bluuebunny/crossref_metadata_2025 huggingface dataset:
Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing
Authors: Chen Fengnong , Zhang Yao , Zhang Jingcheng , Liu Lianmeng , Wu Kaihua
Published: February 2022

Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and U. virens infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work.
Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring
Authors: Angulo-Saucedo Gilbert A. , Leon-Medina Jersson X. , Pineda-Muñoz Wilman Alonso , Torres-Arredondo Miguel Angel , Tibaduiza Diego A.
Published: February 2022

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.
Low-Complexity Self-Interference Cancellation for Multiple Access Full Duplex Systems
Authors: Shayovitz Shachar , Krestiantsev Andrey , Raphaeli Dan
Published: February 2022

Self-interference occurs when there is electromagnetic coupling between the transmission and reception of the same node; thus, degrading the RX sensitivity to incoming signals. In this paper we present a low-complexity technique for self-interference cancellation in multiple carrier multiple access systems employing whole band direct to digital sampling. In this scenario, multiple users are simultaneously received and transmitted by the system at overlapping arbitrary bandwidths and powers. Traditional algorithms for self-interference mitigation based on recursive least squares (RLS) or least mean squares (LMS), fail to provide sufficient rejection, since the incoming signal is far from being spectrally flat, which is critical for their performance. The proposed algorithm mitigates the interference by modeling the incoming multiple user signal as an autoregressive (AR) process and jointly estimates the AR parameters and self-interference. The resulting algorithm can be implemented using a low-complexity architecture comprised of only two RLS modules. The novel algorithm further satisfies low latency constraints and is adaptive, supporting time varying channel conditions. We compare this to many self-interference cancellation algorithms, mostly adopted from the acoustic echo cancellation literature, and show significant performance gain.
A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph
Authors: Castejón-Limas Manuel , Fernández-Robles Laura , Alaiz-Moretón Héctor , Cifuentes-Rodriguez Jaime , Fernández-Llamas Camino
Published: February 2022

Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn’s Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn’s objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to (X,y) entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.
Integrating the IEEE 1451 and IEC 61499 Standards with the Industrial Internet Reference Architecture
Authors: da Rocha Helbert , Abrishambaf Reza , Pereira João , Espirito Santo Antonio
Published: February 2022

Industrial Internet of Things focuses on the manufacturing process and connects with other associated concepts such as Industry 4.0, Cyber-Physical Systems, and Cyber-Physical Production Systems. Because of the complexity of those components, it is necessary to define reference architectures models to manage Industry 4.0 and the Industrial Internet of Things. The reference architecture models aim to solve the interoperability problem enabling the syntactical and semantic levels of interoperability. A reference architecture model provides a bottom/top view of an industrial process, from the physical transducers at the physical layer to the business layer. The physical layer provides access to a twin representation of a physical thing in the digital world, extending the functionalities in the manufacturing process. This paper studies the syntactic interoperability between the IEEE 1451 and IEC 61499 in an industrial environment. The IEEE 1451 family of standards has the essential characteristics to support the information exchange between smart transducers (sensors and actuators), building the digital elements and meeting the Industry 4.0 requirements. The IEC 61499 standard enables industrial control and automation. These two standards combined at the syntactic level solve an interoperability problem. The IEC 61499 also provides data to the framework layer, supplying all the parameters defined for the communication layer specified by a reference architecture model. This paper combines the IEEE 1451 with the IEC 61499, enabling data exchange in a reference architecture model proposed for Industry 4.0. Network performance at the communication level of a reference architecture model in a local network and an external network is evaluated for the proposed application. The IEEE 1451 standard implementation and adoption to acquire data and communicate it inside an industrial process allowed the IEC 61499 standard to control an industrial process. The IEEE 1451 standard is implemented in a MSP430 low power microcontroller. A Raspberry Pi running FORTE and 4diac in the USA and Portugal were used to test a local network in Portugal and an external network in USA. Data related to network performance was obtained with Wireshark and processed with MATLAB. Tests using the Message Queuing Transport Telemetry Transport and Hypertext Transport Protocols verified the performance of these protocols, supported by the IEEE 1451 and IEC 61499 standards, showing that communication inside an Industry 4.0 environment is possible. MQTT protocol is faster, has a small packet size, and consumes less bandwidth. The HTTP protocol uses more bandwidth but is more reliable for real-time communication, essential for Industry 4.0.
Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring
Authors: Scheeren Bart , Kaminski Miroslaw Lech , Pahlavan Lotfollah
Published: February 2022

