bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques
Authors: Mundt Marion , Koeppe Arnd , Bamer Franz , David Sina , Markert Bernd
Published: August 2020

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.
Validation of Spatiotemporal and Kinematic Measures in Functional Exercises Using a Minimal Modeling Inertial Sensor Methodology
Authors: Hindle Benjamin R. , Keogh Justin W.L. , Lorimer Anna V.
Published: August 2020

This study proposes a minimal modeling magnetic, angular rate and gravity (MARG) methodology for assessing spatiotemporal and kinematic measures of functional fitness exercises. Thirteen healthy persons performed repetitions of the squat, box squat, sandbag pickup, shuffle-walk, and bear crawl. Sagittal plane hip, knee, and ankle range of motion (ROM) and stride length, stride time, and stance time measures were compared for the MARG method and an optical motion capture (OMC) system. The root mean square error (RMSE), mean absolute percentage error (MAPE), and Bland–Altman plots and limits of agreement were used to assess agreement between methods. Hip and knee ROM showed good to excellent agreement with the OMC system during the squat, box squat, and sandbag pickup (RMSE: 4.4–9.8°), while ankle ROM agreement ranged from good to unacceptable (RMSE: 2.7–7.2°). Unacceptable hip and knee ROM agreement was observed for the shuffle-walk and bear crawl (RMSE: 3.3–8.6°). The stride length, stride time, and stance time showed good to excellent agreement between methods (MAPE: (3.2 ± 2.8)%–(8.2 ± 7.9)%). Although the proposed MARG-based method is a valid means of assessing spatiotemporal and kinematic measures during various exercises, further development is required to assess the joint kinematics of small ROM, high velocity movements.
Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems
Authors: Chan Ting On , Xia Linyuan , Lichti Derek D. , Sun Yeran , Wang Jun , Jiang Tao , Li Qianxia
Published: August 2020

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.
Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description
Authors: Deng Xiaogang , Zhang Zheng
Published: August 2020

As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.
Crop Disease Classification on Inadequate Low-Resolution Target Images
Authors: Wen Juan , Shi Yangjing , Zhou Xiaoshi , Xue Yiming
Published: August 2020

Currently, various agricultural image classification tasks are carried out on high-resolution images. However, in some cases, we cannot get enough high-resolution images for classification, which significantly affects classification performance. In this paper, we design a crop disease classification network based on Enhanced Super-Resolution Generative adversarial networks (ESRGAN) when only an insufficient number of low-resolution target images are available. First, ESRGAN is used to recover super-resolution crop images from low-resolution images. Transfer learning is applied in model training to compensate for the lack of training samples. Then, we test the performance of the generated super-resolution images in crop disease classification task. Extensive experiments show that using the fine-tuned ESRGAN model can recover realistic crop information and improve the accuracy of crop disease classification, compared with the other four image super-resolution methods.
Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices
Authors: Cho Chiwoo , Choi Wooyeol , Kim Taewoon
Published: August 2020

Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity.
ARES: Automated Risk Estimation in Smart Sensor Environments
Authors: Dimitriadis Athanasios , Flores Jose Luis , Kulvatunyou Boonserm , Ivezic Nenad , Mavridis Ioannis
Published: August 2020

Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations.
Traditional Chinese Medicine Pulse Diagnosis on a Smartphone Using Skin Impedance at Acupoints: A Feasibility Study
Authors: Lan Kun-Chan , Litscher Gerhard , Hung Te-Hsuan
Published: August 2020

In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosis. A pulse can be felt by applying firm fingertip pressure to the skin where the arteries travel. The pulse diagnosis has become an important tool not only for TCM practitioners but also for several areas of Western medicine. Many pulse measuring devices have been proposed to obtain objective pulse conditions. In the past, pulse diagnosis instruments were single-point sensing methods, which missed a lot of information. Later, multi-point sensing instruments were developed that resolved this issue but were much higher in cost and lacked mobility. In this article, based on the concept of sensor fusion, we describe a portable low-cost system for TCM pulse-type estimation using a smartphone connected to two sensors, including one photoplethysmography (PPG) sensor and one galvanic skin response (GSR) sensor. As a proof of concept, we collected five-minute PPG pulse information and skin impedance on 24 acupoints from 80 subjects. Based on these collected data, we implemented a fully connected neural network (FCN), which was able to provide high prediction accuracy (>90%) for patients with a TCM wiry pulse.
Concept Design and Performance Evaluation of a Fossil-Free Operated Cargo Ship with Unlimited Range
Authors: Julià Enric , Tillig Fabian , Ringsberg Jonas W.
Published: August 2020

