bluuebunny/crossref_metadata_2025 huggingface dataset:
Pd-Bi-Based Catalysts for Selective Oxidation of Glucose into Gluconic Acid: The Role of Local Environment of Nanoparticles in Dependence of Their Composition
Authors: Shcherbakova-Sandu Mariya P. , Saraev Andrey A. , Knyazev Alexey S. , Kurzina Irina A.
Published: January 2024

Palladium–bismuth nanomaterials are used in various chemical applications such detectors, electrodes, and catalysts. Pd-Bi catalysts are attracting widespread interest because these catalysts enable the production of valuable products quickly and efficiently, and are environmentally friendly. However, the composition of the catalyst can have a significant impact on its catalytic performance. In this work, we identified a correlation between the composition of the catalyst and its efficiency in converting glucose into sodium gluconate. It was found that the conversion decreases with increasing bismuth content. The most active catalyst was the 0.35Bi:Pd sample with a lower bismuth content (glucose conversion of 57%). TEM, SEM, EXAFS, and XANES methods were used to describe, in detail, the surface properties of the xBi:Pd/Al2O3 catalyst samples. The increase in particle size with increasing bismuth content, observed in the TEM micrographs, was associated with the low melting point of bismuth (271 °C). The SEM method showed that palladium and bismuth particles were uniformly distributed over the surface of the support in close proximity to each other, which allowed us to conclude that an alloy of non-stoichiometric composition was formed. The EXAFS and XANES methods established that bismuth was located on the surface of the nanoparticle predominantly in an oxidized state.
Alterations in Th17 Cells and Non-Classical Monocytes as a Signature of Subclinical Coronary Artery Atherosclerosis during ART-Treated HIV-1 Infection
Authors: Wiche Salinas Tomas Raul , Zhang Yuwei , Gosselin Annie , Rosario Natalia Fonseca , El-Far Mohamed , Filali-Mouhim Ali , Routy Jean-Pierre , Chartrand-Lefebvre Carl , Landay Alan L. , Durand Madeleine , Tremblay Cécile L. , Ancuta Petronela
Published: January 2024

Cardiovascular disease (CVD) remains an important comorbidity in people living with HIV-1 (PLWH) receiving antiretroviral therapy (ART). Our previous studies performed in the Canadian HIV/Aging Cohort Study (CHACS) (>40 years-old; Framingham Risk Score (FRS) > 5%) revealed a 2–3-fold increase in non-calcified coronary artery atherosclerosis (CAA) plaque burden, measured by computed tomography angiography scan (CTAScan) as the total (TPV) and low attenuated plaque volume (LAPV), in ART-treated PLWH (HIV+) versus uninfected controls (HIV−). In an effort to identify novel correlates of subclinical CAA, markers of intestinal damage (sCD14, LBP, FABP2); cell trafficking/inflammation (CCL20, CX3CL1, MIF, CCL25); subsets of Th17-polarized and regulatory (Tregs) CD4+ T-cells, classical/intermediate/non-classical monocytes, and myeloid/plasmacytoid dendritic cells were studied in relationship with HIV and TPV/LAPV status. The TPV detection/values coincided with higher plasma sCD14, FABP2, CCL20, MIF, CX3CL1, and triglyceride levels; lower Th17/Treg ratios; and classical monocyte expansion. Among HIV+, TPV+ versus TPV− exhibited lower Th17 frequencies, reduced Th17/Treg ratios, higher frequencies of non-classical CCR9lowHLADRhigh monocytes, and increased plasma fibrinogen levels. Finally, Th17/Treg ratios and non-classical CCR9lowHLADRhigh monocyte frequencies remained associated with TPV/LAPV after adjusting for FRS and HIV/ART duration in a logistic regression model. These findings point to Th17 paucity and non-classical monocyte abundance as novel immunological correlates of subclinical CAA that may fuel the CVD risk in ART-treated PLWH.
Assessing Future Precipitation Patterns, Extremes and Variability in Major Nile Basin Cities: An Ensemble Approach with CORDEX CORE Regional Climate Models
Authors: Gamal Gamil , Nejedlik Pavol , El Kenawy Ahmed M.
Published: January 2024

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study’s findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change.
Investigation of Impact of C/Si Ratio on the Friction and Wear Behavior of Si/SiC Coatings Prepared on C/C-SiC Composites by Slurry Reaction Sintering and Chemical Vapor Infiltration
Authors: Zhao Daming , Cheng Kaifeng , Chen Baiyang , Gao Peihu , Guo Qiaoqin , Cheng Hao , Naumov Anton , Li Qiao , Kang Wenjie
Published: January 2024