In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed.
Potentialities of High-Resolution 3-D CZT Drift Strip Detectors for Prompt Gamma-Ray Measurements in BNCT
Authors: Abbene Leonardo , Principato Fabio , Buttacavoli Antonino , Gerardi Gaetano , Bettelli Manuele , Zappettini Andrea , Altieri Saverio , Auricchio Natalia , Caroli Ezio , Zanettini Silvia , Protti Nicoletta
Published: February 2022

Recently, new high-resolution cadmium–zinc–telluride (CZT) drift strip detectors for room temperature gamma-ray spectroscopic imaging were developed by our group. The CZT detectors equipped with orthogonal anode/cathode collecting strips, drift strips and dedicated pulse processing allow a detection area of 6 × 20 mm2 and excellent room temperature spectroscopic performance (0.82% FWHM at 661.7 keV). In this work, we investigated the potentialities of these detectors for prompt gamma-ray spectroscopy (PGS) in boron neutron capture therapy (BNCT). The detectors, exploiting the measurement of the 478 keV prompt gamma rays emitted by 94% 7Li nuclides from the 10B(n, α)7Li reaction, are very appealing for the development of single-photon emission computed tomography (SPECT) systems and Compton cameras in BNCT. High-resolution gamma-ray spectra from 10B samples under thermal neutrons were measured at the T.R.I.G.A. Mark II research nuclear reactor of the University of Pavia (Italy).
Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data
Authors: Senal Maria , Møller Anders , Koganti Triven , Iversen Bo
Published: February 2022

Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3−) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale.
Improved Spoken Language Representation for Intent Understanding in a Task-Oriented Dialogue System
Authors: Kim June-Woo , Yoon Hyekyung , Jung Ho-Young
Published: February 2022

Successful applications of deep learning technologies in the natural language processing domain have improved text-based intent classifications. However, in practical spoken dialogue applications, the users’ articulation styles and background noises cause automatic speech recognition (ASR) errors, and these may lead language models to misclassify users’ intents. To overcome the limited performance of the intent classification task in the spoken dialogue system, we propose a novel approach that jointly uses both recognized text obtained by the ASR model and a given labeled text. In the evaluation phase, only the fine-tuned recognized language model (RLM) is used. The experimental results show that the proposed scheme is effective at classifying intents in the spoken dialogue system containing ASR errors.
Political Fragility and the Timing of Conflict Mediation
Authors: Beckerman Carly
Published: February 2022

In recent years, much of the public discourse regarding conflict in the Middle East has pondered the possibility of military intervention, but far less attention has been paid to the optimal mechanisms for conflict mediation. There remains considerable confusion in the study of conflict resolution about how to locate the right time, or ‘ripe moment’ for this type of third-party involvement. This is a crucial area of policy relevant research. When attempting to model ripeness, most of the literature has relied on expected utility models of decision-making and found that crucial but nebulous factors that are important in the MENA region, such as conflicting parties’ psychology, religious and political beliefs, as well as grievances compounded over time, cannot easily be incorporated into the framework. This paper offers a plausibility probe to highlight the potential of an augmented approach. Using Poliheuristic (PH) Theory that reflects the non-compensatory nature of political risk, it creates a litmus test for third-party mediation based not on what conflicting parties aim to achieve, but what outcomes and processes they must avoid. The result is a relatively simple identification of ‘bad’ timing, as well as theory-informed mechanisms designed to help practitioners generate better conditions for mediation. This probe contributes to our understanding of the relationship between political fragility and conflict in the MENA region by indicating how political fragility might be conceptualized as a process that can be mapped and perhaps interrupted.
Proposal for a Preliminary Model of Integrated Product Development Process Oriented by Green Supply Chain Management
Authors: Uemura Reche Alda Yoshi , Canciglieri Junior Osiris , Szejka Anderson Luis , Rudek Marcelo
Published: February 2022

