bluuebunny/crossref_metadata_2025 huggingface dataset:
Prognostic Role of Cell Blood Count in Chronic Myeloid Neoplasm and Acute Myeloid Leukemia and Its Possible Implications in Hematopoietic Stem Cell Transplantation
Authors: Mulas Olga , Mola Brunella , Madeddu Clelia , Caocci Giovanni , Macciò Antonio , Nasa Giorgio La
Published: October 2022

Numerous prognostic indexes have been developed in hematological diseases based on patient characteristics and genetic or molecular assessment. However, less attention was paid to more accessible parameters, such as neutrophils, lymphocytes, monocytes, and platelet counts. Although many studies have defined the role of neutrophil-to-lymphocyte or platelet-to-lymphocyte in lymphoid malignancies, few applications exist for myeloid neoplasm or hematopoietic stem cell transplantation procedures. In this review, we synthesized literature data on the prognostic value of count blood cells in myeloid malignancies and hematopoietic stem cell transplantation in the context of classical prognostic factors and clinical outcomes.
Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners
Authors: Yamagiwa Ken , Tsuchiya Junichi , Yokoyama Kota , Watanabe Ryosuke , Kimura Koichiro , Kishino Mitsuhiro , Tateishi Ukihide
Published: October 2022

Deep learning (DL) image quality improvement has been studied for application to 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). It is unclear, however, whether DL can increase the quality of images obtained with semiconductor-based PET/CT scanners. This study aimed to compare the quality of semiconductor-based PET/CT scanner images obtained by DL-based technology and conventional OSEM image with Gaussian postfilter. For DL-based data processing implementation, we used Advanced Intelligent Clear-IQ Engine (AiCE, Canon Medical Systems, Tochigi, Japan) and for OSEM images, Gaussian postfilter of 3 mm FWHM is used. Thirty patients who underwent semiconductor-based PET/CT scanner imaging between May 6, 2021, and May 19, 2021, were enrolled. We compared AiCE images and OSEM images and scored them for delineation, image noise, and overall image quality. We also measured standardized uptake values (SUVs) in tumors and healthy tissues and compared them between AiCE and OSEM. AiCE images scored significantly higher than OSEM images for delineation, image noise, and overall image quality. The Fleiss kappa value for the interobserver agreement was 0.57. Among the 21 SUV measurements in healthy organs, 11 (52.4%) measurements were significantly different between AiCE and OSEM images. More pathological lesions were detected in AiCE images as compared with OSEM images, with AiCE images showing higher SUVs for pathological lesions than OSEM images. AiCE can improve the quality of images acquired with semiconductor-based PET/CT scanners, including the noise level, contrast, and tumor detection capability.
Impact of Frozen and Conventional Elephant Trunk on Aortic New-Onset Thrombus and Inflammatory Response
Authors: Marchiori Elena , Oberhuber Alexander , Martens Sven , Rukosujew Andreas , Ibrahim Abdulhakim
Published: October 2022

(1) Aim: The primary endpoint of this study was to evaluate the impact of frozen elephant trunk (FET) and conventional elephant trunk (CET) on aortic mural thrombus. The secondary endpoint was to investigate the incidence of persistent inflammatory response (IR) in the form of post-implantation syndrome (PIS) or persistent fever without infection focus after FET and CET, respectively, as well as the risk factors associated with its occurrence. (2) Methods: A single-center, retrospective, observational study of 57 consecutive patients treated with FET and CET between April 2015 and June 2020 was performed. Demographics, procedural data, perioperative laboratory exams as well as vital parameters were recorded. Pre- and postoperative computer tomography angiography (CTA) scans were analyzed with a dedicated software. IR was defined as the presence of continuous fever (>38°, lasting > 24 h) and leukocytosis (white blood cell count > 12 × 1000/µL) developing after surgery in the absence of an infection focus. (3) Results: Fifty-seven consecutive patients (mean age 58.4 ± 12.6 years, 36.8% females) treated with FET (66.6%) or CET (33.3%) for acute aortic dissection (56.1%), post-dissection-aneurysm (19.2%) or aortic aneurysm (24.5%) were included. The median thrombus volume on CTA preoperatively was 10.1 cm3 (range 2–408 cm3). After surgery, the median new-onset mural thrombus was 9.7 cm3 (range 0.2–376 cm3). Nineteen (33.3%) patients developed IR; patients with IR were significantly younger (p = 0.027), less frequently of female gender (p = 0.003) and more frequently affected from acute dissection (p = 0.002) and stayed in the intensive care unit (ICU) significantly longer (p = 0.033) than those without IR. Postoperatively, the volume of new-onset thrombus was significantly greater in the IR group (84.4 vs. 3.2 cm3, p < 0.001). (4) Conclusions: In the context of CET and FET, the persistent inflammatory response occurred in 33.3% of the patients with persistent fever without infection focus. IR was associated with a higher volume of new-onset thrombus and significantly prolonged ICU stay. Further studies to investigate these observations are needed.
Association between Patient–Provider Communication and Self-Perceived Mental Health in US Adults with Cancer: Real-World Evidence through Medical Expenditure Panel Survey
Authors: Choi Briana M. , Obeng-Kusi Mavis , Axon David R.
Published: October 2022