Carbon/carbon (C/C)-SiC composite materials have a series of outstanding advantages, such as a light weight, resistance to thermal degradation, excellent friction performance, and good stability in complex environments. In order to improve the wear resistance of the C/C-SiC composite matrix, Si/SiC coatings were prepared by a combination of chemical vapor infiltration and reactive sintering. The wear performance of Si/SiC coatings with different amounts of silicon carbide was investigated. When the carbon silicon ratio in the slurry was 1:3, the SiC particle content in the coating was 93.0 wt.%; the prepared Si/SiC coating exhibited the lowest wear rate of 3.2 × 10−3 mg·N−1·m−1 among the four coatings; and its frictional coefficient was 0.95, which was higher than that of the substrate. As the residual Si content in the coating decreased, the continuity between SiC particles in the coating was improved. Both the high hardness of SiC and the dense coating contributed significantly to enhancing the coating’s wear resistance.
Fully Microscopic Treatment of Magnetic Field Using Bogoliubov–De Gennes Approach
Authors: Neverov Vyacheslav D. , Kalashnikov Alexander , Lukyanov Alexander E. , Krasavin Andrey V. , Croitoru Mihail D. , Vagov Alexei
Published: January 2024

This work introduces an algorithm designed to solve the Bogoliubov–de Gennes equations of superconductivity theory. What sets this algorithm apart is its remarkable ability to precisely and consistently consider the impact of an external magnetic field, all within the microscopic approach. The computation scheme’s convergence is guaranteed by addressing the Biot–Savart equation for the field where the vector potential appears on both of its sides. To showcase the capabilities of this approach, we provide several key examples: the Abrikosov lattice, vortex core states, and the vortex structure in the intermediate mixed state of a superconductor. This method promises to offer valuable insights into the microscopic physics of intertype superconductivity.
FL* Approach to the Coexistence of Fermi Arcs with Metal–Insulator Crossover in Strongly Underdoped Cuprates
Authors: Marchetti Pieralberto
Published: January 2024

We propose that one can explain the coexistence in the same range of doping and temperature of gapless Fermi arcs with the metal–insulator crossover of in-plane resistivity in strongly underdoped cuprates in terms of the FL* fractionalized Fermi liquid nature of these systems, and that such coexistence is not due simply to disorder effects in the resistivity. The particle excitations of this FL* system derived from variants of the t-J model are the gapless holon carrying charge with small Fermi momentum proportional to the doping, the gapful spinon carrying spin 1/2, and an emergent gauge field coupling them and the hole as a spinon–holon bound state, or more precisely resonance, due to gauge binding, with a Fermi surface respecting the topological Luttinger theorem. In our proposal, Fermi arcs are determined by the hole resonance, whereas the metal–insulator crossover is dominated by spinon–spinon (with subleading holon–holon) gauge interactions, and this dichotomy is able to explain their coexistence.
Examining the Role of Social Determinants of Health and COVID-19 Risk in 28 African Countries
Authors: Moise Imelda K. , Ortiz-Whittingham Lola R. , Owolabi Kazeem , Halwindi Hikabasa , Miti Bernard A.
Published: January 2024

While the impact of the pandemic has varied between and within countries, there are few published data on the relationship between social determinants of health (SDoH) and COVID-19 in Africa. This ecological cross-sectional study examines the relationship between COVID-19 risk and SDoH among 28 African countries. Included were countries with a recent demographic and health survey (years 2010 to 2018). The response variables were COVID-19 case rates and death rates (reported as of 15 August 2020); and the covariates comprised eight broad topics common to multiple SDoH frameworks aggregated to the country level: geography (urban residence), wealth index, education, employment, crowding, and access to information. A negative binomial regression was used to assess the association between aspects of SDoH and COVID-19 outcomes. Our analysis indicated that 1 in 4 (25.1%) households in study countries are without safe and clean water and a space for handwashing. The odds of COVID-19 morbidity and deaths were higher in countries with a high proportion of households without access to safe and clean water. Having a high proportional of educated women (1.003: 95% CI, 1.001–1.005) and living in a less crowded home (0.959: 95% CI, 0.920–1.000) were negatively associated with COVID-19 deaths, while being insured and owning a mobile phone predicted illness. Overall, aspects of SDoH contribute either negatively or positively to COVID-19 outcomes. Thus, addressing economic and environmental SDoH is critical for mitigating the spread of COVID-19 and re-emerging diseases on the African continent.
Cobalt(II) Paddle-Wheel Complex with 3,5-Di(tert-butyl)-4-hydroxybenzoate Bridges: DFT and ab initio Calculations, Magnetic Dilution, and Magnetic Properties
Authors: Astaf’eva Tatiana V. , Nikolaevskii Stanislav A. , Egorov Evgeniy N. , Melnikov Stanislav N. , Yambulatov Dmitriy S. , Matiukhina Anna K. , Nikiforova Marina E. , Shmelev Maxim A. , Kolchin Aleksandr V. , Efimov Nikolay N. , Veber Sergey L. , Bogomyakov Artem S. , Zorina-Tikhonova Ekaterina N. , Eremenko Igor L. , Kiskin Mikhail A.
Published: January 2024