For sustainable product development, activities related to the selection of raw materials, product design, manufacturing, packaging, and distribution must be considered in a way that respects environmental limits and implements alternatives to reduce the consumption of natural resources. Due to this context, companies have sought to create environmentally sustainable alternatives for products and processes, once exposed to government, market, and regulatory pressures. Given this scenario, the question of this study is presented: Is it possible to define a conceptual model from existing models in the literature that guides companies in their Integrated Product Development Process (IPDP) and is oriented by Green Supply Chain Management (GSCM)? To answer the research problem question, a preliminary model integrating the IPDP and GSCM themes was presented. The elaboration of the preliminary model was only possible from the systematic literature review and content analysis previously carried out and presented in the general data in this article. The general aim of the study is to present a preliminary model in which IPDP is oriented to GSCM. As specific objectives, this study intends to present models previously published that have some relation to the IPDP related to green design, green purchase, green manufacturing, green distribution, and reverse logistics, which are related to GSCM; in addition, it presents guidelines to integrate the stages of product development and GSCM to reduce the environmental impact. The contribution of the preliminary model for companies is to present criteria that reduce the environmental impact of products in different GSCM activities within the IPDP. The contribution of this study is to present an analysis of the existing models, which will be the basis for the development of a conceptual model.
Psychological Barriers to Sustainable Dietary Patterns: Findings from Meat Intake Behaviour
Authors: Lourenco Carlos Eduardo , Nunes-Galbes Nadine Marques , Borgheresi Riccardo , Cezarino Luciana Oranges , Martins Flavio Pinheiro , Liboni Lara Bartocci
Published: February 2022

Sustainable diets are patterns that change consumer behaviour towards more balanced and plant-based habits. This study investigates the effect of individuals’ predisposition to adopt sustainable diets while reducing meat intake by measuring their psychological proximity to meat. Using an online consumer-based platform, a cross-sectional survey collected responses from 497 individuals. Questionnaire items included 43 questions prospected from the literature, coded as practical and essential barriers and measured on a modified 7-point Likert scale. The results showed some paradoxes, as a high predisposition to adopt a sustainable diet coexists with a high predisposition to consume animal-based proteins. The main perceived barrier to adopting a plant-based diet was the enjoyment of eating meat, followed by the lack of information about plant-based diets. Barriers related to meat alternatives remained the highest above all the others. It confirms that, for local consumers, meat remains a usual option and is easier to prepare. When investigating the moderating effect of barriers on the predisposition to behaviours towards meat intake reduction, no effect was confirmed. However, we found a U-shaped moderating effect for plant-based meal increase, confirming the psychological proximity of the meat consumption effects under the lens of the Construal Level Theory and Transtheoretical Model. These findings call for further research on the effectiveness of measuring psychological barriers related to sustainable diet adoption.
Coordination of Digital Transformation in International Manufacturing Networks—Challenges and Coping Mechanisms from an Organizational Perspective
Authors: Badasjane Viktorija , Granlund Anna , Ahlskog Mats , Bruch Jessica
Published: February 2022

Coordinating the digital transformation of globally dispersed factories within international manufacturing networks has become a critical issue for competitiveness, yet there has been limited attention paid to this issue in previous research. Therefore, the purpose of this paper is to, from an organizational perspective, explore the challenges in coordinating the digital transformation in an international manufacturing network and the coping mechanisms to overcome those challenges. A case study is conducted in a manufacturing company within the heavy vehicle industry, thus contributing to the limited empirical research covering coordination of digital transformation. The data is analyzed through organizational structure and design theory, and the findings are mapped into four core dimensions: differentiation, integration, centralization, and formalization. The results show 15 challenges and 11 coping mechanisms for coordination of digital transformation in international manufacturing networks, identifying the significance of the coordination uncertainty within the formalization dimension that is particularly exposed to the changes induced by digital transformation. The findings include the need for a coordination-oriented organizational structure that incorporates how and where coordination can be actualized. The research implications contribute with new insights by providing a detailed description of the created organizational structure and, in contrast to previous research, focuses specifically on the coordination aspect of digital transformation in an international manufacturing network.
The Relationship between Chinese Teachers’ Emotional Labor, Teaching Efficacy, and Young Children’s Social-Emotional Development and Learning
Authors: Xie Sha , Wu Dandan , Li Hui
Published: February 2022

