bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of Piezoelectricity of (Bi0.5Na0.5)0.94Ba0.06TiO3 Ceramics Modified by a Combination of Porosity and Sm3+ Doping
Authors: Xia Siyu , Du Huiling , Li Zhuo , Zhao Fan , Li Qianqian , Hu Yuxuan , Kang Le
Published: April 2023

Porous lead-free piezoelectric ceramics are characterized by their environment-friendly, light weight, and large specific surface area. The optimization of porous Na0.5Bi0.5TiO3-based lead-free piezoelectric ceramics can improve piezoelectric properties, enhance force–electric coupling characteristics, and effectively promote energy conversion, expanding the application in force-electric coupling devices. This study aimed to prepare [Smx(Bi0.5Na0.5)1−3x/2]0.94Ba0.06TiO3 (x = 0, 0.01, 0.02, 0.03, 0.04) lead-free ceramics with porous structures, resulting in the piezoelectric constant d33 = 131 pC/N and the plane electromechanical coupling coefficient kp = 0.213 at x = 0.01. The presence of pores in lead-free ceramics has a direct impact on the domain structure and can cause the depolarization process to relax. Then, the soft doping of Sm3+ makes the A-site ion in porous (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics occupancy inhomogeneous and generates cation vacancies, which induces lattice distortion and makes the domain wall motion easier, resulting in the improvement of piezoelectric properties and electromechanical coupling parameters. Furthermore, the piezoelectric oscillator exhibits greater resistance to resonant coupling in the radial extension vibration mode. These results infer that a combination of porosity and Sm3+ doping renders (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics base material for piezoelectric resonators, providing a scientific basis for their application in force–electric coupling devices, such as piezoelectric resonant gas sensors.
You Look like You’ll Buy It! Purchase Intent Prediction Based on Facially Detected Emotions in Social Media Campaigns for Food Products
Authors: Tzafilkou Katerina , Economides Anastasios A. , Panavou Foteini-Rafailia
Published: April 2023

Understanding the online behavior and purchase intent of online consumers in social media can bring significant benefits to the ecommerce business and consumer research community. Despite the tight links between consumer emotions and purchase decisions, previous studies focused primarily on predicting purchase intent through web analytics and sales historical data. Here, the use of facially expressed emotions is suggested to infer the purchase intent of online consumers while watching social media video campaigns for food products (yogurt and nut butters). A FaceReader OnlineTM multi-stage experiment was set, collecting data from 154 valid sessions of 74 participants. A set of different classification models was deployed, and the performance evaluation metrics were compared. The models included Neural Networks (NNs), Logistic Regression (LR), Decision Trees (DTs), Random Forest (RF,) and Support Vector Machine (SVM). The NNs proved highly accurate (90–91%) in predicting the consumers’ intention to buy or try the product, while RF showed promising results (75%). The expressions of sadness and surprise indicated the highest levels of relative importance in RF and DTs correspondingly. Despite the low activation scores in arousal, micro expressions of emotions proved to be sufficient input in predicting purchase intent based on instances of facially decoded emotions.
Triplet Therapy in Metastatic Castrate Sensitive Prostate Cancer (mCSPC)—A Potential New Standard of Care
Authors: Mittal Abhenil , Sridhar Srikala S. , Ong Michael , Jiang Di Maria
Published: April 2023

The treatment paradigm for metastatic castrate-sensitive prostate cancer (mCSPC) has evolved rapidly in the past decade with the approval of several life-prolonging therapies including docetaxel chemotherapy and multiple androgen receptor pathway inhibitors (ARPI) in combination with androgen deprivation therapy (ADT). Recently reported phase-three trials have demonstrated a survival benefit of upfront triplet therapy with ADT, docetaxel plus either abiraterone acetate or darolutamide when compared to ADT plus docetaxel alone. However, multiple questions including the incremental benefit of docetaxel to a combination of ADT and ARPI, the timing of ARPI, optimal patient selection for triplet therapy and clinical and genomic biomarkers still remain to be answered. Moreover, real-world data suggest suboptimal treatment intensification with many patients treated with ADT alone highlighting challenges in implementation. In this article, we review the phase-three data associated with triplet therapy in mCSPC. We also discuss the knowledge gaps that exist despite the completion of these studies and how ongoing studies are likely to change the paradigm in the near future. Finally, we provide a simple algorithm based on current data that clinicians can use in daily practice to select patients for appropriate treatment strategies.
Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach
Authors: Velu Malathi , Dhanaraj Rajesh , Balusamy Balamurugan , Kadry Seifedine , Yu Yang , Nadeem Ahmed , Rauf Hafiz
Published: April 2023