A new binuclear "paddle-wheel" complex, [Co2(bhbz)4(EtOH)2]·4EtOH (1, Hbhbz-3,5-di(tert-butyl)-4-hydroxybenzoic acid); an isostructural zinc complex (2); a and magnetically diluted sample of [Zn1.93Co0.07(bhbz)4(EtOH)2]·4EtOH (3) were obtained. Molecular structures of 1 and 2 were determined by single-crystal X-ray diffraction. DFT calculations for 1 indicate strong Co-Co antiferromagnetic exchange interactions in the binuclear fragment. It was shown that when one paramagnetic ion in the binuclear molecule is replaced by a diamagnetic zinc(II) ion, the remaining cobalt(II) ion can be considered as an isolated center with magnetic anisotropy, the parameters of which are determined by ab initio calculations. Magnetic properties for samples 1 and 3 were investigated and analyzed in detail.
Characterizing the Digital Twin in Structural Mechanics
Authors: Richstein Rebecca , Schröder Kai-Uwe
Published: January 2024

The Digital Twin is one of the major technology trends of the last decade. During the course of its rapid expansion into various fields of application, many definitions of the Digital Twin emerged, tailored to its respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a systematic characterization of the Digital Twin in the context of structural mechanics and lightweight design. While the importance of a shared understanding and the development of holistic solutions for implementing Digital Twins in various application areas is widely recognized, a general framework for implementing Digital Twins in structural mechanics has not yet been established. In this paper, we systematically characterize Digital Twins and develop a framework for their application in structural mechanics, enabling the digital design and monitoring of structures for improved performance and maintenance strategies. The key contributions include collecting and clustering design and operational requirements and deriving two central archetypes: structure-designing and structure-monitoring Digital Twins. The primary goal is to reduce the complexity of conceptualizing Digital Twins of structures by providing a preliminary framework and reconsidering the Digital Twins of structures as a holistic system throughout the product life cycle. Overall, in this paper, we take a systematic approach to enhancing the conceptualization and implementation of Digital Twins in structural mechanics.
The Fluoroless Future in Electrophysiology: A State-of-the-Art Review
Authors: Preda Alberto , Bonvicini Eleonora , Coradello Elena , Testoni Alessio , Gigli Lorenzo , Baroni Matteo , Carbonaro Marco , Vargiu Sara , Varrenti Marisa , Colombo Giulia , Paolucci Marco , Mazzone Patrizio , Guarracini Fabrizio
Published: January 2024

Fluoroscopy has always been the cornerstone imaging method of interventional cardiology procedures. However, radiation exposure is linked to an increased risk of malignancies and multiorgan diseases. The medical team is even more exposed to X-rays, and a higher incidence of malignancies was reported in this professional group. In the last years, X-ray exposure has increased rapidly, involving, above all, the medical team and young patients and forcing alternative fluoroless imaging methods. In cardiac electrophysiology (EP) and pacing, the advent of 3D electroanatomic mapping systems with dedicated catheters has allowed real-time, high-density reconstruction of both heart anatomy and electrical activity, significantly reducing the use of fluoroscopy. In addition, the diffusion of intracardiac echocardiography has provided high anatomical resolution of moving cardiac structures, providing intraprocedural guidance for more complex catheter ablation procedures. These methods have largely demonstrated safety and effectiveness, allowing for a dramatic reduction in X-ray delivery in most arrhythmias’ ablations. However, some technical concerns, as well as higher costs, currently do not allow their spread out in EP labs and limit their use to only procedures that are considered highly complex and time-consuming and in young patients. In this review, we aim to update the current employment of fluoroless imaging in different EP procedures, focusing on its strengths and weaknesses.
Automatization of CT Annotation: Combining AI Efficiency with Expert Precision
Authors: Edelmers Edgars , Kazoka Dzintra , Bolocko Katrina , Sudars Kaspars , Pilmane Mara
Published: January 2024

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.
Evolving Landscape of Paediatric Inflammatory Bowel Disease: Insights from a Decade-Long Study in North-East Slovenia on Incidence, Management, Diagnostic Delays, and Early Biologic Intervention
Authors: Klemenak Martina , Zupan Manca , Riznik Petra , Krencnik Tomaz , Dolinsek Jernej
Published: January 2024

