bluuebunny/crossref_metadata_2025 huggingface dataset:
Shape Characterizing of Aggregates Produced through Different Crushing Techniques
Authors: Yaseen Ghulam , Alaloul Wesam Salah , Hafeez Imran , Qureshi Abdul Hannan
Published: September 2021

The aggregate shape properties produced from the different crushing techniques influence the performance properties of the asphalt mixtures. The objective of this study was to classify the aggregates into spherical, flat, elongated and flat, and elongated shapes, collected from impact crusher and jaw crusher of two sources, and to calculate the shape parameters, such as aspect ratio, shape factor, form factor, sphericity, roundness, and angularity index. In addition, this study also investigated the effects of this classification on the Marshall stability and volumetric properties of asphalt mixtures prepared from the respective shape of aggregates. The results showed that the aggregate of different fractions (passing 37.5 mm and retained on 4.75 mm) produced from the jaw crusher of Margalla quarry showed better shape parameters. The spherical aggregates collected from all crushers showed 20–30% higher Marshall stability of the blends by improving the mechanical and volumetric properties of the asphalt mixtures.
p-CuO/n-ZnO Heterojunction Structure for the Selective Detection of Hydrogen Sulphide and Sulphur Dioxide Gases: A Theoretical Approach
Authors: Albargi Hasan , Ammar Hussein , Badran Heba , Algadi Hassan , Umar Ahmad
Published: September 2021

DFT calculations at the B3LYP/LanL2DZ level of theory were utilized to investigate the adsorption of H2S and SO2 gases on the electronic properties of CuO-ZnO heterojunction structures. The results were demonstrated from the standpoint of adsorption energies (Eads), the density of states (DOS), and NBO atomic charges. The obtained values of the adsorption energies indicated the chemisorption of the investigated gases on CuO-ZnO heterojunction. The adsorption of H2S and SO2 gases reduced the HOMO-LUMO gap in the Cu2Zn10O12 cluster by 4.98% and 43.02%, respectively. This reveals that the Cu2Zn10O12 cluster is more sensitive to the H2S gas than the SO2 gas. The Eads values for SO2 and H2S were −2.64 and −1.58 eV, respectively. Therefore, the Cu2Zn10O12 cluster exhibits a higher and faster response-recovery time to H2S than SO2. Accordingly, our results revealed that CuO-ZnO heterojunction structures are promising candidates for H2S- and SO2-sensing applications.
Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review
Authors: Sánchez-Aguayo Marco , Urquiza-Aguiar Luis , Estrada-Jiménez José
Published: September 2021

Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.
Imaging Modalities Employed in Diabetic Retinopathy Screening: A Review and Meta-Analysis
Authors: Kanclerz Piotr , Tuuminen Raimo , Khoramnia Ramin
Published: September 2021

Introduction: Urbanization has caused dramatic changes in lifestyle, and these rapid transitions have led to an increased risk of noncommunicable diseases, such as type 2 diabetes. In terms of cost-effectiveness, screening for diabetic retinopathy is a critical aspect in diabetes management. The aim of this study was to review the imaging modalities employed for retinal examination in diabetic retinopathy screening. Methods: The PubMed and Web of Science databases were the main sources used to investigate the medical literature. An extensive search was performed to identify relevant articles concerning “imaging”, “diabetic retinopathy” and “screening” up to 1 June 2021. Imaging techniques were divided into the following: (i) mydriatic fundus photography, (ii) non-mydriatic fundus photography, (iii) smartphone-based imaging, and (iv) ultrawide-field imaging. A meta-analysis was performed to analyze the performance and technical failure rate of each method. Results: The technical failure rates for mydriatic and non-mydriatic digital fundus photography, smartphone-based and ultrawide-field imaging were 3.4% (95% CI: 2.3–4.6%), 12.1% (95% CI: 5.4–18.7%), 5.3% (95% CI: 1.5–9.0%) and 2.2% (95% CI: 0.3–4.0%), respectively. The rate was significantly different between all analyzed techniques (p < 0.001), and the overall failure rate was 6.6% (4.9–8.3%; I2 = 97.2%). The publication bias factor for smartphone-based imaging was significantly higher than for mydriatic digital fundus photography and non-mydriatic digital fundus photography (b = −8.61, b = −2.59 and b = −7.03, respectively; p < 0.001). Ultrawide-field imaging studies were excluded from the final sensitivity/specificity analysis, as the total number of patients included was too small. Conclusions: Regardless of the type of the device used, retinal photographs should be taken on eyes with dilated pupils, unless contraindicated, as this setting decreases the rate of ungradable images. Smartphone-based and ultrawide-field imaging may become potential alternative methods for optimized DR screening; however, there is not yet enough evidence for these techniques to displace mydriatic fundus photography.
Low-Grade Ovarian Serous Adenocarcinoma with Lymph Node Metastasis in Neck
Authors: Chen Shih-Lung , Hsieh Tsan-Yu , Yang Shih-Wei
Published: September 2021