Using real-world data, this retrospective cross-sectional study aimed to analyze the association between patient–physician communication and self-reported mental health from cancer patients using publicly available 2019 Medical Expenditure Panel Survey (MEPS) household component data. Four adjusted logistic regression models were conducted to analyze the association between patient–physician communication and self-perceived mental health among United States adult cancer patients, with each model assessing different aspects of patient–physician communication: being respected (respect), being listened to (listen), spending enough time (time), and being explained (explain). The main finding from this study was that only the time model showed a statistical significance, where patients who reported that their physician always spent enough time perceived their mental health as good. Other covariates that showed statistical significance with self-perceived mental health in cancer patients included age, income level, physical limitation, and limitation from pain. In conclusion, there were generally no statistically significant associations between patient–physician communication and mental health among cancer patients.
Fexinidazole for Human African Trypanosomiasis, the Fruit of a Successful Public-Private Partnership
Authors: Bernhard Sonja , Kaiser Marcel , Burri Christian , Mäser Pascal
Published: October 2022

After 100 years of chemotherapy with impractical and toxic drugs, an oral cure for human African trypanosomiasis (HAT) is available: Fexinidazole. In this case, we review the history of drug discovery for HAT with special emphasis on the discovery, pre-clinical development, and operational challenges of the clinical trials of fexinidazole. The screening of the Drugs for Neglected Diseases initiative (DNDi) HAT-library by the Swiss TPH had singled out fexinidazole, originally developed by Hoechst (now Sanofi), as the most promising of a series of over 800 nitroimidazoles and related molecules. In cell culture, fexinidazole has an IC50 of around 1 µM against Trypanosoma brucei and is more than 100-fold less toxic to mammalian cells. In the mouse model, fexinidazole cures both the first, haemolymphatic, and the second, meningoencephalitic stage of the infection, the latter at 100 mg/kg twice daily for 5 days. In patients, the clinical trials managed by DNDi and supported by Swiss TPH mainly conducted in the Democratic Republic of the Congo demonstrated that oral fexinidazole is safe and effective for use against first- and early second-stage sleeping sickness. Based on the positive opinion issued by the European Medicines Agency in 2018, the WHO has released new interim guidelines for the treatment of HAT including fexinidazole as the new therapy for first-stage and non-severe second-stage sleeping sickness caused by Trypanosoma brucei gambiense (gHAT). This greatly facilitates the diagnosis and treatment algorithm for gHAT, increasing the attainable coverage and paving the way towards the envisaged goal of zero transmission by 2030.
Bounds for Coding Theory over Rings
Authors: Gassner Niklas , Greferath Marcus , Rosenthal Joachim , Weger Violetta
Published: October 2022

Coding theory where the alphabet is identified with the elements of a ring or a module has become an important research topic over the last 30 years. It has been well established that, with the generalization of the algebraic structure to rings, there is a need to also generalize the underlying metric beyond the usual Hamming weight used in traditional coding theory over finite fields. This paper introduces a generalization of the weight introduced by Shi, Wu and Krotov, called overweight. Additionally, this weight can be seen as a generalization of the Lee weight on the integers modulo 4 and as a generalization of Krotov’s weight over the integers modulo 2s for any positive integer s. For this weight, we provide a number of well-known bounds, including a Singleton bound, a Plotkin bound, a sphere-packing bound and a Gilbert–Varshamov bound. In addition to the overweight, we also study a well-known metric on finite rings, namely the homogeneous metric, which also extends the Lee metric over the integers modulo 4 and is thus heavily connected to the overweight. We provide a new bound that has been missing in the literature for homogeneous metric, namely the Johnson bound. To prove this bound, we use an upper estimate on the sum of the distances of all distinct codewords that depends only on the length, the average weight and the maximum weight of a codeword. An effective such bound is not known for the overweight.
Aspects Affecting Growth of Family Businesses
Authors: Novotná Katarína , Lušňáková Zuzana , Hanová Martina
Published: October 2022

Family businesses form an inseparable part of each national economy. Therefore, it is necessary to pay significant attention to the aspects that will affect their growth and sustainability in the future. The main aim of this paper is to verify the implementation of key aspects within the entrepreneurial practice of family businesses that considerably affect their growth. Those aspects were identified while processing theoretical input data. On the basis of such knowledge, the research assumptions were determined, and their validity was subsequently verified through the Mann–Whitney U test, the non-parametric Kruskal–Wallis test and the χ2 test of independence. Afterwards, we were able to say that key determinants, such as a formal organisational structure, finance, a constituted family council and a proper code of ethics, have a considerable effect on the growth of family businesses in Slovakia. However, proper legislation that would be a fundamental pillar that family businesses may lean on in any of their operations and activities still needs to be enacted and put into practice.
Collaboration in East Africa: A Contextual Definition
Authors: Fontana Mónica , Peverelli Francesca , Giacomazzi Mauro
Published: October 2022