Background: In the past decade, significant progress has been achieved in the care of children with inflammatory bowel disease (IBD). Our study concentrated on assessing the incidence and management of IBD in children in North-Eastern Slovenia over a 10-year period. Methods: Medical data from children and adolescents diagnosed with IBD in North-Eastern Slovenia (2014–2023) was analysed. Disease incidence and management of children were assessed. Findings were compared between two periods (2014–2019 and 2020–2023, coinciding with the COVID-19 pandemic). Results: 87 patients (median age 15.5 year; 50.6% male) with IBD (43.7% Crohn’s disease (CD)), diagnosed between 2014 and 2023 were included. Extraintestinal manifestations were more common in CD than ulcerative colitis (UC) (15.8% vs. 2.4%, p < 0.05). Median delay from symptom onset to diagnosis was 2 months, lower in UC than CD (NS). Mean annual IBD incidence per 100,000 children aged 0 to 19 years was 6.4 (95% CI 4.4–8.3), slightly lower for CD than UC (2.8/100,000 vs. 3.1/100,000). In the second period, IBD incidence significantly rose (9.1 vs. 4.6, p < 0.05). During this period, 53% of CD patients transitioned to biological treatment within three months of diagnosis. Conclusion: IBD incidence rose among children in North-Eastern Slovenia over the past decade. Additionally, more children with CD underwent biological therapy in the second period.
Asymmetric and Nonlinear Foreign Debt–Inflation Nexus in Brazil: Evidence from NARDL and Markov Regime Switching Approaches
Authors: Sharaf Mesbah Fathy , Shahen Abdelhalem Mahmoud , Binzaid Badr Abdulaziz
Published: January 2024

This paper augments the sparse literature on the inflationary impact of foreign debt in Brazil while addressing methodological caveats in previous studies. We depart from the linearity assumption and employ two nonlinear techniques: the nonlinear autoregressive distributed lag (NARDL) model and a Markov Switching Regression (MSR) to investigate the connection between foreign debt and inflation within a multivariate framework. The analyses consider the presence of structural breaks via assessing variable stationarity using the Zivot and Andrew unit root test and incorporating a residual-based cointegration test proposed by Gregory and Hansen. Additionally, we apply a multiple structural breakpoints test by Bai and Perron to determine the presence of structural breaks in the impact of foreign debt on inflation. Our findings robustly indicate that the domestic money supply has a statistically significant positive effect, while the nominal effective exchange rate has a negative effect on inflation in both the short and long run. The NARDL model reveals that only positive changes in foreign debt have a statistically significant negative effect on inflation in the short run, whereas both positive and negative foreign debt changes significantly affect inflation in the long run. The results from the MSR model are generally consistent with those of the NARDL model.
A Quasi-Experimental Study on the Development of Creative Writing Skills in Primary School Students
Authors: Vicol Marta-Iuliana , Gavriluț Monalisa-Laura , Mâță Liliana
Published: January 2024

The purpose of this research was to evaluate the impact of using an educational program on the development of creative writing skills in fourth-grade students. Creative writing is an important component of teaching, learning, and assessment at the level of primary education. The objectives of the study aimed to identify the general level of development of creative writing skills and establish any significant differences in the development of specific components of creative writing skills (narrative voice, characterization, mood and atmosphere, language and technical aspects of writing, dialogue, narration, setting, image and plot). A quasi-experimental design with intervention and control groups was used. A total of 146 students from 3 public school institutions in the city of Bacau, Romania, that include elementary grades participated in the study. The research group consisted of 73 students in the intervention group and 73 students in the control group. A paired-samples t test was used to compare the scores for two paired variables. The research results have shown that creative writing skills are developing in young school-age students. Also, it was found that there are significant differences in the students from the experimental group, compared to the students in the control group, at the level of the nine components of creative writing skills.
Resilience and Social Support Improve Mental Health and Quality of Life in Patients with Post-COVID-19 Syndrome
Authors: Moisoglou Ioannis , Katsiroumpa Aglaia , Kolisiati Antigoni , Vraka Irene , Kosiara Katerina , Siskou Olga , Kaitelidou Daphne , Konstantakopoulou Olympia , Katsoulas Theodoros , Gallos Parisis , Galanis Petros
Published: January 2024