To meet the IMO goals of emissions reduction in shipping, drastic actions must be taken. Wind-assisted propulsion and renewable energy sources are today discussed frequently as realistic alternatives for future ship propulsion and energy production. This study presents a new and innovative concept of a fossil-free operated cargo ship aiming to achieve an unlimited range. The purpose of the study is to present the feasibility but also the limitations of a ship propelled and operated purely on renewable energy harnessed at sea, independent from shore-based energy sources. Aside from Flettner rotors for propulsion, the ship concept incorporates photovoltaic generators, wind turbines, and a dual-mode propeller to produce energy for the auxiliary systems and for the Flettner rotors, as well as batteries to balance the energy production and consumption. The dual-mode propeller can be used for energy generation and propulsion, thus levelling out any speed drops or peaks and thereby ensuring more reliable operation. The whole system is modelled numerically, and full ship voyages are simulated using the ship performance model ShipCLEAN. Results show feasible achieved speeds on a route with realistic weather conditions. However, negative energy balances limit the pure renewable sailing conditions. Further logistic and technical challenges are discussed.
Application of the DEA Double Bootstrap to Analyze Efficiency in Galician Sheltered Workshops
Authors: López-Penabad María-Celia , Maside-Sanfiz José Manuel , Torrelles Manent Juan , Iglesias-Casal Ana
Published: August 2020

Sheltered workshops (SW), as social enterprises, need to be efficient and maintain a balance between social aspects and economic prosperity. An important part of research on the subject has been focused on measuring the economic value created by these entities. In this study, we analyzed performance of SWs in Galicia (Spain), from the point of view of efficiency, combining social and economic aspects and investigating its key determinants. Using panel data from 609 entities from 2008 to 2017, we followed Simar and Wilson’s two-stage approach (2007). Specifically, we used data envelopment analysis (DEA) at the first stage to estimate efficiency scores and then used truncated regression estimation with double-bootstrap to test the significance of explanatory variables. Our results show that SWs have high levels of performance, higher in economic than in social terms, and we found that several factors, such as size and age, positively influence total, economic and social efficiency individually. We also found a positive, significant relationship between social efficiency and economic profitability.
Mass Media Reporting and Illicit Harvesting of Russian Crab: Implications for Sustainable Fishery
Authors: Belov Andrey , Soboleva Genrietta
Published: August 2020

Existing methods of combating the shadow economy do not always give reliable results. This is particularly true for the illegal use of renewable natural resources. In some parts of the Northwest Pacific basin, illegal, unreported, and unregulated (IUU) fishing has become an issue of growing concern for the sustainability of resource management, ecology, and the social environment. Many factors combine to produce these harmful phenomena. The complex legal rights for shared natural marine resources, the weak capacity of state institutions, and the lack of international cooperation between exporters and importers are all relevant. These factors can be eliminated by supplementing the “traditional” analysis of the shadow economy with new data mined from the media. For the crab harvesting regions of Russia, long-lasting benefits can be achieved through improvements in governance, accountability, and public awareness, or more specifically, through extensive mass media coverage of relevant topics. We argue that in the Russian Pacific, levels of illegal crab harvesting and smuggling correlates closely to the frequency of media references. The results suggest possible applications of mass media analysis: developing additional metrics for the dynamics of shadow economies; and the formulation of effective policy recommendations for sustainable fishing.
Scale and Drivers of Female Agricultural Labor: Evidence from Pakistan
Authors: Mohiuddin Iqra , Kamran Muhammad Asif , Jalilov Shokhrukh-Mirzo , Ahmad Mobin-ud-Din , Adil Sultan Ali , Ullah Raza , Khaliq Tasneem
Published: August 2020

