bluuebunny/crossref_metadata_2025 huggingface dataset:
Battery Energy Storage System Performance in Providing Various Electricity Market Services
Authors: Jaffal Hussein , Guanetti Leopoldo , Rancilio Giuliano , Spiller Matteo , Bovera Filippo , Merlo Marco
Published: February 2024

The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project.
Druggable Sterol Metabolizing Enzymes in Infectious Diseases: Cell Targets to Therapeutic Leads
Authors: Nes W. David , Chaudhuri Minu , Leaver David J.
Published: February 2024

Sterol biosynthesis via the mevalonate-isoprenoid pathway produces ergosterol (24β-methyl cholesta-5,7-dienol) necessary for growth in a wide-range of eukaryotic pathogenic organisms in eukaryotes, including the fungi, trypanosomes and amoebae, while their animal hosts synthesize a structurally less complicated product—cholesterol (cholest-5-enol). Because phyla-specific differences in sterol metabolizing enzyme architecture governs the binding and reaction properties of substrates and inhibitors while the order of sterol metabolizing enzymes involved in steroidogenesis determine the positioning of crucial chokepoint enzymes in the biosynthetic pathway, the selectivity and effectiveness of rationally designed ergosterol biosynthesis inhibitors toward ergosterol-dependent infectious diseases varies greatly. Recent research has revealed an evolving toolbox of mechanistically distinct tight-binding inhibitors against two crucial methylation-demethylation biocatalysts—the C24 sterol methyl transferase (absent from humans) and the C14-sterol demethylase (present generally in humans and their eukaryotic pathogens). Importantly for rational drug design and development, the activities of these enzymes can be selectively blocked in ergosterol biosynthesis causing loss of ergosterol and cell killing without harm to the host organism. Here, we examine recent advances in our understanding of sterol biosynthesis and the reaction differences in catalysis for sterol methylation-demethylation enzymes across kingdoms. In addition, the novelties and nuances of structure-guided or mechanism-based approaches based on crystallographic mappings and substrate specificities of the relevant enzyme are contrasted to conventional phenotypic screening of small molecules as an approach to develop new and more effective pharmacological leads.
Comparative Analysis of Dehydrins from Woody Plant Species
Authors: Karas Milan , Vešelényiová Dominika , Boszorádová Eva , Nemeček Peter , Gerši Zuzana , Moravčíková Jana
Published: February 2024

We conducted analyses on 253 protein sequences (Pfam00257) derived from 25 woody plant species, including trees, shrubs, and vines. Our goal was to gain insights into their architectural types, biochemical characteristics, and potential involvement in mitigating abiotic stresses, such as drought, cold, or salinity. The investigated protein sequences (253) comprised 221 angiosperms (85 trees/shrubs and 36 vines) and 32 gymnosperms. Our sequence analyses revealed the presence of seven architectural types: Kn, KnS, SKn, YnKn, YnSKn, FSKn, and FnKn. The FSKn type predominated in tree and shrub dehydrins of both gymnosperms and angiosperms, while the YnSKn type was more prevalent in vine dehydrins. The YnSKn and YnKn types were absent in gymnosperms. Gymnosperm dehydrins exhibited a shift towards more negative GRAVY scores and Fold Indexes. Additionally, they demonstrated a higher Lys content and lower His content. By analyzing promoter sequences in the angiosperm species, including trees, shrubs, and vines, we found that these dehydrins are induced by the ABA-dependent and light-responsive pathways. The presence of stress- and hormone-related cis-elements suggests a protective effect against dehydration, cold, or salinity. These findings could serve as a foundation for future studies on woody dehydrins, especially in the context of biotechnological applications.
Innovative Ultrasound Criteria for the Diagnosis of Adenomyosis and Correlation with Symptoms: A Retrospective Re-Evaluation
Authors: Biasioli Anna , Degano Matilde , Restaino Stefano , Bagolin Margherita , Moro Francesca , Ciccarone Francesca , Testa Antonia Carla , Greco Pantaleo , Scambia Giovanni , Vizzielli Giuseppe , Driul Lorenza ,   The Udine Hospital Endometriosis Group
Published: February 2024

The 2022 Delphi revision of the MUSA (Morphological Uterus Sonographic Assessment) criteria for the ultrasound diagnosis of adenomyosis divides the ultrasound signs for diagnosis into direct and indirect ones, considering the presence of at least one direct sign as a mandatory criterion. This study aimed to reclassify the patients referred to the Pelvic Pain specialist outpatient clinic of the Gynecological Clinic of Udine according to the new criteria, evaluating the number of overdiagnoses and the possible correlation between the direct and indirect signs and the patients’ symptoms. 62 patients affected by adenomyosis were retrospectively recruited. The patients were then re-evaluated by ultrasound and clinically. At least one direct sign of adenomyosis was found in 52 patients, while 16% of the population examined did not present any. There was no statistically significant difference between patients presenting direct signs and those presenting none for the symptoms considered. According to the new criteria, 16% of the patients examined were not affected by adenomyosis; applying the new consensus to symptomatic patients could increase false negatives. In a population of symptomatic patients, the diagnosis of adenomyosis is still highly probable even without direct ultrasound signs, given the clinical symptoms and having ruled out other causes of such symptoms.
Hemodialysis Efficiency Predictor in End-Stage Kidney Disease Using Real-Time Heart Rate Variability
Authors: Im Sung Il , Kim Ye Na , Kim Hyun Su , Kim Soo Jin , Bae Su Hyun , Kim Bong Joon , Heo Jung Ho , Jung Yeonsoon , Rim Hark , Cho Sung Pil , Park Jung Hwan , Shin Ho Sik
Published: February 2024