Physical and mental health problems among post-COVID-19 patients are common, even a year after infection. As there is no prior study available, we investigated the impacts of resilience and social support on anxiety, depression, and quality of life among patients with post-COVID-19 syndrome. We conducted a cross-sectional study with a convenience sample. The measures included the demographic and clinical characteristics of patients, the Brief Resilience Scale, the Multidimensional Scale of Perceived Social Support, the Patient Health Questionnaire-4 (PHQ-4), and the EuroQol-5D-3L. The mean age of patients was 44.8 years. The total PHQ-4 score suggested that 32.8% of patients with post-COVID-19 syndrome experienced severe psychological distress, 32.8% experienced moderate distress, 23% experienced mild distress, and 11.5% had no distress. Moreover, 60.7% of patients had anxiety scores of ≥3 and 69.7% had depression scores of ≥3, indicating possible major anxiety or depression disorder. The mean EQ-5D-3L index value was 0.36, and the mean EQ-5D-3L VAS was 54.1. Multivariable analysis identified that resilience and social support reduced anxiety and depression among patients. Also, we found a significant positive relationship between resilience and social support, and quality of life. Our findings suggest that resilience and social support can be protective by reducing anxiety and depression and improving quality of life among patients with post-COVID-19 syndrome. Policymakers should develop and implement healthcare management programs to provide psychological support to these patients.
Sleep Analysis by Evaluating the Cyclic Alternating Pattern A Phases
Authors: Alves Arturo , Mendonça Fábio , Mostafa Sheikh Shanawaz , Morgado-Dias Fernando
Published: January 2024

Sleep is a complex process divided into different stages, and a decrease in sleep quality can lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist in assessing sleep-related disorders such as sleep apnea. However, manually detecting CAP-related events is time-consuming and challenging. Therefore, automatic detection is needed. Despite their usually higher performance, the utilization of deep learning solutions may result in models that lack interpretability. Addressing this issue can be achieved through the implementation of feature-based analysis. Nevertheless, it becomes necessary to identify which features can better highlight the patterns associated with CAP. Such is the purpose of this work, where 98 features were computed from the patient’s electroencephalographic signals and used to train a neural network to identify the CAP activation phases. Feature selection and model tuning with a genetic algorithm were also employed to improve the classification results. The proposed method’s performance was found to be among the best state-of-the-art works that use more complex models.
Accelerating Strawberry Ripeness Classification Using a Convolution-Based Feature Extractor along with an Edge AI Processor
Authors: Park Joungmin , Shin Jinyoung , Kim Raehyeong , An Seongmo , Lee Sangho , Kim Jinyeol , Oh Jongwon , Jeong Youngwoo , Kim Soohee , Jeong Yue Ri , Lee Seung Eun
Published: January 2024

Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment. In this paper, we propose a strawberry ripeness classification system utilizing a convolution-based feature extractor (CoFEx) for accelerating convolution operations and an edge AI processor, Intellino, for replacing FC layer operations. We accelerated feature map extraction utilizing the CoFEx constructed with systolic array (SA) and alleviated the computational burden and memory overhead associated with the FC layer operations by replacing them with the k-nearest neighbors (k-NN) algorithm. The CoFEx and the Intellino both were designed with Verilog HDL and implemented on a field-programmable gate array (FPGA). The proposed system achieved a high precision of 93.4%, recall of 93.3%, and F1 score of 0.933. Therefore, we demonstrated a feasibility of the strawberry ripeness classification system operating in an embedded environment.
Adaptive Control of Unmanned Aerial Vehicles with Varying Payload and Full Parametric Uncertainties
Authors: Imran Imil Hamda , Wood Kieran , Montazeri Allahyar
Published: January 2024

This article investigates an adaptive tracking control problem for a six degrees of freedom (6-DOF) nonlinear quadrotor unmanned aerial vehicle (UAV) with a variable payload mass. The changing payload introduces time-varying parametric uncertainties into the dynamical model, rendering a static control strategy no longer effective. To handle this issue, two adaptive schemes are developed to maintain the uncertainties in the translational and rotational dynamics. Initially, a virtual proportional derivative (PD) is designed to stabilize the horizontal position; however, due to an unknown and time-varying mass, an adaptive controller is proposed to generate the total thrust of the UAV. Furthermore, an adaptive controller is designed for the rotational dynamics, to handle parametric uncertainties, such as inertia and external disturbance parameters. In both schemes, a standard adaptive scheme using the certainty equivalence principle is extended and designed. A stability analysis was conducted with rigorous analytical proofs to show the performance of our proposed controllers, and simulations were implemented to assess the performance against other existing methods. Tracking fitness and total control efforts were calculated and compared with closed-loop adaptive tracking control (CLATC) and adaptive sliding mode control (ASMC). The results indicated that the proposed design better maintained UAV stability.
Lightweight Strong PUF for Resource-Constrained Devices
Authors: Korona Mateusz , Giermakowski Radosław , Biernacki Mateusz , Rawski Mariusz
Published: January 2024