The existing literature has established the effects of emotional labor on teachers’ wellbeing indicators and teaching efficacy, leaving its impact on students’ outcomes unexplored. Following Grandey’s integrative model of emotional labor and social-emotional learning (SEL) framework, this study explored the relationship between teachers’ emotional labor, teaching efficacy, and young children’s social-emotional development and learning in early childhood settings. Thirteen preschools were recruited through stratified random sampling in Shenzhen, China. Altogether, 49 classrooms were involved, and three teachers and six children were sampled from each classroom, resulting in a sample of 124 teachers and 241 children. Teachers’ emotional labor strategy, sense of efficacy, and children’s social-emotional development and learning were surveyed. Structural equation modeling has confirmed that teachers’ natural and surface acting predicted their teaching efficacy. Bootstrapped mediation analysis revealed that the mediation paths from teachers’ emotional labor to children’s learning approaches and social-emotional development varied significantly for teachers in different positions. The study implies that different guidelines and training are needed for teachers in different positions to help them cope with varied emotional labor at work and promote their teaching efficacy for young children’s better development.
Development and Experiments on a Batch-Type Solar Roaster—An Innovative Decentralized System for Coffee Roasting
Authors: Majeed Faizan , Raza Ali , Munir Anjum , Hensel Oliver
Published: February 2022

About 70% of the harvested coffee is exported to the industrialized nations for value addition due to lack of processing and logistic facilities in developing coffee producer countries, thus leaving behind a marginal economic return for the growers. This research was conducted to investigate the roasting capacity of an innovatively developed batch-type directly solar radiated roasting system for the decentralized processing of coffee using solar energy. Central composite rotatable design (CCRD) was employed to design the experiments to optimize the coffee roasting process. Experimental results revealed that with an average solar direct normal irradiance (DNI) of 800 W/m2, the roaster was capable of roasting a batch of 2 kg coffee beans in 20, 23, and 25 min subjected to light roasts, medium roasts, and dark roasts, respectively at a drum speed of two revolutions per minute (rpm). The batch-type solar roaster has the capacity to roast 28.8–36 kg of coffee beans depending on dark to light roasting conditions on a clear sunny day with DNI ranging from 650 to 850 W/m2. The system thermal efficiency during coffee roasting was determined to be 62.2%, whereas the roasting efficiency at a corresponding light roast, medium roast, and dark roast was found to be 97.5%, 95.2%, and 91.3%, respectively. The payback period of the solar roaster unit was estimated to be 1038 working sunshine hours, making it viable for commercialization.
Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images
Authors: Nisa Mehrun , Buzdar Saeed Ahmad , Khan Khalil , Ahmad Muhammad Saeed
Published: February 2022

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.
Practical Application of Urinary Zearalenone Monitoring System for Feed Hygiene Management of a Japanese Black Cattle Breeding Herd—The Relationship between Monthly Anti-Müllerian Hormone and Serum Amyloid A Concentrations
Authors: Widodo Oky Setyo , Etoh Makoto , Kokushi Emiko , Uno Seiichi , Yamato Osamu , Pambudi Dhidhi , Okawa Hiroaki , Taniguchi Masayasu , Lamid Mirni , Takagi Mitsuhiro
Published: February 2022