Collaboration is a crucial skill for the improvement of educational outcomes in adolescents. Culture affects the way people collaborate to solve problems, share challenges, and make decisions. Yet, there are only a few studies conducted in the African context that investigate local understandings of the concept of collaboration. This article aimed at investigating the concept of collaboration in the East African context and how this resonates with the local culture. The approach used to develop this study is the qualitative comparative method. One of the most relevant results from these analyses is working together or staying together as frequently used definitions of collaboration. The local definition of collaboration underscores a dimension related to being with another person; for the East African population, collaboration can be described as a way of living and facing reality in a community while being accompanied by others.
Mitigation of Thermal Stability Concerns in FinFET Devices
Authors: Bender Emmanuel , Bernstein Joseph B. , Boning Duane S.
Published: October 2022

Here, we developed a procedure for mitigating thermal hazards in packaged FinFET devices. A monitoring system was installed into devices, based on self-heating impact analysis in the system and device levels, to allow for the observation and alerting of chip temperature and reliability risks. A novel algorithm for reducing measurement noise by means of temperature fluctuation compensation and the filtering of invalid data is presented and demonstrated on packaged devices. The results presented in this work show that the proposed techniques make exceptional improvements to sensory accuracy. Using this methodology enables the mitigation of thermal concerns in systems, including large data servers, and accelerates development of smart resource allocation formations.
A New Scheme for Ransomware Classification and Clustering Using Static Features
Authors: Yamany Bahaa , Elsayed Mahmoud Said , Jurcut Anca D. , Abdelbaki Nashwa , Azer Marianne A.
Published: October 2022

Ransomware is a strain of malware that disables access to the user’s resources after infiltrating a victim’s system. Ransomware is one of the most dangerous malware organizations face by blocking data access or publishing private data over the internet. The major challenge of any entity is how to decrypt the files encrypted by ransomware. Ransomware’s binary analysis can provide a means to characterize the relationships between different features used by ransomware families to track the ransomware encryption mechanism routine. In this paper, we compare the different ransomware detection approaches and techniques. We investigate the criteria, parameters, and tools used in the ransomware detection ecosystem. We present the main recommendations and best practices for ransomware mitigation. In addition, we propose an efficient ransomware indexing system that provides search functionalities, similarity checking, sample classification, and clustering. The new system scheme mainly targets native ransomware binaries, and the indexing engine depends on hybrid data from the static analyzer system. Our scheme tracks and classifies ransomware based on static features to find the similarity between different ransomware samples. This is done by calculating the absolute Jaccard index. Results have shown that Import Address Table (IAT) feature can be used to classify different ransomware more accurately than the Strings feature.
A Study on the Stability of Graph Edit Distance Heuristics
Authors: Jia Linlin , Tognetti Vincent , Joubert Laurent , Gaüzère Benoit , Honeine Paul
Published: October 2022

Graph edit distance (GED) is a powerful tool to model the dissimilarity between graphs. However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods of GED were introduced, e.g., bipartite and IPFP, during which heuristics were employed. The stochastic nature of these methods induces the stability issue. In this paper, we propose the first formal study of stability of GED heuristics, starting with defining a measure of these (in)stabilities, namely the relative error. Then, the effects of two critical factors on stability are examined, namely, the number of solutions and the ratio between edit costs. The ratios are computed on five datasets of various properties. General suggestions are provided to properly choose these factors, which can reduce the relative error by more than an order of magnitude. Finally, we verify the relevance of stability to predict performance of GED heuristics, by taking advantage of an edit cost learning algorithm to optimize the performance and the k-nearest neighbor regression for prediction. Experiments show that the optimized costs correspond to much higher ratios and an order of magnitude lower relative errors than the expert cost.
Methods of Pre-Clustering and Generating Time Series Images for Detecting Anomalies in Electric Power Usage Data
Authors: Oh Sangwon , Oh Seungmin , Um Tai-Won , Kim Jinsul , Jung Young-Ae
Published: October 2022

As electricity supply expands, it is essential for providers to predict and analyze consumer electricity patterns to plan effective electricity supply policies. In general, electricity consumption data take the form of time series data, and to analyze the data, it is first necessary to check if there is no data contamination. For this, the process of verifying that there are no abnormalities in the data is essential. Especially for power data, anomalies are often recorded over multiple time units rather than a single point. In addition, due to various external factors, each set of power consumption data does not have consistent data features, so the importance of pre-clustering is highlighted. In this paper, we propose a method using a CNN model using pre-clustering-based time series images to detect anomalies in time series power usage data. For pre-clustering, the performances were compared using k-means, k-shapes clustering, and SOM algorithms. After pre-clustering, a method using the ARIMA model, a statistical technique for anomaly detection, and a CNN-based model by converting time series data into images compared the methods used. As a result, the pre-clustered data produced higher accuracy anomaly detection results than the non-clustered data, and the CNN-based binary classification model using time series images had higher accuracy than the ARIMA model.
UAS-Borne Radar for Remote Sensing: A Review
Authors: Miccinesi Lapo , Beni Alessandra , Pieraccini Massimiliano
Published: October 2022