While the world is working quietly to repair the damage caused by COVID-19’s widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor–Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.
A Systematic Review of Diagnostic and Prognostic Biomarkers for Head and Neck Cancer of Unknown Primary: An Unmet Clinical Need
Authors: Filippini Daria Maria , Broseghini Elisabetta , Carosi Francesca , Molin Davide Dal , Riefolo Mattia , Fabbri Laura , Abeshi Andi , Fernandez Ignacio Javier , Ferracin Manuela
Published: April 2023

Head and neck cancer of unknown primary (HNCUP) is defined as cervical lymph node metastases without a detectable primary tumor. The management of these patients presents a challenge to clinicians since guidelines in the diagnosis and treatment of HNCUP remain controversial. An accurate diagnostic workup is fundamental for the search for the hidden primary tumor to allow the best adequate treatment strategy. The purpose of this systematic review is to present the currently available data about the diagnostic and prognostic molecular biomarkers for HNCUP. Systematic research in an electronic database was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol and identified 704 articles, of which 23 studies were selected and included in the analysis. Fourteen studies investigated HNCUP diagnostic biomarkers and focused on the human papilloma virus (HPV) and the Epstein–Barr virus (EBV) due to the strong associations with oropharyngeal cancer and nasopharyngeal cancer, respectively. HPV status was shown to possess prognostic value, correlating with longer disease-free survival and overall survival. HPV and EBV are the only available HNCUP biomarkers, and they are already used in clinical practice. A better characterization of the molecular profiling and the development of tissue-of-origin classifiers are necessary to improve the diagnosis, staging, and therapeutic management of patients with HNCUP.
An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI
Authors: Terzi Ramazan
Published: April 2023

This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.
Multiparametric Skin Assessment in a Monocentric Cohort of Systemic Sclerosis Patients: Is There a Role for Ultra-High Frequency Ultrasound?
Authors: Di Battista Marco , Barsotti Simone , Vitali Saverio , Palma Marco , Granieri Giammarco , Oranges Teresa , Aringhieri Giacomo , Dini Valentina , Della Rossa Alessandra , Neri Emanuele , Romanelli Marco , Mosca Marta
Published: April 2023

Background: To assess skin involvement in a cohort of patients with systemic sclerosis (SSc) by comparing results obtained from modified Rodnan skin score (mRSS), durometry and ultra-high frequency ultrasound (UHFUS). Methods: SSc patients were enrolled along with healthy controls (HC), assessing disease-specific characteristics. Five regions of interest were investigated in the non-dominant upper limb. Each patient underwent a rheumatological evaluation of the mRSS, dermatological measurement with a durometer, and radiological UHFUS assessment with a 70 MHz probe calculating the mean grayscale value (MGV). Results: Forty-seven SSc patients (87.2% female, mean age 56.4 years) and 15 HC comparable for age and sex were enrolled. Durometry showed a positive correlation with mRSS in most regions of interest (p = 0.025, ρ = 0.34 in mean). When performing UHFUS, SSc patients had a significantly thicker epidermal layer (p < 0.001) and lower epidermal MGV (p = 0.01) than HC in almost all the different regions of interest. Lower values of dermal MGV were found at the distal and intermediate phalanx (p < 0.01). No relationships were found between UHFUS results either with mRSS or durometry. Conclusions: UHFUS is an emergent tool for skin assessment in SSc, showing significant alterations concerning skin thickness and echogenicity when compared with HC. The lack of correlations between UHFUS and both mRSS and durometry suggests that these are not equivalent techniques but may represent complementary methods for a full non-invasive skin evaluation in SSc.
Comparison of Diagnostic Yield and Safety of Serial Pancreatic Juice Aspiration Cytologic Examination (SPACE) with Different Indications
Authors: Satoh Tatsunori , Kawaguchi Shinya , Takeda Shodai , Ishiguro Yuya , Asahara Kazuhisa , Terada Shuzo , Endo Shinya , Shirane Naofumi , Kanemoto Hideyuki , Ohno Kazuya
Published: April 2023