Agricultural labor is largely informal, particularly for female agricultural labor in developing countries. Despite significant participation in the agricultural labor force in Pakistan, women’s contribution is not properly acknowledged and rewarded. The issue is further aggravated by the dearth of literature on gender–labor relations in cropping and livestock activities. Considering this gap in the literature, the current study was conducted with the specific objective of exploring the labor composition of different agricultural activities in different farm size categories in general and, particularly, female agricultural labor (family and hired labor) participation and its determinants in the rice–wheat cropping system of the Punjab province, Pakistan. The data were collected from 300 households across four districts of the province. Labor participation was calculated on an official farm size classification basis, i.e., small (<12.5 acres), medium (12.6–25 acres) and large (>25 acres) farms. The findings show that female labor is predominantly demanded in the manual harvesting of wheat, rice nursery transplantation and harvesting, and the majority of the livestock-related activities. The regression model results showed that family female labor and hired female labor participation significantly depend on the landholding status of farmers, household size, family type and level of education. The interviews also illustrated that labor relations are rapidly changing—ongoing mechanization threatens conventional female labor activities due to the lack of machinery operation skills among females, caused by informal state policies and cultural barriers. The findings of the study have important policy implications for mainstreaming gender status in agricultural policy and rural development and contribute directly to the Sustainable Development Goals on Gender Equality (SDG#5) and Decent Work and Economic Growth (SDG#8), and indirectly to No Poverty (SDG#1), Zero Hunger (SDG#2), Responsible Consumption and Production (SDG#12) and Climate Action (SDG#13).
The General Concept of the Revenue Model for Sustainability Growth
Authors: Remeňová Katarína , Kintler Jakub , Jankelová Nadežda
Published: August 2020

Since revenue streams provide financial stability for business development, companies are tasked with conducting an individual revenue model, that ensures their healthy and sustainable growth. Therefore, it is important to take into account the manner of creating revenues. This initiative relates to the revenue generation mechanism which describes how revenue is generated from the offer. Our research aims to find out whether the number of revenue streams (scope of the revenue model) and other financial and production indicators can predict the amount of revenue, as well as which factors can predict the scope of the revenue model. Concurrently we have focused on analyzing the sources of revenue streams and have selected key business variables in the wine producing industry. This sector had been selected as there is a significant difference between revenue streams based on manufacturing and those based on tourism activities. This concept was created by using theoretical knowledge about the value, pricing and segmentation. The goal of our research article was to design the General Concept of the Revenue Model and to identify variables with a strong influence on it. Results of the multiple linear regression analysis confirmed the significant influence of particular predictors on the scope of the revenue model, whereby the model explains up to 80% of the variance.
SolarSPELL Assessment: Impact of a Solar-Powered Digital Library as a Teaching-Learning Resource on Climate Change
Authors: Hosman Laura , Gómez Zermeño Marcela Georgina , Alemán de la Garza Lorena
Published: August 2020

Inclusive and quality education can provide nations with the tools to solve global problems. However, some barriers prevent equal access to this education. These obstacles include the lack of basic resources such as electricity and internet availability, which prevents appropriate training in the skills necessary for sustainable community development. Therefore, we have responded with the Solar-Powered Educational Learning Library (SolarSPELL) initiative, which offers a solar-powered digital library and provides an internet-like experience through its offline WiFi network. This educational innovation has been implemented in rural schools across the Pacific Islands, including in some of the more remote islands of Fiji, an area strongly affected by climate change. The objective of the study was to understand the impact of SolarSPELL on teaching and learning about climate change in the schools where it was implemented. This research used a case study method in which quantitative tools were applied to understand the characteristics of the schools and the impact of this educational innovation. The results showed that the SolarSPELL library was an impactful pedagogical resource in the schools where it was implemented. It served as support for teachers and motivated the students, promoted the democratization of knowledge in vulnerable areas, and provided appropriate educational resources to generate knowledge about problem-solving actions that can respond to climate change. The importance of this educational innovation lies in presenting strategies and best practices that help improve the quality of education, making it more inclusive and eliminating barriers to the acquisition of knowledge.
Occurrence, Ecological and Health Risk Assessment of Phthalate Esters in Surface Water of U-Tapao Canal, Southern, Thailand
Authors: Kingsley Okpara , Witthayawirasak Banchong
Published: August 2020