Since the 1950s, radar sensors have been widely used for the monitoring of the earth’s surface. The current radars for remote sensing can be divided into two main categories: Space/aerial-borne and ground-based systems. The unmanned aerial system (UAS) could bridge the gap between these two technologies. Indeed, UAS-borne radars can perform long scans (up to 100/200 m) in a brief time (a few minutes). From the 2010s, the interest in UAS-borne radars has increased in the research community, and it has led to the development of some commercial equipment and more than 150 papers. This review aims to present a study on the state-of-the-art of UAS-borne radars and to outline the future potential of this technology. In this work, the scientific literature was categorized in terms of application, purpose of the paper, radar technology, and type of UAS. In addition, a brief review of the main national UAS regulations is presented. The review on the technological state-of-the-art shows that there is currently no standard in terms of radar technology, and that the multi-helicopter could be the most used UAS in the near future. Moreover, the UAS-borne radar can be used for several remote sensing applications: From landmine detection to smart agriculture, and from archeological survey to research and rescue applications. Finally, the UAS-borne radar appears to be a mature technology, which is almost ready for industrialization. The main developmental limit may be found in the flight regulation, which does not allow for many operations and imposes strict limits on the payload weight.
A Graph-Based Metadata Model for DevOps in Simulation-Driven Development and Generation of DCP Configurations
Authors: Reiterer Stefan H. , Schiffer Clemens , Benedikt Martin
Published: October 2022

With the goal of improving the quality of model-based development and to reduce testing effort, DevOps practices have gained more and more importance. However, most system engineers are not DevOps specialists, and there are a lot of manual steps involved when writing build pipelines and configurations of simulations. For this purpose, an abstract graph-based metadata model is proposed. This allows the autogeneration of scenario descriptions for simulations and code for the build server where the simulation environment is set up and executed. This is demonstrated by applying this process to the DCP standard. In this paper, we will discuss three simple use cases which are motivated by practical problems that arise in complex development environments and how the proposed solutions can be used to tackle them. Detailed descriptions and implementations of the use cases show how the proposed methods can be applied in practice and help solve the described problems. Furthermore, a Python implementation of a DCP master and a simple FMI-to-DCP wrapper are presented in this work.
Deep Learning-Based Intrusion Detection Methods in Cyber-Physical Systems: Challenges and Future Trends
Authors: Umer Muhammad , Sadiq Saima , Karamti Hanen , Alhebshi Reemah M. , Alnowaiser Khaled , Eshmawi Ala’ Abdulmajid , Song Houbing , Ashraf Imran
Published: October 2022

A cyber-physical system (CPS) integrates various interconnected physical processes, computing resources, and networking units, as well as monitors the process and applications of the computing systems. Interconnection of the physical and cyber world initiates threatening security challenges, especially with the increasing complexity of communication networks. Despite efforts to combat these challenges, it is difficult to detect and analyze cyber-physical attacks in a complex CPS. Machine learning-based models have been adopted by researchers to analyze cyber-physical security systems. This paper discusses the security threats, vulnerabilities, challenges, and attacks of CPS. Initially, the CPS architecture is presented as a layered approach including the physical layer, network layer, and application layer in terms of functionality. Then, different cyber-physical attacks regarding each layer are elaborated, in addition to challenges and key issues associated with each layer. Afterward, deep learning models are analyzed for malicious URLs and intrusion detection in cyber-physical systems. A multilayer perceptron architecture is utilized for experiments using the malicious URL detection dataset and KDD Cup99 dataset, and its performance is compared with existing works. Lastly, we provide a roadmap of future research directions for cyber-physical security to investigate attacks concerning their source, complexity, and impact.
High Density Sensor Networks Intrusion Detection System for Anomaly Intruders Using the Slime Mould Algorithm
Authors: Alwan Mohammed Hasan , Hammadi Yousif I. , Mahmood Omar Abdulkareem , Muthanna Ammar , Koucheryavy Andrey
Published: October 2022

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA’s role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works.
Deep Learning Reader for Visually Impaired
Authors: Ganesan Jothi , Azar Ahmad Taher , Alsenan Shrooq , Kamal Nashwa Ahmad , Qureshi Basit , Hassanien Aboul Ella
Published: October 2022