Low-grade ovarian serous adenocarcinoma is rarely encountered in the neck region. The diagnosis of this rare malignancy entity in the neck is challenging for both clinicians and pathologists. A 53-year-old female with a chief complaint of a right lower neck mass that had been growing for approximately 2 weeks. The ultrasound-guided fine needle aspiration cytology favored malignancy. The positron emission tomography/computed tomography scan revealed the clustered enlarged lymph nodes with increased radioactivity uptake in the right neck level V, and strong radioactivity uptake was also displayed in the right ovarian regions. Pelvis magnetic resonance imaging displayed right adnexal complex mass supporting the ovarian cancer. An en bloc resection of the right neck lymph node was conducted. Ovarian serous adenocarcinoma with metastasis of lymph nodes in the neck was confirmed through histopathological findings. This study reviews the clinical features of low-grade ovarian serous carcinoma metastasizing to lymph nodes in neck. Although very rare, ovarian cancer with neck metastasis should be considered in the differential diagnosis of a neck mass lesion. The clinical staging would be relatively high due to the quiet entity of the cancer.
[68Ga]Ga-NODAGA-E[(cRGDyK)]2 Angiogenesis PET/MR in a Porcine Model of Chronic Myocardial Infarction
Authors: Bentsen Simon , Clemmensen Andreas , Loft Mathias , Flethøj Mette , Debes Karina Poulsdóttir , Ludvigsen Trine Pagh , Larsen Cecilie Bjørstrup , Kirchhoff Jeppe , Olsen Lisbeth Høier , Møller Jacob Eifer , Andersen Thomas Lund , Johannesen Helle Hjorth , Jespersen Thomas , Kjaer Andreas
Published: September 2021

Angiogenesis is crucial in tissue repair and prevents scar tissue formation following an ischemic event such as myocardial infarction. The ischemia induces formation of new capillaries, which have high expression of integrin αvβ3. [68Ga]Ga-NODAGA-E[(cRGDyK)]2 ([68Ga]Ga-RGD) is a promising PET-radiotracer reflecting angiogenesis by binding to integrin αvβ3. A Göttingen mini-pig underwent transient catheter-induced left anterior descending artery (LAD) occlusion for 120 min, and after 8 weeks was imaged on a Siemens mMR 3T PET/MR system. A large antero-septal infarction was evident by late gadolinium enhancement (LGE) on the short axis and 2–4 chamber views. The infarcted area corresponded to the area with high [68Ga]Ga-RGD uptake on the fused PET/MR images, with no uptake in the healthy myocardium. To support the hypothesis that [68Ga]Ga-RGD uptake reflects angiogenesis, biopsies were sampled from the infarct border and healthy myocardium. Expression of αvβ3 was evaluated using immunohistochemistry. The staining showed higher αvβ3 expression in the capillaries of the infarct border compared to those in the healthy myocardium. These initial data confirm in vivo detection of angiogenesis using [68Ga]Ga-RGD PET in a translational model, which overall support the method applicability when evaluating novel cardio-protective therapies.
Recombinant Protein Expression and Purification of N, S1, and RBD of SARS-CoV-2 from Mammalian Cells and Their Potential Applications
Authors: García-Cordero Julio , Mendoza-Ramírez Juvenal , Fernández-Benavides David , Roa-Velazquez Daniela , Filisola-Villaseñor Jessica , Martínez-Frías Sandra Paola , Sanchez-Salguero Erik Saul , Miguel-Rodríguez Carlos E. , Maravillas Montero Jose L. , Torres-Ruiz Jose J. , Gómez-Martín Diana , Argumedo Leopoldo Santos , Morales-Ríos Edgar , Alvarado-Orozco Juan M. , Cedillo-Barrón Leticia
Published: September 2021

The coronavirus disease 2019 (COVID-19) pandemic has reached an unprecedented level. There is a strong demand for diagnostic and serological supplies worldwide, making it necessary for countries to establish their own technologies to produce high-quality biomolecules. The two main viral antigens used for the diagnostics for severe acute respiratory syndrome coronavirus (SARS-CoV-2) are the structural proteins spike (S) protein and nucleocapsid (N) protein. The spike protein of SARS-CoV-2 is cleaved into S1 and S2, in which the S1 subunit has the receptor-binding domain (RBD), which induces the production of neutralizing antibodies, whereas nucleocapsid is an ideal target for viral antigen-based detection. In this study, we designed plasmids, pcDNA3.1/S1 and pcDNA3.1/N, and optimized their expression of the recombinant S1 and N proteins from SARS-CoV-2 in a mammalian system. The RBD was used as a control. The antigens were successfully purified from Expi293 cells, with high yields of the S1, N, and RBD proteins. The immunogenic abilities of these proteins were demonstrated in a mouse model. Further, enzyme-linked immunosorbent assays with human serum samples showed that the SARS-CoV-2 antigens are a suitable alternative for serological assays to identify patients infected with COVID-19.
Single- and Dual-Source CT Myelography: Comparison of Radiation Exposure and Establishment of Diagnostic Reference Levels
Authors: Zensen Sebastian , Bos Denise , Opitz Marcel , Forsting Michael , Guberina Nika , Deuschl Cornelius , Wetter Axel
Published: September 2021