Phthalate esters (PAEs) are well known for their environmental contamination and endocrine-disrupting effects on wildlife and humans. In this study, the occurrence of PAEs and ecotoxicological risk assessments were performed in one of the significant canals in southern Thailand, named U-Tapao. Water samples were collected and analyzed for the six most common PAEs by using a gas chromatograph-mass spectrometer (GC-MS). Of the 6 PAEs analyzed, only three PAEs, including di-n-butyl phthalate (DBP), di (2-Ethylhexyl) phthalate (DEHP), and diisononyl phthalate (DiNP) were detected in water samples. The total concentration of PAEs ranged from 1.44 to 12.08 µg/L, with a mean level of 4.76 µg/L. The total average concentration of PAEs found in the canal was higher than the criteria of 3 µg/L for PAEs recommended for the protection of fish and other aquatic organisms by the United States Environmental Protection Agency (USEPA). The results of the potential ecological risk assessment of the risk quotient (RQ) method revealed that DEHP and DiNP posed a high risk to algae and crustacean and crustacean and fish, respectively, whereas DBP posed a medium risk to the different aquatic species. However, current levels of noncarcinogenic and carcinogenic risks via ingestion and dermal exposure in children and adults are within acceptable limits. The baseline data of PAEs in this canal will be beneficial to the strategic and future pollutant control along the canal network.
Seyfert Galaxies Astrophysics
Authors: Foschini Luigi
Published: August 2020

Looking back in time, it is somehow surprising to see the enormous advancement made by astrophysics in just about one century [...]
Pediatric Enteroviral Central Nervous System Infections in Bialystok, Poland: Epidemiology, Viral Types, and Drivers of Seasonal Variation
Authors: Toczylowski Kacper , Wieczorek Magdalena , Bojkiewicz Ewa , Wietlicka-Piszcz Magdalena , Gad Beata , Sulik Artur
Published: August 2020

Enteroviruses are common causes of infections of the central nervous system (CNS) that in temperate climates tend to peak in the summer. The aim of the study was to describe epidemiology, drivers of seasonality, and types of enteroviruses causing infections of the CNS in children in Northeastern Poland. We prospectively collected data on children hospitalized with infection of the CNS attributed to enteroviruses in Bialystok, Poland, from January 2015 to December 2019. In total, 224 children were included. Nineteen different enterovirus types were identified in isolates collected from 188 children. Coxsackie B5 (32%), echovirus 30 (20%), and echovirus 6 (14%) were the three most common types. Enteroviruses were more prevalent during the summer–fall season. Infections caused by echovirus 30 peaked early in June and coxsackievirus B5 in July, whereas echovirus 6 peaked late in October. Phylogenetic analyses of these three enterovirus types showed multiple lineages co-circulating in this region. Mean air temperatures and precipitation rates were independently associated with monthly number of cases. Considering lack of effective treatment or vaccine, easy transmission of enteroviruses between susceptible individuals, their high mutation rate and prolonged time of viral shedding, continued monitoring and surveillance are imperative to recognize enteroviral infections of the CNS and the changes in circulation of enteroviruses in Poland.
Imaging the Choroid
Authors: Singh Sumit Randhir , Chhablani Jay
Published: August 2020

The choroid is the most vascular tissue of the eye, sandwiched between sclera and retina, and responsible for blood supply of the outer retina [...]
EX-PRESS Glaucoma Filtration Device: Management of Complications
Authors: Nicolai Michele , Franceschi Alessandro , Pelliccioni Paolo , Pirani Vittorio , Mariotti Cesare
Published: August 2020

The EX-PRESS glaucoma filtration device appears to be an effective addition to our options to treat refractory glaucoma. The possibility to create a sclerostomy without tissue excision provides a safe and reliable outflow pathway for aqueous that is standard in size, reducing much of the variability associated with a surgical procedure. Prospective randomized studies comparing EX-PRESS implantation with trabeculectomy show encouraging results. However, complications usually encountered in filtration surgery have been reported, and EX-PRESS implantation can also lead to specific device-related complications. This article reviews the most common complications associated with this procedure.
Development of a Fuzzy-Function-Based Performance Indicator for Water Distribution System’s Emergency Condition
Authors: Jun Hwandon , Gim Arin , Oak Sueyeun , Jung Donghwi
Published: August 2020