This study addresses an advantageous application of a urinary zearalenone (ZEN) monitoring system not only for surveillance of ZEN exposure at the production site of breeding cows but also for follow-up monitoring after improvement of feeds provided to the herd. As biomarkers of effect, serum levels of the anti-Müllerian hormone (AMH) and serum amyloid A (SAA) concentrations were used. Based on the results of urinary ZEN measurement, two cows from one herd had urinary ZEN concentrations which were two orders of magnitude higher (ZEN: 1.34 mg/kg, sterigmatocystin (STC): 0.08 mg/kg in roughages) than the levels of all cows from three other herds (ZEN: not detected, STC: not detected in roughages). For the follow-up monitoring of the herd with positive ZEN and STC exposure, urine, blood, and roughage samples were collected from five cows monthly for one year. A monitoring series in the breeding cattle herd indicated that feed concentrations were not necessarily reflected in urinary concentrations; urinary monitoring assay by ELISA may be a simple and accurate method that reflects the exposure/absorption of ZEN. Additionally, although the ZEN exposure level appeared not to be critical compared with the Japanese ZEN limitation in dietary feeds, a negative regression trend between the ZEN and AMH concentrations was observed, indicating that only at extremely universal mycotoxin exposure levels, ZEN exposure may affect the number of antral follicles in cattle. A negative regression trend between the ZEN and SAA concentrations could also be demonstrated, possibly indicating the innate immune suppression caused by low-level chronic ZEN exposure. Finally, significant differences (p = 0.0487) in calving intervals between pre-ZEN monitoring (mean ± SEM: 439.0 ± 41.2) and post-ZEN monitoring (349.9 ± 6.9) periods were observed in the monitored five cows. These preliminary results indicate that the urinary ZEN monitoring system may be a useful practical tool not only for detecting contaminated herds under field conditions but also provides an initial look at the effects of long-term chronic ZEN/STC (or other co-existing mycotoxins) exposure on herd productivity and fertility.
A Battle against COVID-19: Vaccine Hesitancy and Awareness with a Comparative Study between Sinopharm and AstraZeneca
Authors: Boshra Marian S. , Hussein Raghda R. S. , Mohsen Marwa , Elberry Ahmed A. , Altyar Ahmed E. , Tammam Mahmoud , Sarhan Rania M.
Published: February 2022

Background: Awareness about the COVID-19 vaccine’s adverse effects is crucial for gaining public trust. As we still lack proof of vaccines’ safety, this survey aimed to investigate Egyptians’ general awareness of the Sinopharm and AstraZeneca vaccines against COVID-19 and provide considerable evidence on their side effects and complications. Methods: A cross-sectional questionnaire-based study was conducted in Egypt between 20 September and 10 October in 2021, with multiple-choice questions (MCQs) covering all data on vaccine administration confusion, adverse effects or intensity, and complications. Results: Among the 390 participants, 42.3% reported being hesitant before receiving one of the vaccines. About 40.3% of participants were previously infected before getting vaccinated while only 4.6% reported being infected after vaccination. The AstraZeneca vaccine demonstrated higher side effects and symptoms than the Sinopharm vaccine while the Sinopharm vaccine showed a significantly higher rate of COVID-19 infection after vaccination. Conclusions: People with higher educational levels and chronic respiratory diseases represent an excellent model for accepting COVID-19 vaccination. A booster shot is recommended for people vaccinated with the Sinopharm vaccine due to a significantly higher rate of COVID-19 infection after vaccination; however, the Sinopharm vaccine shows a more acceptable safety profile.
Breaking Solitary Wave Impact on a Vertical Seawall
Authors: Wu Yun-Ta
Published: February 2022

Wave interactions with vertical and sloping seawalls are indeed complicated, especially for the impacts due to breaking waves, which are unsteady, turbulent and multi-phase. Available studies successfully measured the impact pressure due to waves acting on seawalls, whereas the associated flow velocity and turbulence characteristic received limited attention, indicating that the momentum of such violent free-surface flows cannot be determined. In this study, new experiments were carried out in a laboratory-scale wave flume using a non-intrusive image-based measuring technique (bubble image velocimetry, BIV) to measure the flow velocities due to a shoaling solitary wave impinging on and overtopping a vertical seawall. By varying the wave height of solitary waves, the breaking point of a shoaling wave can be changed. As such, the impact point of a breaking wave in relation to the seawall can be thereby adjusted. Considering the same still-water depth, two wave height conditions are studied so as to produce different levels of aerated flows. Effects of high- and low-aerated cases on free surface elevations, flow velocities and turbulence characteristics are presented so as to develop a better understanding of wave-structure interactions. More specifically, the maximum velocities and turbulence intensities at different evolutionary phases are identified for these two cases.
Fluoride Adsorption Comparison from Aqueous Solutions Using Al- and La-Modified Adsorbent Prepared from Polygonum orientale Linn.
Authors: Shi Shengli , Zhao Kangxu , Zhao Qiyue , Luo Aiguo , Xie Shulian , Feng Jia
Published: February 2022

