond indices as well as the rescaling by their volatilities over different horizons (since inception to Q2-2020). Contrarily to some literature, we find that the realized distributions of logarithmic returns, scaled or not by the standard deviations, are skewed and that they may be better fitted by t-skew distributions. Our finding holds true across asset classes, maturity and developed and developing markets. This may explain why models based on dynamic conditional score (DCS) have superior performance when the underlying distribution belongs to the t-skew family. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example, extrapolating yearly scenarios from daily performances may prove not to be correct.
Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach
Authors: Sheraz Muhammad , Nasir Imran
Published: May 2021

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.
Determination of Key Phenological Phases of Winter Wheat Based on the Time-Weighted Dynamic Time Warping Algorithm and MODIS Time-Series Data
Authors: Zhao Fa , Yang Guijun , Yang Xiaodong , Cen Haiyan , Zhu Yaohui , Han Shaoyu , Yang Hao , He Yong , Zhao Chunjiang
Published: May 2021

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data.
Towards Vine Water Status Monitoring on a Large Scale Using Sentinel-2 Images
Authors: Laroche-Pinel Eve , Duthoit Sylvie , Albughdadi Mohanad , Costard Anne D. , Rousseau Jacques , Chéret Véronique , Clenet Harold
Published: May 2021

Wine growing needs to adapt to confront climate change. In fact, the lack of water becomes more and more important in many regions. Whereas vineyards have been located in dry areas for decades, so they need special resilient varieties and/or a sufficient water supply at key development stages in case of severe drought. With climate change and the decrease of water availability, some vineyard regions face difficulties because of unsuitable variety, wrong vine management or due to the limited water access. Decision support tools are therefore required to optimize water use or to adapt agronomic practices. This study aimed at monitoring vine water status at a large scale with Sentinel-2 images. The goal was to provide a solution that would give spatialized and temporal information throughout the season on the water status of the vines. For this purpose, thirty six plots were monitored in total over three years (2018, 2019 and 2020). Vine water status was measured with stem water potential in field measurements from pea size to ripening stage. Simultaneously Sentinel-2 images were downloaded and processed to extract band reflectance values and compute vegetation indices. In our study, we tested five supervised regression machine learning algorithms to find possible relationships between stem water potential and data acquired from Sentinel-2 images (bands reflectance values and vegetation indices). Regression model using Red, NIR, Red-Edge and SWIR bands gave promising result to predict stem water potential (R2=0.40, RMSE=0.26).
Identifying Spatial and Temporal Variations in Concrete Bridges with Ground Penetrating Radar Attributes
Authors: Kumar Vivek , Morris Isabel M. , Lopez Santiago A. , Glisic Branko
Published: May 2021

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.
Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018
Authors: Feng Chunjie , Zhang Xiaotong , Wei Yu , Zhang Weiyu , Hou Ning , Xu Jiawen , Yang Shuyue , Xie Xianhong , Jiang Bo
Published: May 2021

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.
Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
Authors: Mu Haowei , Li Xuecao , Du Xiaoping , Huang Jianxi , Su Wei , Hu Tengyun , Wen Yanan , Yin Peiyi , Han Yuan , Xue Fei
Published: May 2021

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.
Lifting Scheme-Based Sparse Density Feature Extraction for Remote Sensing Target Detection
Authors: Tian Ling , Cao Yu , Shi Zishan , He Bokun , He Chu , Li Deshi
Published: May 2021

The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency.
Assessing MITRE ATT&CK Risk Using a Cyber-Security Culture Framework
Authors: Georgiadou Anna , Mouzakitis Spiros , Askounis Dimitris
Published: May 2021

The MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures. Its innovative approach has been broadly welcomed by both vendors and enterprise customers in the industry. Its usage extends from adversary emulation, red teaming, behavioral analytics development to a defensive gap and SOC (Security Operations Center) maturity assessment. While extensive research has been done on analyzing specific attacks or specific organizational culture and human behavior factors leading to such attacks, a holistic view on the association of both is currently missing. In this paper, we present our research results on associating a comprehensive set of organizational and individual culture factors (as described on our developed cyber-security culture framework) with security vulnerabilities mapped to specific adversary behavior and patterns utilizing the MITRE ATT&CK framework. Thus, exploiting MITRE ATT&CK’s possibilities towards a scientific direction that has not yet been explored: security assessment and defensive design, a step prior to its current application domain. The suggested cyber-security culture framework was originally designed to aim at critical infrastructures and, more specifically, the energy sector. Organizations of these domains exhibit a co-existence and strong interaction of the IT (Information Technology) and OT (Operational Technology) networks. As a result, we emphasize our scientific effort on the hybrid MITRE ATT&CK for Enterprise and ICS (Industrial Control Systems) model as a broader and more holistic approach. The results of our research can be utilized in an extensive set of applications, including the efficient organization of security procedures as well as enhancing security readiness evaluation results by providing more insights into imminent threats and security risks.
Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission
Authors: Cano-Garcia Helena , Kshirsagar Rohit , Pricci Roberto , Teyeb Ahmed , O’Brien Fergus , Saha Shimul , Kosmas Panagiotis , Kallos Efthymios
Published: May 2021

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.
An Appliance Scheduling System for Residential Energy Management
Authors: Apaydin-Özkan Hanife
Published: May 2021

In this work, an Appliance Scheduling-based Residential Energy Management System (AS-REMS) for reducing electricity cost and avoiding peak demand while keeping user comfort is presented. In AS-REMS, based on the effects of starting times of appliances on user comfort and the user attendance during their operations, appliances are divided into two classes in terms of controllability: MC-controllable (allowed to be scheduled by the Main Controller) and user-controllable (allowed to be scheduled only by a user). Use of all appliances are monitored in the considered home for a while for recording users’ appliance usage preferences and habits on each day of the week. Then, for each MC-controllable appliance, preferred starting times are determined and prioritized according to the recorded user preferences on similar days. When scheduling, assigned priorities of starting times of these appliances are considered for maintaining user comfort, while the tariff rate is considered for reducing electricity cost. Moreover, expected power consumptions of user-controllable appliances corresponding to the recorded user habits and power consumptions of MC-controllable appliances corresponding to the assigned starting times are considered for avoiding peak demand. The corresponding scheduling problem is solved by Brute-Force Closest Pair method. AS-REMS reduces the peak demand levels by 45% and the electricity costs by 39.6%, while provides the highest level of user comfort by 88%. Thus, users’ appliance usage preferences are sustained at a lower cost while their comfort is kept impressively.
A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique
Authors: Chen Hui , Huang Bin , Tee Kong Fah , Lu Bo
Published: May 2021

This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
Authors: Beauchamp Jeremy , Bunescu Razvan , Marling Cindy , Li Zhongen , Liu Chang
Published: May 2021

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.
2020 Safety Young Investigator Award: Announcement and Interview with the Winner
Authors:   Safety Editorial Office
Published: May 2021

After an extensive voting period, we are proud to present the winner of the Safety Young Investigator Award: [...]
LC-ESI-QTOF-MS/MS Profiling and Antioxidant Activity of Phenolics from Custard Apple Fruit and By-Products
Authors: Du Junxi , Zhong Biming , Subbiah Vigasini , Barrow Colin , Dunshea Frank , Suleria Hafiz
Published: May 2021

Custard apple is an edible fruit grown in tropical and subtropical regions. Due to its abundant nutrient content and perceived health benefits, it is a popular food for consumption and is utilized as a medicinal aid. Although some published research had provided the phenolic compound of custard apple, the comprehensive phenolic profiling of Australian grown custard apple is limited. Hence, this research aimed to evaluate the phenolic content and antioxidant potential by various phenolic content and antioxidant assays, followed by characterization and quantification of the phenolic profile using LC-ESI-QTOF-MS/MS and HPLC-PDA. African Pride peel had the highest value in TPC (61.69 ± 1.48 mg GAE/g), TFC (0.42 ± 0.01 mg QE/g) and TTC (43.25 ± 6.70 mg CE/g), followed by Pink’s Mammoth peel (19.37 ± 1.48 mg GAE/g for TPC, 0.27 ± 0.03 mg QE/g for TFC and 10.25 ± 1.13 mg CE/g for TTC). African Pride peel also exhibited the highest antioxidant potential for TAC (43.41 ± 1.66 mg AAE/g), FRAP (3.60 ± 0.14 mg AAE/g) and ABTS (127.67 ± 4.60 mg AAE/g), whereas Pink’s Mammoth peel had the highest DPPH (16.09 ± 0.34 mg AAE/g), RPA (5.32 ± 0.14 mg AAE/g), •OH-RSA (1.23 ± 0.25 mg AAE/g) and FICA (3.17 ± 0.18 mg EDTA/g). LC-ESI-QTOF-MS/MS experiment successfully characterized 85 phenolic compounds in total, encompassing phenolic acids (20), flavonoids (42), stilbenes (4), lignans (6) and other polyphenols (13) in all three parts (pulp, peel and seeds) of custard apple. The phenolic compounds in different portions of custard apples were quantified by HPLC-PDA, and it was shown that African Pride peel had higher concentrations of the most abundant phenolics. This is the first study to provide the comprehensive phenolic profile of Australian grown custard apples, and the results highlight that each part of custard apple can be a rich source of phenolics for the utilization of custard apple fruit and waste in the food, animal feeding and nutraceutical industries.
Muslim Solidarity and the Lack of Effective Protection for Rohingya Refugees in Southeast Asia
Authors: Missbach Antje , Stange Gunnar
Published: May 2021