CT myelography (CTM) is a diagnostic technique for the evaluation of various spinal pathologies, and plays an important role in diagnosis of different diseases such as spontaneous intracranial hypotension and postoperative cerebrospinal fluid leaks. The aims of this study were to examine radiation exposure, establish diagnostic reference levels (DRLs) and compare radiation doses of single- and dual-source examinations and different CTM protocols. In this retrospective study, 183 CTMs comprising 155 single-source and 28 dual-source examinations, performed between May 2015 and December 2020, were analyzed. Dose data included 31 whole spine (A), 23 cervical (B), 10 thoracic (C), and 119 lumbar (D) CTMs. Radiation exposure was reported for volume-weighted CT dose index (CTDIvol) and dose-length product (DLP). Radiation doses for CTDIvol and DLP were distributed as follows (median, IQR): A: 7.44 mGy (6.01–11.17 mGy)/509.7 mGy·cm (382.4–682.9 mGy·cm), B: 9.31 mGy (7.20–14.64 mGy)/214.5 mGy·cm (153.7–308.2 mGy·cm), C: 6.80 mGy (6.14–8.26 mGy)/365.4 mGy·cm (222.8–432.4 mGy·cm), D: 11.02 mGy (7.97–14.89 mGy)/308.0 mGy·cm (224.7–413.7 mGy·cm). Local DRLs could be depicted as follows (CTDIvol/DLP): A: 11 mGy/683 mGy·cm, B: 15 mGy/308 mGy·cm, C: 8 mGy/432 mGy·cm, D: 15 mGy/414 mGy·cm. High image quality was achieved for all anatomical regions. Basically, radiation exposure of CTM differs according to anatomical location.
On Building and Evaluating a Medical Records Exploration Interface Using Text Mining Techniques
Authors: Torres Parejo Úrsula , Campaña Jesús Roque , Vila María Amparo , Delgado Miguel
Published: September 2021

Medical records contain many terms that are difficult to process. Our aim in this study is to allow visual exploration of the information in medical databases where texts present a large number of syntactic variations and abbreviations by using an interface that facilitates content identification, navigation, and information retrieval. We propose the use of multi-term tag clouds as content representation tools and as assistants for browsing and querying tasks. The tag cloud generation is achieved by using a novelty mathematical method that allows related terms to remain grouped together within the tags. To evaluate this proposal, we have carried out a survey over a spanish database with 24,481 records. For this purpose, 23 expert users in the medical field were tasked to test the interface and answer some questions in order to evaluate the generated tag clouds properties. In addition, we obtained a precision of 0.990, a recall of 0.870, and a F1-score of 0.904 in the evaluation of the tag cloud as an information retrieval tool. The main contribution of this approach is that we automatically generate a visual interface over the text capable of capturing the semantics of the information and facilitating access to medical records, obtaining a high degree of satisfaction in the evaluation survey.
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Authors: Leadbeater Chiara , Sharrock Louis , Coyle Brian , Benedetti Marcello
Published: September 2021

Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using f-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any f-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the born machine. The first is based on f-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing f-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback–Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another f-divergence, namely, the Pearson divergence.
Optimal Heat Exchanger Area Distribution and Low-Temperature Heat Sink Temperature for Power Optimization of an Endoreversible Space Carnot Cycle
Authors: Wang Tan , Ge Yanlin , Chen Lingen , Feng Huijun , Yu Jiuyang
Published: September 2021

Using finite-time thermodynamics, a model of an endoreversible Carnot cycle for a space power plant is established in this paper. The expressions of the cycle power output and thermal efficiency are derived. Using numerical calculations and taking the cycle power output as the optimization objective, the surface area distributions of three heat exchangers are optimized, and the maximum power output is obtained when the total heat transfer area of the three heat exchangers of the whole plant is fixed. Furthermore, the double-maximum power output is obtained by optimizing the temperature of a low-temperature heat sink. Finally, the influences of fixed plant parameters on the maximum power output performance are analyzed. The results show that there is an optimal temperature of the low-temperature heat sink and a couple of optimal area distributions that allow one to obtain the double-maximum power output. The results obtained have some guidelines for the design and optimization of actual space power plants.
Adaptive Event-Triggered Synchronization of Uncertain Fractional Order Neural Networks with Double Deception Attacks and Time-Varying Delay
Authors: Shen Zhuan , Yang Fan , Chen Jing , Zhang Jingxiang , Hu Aihua , Hu Manfeng
Published: September 2021