Emergency interconnected operation (EIO) between adjacent blocks in a water distribution system (WDS) is one of the most effective countermeasures of a water utility when abnormal conditions (e.g., suspended water supply, pipe burst) occur at a certain location. For improved WDS operation and management, calculating a reliable performance indicator that accurately represents the states of consumers under both normal and abnormal conditions is essential. In this study, a water supply performance indicator (WSPI) was developed for assessing the hydraulic performance of a WDS in terms of consumer usability and satisfaction. A fuzzy function is used to represent the consumer satisfaction with the water supply level, and the satisfaction of different consumers can be adjusted by setting the variables of the fuzzy function. The WSPI can be applied to a hydraulic analysis model based on data from an actual WDS to assess the water supply capacity for each node and the entire network. It can also be used in an advanced pressure-driven analysis model to assess the WDS performance under various abnormal conditions. The proposed WSPI was applied to six suspended water supply scenarios of an actual WDS with and without EIO to assess the effectiveness of this countermeasure.
Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN
Authors: Almuhaylan Mohammed R. , Ghumman Abdul Razzaq , Al-Salamah Ibrahim Saleh , Ahmad Afaq , Ghazaw Yousry M. , Haider Husnain , Shafiquzzaman Md.
Published: August 2020

In arid regions, the groundwater drawdown consistently increases, and even for a constant pumping rate, long-term predictions remain a challenge. The present research applies the modular three-dimensional finite-difference groundwater flow (MODFLOW) model to a unique aquifer facing challenges of undefined boundary conditions. Artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS) have also been investigated for predicting groundwater levels in the aquifer. A framework is developed for evaluating the impact of various scenarios of groundwater pumping on aquifer depletion. A new code in MATLAB was written for predictions of aquifer depletion using ANN/ANFIS. The geotechnical, meteorological, and hydrological data, including discharge and groundwater levels from 1980 to 2018 for wells in Qassim, were collected from the ministry concerned. The Nash–Sutcliffe efficiency and mean square error examined the performance of the models. The study found that the existing pumping rates can result in an alarming drawdown of 105 m in the next 50 years. Appropriate water conservation strategies for maintaining the existing pumping rate can reduce the impact on aquifer depletion by 33%.
Static Liquefaction Capacity of Saturated Undisturbed Loess in South Jingyang Platform
Authors: Yan Rui-Xin , Peng Jian-Bing , Zhang Jin-Yuan , Wang Shao-kai
Published: August 2020

According to a previous geological investigation, high-speed and long-distance loess landslides in the South Jingyang platform in Shaanxi Province are closely related to the static liquefaction of loess. Considering the typical loess landslides in this area, isotropic consolidated undrained (ICU) triaxial tests and scanning electron microscopy analyses were conducted in this study. The main conclusions are as follows: (1) The stress-strain curves indicate strong strain softening under different confining pressures. The pore water pressure increases significantly and then remains at a high level; (2) The liquefaction potential index (LPI) shows an increasing trend followed by stabilization; the larger the LPI is, the smaller the state parameter (Ψ) is. The steady-state points of the loess are in the instability region; however, the steady-state strength is not zero; (3) Based on the ICU test results, the average pore diameter decreases; the shape ratio remains essentially unchanged; and the fractal dimension and roundness show different trends. The proportions of the macropore and mesopore decrease; that of the small pore increases slightly; and that of the micropore increases significantly; (4) The compression deformation of the highly spaced pores causes rapid strain hardening. A rapid strain softening results from the pore throat blockage at the beginning of particle rearrangement and reorganization. A stable strain softening is related to the agglomeration blocking of the reconstructed pore throat in the gradually stable stage of particle rearrangement and reorganization.
Biosand Filter as a Point-of-Use Water Treatment Technology: Influence of Turbidity on Microorganism Removal Efficiency
Authors: Duran Romero Daniela A. , de Almeida Silva Maria Cristina , M. Chaúque Beni J. , D. Benetti Antônio
Published: August 2020