Physical Unclonable Functions are security primitives that exploit the variation in integrated circuits’ manufacturing process, and, as a result, each instance processes applied stimuli differently. This feature can be used to provide a unique fingerprint of the electronic device, or as an interesting alternative to classic key storage methods. Due to their nature, they are often considered an element of the Internet of Things nodes. However, their application heavily depends on resource consumption. Lightweight architectures are proposed in the literature but are technology-dependent or still introduce significant hardware overhead. This paper presents a lightweight, Strong PUF based on ring oscillator architecture, which offers small hardware overhead and sufficient security levels for resource-constrained Internet of Things devices. The PUF design utilizes a Linear Feedback Shift Register-based scramble module to generate many challenge–response pairs from a small number of ring oscillators and a control module to manage the response generation process. The proposed PUF can be used as a Weak PUF for key generation or a Strong PUF for device authentication.
A Hybrid Fault Diagnosis Method for Autonomous Driving Sensing Systems Based on Information Complexity
Authors: Jin Tianshi , Zhang Chenxi , Zhang Yikang , Yang Mingliang , Ding Weiping
Published: January 2024

In the context of autonomous driving, sensing systems play a crucial role, and their accuracy and reliability can significantly impact the overall safety of autonomous vehicles. Despite this, fault diagnosis for sensing systems has not received widespread attention, and existing research has limitations. This paper focuses on the unique characteristics of autonomous driving sensing systems and proposes a fault diagnosis method that combines hardware redundancy and analytical redundancy. Firstly, to ensure the authenticity of the study, we define 12 common real-world faults and inject them into the nuScenes dataset, creating an extended dataset. Then, employing heterogeneous hardware redundancy, we fuse MMW radar, LiDAR, and camera data, projecting them into pixel space. We utilize the “ground truth” obtained from the MMW radar to detect faults on the LiDAR and camera data. Finally, we use multidimensional temporal entropy to assess the information complexity fluctuations of LiDAR and the camera during faults. Simultaneously, we construct a CNN-based time-series data multi-classification model to identify fault types. Through experiments, our proposed method achieves 95.33% accuracy in detecting faults and 82.89% accuracy in fault diagnosis on real vehicles. The average response times for fault detection and diagnosis are 0.87 s and 1.36 s, respectively. The results demonstrate that the proposed method can effectively detect and diagnose faults in sensing systems and respond rapidly, providing enhanced reliability for autonomous driving systems.
SEPIC-Boost-Based Unidirectional PFC Rectifier with Wide Output Voltage Range
Authors: Cheng Hong , Li Xin , Wang Cong , Zhao Zhihao , Shen Yucheng , Yuan Wei
Published: January 2024

A novel unidirectional hybrid PFC rectifier topology based on SEPIC and boost converters is proposed, which is applicable to various industrial applications such as electric vehicle charging stations, variable speed AC drives, and energy storage systems. Compared to other rectifiers, the proposed SEPIC-boost-based rectifier exhibits continuous current on the AC side, lower voltage stress on the active switches, a wider range of DC output voltage, no auxiliary DC-DC converters, and a high step-up static voltage gain operating with low input voltage and a low step-up static gain for the high-input-voltage operation. These traits allow the SEPIC-boost-based rectifier to utilize smaller input-side harmonic filtering inductors and adopt active switches with lower voltage ratings, resulting in reduced conduction losses. Additionally, the proposed rectifier features power factor correction and high boost/buck voltage-gain capabilities, simplifying control for electric vehicle charging and expanding its range of applications. In this paper, the operating principle of the novel topology is presented first, and then the mathematical model of the proposed rectifier is built. Based on this, the comparison between the proposed topology and conventional boost and SEPIC converters is given. Furthermore, the control strategy, including the high-power-factor control and the balancing control to the DC capacitor voltages, is discussed. Finally, to validate the accuracy of the proposed rectifier’s theoretical research, a 500-W SEPIC-boost rectifier system has been constructed in the laboratory, generating a 200/120 Vdc output voltage from a 155 Vpk/50 Hz power source.
Research Progress of Wireless Positioning Methods Based on RSSI
Authors: Chen Bohang , Ma Jun , Zhang Lingfei , Zhou Jiacheng , Fan Jinyu , Lan Haiming
Published: January 2024

Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.
HoneyFactory: Container-Based Comprehensive Cyber Deception Honeynet Architecture
Authors: Yu Tianxiang , Xin Yang , Zhang Chunyong
Published: January 2024