We assessed whether there are differences in the diagnostic yield and safety of serial pancreatic juice aspiration cytologic examination (SPACE) among different indications. We retrospectively analyzed 226 patients who underwent SPACE. They were classified into group A (patients with pancreatic masses, including advanced adenocarcinoma, sclerosing pancreatitis, or autoimmune pancreatitis), group B (suspicious pancreatic carcinoma patients without obvious pancreatic masses, including small pancreatic carcinoma, carcinoma in situ, or benign pancreatic duct stenosis), and group C (intraductal papillary mucinous neoplasm, IPMN). There were 41, 66, and 119 patients, with malignancy diagnosed in 29, 14, and 22 patients, in groups A, B, and C, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 69%, 100%, 100%, 57%, and 78% in group A; 79%, 98%, 92%, 94%, and 94% in group B; and 27%, 87%, 32%, 84%, and 76% in group C, respectively. PEP was observed in three (7.3%), three (4.5%), and fifteen (13%) patients in group A, B, and C, respectively (p = 0.20). SPACE is useful and safe in patients with suspicious small pancreatic carcinoma. However, it has limited efficacy and might not be recommended in patients with IPMN because of the high frequency of PEP.
A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions
Authors: Almufareh Maram Fahaad , Tehsin Samabia , Humayun Mamoona , Kausar Sumaira
Published: April 2023

Monkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has shown its spread to Europe, Australia, the United States, and Africa. Typically, diagnosis of MPX is performed through PCR, by taking a sample of the skin lesion. This procedure is risky for medical staff, as during sample collection, transmission and testing, they can be exposed to MPXV, and this infectious disease can be transferred to medical staff. In the current era, cutting-edge technologies such as IoT and artificial intelligence (AI) have made the diagnostics process smart and secure. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. Keeping in view the importance of these cutting-edge technologies, this paper presents a non-invasive, non-contact, computer-vision-based method for diagnosis of MPX by analyzing skin lesion images that are more smart and secure compared to traditional methods of diagnosis. The proposed methodology employs deep learning techniques to classify skin lesions as MPXV positive or not. Two datasets, the Kaggle Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Skin Image Dataset (MSID), are used for evaluating the proposed methodology. The results on multiple deep learning models were evaluated using sensitivity, specificity and balanced accuracy. The proposed method has yielded highly promising results, demonstrating its potential for wide-scale deployment in detecting monkeypox. This smart and cost-effective solution can be effectively utilized in underprivileged areas where laboratory infrastructure may be lacking.
Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence
Authors: Elmannai Hela , El-Rashidy Nora , Mashal Ibrahim , Alohali Manal Abdullah , Farag Sara , El-Sappagh Shaker , Saleh Hager
Published: April 2023

Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healthcare systems to reduce the disease’s problems and complications. Machine learning (ML) and ensemble learning have recently shown promising results in medical diagnostics. The main goal of our research is to provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations. Feature selection methods with different types of ML models (logistic regression (LR), random forest (RF), decision tree (DT), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), xgboost, and Adaboost algorithm to get optimal feature selection and best model. Stacking ML models that combine the best base ML models with meta-learner are proposed to improve performance. Bayesian optimization is used to optimize ML models. Combining SMOTE (Synthetic Minority Oversampling Techniques) and ENN (Edited Nearest Neighbour) solves the class imbalance. The experimental results were made using a benchmark PCOS dataset with two ratios splitting 70:30 and 80:20. The result showed that the Stacking ML with REF feature selection recorded the highest accuracy at 100 compared to other models.
Issues of EU Member Nations’ Shared Sovereignty, Institutions, and Economic Development
Authors: Mardanov Ismatilla
Published: April 2023

To investigate the effects of the European Union’s (EU) member nations’ shared sovereignty on economic growth. The member nations have lost substantial political and economic independence (sovereignty) and democracy. Therefore, their governments cannot facilitate rapid economic growth in their countries, affecting the EU as a whole. Data from the World Bank, institutional research entities, and the EU were utilized. The dependent variable is economic growth, and the independent and moderating variables are mainly institutions and the European Sovereignty Index. Shared sovereignty and its specific categories and foreign direct investment (FDI) outflows negatively impact economic development in the EU. Shared sovereignty negatively moderates the relationship between political rights and economic development and between FDI outflows and economic development. Democracy in member nations is formal rather than real. The present study focused on the EU’s problems rather than its achievements and empirically investigated the direct and moderating effects of national sovereignty and member-country institutions on member-country economic growth. This focus and the nature of the investigation constitute the originality of the present study and reduce the gap in the literature about the effects of sovereignty, institutions, and capital spillovers (FDI outflows) on economic growth in Europe. The value of the study is in its findings, which should trigger holistic research efforts on the pros and cons of the EU for Europe, democracy, the economy, and the world.
A Novel Single X-Wire AM Mini-LED Backlight Unit with Motion Blur Reduction for High-Image-Quality LCD TVs
Authors: Ji Hansai , Lim Youngjin , Yan Chenghui , Li Jinle , Li Zhiguo , Geng Di
Published: April 2023