This paper investigates the problem of adaptive event-triggered synchronization for uncertain FNNs subject to double deception attacks and time-varying delay. During network transmission, a practical deception attack phenomenon in FNNs should be considered; that is, we investigated the situation in which the attack occurs via both communication channels, from S-C and from C-A simultaneously, rather than considering only one, as in many papers; and the double attacks are described by high-level Markov processes rather than simple random variables. To further reduce network load, an advanced AETS with an adaptive threshold coefficient was first used in FNNs to deal with deception attacks. Moreover, given the engineering background, uncertain parameters and time-varying delay were also considered, and a feedback control scheme was adopted. Based on the above, a unique closed-loop synchronization error system was constructed. Sufficient conditions that guarantee the stability of the closed-loop system are ensured by the Lyapunov-Krasovskii functional method. Finally, a numerical example is presented to verify the effectiveness of the proposed method.
Guidelines for Teacher Training in Mobile Augmented Reality Games: Hearing the Teachers’ Voices
Authors: Pombo Lúcia , Marques Margarida M.
Published: September 2021

Mobile augmented reality games (MARGs) can be leveraged for educational purposes, as there are several examples in the literature revealing their educational value. The supporting technologies for MARGs’ implementation in education are increasingly pervasive and popular, so it is time for their adoption in teacher practices. However, the integration of new practices in schools, with an impact on students’ learning, requires teacher training. For that, a 50 h workshop was con-ducted to promote the collaborative development of MARGs for science, technology, engineering, and mathematics learning, which was attended by 16 in-service teachers, in Portugal. The aim of this study is to propose a set of guidelines for teacher training on MARGs, emergent from the perspectives of teachers who participated in the workshop. This is a descriptive qualitative study focusing on documental analysis of the individual final reflections of the participating teachers. The results showed that the workshop answered teachers’ personal and professional needs, with an impact on their practices, in what concerns the integration of mobile and AR technology, and of games. Producing a new game during training and making it freely available for others to use seemed to be very satisfactory for teachers, and a good practice to follow. From teachers’ reflections, it was possible to propose a set of guidelines useful for international readership, both researchers and teacher trainers, who aim to conceive and develop continuous professional development initiatives for MARGs’ integration in teacher practices.
The Impact of Procedural Knowledge on the Formation of Declarative Knowledge: How Accomplishing Activities Designed for Developing Learning Skills Impacts Teachers’ Knowledge of Learning Skills
Authors: Saks Katrin , Ilves Helen , Noppel Airi
Published: September 2021

In addition to content knowledge, it is necessary to teach learning skills. Using relevant learning skills assures better learning outcomes and abilities for the whole life course. Due to a packed curriculum or teachers having insufficient skills, student learning skills are often neglected. The aim of this study is to find out whether it is possible to support the development of declarative knowledge in learning skills in teachers through procedural knowledge acquired via practical teaching activities. A quasi-experimental study using semi-structured pre- and post-intervention interviews and six-month intervention activities was conducted with 14 teachers. The procedural knowledge of the teachers was assessed based on their practical experience with the intervention activities. Their declarative knowledge was assessed based on pre- and post-intervention interview reflections. The findings revealed that procedural knowledge, being predominantly teacher centred, mostly appears through experience and reflections on the teaching practice that guides them. It also appeared that the intervention focusing on advancing procedural knowledge among teachers advanced their declarative knowledge in terms of metacognition, revealing new aspects of motivation and sense-giving for their learning. As follow-up research, we plan to continue studying conditional knowledge in teachers in order to identify situations where this is applied.
The Broken Yellow Brick Road: A Narrative Exploration of the College-Going Decisions and Trajectory of a Low-Income Single Mother of Color
Authors: Iloh Constance
Published: September 2021

Communities frequently treated as ‘have-nots’ in higher education are a window into the condition of postsecondary education access, exclusion, inequities, and outcomes. This reality is no more evident than with the college-going trajectories of low-income single mothers of color. Evoking the possibilities of narrative inquiry in general, and life history method in particular, the author explores the college-going ecology, decisions, and trajectory of a 35-year-old low-income Filipina single mother. Through this empirical undertaking, particular attention is paid to the challenges present within the informant’s context of information, time, and opportunity—the three dimensions of the Iloh Model of College-Going Decisions and Trajectories. Findings of this narrative include prolonged and disjointed experiences; poor navigational structures and asymmetries of information; and institutional constraints, barriers, and disregard. In addition to insights that reflect decision-making challenges and buyer’s remorse of minoritized students; this study situates new directions for addressing concerning contexts of time, information, and opportunity for single mothers pursuing college.
Link and System-Level NOMA Simulator: The Reproducibility of Research
Authors: Khan Arsla , Usman Muhammad Arslan , Usman Muhammad Rehan , Ahmad Muneeb , Shin Soo-Young
Published: September 2021