The number of people living without access to clean water can be reduced by the implementation of point-of-use (POU) water treatment. Among POU treatment systems, the domestic biosand filter (BSF) stands out as a viable technology. However, the performance of the BSF varies with the inflow water quality characteristics, especially turbidity. In some locations, people have no choice but to treat raw water that has turbidity above recommended levels for the technology. This study aimed to measure the efficiency with which the BSF removes microorganisms from well water and from fecal-contaminated water with turbidity levels of 3, 25, and 50 NTU. Turbidity was controlled by the addition of kaolin to water. Turbidity removal varied from 88% to 99%. Reductions in total coliform (TC) and Escherichia coli ranged from 0.54–2.01 and 1.2–2.2 log removal values (LRV), respectively. The BSF that received water with a higher level of turbidity showed the greatest reduction in the concentration of microorganisms. Additional testing with water contaminated with four bacterial pure cultures showed reductions between 2.7 and 3.6 LRV. A higher reduction in microorganisms was achieved after 30–35 days in operation. Despite the filter’s high efficiency, the filtrates still had some microorganisms, and a disinfection POU treatment could be added to increase water safety.
Mekanisme Persyaratan Teknik Perizinan Pemasukan Benih Tumbuhan dari Luar Negeri ke Sumatera Utara
Authors: Siagian Roberty Melva , Pakpahan Kartina , Akbar Fitra Fajar
Published: August 2020

North Sumatra Province requires the provision of quality seeds to meet the demand for import. The research objective is to analyze positive laws, technical requirements mechanisms, licensing constraints for importation of seeds from abroad in North Sumatra. Using empirical juridical research methods, with a qualitative approach. Enforcement or implementation of normative legal provisions in action on legal events that occur. Conducted interviews with the staff of the Class II Medan Agricultural Quarantine Center. The conclusion is that the import of seeds is specifically regulated in Law Number 21 of 2019 concerning Animal, Fish and Plant Quarantine. Government Regulation Number 82 of 2000, Regulation of the Minister of Agriculture Number 15 of 2017 concerning Importation and Exportation of Horticultural Seeds and Regulation of the Minister of Agriculture Number 127 of 2014 concerning Importation and Exportation of Plant Seeds. Obtaining a permit from the Minister of Agriculture accompanied by a certificate from the country of origin states that the seeds are free from plant-disturbing organisms, namely HpHK, HPIK, or OPTK. The officers can control the seeds that contain plant pests in plant media in North Sumatra by conducting intensive supervision at the entry point as an effort to protect against harm to humans, including damaging the preservation of natural resources.
A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction
Authors: Bashir Kamal , Li Tianrui , Yahaya Mahama
Published: August 2020

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process
Le journal de Maurice Dartevelle, sous-lieutenant au Fort d’Andoy près de Namur (8 juillet-26 août 1914)
Authors: Lierneux Pierre , Van Belle Jean-Louis
Published: August 2020

Cette étude concerne la publication d’un journal de guerre (1914-1918) de Maurice Dartevelle (1890-1974) qui tout jeune sous-lieutenant d’artillerie sera affecté le 30 mai 1914 au fort d’Andoy destiné à assurer la défense de la forteresse de Namur. Il y décrit sa vie, l’atmosphère régnant du 4 au 24 août 1914, date à laquelle à la suite d’un bombardement intense ce fort dut se rendre, reddition qui le fit prisonnier. L’édition de ce texte est précédée d’une introduction retraçant la vie et la carrière de l’auteur.
De eerste oorlogsweken van een jonge vrijwilliger : het dagboek van Pierre Pirenne, 4 augustus -18 September 1914
Authors: Keymeulen Sarah
Published: August 2020

Pierre Pirenne (1895-1914) était l’un des quatre fils de l’historien Henri Pirenne et son épouse Jenny Vanderhaeghen. Il s’est porté volontaire de guerre à l’âge de dix-neuf ans et a déjà été tué après quelques mois. Son journal de guerre, dont une partie seulement a été conservée, ne couvre que les premières semaines de la guerre, mais n’est pourtant pas moins envoûtant. Il peint l’image d’un jeune homme déterminé à se battre -non seulement contre l’ennemi allemand, mais aussi contre l’image de «fils à papa».
Déconstruire l’image de la Belgique «germanophile» à l’Étranger (1915-1916) -Propagande et relations internationales. Quatorze lettres et notes inédites d’Eugène Beyens, Paul Hymans et Georges Lorand
Authors: Genin Vincent
Published: August 2020