We have demonstrated a high-image-quality 85-inch 4 K high-dynamic-range (HDR) LCD display with 2304 dimming zones using a single X-wire AM mini-LED backlight unit (BLU) with motion blur and residual image reduction technology. Our latest work proposed the new single X-wire AM driving method, consisting of one matrix control IC/dimming controller (DCON) and X-wires of mini-LED driver ICs (dimmers), which eliminates extra scanning, sensing lines and pixel/source driver IC using an adaptive addressing dimming method. Additionally, a single X-wire controlled system can realize a flexible and expandable layout, which can achieve ultra thin and low cost in ultra-high dimming zones and large size display. Furthermore, the horizontal-scan (H-scan) driving method, which reduces motion blur and residual images using a scanning black frame insertion (BFI) technology, is utilized. The proposed mini-LED BLU can save about 9% power consumption compared to the conventional one in normal displays.
Detecting Plant Disease in Corn Leaf Using EfficientNet Architecture—An Analytical Approach
Authors: Rajeena P. P. Fathimathul , S. U. Aswathy , Moustafa Mohamed A. , Ali Mona A. S.
Published: April 2023

The various corn diseases that affect agriculture go unnoticed by farmers. Each day, more crops fail due to diseases as there is no effective treatment or a way to identify the illness. Common rust, blight, and the northern leaf grey spot are the most prevalent corn diseases. The presence of a disease cannot be accurately detected by simply looking at the plant. This will lead to improper pesticide use, which harms people by bringing on chronic diseases. Therefore, maintaining food security depends on accurate and automatic disease detection. It might be possible to save time and stop crop degradation before it takes place by utilising digital technologies. Hence, applying modern digital technologies to identify the disease in the damaged corn fields automatically will be more advantageous to the farmers. Many academics have recently become interested in deep learning, which has aided in creating an exact and autonomous picture classification scheme. The use of deep learning techniques and their adjustments for detecting corn illnesses can greatly assist contemporary agriculture. To find plant leaf diseases, we employ image acquisition, preprocessing, and classification processes. Preprocessing includes procedures such as reading images, resizing images, and data augmentation. The suggested project is based on EfficientNet and improves the precision of the database of corn leaf diseases by tweaking the variables. Tests are run using DenseNet and Resnet on the test dataset to confirm the precision and robustness of this approach. The recognition accuracy of 98.85% that can be achieved using this method, according to experimental results, is significantly higher than those of other cutting-edge techniques.
Development of an Autonomous Robot Replenishment System for Convenience Stores
Authors: Su Chien-Yu , Wang Hsueh-Cheng , Ko Chun-Hsu , Young Kuu-Young
Published: April 2023

With the appealing ability of combining both mobility and manipulability, mobile robot manipulators have been applied to factory automation, telemedicine, warehousing, etc. Due to the high density of convenience stores in Taiwan, Asia, and other countries, we make further use of mobile robot manipulators to develop an autonomous replenishment system. The replenishment task for a convenience store poses challenges given its narrow aisles and the occasional presence of customers. It thus demands thorough consideration of the planning, navigation, and control of the robot, in addition to customer safety. Correspondingly, we propose strategies for task and path planning, in addition to control and sensor fusion. Specifically, a new collision-free path planning algorithm (the DWA-PS) that consider both customer safety and comfort is developed, along with replenishment and control strategies that utilize the flexibility of the mobile robot manipulator to raise working efficiency. For the performance evaluation, we apply the proposed system to conduct a series of replenishment experiments, including in a convenience store near our university.
Design of Vessel Data Lakehouse with Big Data and AI Analysis Technology for Vessel Monitoring System
Authors: Park Sun , Yang Chan-Su , Kim JongWon
Published: April 2023

The amount of data in the maritime domain is rapidly increasing due to the increase in devices that can collect marine information, such as sensors, buoys, ships, and satellites. Maritime data is growing at an unprecedented rate, with terabytes of marine data being collected every month and petabytes of data already being made public. Heterogeneous marine data collected through various devices can be used in various fields such as environmental protection, defect prediction, transportation route optimization, and energy efficiency. However, it is difficult to manage vessel related data due to high heterogeneity of such marine big data. Additionally, due to the high heterogeneity of these data sources and some of the challenges associated with big data, such applications are still underdeveloped and fragmented. In this paper, we propose the Vessel Data Lakehouse architecture consisting of the Vessel Data Lake layer that can handle marine big data, the Vessel Data Warehouse layer that supports marine big data processing and AI, and the Vessel Application Services layer that supports marine application services. Our proposed a Vessel Data Lakehouse that can efficiently manage heterogeneous vessel related data. It can be integrated and managed at low cost by structuring various types of heterogeneous data using an open source-based big data framework. In addition, various types of vessel big data stored in the Data Lakehouse can be directly utilized in various types of vessel analysis services. In this paper, we present an actual use case of a vessel analysis service in a Vessel Data Lakehouse by using AIS data in Busan area.
Analytical Design of Compact Multiband Bandpass Filters with Multiconductor Transmission Lines and Shunt Open Stubs
Authors: Pérez-Escribano Mario , Márquez-Segura Enrique , Sánchez-Martínez Juan José
Published: April 2023