Recent advances in machine and deep learning algorithms and enhanced computational capabilities have revolutionized healthcare and medicine. Nowadays, research on assistive technology has benefited from such advances in creating visual substitution for visual impairment. Several obstacles exist for people with visual impairment in reading printed text which is normally substituted with a pattern-based display known as Braille. Over the past decade, more wearable and embedded assistive devices and solutions were created for people with visual impairment to facilitate the reading of texts. However, assistive tools for comprehending the embedded meaning in images or objects are still limited. In this paper, we present a Deep Learning approach for people with visual impairment that addresses the aforementioned issue with a voice-based form to represent and illustrate images embedded in printed texts. The proposed system is divided into three phases: collecting input images, extracting features for training the deep learning model, and evaluating performance. The proposed approach leverages deep learning algorithms; namely, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), for extracting salient features, captioning images, and converting written text to speech. The Convolution Neural Network (CNN) is implemented for detecting features from the printed image and its associated caption. The Long Short-Term Memory (LSTM) network is used as a captioning tool to describe the detected text from images. The identified captions and detected text is converted into voice message to the user via Text-To-Speech API. The proposed CNN-LSTM model is investigated using various network architectures, namely, GoogleNet, AlexNet, ResNet, SqueezeNet, and VGG16. The empirical results conclude that the CNN-LSTM based training model with ResNet architecture achieved the highest prediction accuracy of an image caption of 83%.
Enhancing Glucose Recovery from Hibiscus cannabinus L. through Phosphoric Acid Pretreatment
Authors: Premjet Duangporn , Wongleang Suwanan , Premjet Siripong
Published: October 2022

Non-food lignocellulosic biomass is an attractive source owing to its abundance as a renewable resource and cost-effectiveness. Hibiscus cannabinus L., commonly known as kenaf, is a fiber-producing plant with high cellulose yield and non-food biomass. This study aimed to enhance the glucose recovery (GR) of kenaf biomass (KB). The bark and core fibers of KB are rich in glucan content and low in lignin content. Based on its glucan and lignin contents, KB has considerable potential as a feedstock for synthesizing monomer sugars, which can produce biofuel and high-value compounds. Therefore, the bark and core fibers were treated at a moderate temperature with various concentrations of phosphoric acid, followed by enzymatic hydrolysis. After pretreatment, the chemical composition of both feedstocks was changed. Phosphoric acid substantially affected the elimination of partial lignin and hemicellulose, which led to enhanced enzymatic hydrolysis. The maximum hydrolysis efficiency (HE) and GR of bark and core fibers were achieved when both feedstocks were treated with 75% phosphoric acid. Compared with untreated feedstocks, HE increased by approximately 5.6 times for bark and 4.7 times for core fibers. However, GR was enhanced approximately 4.9-fold for bark and 4.3-fold for core fibers.
Beeswax–EVA/Activated-Charcoal-Based Fuels for Hybrid Rockets: Thermal and Ballistic Evaluation
Authors: Mahottamananda Sri , Pal Yash , Dinesh Mengu , Ingenito Antonella
Published: October 2022

Beeswax (C46H92O) is a naturally derived substance that has the potential to be used as a solid fuel for hybrid rocket applications and as a substitute for paraffin wax fuel in hybrid rockets. BW burns more efficiently than paraffin wax because of the oxygen molecule it contains. The low thermal stability and poor mechanical properties of BW limit its practical use for upper-stage propulsion applications, and these issues are rarely addressed in the literature on hybrid rockets. This study investigates the thermal stability and ballistic properties of BW using ethylene-vinyl acetate (EVA) and activated charcoal (AC) as an additive. The thermal stability of BW–EVA/AC fuel compositions was analyzed using a thermogravimetric analyzer (TGA). The thermal stability of the blended BW compositions improved significantly. A laboratory-scale hybrid rocket motor was used to evaluate such aspects of ballistic performance as regression rate, characteristic velocity, and combustion efficiency. The results revealed that the pure BW exhibited a higher regression rate of 26.5% at an oxidizer mass flux of 96.4 kg/m2-s compared to BW–EVA/AC blends. The addition of EVA and AC to BW was found to increase the experimental characteristic velocity and combustion efficiency. The combustion efficiency of BW-based fuel was improved from 62% to 94% when 20 wt.% EVA and 2 wt.% AC were added into the fuel matrix.
Evaluation of the Provincial Carbon Neutrality Capacity of the Middle and Lower Yellow River Basin based on the Entropy Weight Matter-Element Model
Authors: Xu Jian , Wang Haiying , Li Zhi
Published: October 2022

In the process of promoting economic development, carbon peaks and carbon neutrality have gradually received more attention. The question of how to steadily and rapidly improve the carbon neutrality capacity of each province and excavate the key factors hindering the carbon neutrality capacity has become particularly important. In this study, the DPSIR (driving, pressure, state, influence, response) framework was used to construct an index system of the provincial carbon neutrality capacity of the middle and lower Yellow River Basin, which included 37 indices. Based on the entropy weight matter-element model, the time evolution, regional differences, and restriction indicators of the carbon neutrality capacity of four provinces in the middle and lower Yellow River Basin from 2008 to 2021 were analyzed. The results showed that the carbon neutrality capacity of the four provinces in the middle and lower Yellow River Basin has significantly improved over time and has gradually reached a grade of “good”. Differences in carbon neutrality capacity among the provinces still exist but are gradually shrinking. the per capita car ownership, urban population density, and other factors have hindered the improvement of the carbon neutrality capacity in each province, but with the reduction in restriction indicators and increase in positive indicators such as urbanization rate, forest grass coverage, and others, the overall development direction tended to be promising.
Permanent Magnet Selections for AFPM Disc Generators
Authors: Radwan-Pragłowska Natalia , Węgiel Tomasz
Published: October 2022