Al- and La-modified adsorbent materials (PO–Al, PO–La) were prepared by impregnating Polygonum orientale Linn. straw with Al2(SO4)3 and La(NO3)3·6H2O solutions. The potential of removing fluoride using these modified adsorbents was examined. In the PO, PO–Al and PO–La adsorption systems, the fluoride adsorption process followed pseudo-second-order kinetics, and the kinetic constants for k2 and R2 were 0.0276 and 0.9609; 0.2070 and 0.9994; 0.1266 and 0.9933, respectively. The adsorption equilibrium results showed the best match with Langmuir isotherms. Moreover, the maximum monolayer adsorption capacity of PO, PO–Al and PO–La are 0.0923, 3.3190 and 1.2514 mg/g, respectively, in 30 °C. The regeneration results show that the effectively regenerating ability of modified adsorbents. Al-modified adsorbent showed the best results in terms of cost-effectiveness and adsorption efficiency for fluoride adsorption.
Experimental Investigation on Bioremediation of Heavy Metal Contaminated Solution by Sporosarcina pasteurii under Some Complex Conditions
Authors: Huang Xiaosong , Zhang Rongjun , Cui Mingjuan , Lai Hanjiang
Published: February 2022

Bioremediation of contaminated solutions has attracted extensive attention in recent years due to its wide range of applicability to various types of contaminants and environmental friendliness. Previous studies adequately confirmed the potential of Sporosarcina pasteurii (i.e., S. pasteurii)-based bioremediation for heavy metal contaminated solutions, but they focused mainly on the bioremediation ability of single-heavy-metal contaminated solutions. This study focuses on S. pasteurii-based bioremediation under more complex pollution conditions. A series of laboratory experiments were performed to explore the efficiency and mechanism of S. pasteurii-based bioremediation to heavy metal contaminated solutions under various conditions, including single-heavy-metal pollution condition, heavy metal pollution under high mineral salinity context and multi-heavy-metal pollution scenarios. The results show that S. pasteurii can effectively remove heavy metals such as Cd, Cr(III), and Zn through biomineralization; for the typical range of mineral salinity (including NaCl and KCl) possibly encountered in practice in some contaminated solutions, such as leachate of landfills, the detrimental influence of high mineral salinity on efficiency of S. pasteurii-based bioremediation can be neglected; more importantly, S. pasteurii-based bioremediation can be considered as a potential option for remedying multi-heavy-metal contaminated solutions, though the addition of some heavy metals tends to produce a substantially detrimental influence on the bioremediation ability of S. pasteurii to other heavy metals.
Analysis and Research on Power Supply Strategies of Electric Vehicles Based on Wind Farms
Authors: Liu Yunjia
Published: February 2022

The widespread growth of electric vehicles could pose significant grid and charging infrastructure challenges, especially in areas with underdeveloped infrastructure. This has affected the ease of charging electric vehicles. In this paper, I design a power supply strategy for electric vehicle charging facilities based on wind farm power supply. In this strategy, a preliminary selection of line conductors is carried out, and several schemes are preliminarily determined. Further comparative analysis is made from the three aspects of conductor, tower type, and bus. Through the PowerWorld software, a simulation model is established for each scheme, and an optimal strategy that takes into account economy, security, and system stability is obtained (AAC 31.5 mm double split, tower A and bus 5). This can assist in the transformation of electric vehicle power supply and the construction of wind farm power supply facilities.
Study on the Photo-Based 3d Scanning Process
Authors: Kristály Álmos , Ficzere Péter
Published: February 2022