Honeynet and honeypot originate as network security tools to collect attack information during the network being compromised. With the development of virtualization and software defined networks, honeynet has recently achieved many breakthroughs. However, existing honeynet architectures treat network attacks as interactions with a single honeypot which is supported by multiple honeypots to make this single one more realistic and efficient. The scale and depth of existing honeynets are limited, making it hard to capture complicated attack information. Existing honeynet frameworks also have low-level simulation of protected network and lacks test metrics. To address these issues, we design and implement a novel container-based comprehensive cyber deception honeynet architecture that consists of five modules, called HoneyFactory. Just like factory producing products according to customer preferences, HoneyFactory generates honeynet using containers based on business networks under protection. In HoneyFactory architecture, we propose a novel honeynet deception model based on hmm model to evaluate deception stage. We also design other modules to make this architecture comprehensive and efficient. Experiments show that HoneyFactory performs better than existing research in communication latency and connections per second. Experiments also show that HoneyFactory can effectively evaluate deception stage and perform deep cyber deception.
A Convolutional Block Base Architecture for Multiclass Brain Tumor Detection Using Magnetic Resonance Imaging
Authors: Khan Muneeb A. , Park Heemin
Published: January 2024

In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex and diverse manifestations of tumors in these scans. In this paper, a convolutional-block-based architecture has been proposed for the detection of multiclass brain tumors using MRI scans. Leveraging the strengths of CNNs, our proposed framework demonstrates robustness and efficiency in distinguishing between different tumor types. Extensive evaluations on three diverse datasets underscore the model’s exceptional diagnostic accuracy, with an average accuracy rate of 97.52%, precision of 97.63%, recall of 97.18%, specificity of 98.32%, and F1-score of 97.36%. These results outperform contemporary methods, including state-of-the-art (SOTA) models such as VGG16, VGG19, MobileNet, EfficientNet, ResNet50, Xception, and DenseNet121. Furthermore, its adaptability across different MRI modalities underlines its potential for broad clinical application, offering a significant advancement in the field of radiological diagnostics and brain tumor detection.
A PVT-Robust and 73.9 mHz High-Pass Corner Instrumentation Amplifier with an SCF-SCR-PR Hybrid Feedback Resistor
Authors: Xu Hao , Liu Yunda , Duan Yachi , Li Tianke , Zhang Jun , Li Zhiqiang , Zhang Haiying
Published: January 2024

Analog front-end (AFE) circuits play an important role in the acquisition of physiological signals with low-level amplitudes (from tens of μV to tens of mV) and broadband low-frequency ranges (from sub-Hz to several hundred Hz). Possessing a high input impedance, an instrumentation amplifier (IA) accurately amplifies signals with low amplitude and low frequency, making it suitable for AFE circuits. This work demonstrates a capacitively coupled IA whose feedback resistance is realized by the proposed hybrid resistor, consisting of a switched-capacitor low-pass filter, a switched-capacitor resistor, and a continuous-time low-pass filter. The capacitively coupled IA achieves tera-ohm (TΩ) resistance and is insensitive to process, voltage, and temperature (PVT) variations. The simulation results show that the proposed IA illustrates a high-pass corner of 73.9 mHz, and the change of its high-pass corner with temperature is 0.05 mHz/°C. With the variation in the PVT corners, the difference between the maximum and minimum values of the high-pass corner of the proposed capacitively coupled IA is only 0.06 Hz. The design was implemented in a 130 nm standard CMOS process. The AFE with the proposed capacitively coupled IA achieves a 53.9 dB signal-to-noise and distortion ratio (SNDR) and 69.5 dB total harmonic distortion (THD).
Analysis of the Influence of Complex Terrain around DC Transmission Grounding Electrodes on Step Voltage
Authors: Xiong Qi , Liu Xiangyi , Li Yanxin , Xi Lingran , Qiu Shuang
Published: January 2024

The distribution of renewable energy sources is geographically limited. In the process of long-distance transmission, the direct current flowing from a ground electrode into the ground will cause a higher step voltage, which will bring serious security risks to the surrounding industry and life. Accurate calculation of the complex soil electrical model around the grounding electrode is crucial for site selection. Existing simulation software like CDEGS results in significant errors, particularly in complex karst topography. Therefore, constructing a finite element model that accurately reflects the characteristics of geotechnical soil near the DC grounding electrode is an essential but unresolved problem. This paper establishes a soil electrical model for karst topography and explores the impact of cave-type caverns and underground rivers on the step voltage distribution of DC grounding electrodes. These research findings can guide the site selection of DC transmission projects in karst topography.
Lactic Acid Fermentation as a Valorising Agent for Brewer’s Spent Yeast—Improving the Sensory Quality and Nutritional Potential
Authors: Jaeger Alice , Nyhan Laura , Sahin Aylin W. , Zannini Emanuele , Arendt Elke K.
Published: January 2024