Southeast Asia has the weakest normative frameworks for refugee protection of any region in the world apart from the Middle East. Only two out of ten member states of the Association of Southeast Asian Nations (ASEAN) have signed the 1951 International Refugee Convention. Nevertheless, the 2012 ASEAN Human Rights Declaration explicitly mentions the right to seek and receive asylum ‘in accordance with the laws of such State and applicable international agreements’ (ASEAN 2012). One of the litmus tests for this right has been the regional treatment of the Rohingya, a Muslim minority from Myanmar that faces forced displacement, discrimination, and large-scale state violence. Based on media content analysis and a scientific literature review, this paper sheds light on how ASEAN’s most prominent Muslim member countries, Indonesia and Malaysia, advocate on behalf of the forcibly displaced Rohingya. In particular, this paper focuses on competing forms of political interventions and shifting notions of Muslim solidarity. While Indonesia and Malaysia have been very vocal in bilateral, regional, and international forums to criticise the Myanmar government for their violation of basic human rights, both countries remain highly reluctant to offer sanctuary to Rohingya refugees, of which several thousand have attempted to reach Indonesia and Malaysia. This research finds that the notion of Muslim solidarity remains a symbolic rhetoric primarily directed at domestic audiences and the failure to render effective protection to refugees has rather increased over the last five years.
Trait and Ability Emotional Intelligence and Its Impact on Sports Performance of Athletes
Authors: Kopp Alexandra , Reichert Markus , Jekauc Darko
Published: May 2021

Emotional intelligence (EI) is considered a determinant of sports performance. Two opposing perspectives have been discussed in the theoretical discourse on EI: EI as an ability versus EI as a trait, both widely differing in content and method of assessment. Previous applied sport psychology research is characterized by a heterogeneous use of different conceptualizations and measurements of EI. However, evidence for the superiority of an EI concept does not exist. This study directly compares the ability and trait EI concepts in the relationship with athletic performance. An online survey was conducted (response rate = 19%). Participants completed the Mayer-Salovey-Caruso Emotional Intelligence Test, the Trait Emotional Intelligence Questionnaire Short Form, a list of questions about biographical information as well as information related to sports performance and sport participation. We used regression analyses and controlled type of sports to investigate how sports performance is influenced by EI. Trait EI positively predicted self-assessment of athletes’ performance (B = 1.02; p < 0.01) whereby ability EI did not predict any outcome of sports performance. The effect of trait EI was independent of the ability EI. Overall, the result indicates some evidence for the superiority of the trait EI in applied sports psychology.
Adult Education: A Sustainable Model for the Reduction of Psychosocial and Educational Risks Caused by COVID-19
Authors: Perea-Rodríguez Manuel-Jesús , Morón-Marchena Juan-Agustín , Muñoz-Díaz María-Carmen , Cobos-Sanchiz David
Published: May 2021