In this article, the field (FEM) and analytical analyses were used for the optimal selection of magnets material for the Axial Flux Permanent Magnet Generator (AFPMG), without building the prototype before. The tested generator is an axial flux machine which consists of a single stator and two rotor discs with Permanent Magnets (PM). Three-dimensional (3D) ANSYS Maxwell package was used for magnetostatic and transient field (FEM) simulations. Two types of PM were selected for the analysis: Ceramic (also known as “Ferrite”) magnets made from Strontium Ferrite powder and Neodymium Iron Boron magnets (NdFeB). The authors compared obtained electromotive forces (EMF) and generator powers for selected magnets materials, performed FFT analyses of voltages and currents and indicated the optimal solutions. In addition to the operational properties of the AFPMG, the magnet and manufacturing costs were compared.
Effect of Heave Plates on the Wave Motion of a Flexible Multicolumn FOWT
Authors: Takata Taisuke , Takaoka Mayuko , Houtani Hidetaka , Hara Kentaro , Oh Sho , Malta Edgard B. , Iijima Kazuhiro , Suzuki Hideyuki , Gonçalves Rodolfo T.
Published: October 2022

Three models with different footing types were used to clarify the effect of heave plates on the hydrodynamic behavior of the elastic response of a flexible multicolumn floating offshore wind turbine (FOWT). The models were tested under regular waves, whose added mass, damping, and motion response results were compared with numerical simulations by NK-UTWind and WAMIT codes. As a whole, the attachment of heave plates was responsible for increasing the added mass and damping levels, consequently modifying the RAO of the models. Regarding the response in a sea condition, a decrease of 33% and 66% of the significant motion height (heave and pitch) was observed. Thus, the heave plate can be a good feature for the future design of FOWT.
Impact of Manufacturing and Material Uncertainties in Performance of a Transverse Flux Machine for Aerospace
Authors: Kulan Mehmet C. , Baker Nick J. , Turvey Simon
Published: October 2022

3-dimensional (3D) flux machines accommodate many design and analysis challenges. Transverse flux machines (TFMs) are mostly useful for low speed, high torque applications but in this research a fault tolerant transverse flux alternator with segmented stator has been prototyped and several important design and manufacturing challenges have been thoroughly investigated as different scenarios. The proposed machine consists of axially separated, 4-single phase to achieve low speed power of 40 Watts at 400 revolutions/minute (rpm). In addition, the machine must achieve several high-speed specifications at 15,000 rpm. It is demonstrated that the proposed TFM could be an alternative to more conventional radial flux machines in aerospace as validated within the paper. However, the necessity of stator segmentation and the use of soft magnetic composites for the proposed 3D flux machine lead to many important design and optimization considerations. Therefore, this paper investigates the manufacturing and material uncertainties in a TFM for an aerospace application. The results indicate that transverse flux alternator might be an option as an aerospace alternator but the peak performance of the prototype machine is still far from the 3D models investigated using finite element electromagnetic simulations.
Power System Fault Diagnosis Method Based on Deep Reinforcement Learning
Authors: Wang Zirui , Zhang Ziqi , Zhang Xu , Du Mingxuan , Zhang Huiting , Liu Bowen
Published: October 2022

Intelligent power grid fault diagnosis is of great significance for speeding up fault processing and improving fault diagnosis efficiency. However, most of the current fault diagnosis methods focus on rule diagnosis, relying on expert experience and logical rules to build a diagnosis model, and lack the ability to automatically extract fault knowledge. For switch refusal events, it is difficult to determine a refusal switch without network topology. In order to realize the non-operating switch identification without network topology, this paper proposes a power grid fault diagnosis method based on deep reinforcement learning for alarm information text. Taking the single alarm information of the non-switch refusal sample as the research object, through the self-learning ability of deep reinforcement learning, it learns the topology connection relationship and action logic relationship between equipment, protection and circuit breakers contained in the alarm information, and realizes the detection of fault events. The correct prediction of the fault removal process after the occurrence, based on this, determines the refusal switch when the switch refuses to operate during the fault removal process. The calculation example results show that the proposed method can effectively diagnose the refusal switch of the switch refusal event, which is feasible and effective.
Double Cathode Modification Improves Charge Transport and Stability of Organic Solar Cells
Authors: Lin Tao , Dai Tingting
Published: October 2022

Introducing a cathode modification layer is an effective method to obtaining highly efficient organic solar cells (OSCs) and improving their stability. Herein, we innovatively introduced a double cathode modification layer (SnO2/ZnO) into a non-fullerene OSCs based on PM7:IT-4F and explored the mechanisms. The effects of SnO2/ZnO film on charge carriers transfer in OSCs are studied via a variety of electrical testing methods including Photo-CELIV measurements. As a result, a cathode buffer layer with low recombination rate and high carrier mobility could be introduced, which is beneficial to electron transport and collection. The champion device based on the double cathode modification layer acquires an efficiency of 12.91%, obviously higher than that of the single cathode modification layer (SnO2 or ZnO) device. Moreover, The SnO2/ZnO double layer is demonstrated to be of great help in the improvement of device stability, and our work could provide a new inspiration for the preparation of OSCs cathode modification layer.
RETRACTED: Abdallah et al. A Critical Review on Recycling Composite Waste Using Pyrolysis for Sustainable Development. Energies 2021, 14, 5748
Authors: Abdallah Ramez , Juaidi Adel , Savaş Mahmut , Çamur Hüseyin , Albatayneh Aiman , Abdala Samer , Manzano-Agugliaro Francisco
Published: October 2022