Brewer’s spent yeast (BSY) is one of the brewing industry’s most plentiful side-streams. Abundant, low-cost and high in nutrients, it has great potential for application in food technology and human nutrition. With the ever-increasing interest in sustainability, waste reduction and circular food systems, the use of BSY as a novel food ingredient may be the route to add exponential value while reducing the environmental impact. However, negative flavour characteristics and high amounts of alcohol severely limit the current applications of BSY. This study explores the use of processing involving lactic acid bacteria (LAB) fermentation technology as a means of improving BSY quality characteristics and examines the effects of this process on the chemical, nutritional and sensory characteristics of BSY. The results reveal that BSY is a suitable substrate for LAB fermentation, successfully supporting the growth of Lactobacillus amylovorus FST 2.11. Compared to the unfermented BSY (CBSY), fermentation significantly reduced the perceptible bitterness of the BSY as detected by a sensory panel, from 6.0 ± 2.8 units to 0.9 ± 0.7 units, respectively. Fermented BSY (PBSY) had enhanced sour and fruity flavours, and a variety of other volatile compounds and metabolites were determined. Protein profiles showed significant protein degradation, and free amino acid levels were greatly increased following fermentation, from 2.8 ± 0.2 g/100 g to 10.5 ± 0.4 g/100 g, respectively. Protein quality was high, with the CBSY and PBSY providing well over the required level (>100%) of essential amino acids per gram protein, with the exception of sulphur amino acids (98%). Major physical differences were observed using scanning electron microscopy. This study concludes that LAB fermentation positively affects the sensory and nutritional characteristics of BSY and can aid in the incorporation of brewer’s spent yeast into foods for human consumption.
Engineering an Artificial Pathway to Improve the Bioconversion of Lysine into Chiral Amino Alcohol 2-Hydroxycadaverine Using a Semi-Rational Design
Authors: Cheng Jie , Xiao Shujian , Luo Qing , Wang Bangxu , Zeng Rumei , Zhao Liming , Zhang Jiamin
Published: January 2024

Amino alcohols are important compounds that are widely used in the polymer and pharmaceutical industry, particularly when used as chiral scaffolds in organic synthesis. The hydroxylation of polyamide polymers may allow crosslinking between molecular chains through the esterification reactions of hydroxyl and carboxyl groups. Therefore, this may alter the functional properties of polyamide polymers. 2-hydroxycadaverine (2HyC), as a new type of chiral amino alcohol, has potential applications in the pharmaceutical, chemical, and polymer industries. Currently, 2HyC production has only been realized via pure enzyme catalysis or two-stage whole-cell biocatalysis, which faces great challenges for scale-up production. However, the use of a cell factory is very promising for the production of 2HyC in industrial applications. Here, we designed and constructed a promising artificial pathway in Escherichia coli for producing 2HyC from biomass-derived lysine. This biosynthesis route expands the lysine catabolism pathway and employs two enzymes to sequentially convert lysine into 2HyC. However, the catalytic activity of wild-type pyridoxal phosphate-dependent decarboxylase from Chitinophage pinensis (DCCp) toward 3-hydroxylysine is lower, resulting in the lower production of 2HyC. Thus, the higher catalytic activity of DCCp is desired for low-cost and expanded industrial applications of 2HyC. To improve the catalytic activity of DCCp, a mutant library of DCCp was first built using a semi-rational design. The Kcat/Km of mutant DCCp (R53D/V94I) increased by 63%. A titer of 359 mg/L 2HyC was produced in shake flasks, with a 2HyC titer increase of 54% compared to control strain ML101. The results show that the production of 2HyC was effectively increased through a semi-rational design strategy. These findings lay the foundation for the development and utilization of renewable resources to produce 2HyC in microorganisms via an efficient, green, and sustainable biosynthetic strategy for further industrial application.
An Update on Microbial Biosynthesis of β-Caryophyllene, a Sesquiterpene with Multi-Pharmacological Properties
Authors: Tsigoriyna Lidia , Sango Chakarvati , Batovska Daniela
Published: January 2024

The sesquiterpene β-caryophyllene (BCP) is a major component of various plant essential oils, to which it confers a unique spicy aroma. It is mainly used as a fragrance additive in the food, cosmetic and perfume industries, with an annual consumption ranging between 100 and 1000 metric tons worldwide. Recently, BCP has attracted attention as a promising precursor for the production of high-density fuels and for its various biological activities and pharmacological effects. These include antioxidant, anti-inflammatory, anticancer, immune–modulatory, and many other activities. Due to its underlying mechanisms, β-caryophyllene interacts with various human receptors, including CB2 of the endocannabinoid system, which defines it as a phytocannabinoid with therapeutic potential for certain serious conditions. Due to β-caryophyllene’s high utility, various green and sustainable strategies for its production in microorganisms have been developed. This article provides an update on the state-of-the-art in this field to identify directions for further development to extend the compound’s potential.