A compact topology for implementing compact multiband bandpass filters is presented in this paper. To achieve the desired frequency response, two identical short-circuited multiconductor transmission lines (MTLs) and properly connected shunt open stubs are interconnected. Using this configuration, it is possible to design multiband bandpass filters effortlessly. As both the MTLs and the stubs are distributed elements, spur-line band-stop filters are added to mitigate the first replica of the structure’s frequency response. To assess the behavior of the developed filter design procedure, a prototype consisting of a six fingers-MTL and two shunt open stubs has been designed, manufactured, and analyzed, showing excellent agreement between analytical and measured results, considering that no simulation or optimization process has been performed during the design process, just for result verification. Furthermore, it is possible to establish a design criterion that allows the fast and reliable synthesis of multiband responses by varying just a small number of geometrical parameters of the MTLs and the corresponding stubs.
Assessing the Economic Impact of Introducing Localised PV Solar Energy Generation and Energy Storage for Fleet Electrification
Authors: Munoz Maria Nunez , Ballantyne Erica E. F. , Stone David A.
Published: April 2023

Greenhouse gas (GHG) emissions from the transport sector and their effect on air quality are now a major concern, and the electrification of road freight transport is seen as one potential solution. However, this presents a challenge with the increased electricity demand on a depot’s grid connection, and increased costs if this has to be upgraded. This study seeks to evaluate the impact on costs of introducing solar (PV) panels and a Battery Energy Storage System (BESS) when a company electrifies its fleet, based on two different priorities. On one hand, avoiding the use of the grid at peak price periods at the expense of upgrading the power connection. On the other hand, avoiding any power connection upgrade at the expense of incurring excess capacity charges. These two priorities aim to represent real-life challenges that logistics and commercial companies are facing when it comes to fleet electrification. The choice of prioritising one over the other may be driven by operational requirements and/or technical constraints. For each approach, a different energy management algorithm is developed using MATLAB and Simulink. The results obtained suggest that there is more flexibility in cost reduction when the upgrade of the power connection is not an obstacle. If the upgrade of the power network is not an option, the installation of PV panels and a BESS must be implemented together with other strategies (i.e., smart charging) to make it an economic option.
Structural Performance of Energy Efficient Geopolymer Concrete Confined Masonry: An Approach towards Decarbonization
Authors: Ajmal Muhammad Mubashir , Qazi Asad Ullah , Ahmed Ali , Mughal Ubaid Ahmad , Abbas Safeer , Kazmi Syed Minhaj Saleem , Munir Muhammad Junaid
Published: April 2023

Geopolymer concrete is preferred over OPC due to its use of energy waste such as fly ash, making it more sustainable and energy-efficient. However, limited research has been done on its seismic characterization in confined masonry, highlighting a gap in sustainable earthquake-resistant structures. Our study compares the performance of alkali-activated fly-ash-based geopolymer concrete bare frame and confined masonry wall panels with conventional concrete. Experimental results showed that geopolymer concrete bare frame has 3.5% higher initial stiffness and 1.0% higher lateral load-bearing capacity compared to conventional concrete. Geopolymer concrete confined masonry exhibited 45.2% higher initial stiffness and 4.1% higher ultimate seismic capacity than traditional concrete. The experimental results were verified using a numerical simulation technique with ANSYS-APDL, showing good correlation. Comparison with previously tested masonry walls revealed that GPC confined masonry has similar structural behavior to cement concrete masonry. This study demonstrates that geopolymer concrete made from waste energy such as fly ash is a sustainable and low-energy substitute for OPC concrete, particularly in highly seismic-prone areas, for a cleaner environment.
A Method for CM EMI Suppression on PFC Converter Using Lossless Snubber with Chaotic Spread Spectrum
Authors: Fan Weifeng , Shi Yilong , Chen Yanming
Published: April 2023