This study focuses on the design of a MATLAB platform for non-orthogonal multiple access (NOMA) based systems with link-level and system-level analyses. Among the different potential candidates for 5G, NOMA is gaining considerable attention owing to the many-fold increase in spectral efficiency as compared to orthogonal multiple access (OMA). In this study, a NOMA simulator is presented for two and more than two users in a single cell for link-level analysis; whereas, for system-level analysis, seven cells and 19 cells scenarios were considered. Long-term evolution (LTE) was used as the baseline for the NOMA simulator, while bit error rate (BER), throughput and spectral efficiency are used as performance metrics to analyze the simulator performance. Moreover, we demonstrated the application of the NOMA simulator for different simulation scenarios through examples. In addition, the performance of multi-carrier NOMA (MC-NOMA) was evaluated in the presence of AWGN, impulse noise, and intercell interference. To circumvent channel impairments, channel coding with linear precoding is suggested to improve the BER performance of the system.
Utilizing Educational Robotics for Environmental Empathy Cultivation in Primary Schools
Authors: Ziouzios Dimitris , Rammos Dimitrios , Bratitsis Tharrenos , Dasygenis Minas
Published: September 2021

The fostering of empathy among primary school students is an important goal because it enhances the improvement of behavior and the development of positive social contacts. Empathy can contribute to understanding and supporting others’ needs. In most cases, empathy in young children is developed through listening to sad stories, experienced first-hand by others. In the educational scenario presented in this article, the dramatic effects of climate change were conveyed to the pupils through a message said to originate from the future, delivered by an educational robot. The message was expressed by a peer living in Iran in 2050. In addition to delivering the message, the robot called on children to prevent climate change from rapidly worsening by changing their own way of thinking and attitudes. Thus, students called upon a formulated educational problem to understand and handle through their own emotional and cognitive performance through the robot’s storytelling. This performance was intensely affected by empathy towards the Iranian peer’s difficult personal living conditions. The research focused on measuring the evidence of empathy development. Additionally, the design and implementation aspects of the robot are presented, utilizing the implemented teaching intervention as means of demonstrating the innovative nature of the robot.
A New Reconfigurable Filter Based on a Single Electromagnetic Bandgap Honey Comb Geometry Cell
Authors: Tavora de Albuquerque Silva Andre , Ferreira Dias Claudio , Rodrigues de Lima Eduardo , Fraidenraich Gustavo , Medeiros de Almeida Larissa
Published: September 2021

This work presents a new unit cell electromagnetic bandgap (EBG) design based on HoneyComb geometry (HCPBG). The new HCPBG takes a uniplanar geometry (UCPBG—uniplanar compact PBG) as a reference and follows similar design methods for defining geometric parameters. The new structure’s advantages consist of reduced occupied printed circuit board area and flexible rejection band properties. In addition, rotation and slight geometry modification in the HCPBG cell allow changing the profile of the attenuation frequency range. This paper also presents a reconfigurable unit cell HCPBG filter strategy, for which the resonance center frequency is shifted by changing the gap capacitance with the assistance of varactor diodes. The HCPBG filter and reconfiguration behavior is demonstrated through electromagnetic (EM) simulations over the FR1 band of the 5G communication network. Intelligent communication systems can use the reconfiguration feature to select the optimal operating frequency for maximum attenuation of unwanted or interfering signals, such as harmonics or intermodulation products.
High Gain SIW H-Plane Horn Antenna with 3D Printed Parasitic E-Plane Horn
Authors: Huang Sheng , Chan King Yuk , Wang Yu , Ramer Rodica
Published: September 2021

Substrate integrated waveguide (SIW) technology that combines 3D and 2D structures has been successfully utilized due to its notable advantages, including in its application to H-plane horn antennas. As this type of antenna is commonly constructed on thin substrates, the E-plane radiation pattern is always wide, thereby limiting the achievable gain performance. In this work, we propose an approach that incorporates 3D printed horns on a prefabricated SIW H-plane horn antenna to successfully narrow the E-plane radiation pattern, thereby improving the gain performance. The proposed E-plane horn is designed at the aperture of the original H-plane horn, providing a smooth and continuous wave transition from the thin substrate to the end-fire direction. This approach improves the directional radiation performance significantly and reduces fabrication time and associated difficulties as the parasitic structures are simply attached to the SIW horn, without the requirement of redesigning or refabricating the original antenna. From 20 to 25 GHz, an optimized prototype shows excellent performance. At 22.7 GHz, it exhibits 35° and 33° for the E- and H-plane half-power beamwidths (HPBWs), with corresponding side-lobe levels (SLLs) of −23 dB and −15 dB. The present research reveals that the proposed design presents high feasibility and a reduced demand for high-precision manufacturing processes at a lower cost, concomitantly providing an effective means to further improve on the radiation characteristics.
Reinforcement Learning for Generating Secure Configurations
Authors: Dass Shuvalaxmi , Siami Namin Akbar
Published: September 2021