The journal retracts the article “A Critical Review on Recycling Composite Waste Using Pyrolysis for Sustainable Development” Energies 2021, 14(18), 5748 [...]
Characteristics of Elastoplastic Consolidation by Compaction and Its Effects on Coal Permeability
Authors: Guo Zhenghuai , Regenauer-Lieb Klaus , Vu Phung Nhu Hao , Guan Zhenliang
Published: October 2022

This paper presents a combined experimental–analytical investigation of coal strain development under fluctuating applied hydrostatic stress. The laboratory setup mimics the isotropic volumetric compaction of coal under burial–uplift cycles in the absence of tectonic stress. Special emphasis is placed on the corresponding permeability evolution of the coal strata. Our results show that the stress–strain path is exponential, approaching a linear relation in the logarithmic stress–strain space with the monotonic increase in stress. A similar behavior is found for the strain–permeability path in the logarithmic strain–permeability space. The permeability recovery undergoes hysteresis with respect to the stress in a stress loading–unloading cycle, but the hysteresis is not manifest with respect to the strain. A theoretical geomechanical consolidation analysis was performed using an elastoplastic modelling framework. The analysis suggests that plastic strain is the cause of the hysteresis of the strain recovery in a stress loading–unloading cycle. The closed hysteresis loops manifested in stress loading–unloading–reloading cycles are promoted by the plastic strain during stress unloading and the difference in evolution rates of the elastic core between loading and unloading. The results of this study are helpful for understanding the mechanism of permeability evolution and optimizing water and coal seam gas production.
Effect of Saccharomyces cerevisiae and Saccharomyces pastorianus Co-Inoculation on Alcoholic Fermentation Behavior and Aromatic Profile of Sauvignon Blanc Wine
Authors: Dimopoulou Maria , Goulioti Elli , Troianou Vicky , Toumpeki Chrisavgi , Paramithiotis Spiros , Gosselin Yves , Dorignac Etienne , Papadopoulos Georgios , Kotseridis Yorgos
Published: October 2022

Enhancing the sensory profile of wines by exposing the aromas of the grape variety through the involvement of microorganisms has always been a challenge in winemaking. The aim of our work was to evaluate the impact of different fermentation schemes by using mixed and pure cultures of different Saccharomyces species to Sauvignon blanc wine chemical composition and sensory profile. The Sauvignon blanc must has been inoculated with mixed and pure cultures of S. pastorianus and S. cerevisiae strains. For the mixed fermentation schemes, one strain of S. pastorianus has been inoculated with different proportions of S. cerevisiae (S. pastorianus to S. cerevisiae: 99%–1%, 95%–5%, 90%–10%, 80%–20% and 70%–30% w/w) in co-inoculation with two commercial strains of S. cerevisiae. A total of 13 fermentations trials, three monocultures and 10 mixed cultures were performed in biological triplicate. The fermentation kinetics have been controlled by density measurement and classical oenological analyses were performed based on the International Organisation of Vine and Wine (OIV) analytical methods. The population dynamics were evaluated by the specific interdelta PCR reaction of the Saccharomyces species at the beginning and at the end of the fermentation process. The volatile compounds of the wine aroma, such as the esters, higher alcohols and thiols were analyzed by GC/MS. Sensory assessment by trained panel was carried out for all produced wines. Complete depletion of the sugars was achieved between 10 and 13 days for all the fermentation trials. The population dynamics analysis revealed that the S. cerevisiae strain was the most predominant at the end of the fermentation process in all inoculation ratios that were tested. The wines that were fermented with S. pastorianus, either in pure or mixed cultures, were characterized by significantly lower acetic acid production and higher malic acid degradation when compared to the wines that were fermented only with S. cerevisiae strains. The aroma profile of the produced wines was highly affected by both inoculation ratio and the S. cerevisiae strain that was used. The presence of S. pastorianus strain enhanced the production of the varietal thiols when compared to the samples that were fermented with the S. cerevisiae pure cultures. The mixed inoculation cultures of Saccharomyces species could lead to wines with unique character which can nicely express the varietal character of the grape variety.
A Unified PUF and Crypto Core Exploiting the Metastability in Latches
Authors: Serrano Ronaldo , Duran Ckristian , Sarmiento Marco , Dang Tuan-Kiet , Hoang Trong-Thuc , Pham Cong-Kha
Published: October 2022