L’édition de cette correspondance permet de mieux connaître les moyens de propagande déployés par la Belgique, durant la Première Guerre mondiale, afin de restaurer son image de pays aux sensibilités germanophiles avant 1914. Cet ensemble de quatorze lettres rédigées en 1915 et 1916 (sans oublier un memorandum rétrospectif de 1919) jette une nouvelle lumière sur les stratégies développées par la Belgique officielle en exil (Paul Hymans, Eugène Beyens, Georges Lorand) afin de purifier son image internationale.
Ethical and Social Values for Paediatric Health Technology Assessment and Drug Policy
Authors: Denburg Avram E. , Giacomini Mita , Ungar Wendy , Abelson Julia
Published: August 2020

Background: Public policy approaches to funding paediatric medicines in advanced health systems remain understudied. In particular, the ethical and social values dimensions of health technology assessment (HTA) and drug coverage decisions for children have received almost no attention in research or policy. Methods: To elicit and understand the social values that influence decision-making for public funding of paediatric drugs, we undertook a series of in-depth, semi-structured interviews with a stratified purposive sample (n = 22) of stakeholders involved with or affected by drug funding decisions for children at the provincial (Ontario) and national levels in Canada. Constructivist grounded theory methodology guided data collection and thematic analysis. Results: Our study provides empirical evidence about the unique ethical and social values dimensions of HTA for children, and describes a novel social values typology for paediatric drug policy decision-making. Three principal categories of values emerged from stakeholder reflections on HTA and drug policy-making for children: procedural values, structural values, and sociocultural values. Key findings include the importance of attention to the procedural legitimacy of HTA for children, with emphasis on the inclusion of child health voices in processes of technology appraisal and policy uptake; a role for HTA institutions to consider the equity impacts of technologies, both in setting review priorities and in assessing the value of technologies for public coverage; and the potential benefits of a distinct national framework to guide drug policy for children. Conclusion: Current approaches to HTA are not well designed for the realities of child health and illness, nor the societal priorities regarding children that our study identified. This research generates new knowledge to inform decision-making on paediatric drugs by HTA institutions and government payers in Canada and other publicly-funded health systems, through insights into the relevant social values for child drug funding decisions from varied stakeholder groups.
Hollow Threats: Transnational Food and Beverage Companies’ Use of International Agreements to Fight Front-of-Pack Nutrition Labeling in Mexico and Beyond
Authors: Crosbie Eric , Carriedo Angela , Schmidt Laura
Published: August 2020

In October 2019, the Mexican government reformed its General Health Law thus establishing the warning approach to front-of-pack nutrition labeling (FOPNL), and in March 2020, modified its national standard, revamping its ineffective FOPNL, one preemptively developed by industry actors. Implementation is scheduled for later in 2020. However, the new regulation faces fierce opposition from transnational food and beverage companies (TFBCs), including Nestlé, Kellogg, Grupo Bimbo, Coca-Cola, PepsiCo through their trade associations, the National Manufacturers, American Bakers Associations, the Confederation of Industrial Chambers of Mexico and ConMéxico. Mexico, as a regional leader, could tip momentum in favor of FOPNL diffusion across Latin America. But the fate of the Mexican FOPNL and the region currently lies in this government’s response to three threats of legal challenges by TFBCs, citing international laws and guidelines including the World Trade Organization (WTO), Codex Alimentarius, and the North American Free Trade Agreement (NAFTA)/US-Mexico-Canada Agreement (USMCA). In this perspective, we argue that these threats should not prevent Mexico or other countries from implementing evidence-informed policies, such as FOPNLs, that pursue legitimate public health objectives.
Chewing Questionnaire Survey Results and Chewing Ability Test Results of 118 People who Participated in the University Festival
Authors: Katayama Naomi , Kondo Shoko
Published: August 2020