Many security problems in software systems are because of vulnerabilities caused by improper configurations. A poorly configured software system leads to a multitude of vulnerabilities that can be exploited by adversaries. The problem becomes even more serious when the architecture of the underlying system is static and the misconfiguration remains for a longer period of time, enabling adversaries to thoroughly inspect the software system under attack during the reconnaissance stage. Employing diversification techniques such as Moving Target Defense (MTD) can minimize the risk of exposing vulnerabilities. MTD is an evolving defense technique through which the attack surface of the underlying system is continuously changing. However, the effectiveness of such dynamically changing platform depends not only on the goodness of the next configuration setting with respect to minimization of attack surfaces but also the diversity of set of configurations generated. To address the problem of generating a diverse and large set of secure software and system configurations, this paper introduces an approach based on Reinforcement Learning (RL) through which an agent is trained to generate the desirable set of configurations. The paper reports the performance of the RL-based secure and diverse configurations through some case studies.
Artificial Intelligence Decision Support System Based on Artificial Neural Networks to Predict the Commercialization Time by the Evolution of Peach Quality
Authors: Ananias Estevão , Gaspar Pedro D. , Soares Vasco N. G. J. , Caldeira João M. L. P.
Published: September 2021

Climacteric fruit such as peaches are stored in cold chambers after harvest and usually are maintained there until the desired ripening is reached to direct these fruit to market. Producers, food industries and or traders have difficulties in defining the period when fruit are at the highest level of quality desired by consumers in terms of the physical-chemical parameters (hardness –H–, soluble solids content –SSC–, and acidity –Ac–). The evolution of peach quality in terms of these parameters depends directly on storage temperature –T– and relative humidity –RH–, as well on the storage duration –t–. This paper describes an Artificial Intelligence (AI) Decision Support System (DSS) designed to predict the evolution of the quality of peaches, namely the storage time required before commercialization as well as the late commercialization time. The peaches quality is stated in terms of the values of SSC, H and Ac that consumers most like for the storage T and RH. An Artificial neuronal network (ANN) is proposed to provide this prediction. The training and validation of the ANN were conducted with experimental data acquired in three different farmers’ cold storage facilities. A user interface was developed to provide an expedited and simple prediction of the marketable time of peaches, considering the storage temperature, relative humidity, and initial physical and chemical parameters. This AI DSS may help the vegetable sector (logistics and retailers), especially smaller neighborhood grocery stores, define the marketable period of fruit. It will contribute with advantages and benefits for all parties—producers, traders, retailers, and consumers—by being able to provide fruit at the highest quality and reducing waste in the process. In this sense, the ANN DSS proposed in this study contributes to new AI-based solutions for smart cities.
EPS-Ledger: Blockchain Hyperledger Sawtooth-Enabled Distributed Power Systems Chain of Operation and Control Node Privacy and Security
Authors: Khan Abdullah Ayub , Laghari Asif Ali , Liu De-Sheng , Shaikh Aftab Ahmed , Ma Dan-Dan , Wang Chao-Yang , Wagan Asif Ali
Published: September 2021

A distributed power system operation and control node privacy and security are attractive research questions that deliver electrical energy systems to the participating stakeholders without being physically connected to the grid system. The increased use of renewable energy in the power grid environment creates serious issues, for example, connectivity, transmission, distribution, control, balancing, and monitoring volatility on both sides. This poses extreme challenges to tackle the entire bidirectional power flow throughout the system. To build distributed monitoring and a secure control operation of node transactions in the real-time system that can manage and execute power exchanging and utilizing, balancing, and maintaining energy power failure. This paper proposed a blockchain Hyperledger Sawtooth enabling a novel and secure distributed energy transmission node in the EPS-ledger network architecture with a robust renewable power infiltration. The paper focuses on a cyber-physical power grid control and monitoring system of renewable energy and protects this distributed network transaction on the blockchain and stores a transparent digital ledger of power. The Hyperledger Sawtooth-enabled architecture allows stakeholders to exchange information related to power operations and control monitoring in a private ledger network architecture and investigate the different activities, preserved in the interplanetary file systems. Furthermore, we design, create, and deploy digital contracts of the cyber–physical energy monitoring system, which allows interaction between participating stakeholders and registration and presents the overall working operations of the proposed architecture through a sequence diagram. The proposed solution delivers integrity, confidentiality, transparency, availability, and control access of the distribution of the power system and maintains an immutable operations and control monitoring ledger by secure blockchain technology.
Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness
Authors: Park Jinhwan , Yoo Donghyeon , Moon Jaemin , Yoon Janghyeok , Park Jungtae , Lee Seungae , Lee Doohee , Kim Changwan
Published: September 2021