Hardware acceleration of cryptography algorithms represents an emerging approach to obtain benefits in terms of speed and side-channel resistance compared to software implementations. In addition, a hardware implementation can provide the possibility of unifying the functionality with some secure primitive, for example, a true random number generator (TRNG) or a physical unclonable function (PUF). This paper presents a unified PUF-ChaCha20 in a field-programmable gate-array (FPGA) implementation. The problems and solutions of the PUF implementation are described, exploiting the metastability in latches. The Xilinx Artix-7 XC7A100TCSG324-1 FPGA implementation occupies 2416 look-up tables (LUTs) and 1026 flips-flops (FFs), reporting a 3.11% area overhead. The PUF exhibits values of 49.15%, 47.52%, and 99.25% for the average uniformity, uniqueness, and reliability, respectively. Finally, ChaCha20 reports a speed of 0.343 cycles per bit with the unified implementation.
Wildfire Risk Levels at the Local Scale: Assessing the Relative Influence of Hazard, Exposure, and Social Vulnerability
Authors: Bergonse Rafaello , Oliveira Sandra , Santos Pedro , Zêzere José Luís
Published: October 2022

Wildfire risk assessment provides important tools to fire management, by analysing and aggregating information regarding multiple, interactive dimensions. The three main risk dimensions hazard, exposure and vulnerability, the latter considered in its social dimension, were quantified separately at the local scale for 972 civil parishes in central mainland Portugal and integrated into a wildfire risk index. The importance of each component in the level of risk varied, as assessed by a cluster analysis that established five different groups of parishes, each with a specific profile regarding the relative importance of each dimension. The highest values of wildfire risk are concentrated in the centre-south sector of the study area, with high-risk parishes also dispersed in the northeast. Wildfire risk level is dominated by the hazard component in 52% of the parishes, although with contrasting levels of magnitude. Exposure and social vulnerability dominate together in 32% of the parishes, with the latter being the main risk driver in only 17%. The proposed methodology allows for an integrated, multilevel assessment of wildfire risk, facilitating the effective allocation of resources and the adjustment of risk reduction policies to the specific reality in each parish that results from distinct combinations of the wildfire risk dimensions.
Point-of-Care Lateral Flow Detection of Viable Escherichia coli O157:H7 Using an Improved Propidium Monoazide-Recombinase Polymerase Amplification Method
Authors: Rani Alka , Dike Charles Chinyere , Mantri Nitin , Ball Andrew
Published: October 2022

The detection of both viable and viable but non-culturable (VBNC) Escherichia coli O157:H7 is a crucial part of food safety. Traditional culture-dependent methods are lengthy, expensive, laborious, and unable to detect VBNC. Hence, there is a need to develop a rapid, simple, and cost-effective detection method to differentiate between viable/dead E. coli O157:H7 and detect VBNC cells. In this work, recombinase polymerase amplification (RPA) was developed for the detection of viable E. coli O157:H7 through integration with propidium monoazide (PMAxx). Initially, two primer sets, targeting two different genes (rfbE and stx) were selected, and DNA amplification by RPA combined with PMAxx treatment and the lateral flow assay (LFA) was carried out. Subsequently, the rfbE gene target was found to be more effective in inhibiting the amplification from dead cells and detecting only viable E. coli O157:H7. The assay’s detection limit was found to be 102 CFU/mL for VBNC E. coli O157:H7 when applied to spiked commercial beverages including milk, apple juice, and drinking water. pH values from 3 to 11 showed no significant effect on the efficacy of the assay. The PMAxx-RPA-LFA was completed at 39 °C within 40 min. This study introduces a rapid, robust, reliable, and reproducible method for detecting viable bacterial counts. In conclusion, the optimised assay has the potential to be used by the food and beverage industry in quality assurance related to E. coli O157:H7.
Basil (Ocimum basilicum L.) Leaves as a Source of Bioactive Compounds
Authors: Romano Raffaele , De Luca Lucia , Aiello Alessandra , Pagano Raffaele , Di Pierro Prospero , Pizzolongo Fabiana , Masi Paolo
Published: October 2022

Basil (Ocimum basilicum L.) is an annual spicy plant generally utilized as a flavouring agent for food. Basil leaves also have pharmaceutical properties due to the presence of polyphenols, phenolic acids, and flavonoids. In this work, carbon dioxide was employed to extract bioactive compounds from basil leaves. Extraction with supercritical CO2 (p = 30 MPa; T = 50 °C) for 2 h using 10% ethanol as a cosolvent was the most efficient method, with a yield similar to that of the control (100% ethanol) and was applied to two basil cultivars: “Italiano Classico” and “Genovese”. Antioxidant activity, phenolic acid content, and volatile organic compounds were determined in the extracts obtained by this method. In both cultivars, the supercritical CO2 extracts showed antiradical activity (ABTS●+ assay), caffeic acid (1.69–1.92 mg/g), linalool (35–27%), and bergamotene (11–14%) contents significantly higher than those of the control. The polyphenol content and antiradical activity measured by the three assays were higher in the “Genovese” cultivar than in the “Italiano Classico” cultivar, while the linalool content was higher (35.08%) in the “Italiano Classico” cultivar. Supercritical CO2 not only allowed us to obtain extracts rich in bioactive compounds in an environmentally friendly way but also reduced ethanol consumption.