The experiment aims to determine the practical applicability of photo-based 3D scanning technologies. With the help of computer software, 3D printable, simulation-ready models are created from regular photographs. The accuracy and consistency of such models are evaluated regarding usability engineering.
Comparative Analysis of Burrs in UD-CFRP Composites Using Advanced Hole Machining Technologies
Authors: Helle Anna Nikoletta , Pereszlai Csongor , Gödri András , Geier Norbert
Published: February 2022

Machining of carbon fibre reinforced polymer (CFRP) composites is challenging due to their inhomogeneous and anisotropic structure as well as the strong effect of the carbon fibres on wear. Burrs are critical machining-induced macro-geometrical defects in the case of the machining of CFRP composites, which may lead to assembly difficulties. Nowadays, although novel hole-machining technologies reduce the likelihood of burrs occurring, these technologies are often more costly and require longer machining times. The current experimental study focuses on the analysis of burrs induced by advanced hole machining technologies (helical milling, tilted helical milling and wobble milling) and comparison with a conventional one (conventional drilling).A total of 32 experiments were carried out in a VHTC 5-axis machining centre using uncoated solid carbide end mills. Furthermore, these technologies are compared and discussed based on the burrs experienced and average material removal rate (AMRR). Experimental results show that conventional drilling caused the lowest amount of burrs, followed by wobble milling, tilted helical milling and helical milling. Even though wobble milling is one of the most advantageous technologies in terms of burrs, the AMRR of conventional drilling is twenty times larger than that of wobble milling, therefore, the further development of wobble milling is recommended.
Analysis of Roughness Parameters Determining Tribological Properties in Hard Turned Surfaces
Authors: Molnár Viktor , Sztankovics István
Published: February 2022

Hard-machined components built into automotive industrial products play an important role because they incorporate working surfaces. The machining of them is crucial; the accuracy, surface quality and lifetime have to be ensured. In this paper the tribological properties of hard-turned surfaces are characterized and analyzed based on 3D and 2D surface roughness parameters. Functional parameters that provide quantitative information about the wear resistance and fluid retention of the machined surfaces were studied. The aim of the study was to summarize the relevant roughness parameters in terms of the functionality of the surfaces and to collect experimental results for their application.
Challenges faced in the conversion of a tertiary healthcare centre into a COVID-19 hospital and developing a free-standing isolation centre in India
Authors: Kumar Ratre Brajesh , Kumar Balbir , Garg Rakesg , Vig Saurabh , Pandit Anuja , Arun Bhopale Shweta , Bhatnagar Sushma
Published: February 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which is a highly virulent disease associated with high mortality. Its outbreak into a pandemic has challenged the healthcare system of most countries around the world. A large number of infectious diseases hospitals are the need of hour to counteract this worrying infection. However, it is not possible to build such hospitals on a large scale within a short period of time; the only possibility is to convert existing healthcare facilities into COVID-19 facilities. The Centers for Disease Prevention and Control (CDC) has released guidelines for the preparation of COVID-19 hospitals, but execution at the ground level comes with many challenges. Here, we share our experience and the challenges we faced during the preparation of a tertiary healthcare centre into a COVID-19 centre, with the hope that it will help other institutions to prepare.
Intra-abdominal cancer risk with abdominal pain: a prospective cohort primary care study
Authors: Price Sarah J , Gibson Niamh , Hamilton William T , King Angela , Shephard Elizabeth A
Published: February 2022

Background

Quantifying cancer risk in primary care patients with abdominal pain informs diagnostic strategies.

Aim

To quantify oesophagogastric, colorectal, liver, pancreatic, ovarian, uterine, kidney, and bladder cancer risks associated with newly reported abdominal pain with or without other symptoms, signs, or abnormal blood tests (that is, features) indicative of possible cancer.

Design and setting

This was an observational prospective cohort study using Clinical Practice Research Datalink records with English cancer registry linkage.

Method

The authors studied 125 793 patients aged ≥40 years with newly reported abdominal pain in primary care between 1 January 2009 and 31 December 2013. The 1-year cumulative incidence of cancer, and the composite 1-year cumulative incidence of cancers with shared additional features, stratified by age and sex are reported.