This paper proposes an improved common mode (CM) electromagnetic interference (EMI) suppression method in switching power supplies. The lossless snubber circuit can reduce du/dt and EMI in the high-frequency band. Nevertheless, it has a weak EMI suppression effect on the low-frequency band. A method combining the chaotic spread spectrum and the lossless snubber (CSS–LS) is proposed to improve the EMI suppression effect of the lossless snubber. It is an effective means to suppress CM EMI further. The paper used a Boost PFC converter as the object of analysis to study the CM EMI suppression effect of CSS–LS. Firstly, a CM EMI-equivalent model of the lossless snubber PFC converter was established. Then, the power spectral density function under chaotic spread-spectrum modulation was derived. The simulation analysis was performed. Finally, an experimental prototype was built, and relevant EMI tests were carried out. The experimental results show that CSS–LS can reduce CM EMI by 4~20 dBµV with little impact on converter stability. Fewer extra costs are needed for this optimization method, which is suitable for high-power-density power electronic devices.
Hybrid Surrogate Model-Based Multi-Objective Lightweight Optimization of Spherical Fuel Element Canister
Authors: Hao Yuchen , Wang Jinhua , Lin Musen , Gong Menghang , Zhang Wei , Wu Bin , Ma Tao , Wang Haitao , Liu Bing , Li Yue
Published: April 2023

A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simultaneously. Accordingly, a hybrid surrogate model-based multi-objective optimization method with the numerical method for the lightweight and safe design of the SFE canister is proposed. To be specific, the drop analysis model of the SFE canister is firstly established where the finite element method—discrete element method (FEM–DEM) coupled method is integrated to simulate the interaction force between the SFE and canister. Through simulation, the design variables, optimization objectives, and constraints are identified. Then the hybrid radial basis function—response surface method (RBF–RSM) surrogate method is carried out to approximate and simplify the accurate numerical model. A non-dominated sorting genetic algorithm (NSGA-II) is used for resolving this multi-objective model. Optimal design is validated using comprehensive comparison, and the reduction of weight and maximum strain can be up to 2.46% and 44.65%, respectively. High-accuracy simulation with high-efficiency optimization is successfully demonstrated to perform the lightweight design on nuclear facilities.
Machine-Learning-Based Classification for Pipeline Corrosion with Monte Carlo Probabilistic Analysis
Authors: Ismail Mohd Fadly Hisham , May Zazilah , Asirvadam Vijanth Sagayan , Nayan Nazrul Anuar
Published: April 2023

Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their width, length and depth. Consecutive in-line inspection data are used to determine the pipeline’s corrosion growth rate and its remnant life, which set the operational and maintenance activities of the pipeline. The traditional approach of manually processing in-line inspection data has various weaknesses, including being time consuming due to huge data volume and complexity, prone to error, subject to biased judgement by experts and challenging for matching of in-line inspection datasets. This paper aimed to contribute to the adoption of machine learning approaches in classifying pipeline defects as per Pipeline Operator Forum requirements and matching in-line inspection data for determining the corrosion growth rate and remnant life of pipelines. Machine learning techniques, namely, decision tree, random forest, support vector machines and logistic regression, were applied in the classification of pipeline defects using Phyton programming. The performance of each technique in terms of the accuracy of results was compared. The results showed that the decision tree classifier model was the most accurate (99.9%) compared with the other classifiers.
Analysis of Space Charge Signal Spatial Resolution Determined with PEA Method in Flat Samples including Attenuation Effects
Authors: Florkowski Marek , Kuniewski Maciej
Published: April 2023

The constant development of the electrical engineering sector, especially in the transmission of electrical energy under high-voltage direct current (HVDC), requires research on new insulation materials and investigations of physical phenomena under ultrahigh electrical fields in solid dielectrics. One of the current problematic issues is the formation of space charge in HV insulation systems, which affects the operational electrical field distribution and can lead to faster insulation degradation. There are several problems that have to be considered before every space charge measurement, such as the attenuation and dispersion of sound waves in tested dielectric materials, reflections at the interfaces, and the spatial resolution of the measured charge profile. The spatial resolution is one of the most important technical factors of the PEA measurement stand. The spatial resolution, as it is assumed, depends on several factors, such as the width of the pulser and the pulse rise time, the thickness of the piezoelectric sensor, and the dispersion of the tested material. The article presents the laboratory measurement results of the impact of pulser parameters, such as pulse width and rise time, and sensor thickness on the equivalent thickness of the measured net charge layer corresponding to the resolution of the method. The dispersion in the tested LDPE material is also presented and analysed. The results show that with an increase in the pulser rise time, a higher resolution of the pea method can be achieved.
Formal Verification of the European Train Control System (ETCS) for Better Energy Efficiency Using a Timed and Asynchronous Model
Authors: Kochan Andrzej , Daszczuk Wiktor B. , Grabski Waldemar , Karolak Juliusz
Published: April 2023