Chewing well and eat slowly are good habits for maintaining good health. A slow rise in blood sugar after eating can keep people away from overeating, obesity, and diabetes. In this study, we conducted a subjective self-administered questionnaire survey on chewing and a chewing ability test using chewing ability chewing gum for 34 high school students, 55 university students, and 23 middle-aged people who participated in the university festival. The chewing ability test, a chewing gum manufactured by LOTTE used. As a result, most of the participants knew xylitol and some word, the 8020 campaign (holding 20 teeth at the age of 80). Also, although many participants could bite apples with skin, a few were confident in their teeth. Many participants replied they were chewing their meals well, but a few participants bite 30 times one bite of food. Participants chewed the chew-ability gum 60 times, and the inspector judged the gum color.
Fundamental Study of Quantitative Evaluation of Radio Wave Output of Wireless Medical Telemetry Transmitters Operable in Medical Field
Authors: Fujii Kiyotaka , Kishimoto Kazumasa , Inoue Munenori
Published: August 2020

Wireless medical telemetry systems (WMTSs) are important medical equipment for monitoring of biological information such as the electrocardiogram of a patient in a remote location in real time. However, because WMTSs have characteristically used channels that are unique to the radio wave spectrum, many institutions have not previously managed WMTSs. Therefore, we examined a method for quantitative evaluation of the radio wave output of a wireless medical telemetry transmitter (WMTT) that can even be implemented in the medical field. In the experiments, we demonstrated the possibility of use of the method for quantitative evaluation of WMTT radio wave output in the medical field using two types of radio wave propagation model: a free-space propagation model and a two-ray ground reflection model. To determine the reference threshold value for use of each model, a breakpoint was found to be important to grasp the change point between short distance and long distance. It was indicated that the measured value was lower than the theoretical value below the breakpoint, while the measured value was slightly higher than the theoretical value beyond the breakpoint.
Surface Defect Detection and Root Cause Analysis
Authors: Liu Tianchen , Zhu Fan , Yu Haoran , Gu Haisong
Published: August 2020

Artificial Intelligence has played an increasingly important role in surface defect detection in recent years. At the same time, there are many challenges using deep learning for this area, such as the detection accuracy, shortage of data and, lack of knowledge of root cause of defects. To solve the problem of data shortage, we propose a taxonomy method called DataonomyTM to extend a meta defect datasets with a small number of samples for training defect classifiers. For the accuracy, we apply two latest deep neural network(DNN) architectures, Inception v3 and fully convolutional networks (FCN) so as not only to classify whether there are defects but also to make a pixel-wise prediction to inference the areas of defects. For those detected defects, we combine DNN with traditional AI methods to find root causes of detected defects. We use a generalized multi-image matting algorithm to extract common defects automatically. We apply this technology to identify defects that stem from systematic errors in the surface operation. Experimental results have shown great capability and versatility of our proposed methods.
Sensorial Profile and Acceptability of Coffee Blends Submitted to Different Roasting Degrees
Authors: Rodrigues de Oliveira Ana Paula Lelis , Neves Ramon Felipe , Horta de Oliveira Gabriel Henrique , Corrêa de Souza Magno Vinicius
Published: August 2020

Industry of roasted and ground coffee uses Coffea arabicaL. (arabica) and Coffeacane-phora (robusta), main coffee species, to form blends consumed worldwide. In addition to blends composition, industries also vary the roast degree to attend the consumer market. Being that stated, this work aimed to evaluate the influence of roasting degree and blends composition, usingsensorial analysis, over the product acceptability. Arabica and robusta coffee were dehulled, classified, and roasted at Agtrons numbers SCAA#65 (medium-light) e SCAA#45 (moderately dark). Afterward, the authors made a preliminary test to select the blends for conventional consumers as a function of robusta coffee percentage. After the selection and determination of adequate proportion (0, 30, and 60 % m/m) of robusta coffee,the researchers invited 49 consumers to perform the tests. The researchers used preliminary tests to indicate that 29 out of the 49 consumers are capableto complete the tests. They performed the sensorial analysis of the blends, signaling grades from 1 to 9 to flavor, aroma, and appearance. Blend composition presented a higher impact over the coffee acceptability than the roast degree, in which coffee with 0% of robusta coffee, independently of the roast degree, followed by sample with 30% of robusta coffee roasted at medium light, presented the highest grades.