Results

With abdominal pain, overall risk was greater in men and increased with age, reaching 3.4% (95% confidence interval [CI] = 3.0 to 3.7, predominantly colorectal cancer 1.9%, 95% CI = 1.6 to 2.1) in men ≥70 years, compared with their expected incidence of 0.88% (95% CI = 0.87 to 0.89). Additional features increased cancer risk; for example, for men, colorectal or pancreatic cancer risk with abdominal pain plus diarrhoea at 60–69 years of age was 3.1% (95% CI = 1.9 to 4.9) predominantly colorectal cancer (2.2%, 95% CI = 1.2 to 3.8).

Conclusion

Abdominal pain increases intra-abdominal cancer risk nearly fourfold in men aged ≥70 years, exceeding the 3% threshold warranting investigation. This threshold is surpassed for the >60 years age group only with additional features. These results will help direct appropriate referral and testing strategies for patients based on their demographic profile and reporting features. The authors suggest non-invasive strategies first, such as faecal immunochemical testing, with safety-netting in a shared decision-making framework.
Why do GPs rarely do video consultations? qualitative study in UK general practice
Authors: Greenhalgh Trisha , Ladds Emma , Hughes Gemma , Moore Lucy , Wherton Joseph , Shaw Sara E , Papoutsi Chrysanthi , Wieringa Sietse , Rosen Rebecca , Rushforth Alexander , Rybczynska-Bunt Sarah
Published: February 2022

Background

Fewer than 1% of UK general practice consultations occur by video.

Aim

To explain why video consultations are not more widely used in general practice.

Design and setting

Analysis of a sub-sample of data from three mixed-method case studies of remote consultation services in various UK settings from 2019–2021.

Method

The dataset included interviews and focus groups with 121 participants from primary care (33 patients, 55 GPs, 11 other clinicians, nine managers, four support staff, four national policymakers, five technology industry). Data were transcribed, coded thematically, and then analysed using the Planning and Evaluating Remote Consultation Services (PERCS) framework.

Results

With few exceptions, video consultations were either never adopted or soon abandoned in general practice despite a strong policy push, short-term removal of regulatory and financial barriers, and advances in functionality, dependability, and usability of video technologies (though some products remained ‘fiddly’ and unreliable). The relative advantage of video was perceived as minimal for most of the caseload of general practice, since many presenting problems could be sorted adequately and safely by telephone and in-person assessment was considered necessary for the remainder. Some patients found video appointments convenient, appropriate, and reassuring but others found a therapeutic presence was only achieved in person. Video sometimes added value for out-of-hours and nursing home consultations and statutory functions (for example, death certification).

Conclusion

Efforts to introduce video consultations in general practice should focus on situations where this modality has a clear relative advantage (for example, strong patient or clinician preference, remote localities, out-of-hours services, nursing homes).
Pengaruh Pendidikan Kesehatan dengan Media Booklet Anti ISPA (BOOKIS) terhadap Perilaku Pencegahan ISPA pada Ibu Balita
Authors: Ameliya Putri Ni Putu Diah , Gama I Ketut
Published: February 2022

Upper Respiratory Tract Infections (URTI) is an acute infection involving the organ of the upper respiratory tract and the bottom of which can cause a range of illnesses from mild infection to weight and turn off. The purpose of this research was to determine the effect of health education with anti-URTI Booklet media on URTI prevention behavior in mothers of children under five years old. This research was conducted in April-May 2019. This type of research is a quasy experiment with a pre-test and post-test design. This research used 61 people as a sample with used the total sampling technique. The results of this study indicate that from 61 respondents before given treatment had goof behavioral categories as much as 57.4% and after being treated 96.7% had good value categories. Hypothesis testing using the Wilcoxon test results in a p value of 0,000 (p value 0.05) which means that there is an effect of health education with the Anti-URTI Booklet media on URTI prevention behavior in mothers of children under five years old. Based on the results of the study it is suggested that it can improve efforts to prevent URTI in infants using the Anti-URTI Booklet (BOOKIS).