The ERTMS/ETCS is the newest automatic train protection system. This is a system that supports the driver in driving the train. It is currently being implemented throughout the European Union. This system’s latest specifications also provide additional functions to increase the energy efficiency of train driving in the form of ATO (automatic train operation). These functions of the ETCS will be valuable, provided they operate without failure. To achieve errorless configuration of the ETCS, a methodology for automatic system verification using the IMDS (Integrated Model of Distributed Systems) formalism and the temporal tool Dedan was applied. The main contribution is asynchronous and timed verification, which appropriately models the distributed nature of the ETCS and allows the designer not only to analyze time dependencies but also to define the range of train velocities in which the operational scenario is valid. Additionally, the novelties of the presented verification methodology are the graphical design of the system components and automated verification freeing the designer from using textual design. We express the verified properties as observer automata rather than in temporal logic. Moreover, we check partial properties related to system fragments, which is crucial in distributed systems. This paper presents the verification of an example ETCS system application. The verification results are presented as sequence diagrams leading to a correct/incorrect final state.
The Classification of Log Decay Classes and an Analysis of Their Physical and Chemical Characteristics Based on Artificial Neural Networks and K-Means Clustering
Authors: Wen Wen , Zhang Wenjun , He Shirong , Hu Haitao , Qiao Hailiang , Wang Xiao , Rao Nan , Yuan Jie
Published: April 2023

Most existing methods for determining log decay levels normally use variations in log surface characteristics, and the results are subject to human subjectivity, which is uncertain and inaccurate. In order to investigate a novel method for the quantitative determination of log decay levels, we randomly selected log samples from four species (Pinus tabulaeformis, Larix principis-ruprechtii, Betula albosinensis and Quercus aliena var. acuteserrata) with different levels of decay and determined their basic physicochemical characteristics in the laboratory. An artificial neural network (ANN) model was used to predict the hardness values of the log samples with different levels of decay at different moisture contents. The hardness was then used as a clustering factor to quantify the decay levels of the log via K-means clustering analysis. The variations in and correlations between the basic physicochemical factors of the log specimens were investigated between the different decay classes and between the different tree species, and then ANOVA and correlation analysis were used to verify the reliability of the clustering results. The results showed that the prediction of the hardness of the decayed log by the ANN was very effective and that the highly significant variability in the dry matter content, basic density and some basic chemical element contents between the log samples that were classified into different decay grades confirmed the reliability of the clustering results. This study explores an innovative method for the quantitative determination of log decay classes.
Distributed Systems for Emerging Computing: Platform and Application
Authors: Wang Xu , Shi Bin , Fang Yili
Published: April 2023

In recent years, the new computing paradigms such as serverless computing, edge computing and blockchain-based computing have attracted much attention in both academia and industrial communities. Distributed systems and applications play fundamental roles in connecting the underlying computers, network and devices for collaboration, as well as providing new services for users. However, due to the increasing complexity of the large-scale and dynamic heterogeneous resource, and the new requirements and features, these distributed systems and applications will face many challenges in terms of their efficiency, flexibility and algorithms. This editorial discusses the state-of-the-art advancements in distributed systems and applications for the emerging computing.
Caspofungin-Loaded Formulations for Treating Ocular Infections Caused by Candida spp.
Authors: Pérez-González Noelia , Rodríguez-Lagunas María J. , Calpena-Campmany Ana C. , Bozal-de Febrer Nuria , Halbaut-Bellowa Lyda , Mallandrich Mireia , Clares-Naveros Beatriz
Published: April 2023

Fungal keratitis causes corneal blindness worldwide. The treatment includes antibiotics, with Natamycin being the most commonly used; however, fungal keratitis is difficult to treat, so alternative therapies are needed. In situ gelling formulations are a promising alternative; this type of formulation has the advantages of eye drops combined with the advantages of ointments. This study was designed to develop and characterize three formulations containing 0.5% CSP: CSP-O1, CSP-O2, and CSP-O3. CSP is an antifungal drug that acts against a diverse variety of fungi, and Poloxamer 407 (P407) is a polymer of synthetic origin that is able to produce biocompatible, biodegradable, highly permeable gels and is known to be thermoreversible. Short-term stability showed that formulations are best stored at 4 °C, and rheological analysis showed that the only formulation able to gel in situ was CSP-O3. In vitro release studies indicated that CSP-O1 releases CSP most rapidly, while in vitro permeation studies showed that CSP-O3 permeated the most. The ocular tolerance study showed that none of the formulations caused eye irritation. However, CSP-O1 decreased the cornea’s transparency. Histological results indicate that the formulations are suitable for use, with the exception of CSP-O3, which induced slight structural changes in the scleral structure. All formulations were shown to have antifungal activity. In view of the results obtained, these formulations could be promising candidates for use in the treatment of fungal keratitis.
Intrafollicular Retinoic Acid Signaling Is Important for Luteinizing Hormone-Induced Oocyte Meiotic Resumption
Authors: Wang Fupeng , Tang Yawen , Cai Yijie , Yang Ran , Wang Zongyu , Wang Xiaodong , Yang Qianying , Wang Wenjing , Tian Jianhui , An Lei
Published: April 2023