Lithium-ion batteries (LIBs) are increasingly employed in electric vehicles (EVs) owing to their advantages, such as low weight, and high energy and power densities. However, the uncertainty encountered in the manufacturing of LIB cells increases the failure rate and causes cell-to-cell variations, thereby degrading the battery capacity and lifetime. In this study, the reliability and robustness of LIB cells were improved using the design of experiments (DOE), and the reliability-based robust design optimization (RBRDO) approaches. First, design factors sensitive to the energy density and power density were selected as design variables through sensitivity analysis using the DOE. RBRDO was performed to maximize the energy density while reducing the failure rate and cell-to-cell variations. To verify the superiority of the reliability and robustness offered by RBRDO, the obtained results were compared with those from conventional deterministic design optimization (DDO), and reliability-based design optimization (RBDO). RBRDO increased the mean of the energy density by 33.5% compared to the initial value and reduced the failure rate by 98.9%, due to improved reliability, compared to DDO. Moreover, RBRDO reduced the standard deviation in the energy density (i.e., cell-to-cell variations) by 30.0% due to the improved robustness compared to RBDO.
Low-Carbon Economy in Schools: Environmental Footprint and Associated Externalities of Five Schools in Southwestern Europe
Authors: Gamarra Ana Rosa , Lago Carmen , Herrera-Orozco Israel , Lechón Yolanda , Almeida Susana Marta , Lage Joana , Silva Filipe
Published: September 2021

This study provides an in-depth assessment of the environmental performance of five public schools in the transition towards a low-carbon economy and a more sustainable model of society. Life cycle assessment (LCA) methodology is used to conduct the study. The school system includes several activities and processes clustered in three subsystems: management of the school building, training and learning activities (T&L) and mobility and transport (M&T). A detailed primary data inventory of energy and resources consumption was collected in five schools located in Spain and Portugal. Findings on climate change (CC), water depletion (WD), particular matter (PM), acidification (Ac), and human health (HH), as well as associated external cost (EC), are reported per student in one school year as reference unit, allowing the schools’ individual performance comparison and identify the potential improvements. Considering the sample of schools, findings reveal that peculiarities of the schools, such as location, specialization, and level of education, are crucial for the environmental performance. Buildings are a relevant contributor to CC as well as heating and electricity needs, although their relevance is dependent on multiple factors. The M&T subsystem also has relevant weight on the metrics evaluated. Educational activities have a lower impact in absolute terms but, in some schools, it becomes the main contributor to HH due to paper and electricity consumption and manufacturing of equipment. External costs results are in the range of 11 to 38 EUR/student·year mainly caused by heating, electricity and wastes from the building subsystem, and the M&T subsystem.
The Bacterial Diversity of Spontaneously Fermented Dairy Products Collected in Northeast Asia
Authors: Yu Zhongjie , Peng Chuantao , Kwok Lai-yu , Zhang Heping
Published: September 2021

Spontaneously fermented dairy products have a long history, and present diverse microorganisms and unique flavors. To provide insight into the bacterial diversity, 80 different types of spontaneously fermented dairy product samples’ sequence data that were downloaded from MG-RAST and NCBI and 8 koumiss and 4 shubat were sequenced by the PacBio SMRT sequencing platform. All samples including butter, sour cream, cottage cheese, yogurt, koumiss, shubat, and cheese, were collected from various regions in Russia, Kazakhstan, Mongolia and Inner Mongolia (China). The results revealed that Firmicutes and Proteobacteria were the most dominant phyla (>99%), and 11 species were identified with a relative abundance exceeding 1%. Furthermore, Streptococcus salivarius, Lactobacillus helveticus, Lactobacillus delbrueckii, Enterobacter xiangfangensis, and Acinetobacter baumannii were the primary bacterial species in the fermented dairy product samples. Principal coordinates analysis showed that koumiss and shubat stood out from the other samples. Moreover, permutational ANOVA tests revealed that the types of fermented dairy products and geographical origin significantly affected microbial diversity. However, different processing techniques did not affect microbial diversity. In addition, results of hierarchical clustering and canonical analysis of the principal coordinates were consistent. In conclusion, geographical origin and types of fermented dairy products determined the bacterial diversity in spontaneously fermented dairy product samples.
The Effect of Arabinoxylan and Wheat Bran Incorporation on Dough Rheology and Thermal Processing of Rotary-Moulded Biscuits
Authors: Molina María Teresa , Lamothe Lisa , Gunes Deniz Z. , Vaz Sandra M. , Bouchon Pedro
Published: September 2021

Wheat bran incorporation into biscuits may increase their nutritional value, however, it may affect dough rheology and baking performance, due to the effect of bran particles on dough structure and an increase in water absorption. This study analyzed the enrichment effect of wheat bran and arabinoxylans, the most important non-starch polysaccharides found in whole wheat flour, on dough rheology and thermal behaviour during processing of rotary-moulded biscuits. The objective was to understand the contribution of arabinoxylans during biscuit-making and their impact when incorporated as wheat bran. Refined flour was replaced at 25, 50, 75, or 100% by whole flour with different bran particle sizes (fine: 4% > 500 μm; coarse: 72% > 500 μm). The isolated effect of arabinoxylans was examined by preparing model flours, where refined flour was enriched with water-extractable and water-unextractable arabinoxylans. Wheat bran had the greatest impact on dough firmness and arabinoxylans had the greatest impact on the elastic response. The degree of starch gelatinization increased from 24 to 36% in biscuits enriched with arabinoxylans or whole flour and coarse bran. The microstructural analysis (SEM, micro-CT) suggested that fibre micropores may retain water inside their capillaries which can be released in a controlled manner during baking.
Continuous Monitoring of Olive Fruit Growth by Automatic Extensimeter in Response to Vapor Pressure Deficit from Pit Hardening to Harvest
Authors: Khosravi Arash , Zucchini Matteo , Giorgi Veronica , Mancini Adriano , Neri Davide
Published: September 2021