Education for health and sustainability has to be understood from a new perspective beyond the traditional conceptual limits. Thus, following the lines of the 2030 sustainable development goals, we examine how permanent education and adult education can become a fundamental element for the achievement of said objectives, serving as a neutraliser of psychosocial risk factors. In other words, a quality education throughout life becomes a dynamic factor for the development of lifestyle habits and healthy aging, purposes that during the pandemic and the state of alarm have been altered by confinement, closure of educational centres, and methodological changes. The objective of the study is to analyse whether the maintenance of educational activity has influenced the psychological state of people, reducing, neutralising, or increasing the psychosocial risk factors linked to confinement and the evolution of COVID-19. For this, an observational study was developed, taking as a case the Universidad Popular Dos Hermanas (Seville, Spain), with a sample of 384 learners over the age of 16 years. The variables considered were sociodemographic means and technical tools, assessment of the institution, teacher assessment, and psychosocial variables related to possible effects caused by the context. The data were collected through a self-developed questionnaire. Descriptive analyses and bivariate correlations were carried out. Methodological diversity and positive correlations were shown in terms of the institution’s function, teaching assessment, maintenance of activity, and reduction of psychopathological risks.
Protecting Food Cultural Biodiversity: From Theory to Practice. Challenging the Geographical Indications and the Slow Food Models
Authors: Mariani Mariagiulia , Casabianca François , Cerdan Claire , Peri Iuri
Published: May 2021

A global expansion in public and private initiatives seeks to strengthen the link between traditional products and sustainable development by creating a niche in the market for these products. Relevant examples are the Geographical Indications and the Slow Food Presidia models. This paper compares both types of Origin Food Schemes (OFS) to disclose the main commonalities and differences in their institutionalization, and their complex outcomes on cultural biodiversity (CB), which is a major concern for the sustainability of rural communities. We used underpinning knowledge dynamics as an analytical lens through the cross-comparison of ethnographic findings collected in four case studies of origin cheeses located in France, Italy and Morocco. Our findings suggest that OFS have high potential to defend CB because of their collective and context-dependent approaches. We argue that knowledge and practices mobilized in OFS are the result of power relations and confrontations among local actors, and show how four identified tensions between different forms and types of knowledge differently shape food culture, food technique, perceptions, and representations. In conclusion, the institutional approaches, practices and knowledge dynamics compared in this analysis show six effective ways to link OFS and CB, facilitating the trajectory toward sustainable development.
Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach
Authors: Aich Satyabrata , Tripathy Sushanta , Joo Moon-Il , Kim Hee-Cheol
Published: May 2021

In the digital era, almost every system is connected to a digital platform to enhance efficiency. Although life is thus improved, security issues remain important, especially in the healthcare sector. The privacy and security of healthcare records is paramount; data leakage is socially unacceptable. Therefore, technology that protects data but does not compromise efficiency is essential. Blockchain technology has gained increasing attention as it ensures transparency, trust, privacy, and security. However, the critical factors affecting efficiency require further study. Here, we define the critical factors that affect blockchain implementation in the healthcare industry. We extracted such factors from the literature and from experts, then used interpretive structural modeling to define the interrelationships among these factors and classify them according to driving and dependence forces. This identified key drivers of the desired objectives. Regulatory clarity and governance (F2), immature technology (F3), high investment cost (F6), blockchain developers (F9), and trust among stakeholders (F12) are key factors to consider when seeking to implement blockchain technology in healthcare. Our analysis will allow managers to understand the requirements for successful implementation.
Effect of Depreciation Method for Long-Term Tangible Assets on Sustainable Management: From a Nuclear Power Generation Cost Perspective under the Nuclear Phase-Out Policy
Authors: Bang SungSig , Park SangYun
Published: May 2021

The Korean Government has been implementing a nuclear phase-out policy since 2017. Nuclear power plants accounted for 30.0% of the total power generation in 2016; this figure fell to 25.9% at the end of 2019, and the average Capacity Factor (CF) of a nuclear power plant approximately dropped from 89.1% to 69.2%. The nuclear phase-out policy presents severe consequences for the sustainable management of the nuclear power industry. Accordingly, the purpose of this study is to analyze the effect of a decrease in the nuclear capacity factor under the nuclear phase-out policy on the depreciation cost per unit using the Straight-Line Method (SLM) and Decelerated Depreciation Method (DDM) and to provide recommendations from a sustainable management perspective. The results show that the decrease in CF of nuclear power plants has a negative impact on sustainable development of the nuclear power industry. DDM is more beneficial than the SLM during this initial stage of depreciation under the nuclear phase-out policy. In addition, in the early stages of projects or immediately after attracting large-scale investments, DDM can offer more positive signs for stockholders by calculating a smaller net loss or a higher net profit.