It has been clear that retinoic acid (RA), the most active vitamin A (VA) derivative, plays a central role in governing oocyte meiosis initiation. However, it has not been functionally determined if RA participates in luteinizing hormone (LH)-induced resumption from long-lasting oocyte meiotic arrest, which is essential for haploid oocyte formation. In the present study, using well-established in vivo and in vitro models, we identified that intrafollicular RA signaling is important for normal oocyte meiotic resumption. A mechanistic study indicated that mural granulosa cells (MGCs) are the indispensable follicular compartment for RA-prompted meiotic resumption. Moreover, retinoic acid receptor (RAR) is essential for mediating RA signaling to regulate meiotic resumption. Furthermore, we found zinc finger protein 36 (ZFP36) is the transcriptional target of RAR. Both RA signaling and epidermal growth factor (EGF) signaling were activated in MGCs in response to LH surge, and two intrafollicular signalings cooperate to induce rapid Zfp36 upregulation and Nppc mRNA decrease, which is critical to LH-induced meiotic resumption. These findings extend our understanding of the role of RA in oocyte meiosis: RA not only governs meiotic initiation but also regulates LH-induced meiotic resumption. We also emphasize the importance of LH-induced metabolic changes in MGCs in this process.
The Operon as a Conundrum of Gene Dynamics and Biochemical Constraints: What We Have Learned from Histidine Biosynthesis
Authors: Del Duca Sara , Semenzato Giulia , Esposito Antonia , Liò Pietro , Fani Renato
Published: April 2023

Operons represent one of the leading strategies of gene organization in prokaryotes, having a crucial influence on the regulation of gene expression and on bacterial chromosome organization. However, there is no consensus yet on why, how, and when operons are formed and conserved, and many different theories have been proposed. Histidine biosynthesis is a highly studied metabolic pathway, and many of the models suggested to explain operons origin and evolution can be applied to the histidine pathway, making this route an attractive model for the study of operon evolution. Indeed, the organization of his genes in operons can be due to a progressive clustering of biosynthetic genes during evolution, coupled with a horizontal transfer of these gene clusters. The necessity of physical interactions among the His enzymes could also have had a role in favoring gene closeness, of particular importance in extreme environmental conditions. In addition, the presence in this pathway of paralogous genes, heterodimeric enzymes and complex regulatory networks also support other operon evolution hypotheses. It is possible that histidine biosynthesis, and in general all bacterial operons, may result from a mixture of several models, being shaped by different forces and mechanisms during evolution.
Late Holocene Environmental History and Norse Settlement in Outer Fjords from South Greenland: A Case Study at Lake Qallimiut
Authors: Gauthier Emilie , Currás Andrés , Massa Charly , Guillemot Typhaine , Richard Hervé , Bichet Vincent
Published: April 2023

To complement discussions about vegetation history and climate variations in south Greenland, especially during the Norse settlement, we developed a sedimentological multiproxy approach to study a 4300-year-old lacustrine core comprising pollen analysis, NPPs analysis, physical measurements (magnetic susceptibility, density, and grain size), and geochemical analyses (X-ray fluorescence, X-ray diffraction, and elemental analyses). Sediment archives were retrieved from a river-fed lake, Lake Qallimiut, located in the outer fjords of the Vatnahverfi area. The pollen analysis indicated a transition from juniper and willow cover to a dwarf birch forest. Non-pollen palynomorphs (NPPs) suggested grazing pressure and the presence of wild herbivores between 2300 and 1800 cal. BC. From ca. 1000 cal. AD, the presence of Norse farmers was evidenced in this area by archaeological surveys, and pollen analyses confirm the presence of human activities from the 11th century to the end of the 13th century. However, human impact progressively vanished between the 12th and 13th centuries, much earlier than at the other Vatnahverfi sites.