Recently, several studies on olive fruit growth have focused on circadian monitoring as an important orchard management tool. The olive fruit growth trend is described by double sigmoid model with four growth phases, where the third phase spans from the end of pit hardening to initial fruit maturation, and the last phase includes olive maturation up to fruit drop. Environmental factors play an important role in fruit growth, with vapor pressure deficit (VPD) being a keystone factor. Our experiment was designed to hourly monitor olive (Olea europaea L. cv. ‘Frantoio’) fruit transversal diameter from approximately initial pit hardening (II Phase), extension (III Phase) until harvest time (IV Phase) in the attempt to determine whether fruit growth dynamically responds to environmental variables such as diurnal VPD change in different stages of fruit development. Automatic extensimeters were applied in open field and VPD was calculated from data of our weather station. Throughout the experiment period, the circadian model of fruit growth showed two steps: shrinkage and expansion. Almost in all days of the third phase of fruit growth, daily response of transversal diameter to VPD formed complete clockwise hysteresis loops. During the fourth phase of fruit growth, with increasing fruit maturation, the complete clockwise hysteresis loop experienced some abnormality. At the fourth stage of fruit growth there were incomplete and partial clockwise hysteresis loops. We conclude that hysteresis can be employed to detect the shift between the end of the third phase (cell expansion) and the beginning of the fourth phase (fruit maturation) of fruit growth. The disappearance of the complete clockwise hysteresis loop and the substitution with incomplete, or partial clockwise hysteresis loops was observable only in the fourth stage of fruit growth. These results can be valuable for any smart fruit management of olive fruit production.
Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis
Authors: Morgan Kelly , Zhou Shang-Ming , Hill Rebecca , Lyons Ronan A. , Paranjothy Shantini , Brophy Sinead T.
Published: September 2021

Background: The growth and maturation of infants reflect their overall health and nutritional status. The purpose of this study is to examine the associations of prenatal and early postnatal factors with infant growth (IG). Methods: A data-driven model was constructed by structural equation modelling to examine the relationships between pre- and early postnatal environmental factors and IG at age 12 months. The IG was a latent variable created from infant weight and waist circumference. Data were obtained on 274 mother–child pairs during pregnancy and the postnatal periods. Results: Maternal pre-pregnancy BMI emerged as an important predictor of IG with both direct and indirect (mediated through infant birth weight) effects. Infants who gained more weight from birth to 6 months and consumed starchy foods daily at age 12 months, were more likely to be larger by age 12 months. Infant physical activity (PA) levels also emerged as a determinant. The constructed model provided a reasonable fit (χ2 (11) = 21.5, p < 0.05; RMSEA = 0.07; CFI = 0.94; SRMR = 0.05) to the data with significant pathways for all examined variables. Conclusion: Promoting healthy weight amongst women of child bearing age is important in preventing childhood obesity, and increasing daily infant PA is as important as a healthy infant diet.
Research on Urban Spatial Connection and Network Structure of Urban Agglomeration in Yangtze River Delta—Based on the Perspective of Information Flow
Authors: Lin Qiaowen , Xiang Mengyu , Zhang Lu , Yao Jinjiang , Wei Chao , Ye Sheng , Shao Hongmei
Published: September 2021

Exploration of urban spatial connections and network structures of urban agglomeration in the Yangtze River Delta, as well as its influencing factors, is of great significance regarding optimization of the development pattern of the Yangtze River Delta urban agglomeration and promotion of regional high-quality development. Therefore, based on Baidu index data in 2015 and 2019, this paper first analyzes the spatiotemporal variation characteristics of information-flow connections in the Yangtze River Delta urban agglomeration. Then it uses social network analysis to explore the information-flow network structure in the Yangtze River Delta urban agglomeration, and finally explores the influencing factors of information-flow intensity in the Yangtze River Delta urban agglomeration. The main conclusions are as follows: (1) The total amount of information flow in the Yangtze River Delta urban agglomeration has had no obvious change, and the coverage of information flow in the central urban circle has expanded. (2) The network hierarchy presents a relatively stable “pyramid” distribution pattern, which tends to develop into a “spindle” pattern. (3) The overall network density of the Yangtze River Delta urban agglomeration is high and is increasing. The backbone network is a “triangle” structure. The central cities in the region are stable, and the subgroups are adjacent to each other geographically. (4) Gross Domestic Product, resident population of the region and the number of Internet broadband subscribers all have important effects on the total information flow, among which the number of Internet broadband subscribers has the greatest effect on the total information flow. In addition, urban functions and their positioning, urban events, history and culture, and other factors that are difficult to quantify also have a certain impact on the information-flow network among cities.