Background: Autonomic dysfunction as a long-term complication may occur in end-stage kidney disease (ESKD) patients and can be diagnosed using heart rate variability (HRV) analyzed from electrocardiogram (ECG) recordings. There is limited data about HRV using real-time ECG to predict hemodialysis (HD) efficiency in patients with ESKD who are routinely doing HD in the real world. Methods: A total of 50 patients (62.1 ± 10.7 years) with ESKD underwent continuous real-time ECG monitoring (237.4 ± 15.3 min) during HD for HRV using remote monitoring system. Their electrolyte levels were checked before and after HD. We compared HRV according to electrolyte levels. Results: During the monitor, we checked the ECG and electrolyte levels simultaneously a total of 2374 times for all of the patients. Both time and frequency domain HRV were higher when the patients had lower K+ level (<0.5 mEq/L) and P+ level change (<2 mEq/L) before and after HD as compared to those with a higher K+ level (≥0.5 mEq/L) and P+ level change (≥2 mEq/L). Additionally, patients with lower K+ and P+ level change groups had higher incidences of arrhythmic events including atrial/ventricular premature complexes, despite no difference of mean heart rate (p < 0.001). Conclusions: Higher HRV was independently associated with a poorly controlled K+ and P+ level during HD in patients with ESKD. This is consistently evidenced by the independent association between higher HRV, K+ and P+ levels in real time, suggesting that low electrolyte changes before and after HD alone may cause cardiac autonomic dysfunction.
Facial Expression Realization of Humanoid Robot Head and Strain-Based Anthropomorphic Evaluation of Robot Facial Expressions
Authors: Yan Zhibin , Song Yi , Zhou Rui , Wang Liuwei , Wang Zhiliang , Dai Zhendong
Published: February 2024

The facial expressions of humanoid robots play a crucial role in human–computer information interactions. However, there is a lack of quantitative evaluation methods for the anthropomorphism of robot facial expressions. In this study, we designed and manufactured a humanoid robot head that was capable of successfully realizing six basic facial expressions. The driving force behind the mechanism was efficiently transmitted to the silicone skin through a rigid linkage drive and snap button connection, which improves both the driving efficiency and the lifespan of the silicone skin. We used human facial expressions as a basis for simulating and acquiring the movement parameters. Subsequently, we designed a control system for the humanoid robot head in order to achieve these facial expressions. Moreover, we used a flexible vertical graphene sensor to measure strain on both the human face and the silicone skin of the humanoid robot head. We then proposed a method to evaluate the anthropomorphic degree of the robot’s facial expressions by using the difference rate of strain. The feasibility of this method was confirmed through experiments in facial expression recognition. The evaluation results indicated a high degree of anthropomorphism for the six basic facial expressions which were achieved by the humanoid robot head. Moreover, this study also investigates factors affecting the reproduction of expressions. Finally, the impulse was calculated based on the strain curves of the energy consumption of the humanoid robot head to complete different facial expressions. This offers a reference for fellow researchers when designing humanoid robot heads, based on energy consumption ratios. To conclude, this paper offers data references for optimizing the mechanisms and selecting the drive components of the humanoid robot head. This was realized by considering the anthropomorphic degree and energy consumption of each part. Additionally, a new method for evaluating robot facial expressions is proposed.
Susceptibility of Tetranychus urticae to the Alkaloidal Extract of Zanthoxylum schreberi Bark: Phenotypic and Biochemical Insights for Biotechnological Exploitation
Authors: Rincón Ricardo A. , Rodríguez Daniel , Coy-Barrera Ericsson
Published: February 2024

Tetranychus urticae Koch, a phytophagous mite, is one of the most significant crop pests globally. The primary method employed for controlling T. urticae involves chemical means, utilizing synthesized products, posing the risk of developing resistance. The urgency for novel strategies integrated into pest management programs to combat this mite is becoming increasingly imperative. Botanical pesticides emerge as a promising tool to forestall arthropod resistance. Among these, extracts from Rutaceae plants, abundant in bioactive specialized metabolites, have demonstrated potential as insecticides and miticides. In this study, various concentrations of alkaloidal extracts sourced from the bark of Zanthoxylum schreberi J.F.Gmel. (Rutaceae) were evaluated against T. urticae adult females. Furthermore, the extract’s combination with three distinct commercial acaricides (i.e., chlorfenapyr, cyflumetofen, and abamectin) was also assessed for this mite. Chemical characterization of the extract via LC-MS allowed for the annotation of various compounds related to ten benzylisoquinoline-derived alkaloids. The extract, both alone and in combination with commercial insecticides, yielded varying responses, inducing over 40% mortality at 2% w/w, demonstrating a 90% repellency rate at the same concentration, and exerting a moderate impact on fecundity. These treatments extended beyond phenotypic responses, delving into the biochemical effects on treated T. urticae females through an exploration of the impact on four enzymes, i.e., acetylcholinesterase (AChE), glutathione S-transferase (GST), esterases (GE), and P450-like monooxygenases (PMO). Employing consensus docking studies and in vitro enzymatic evaluations, it was discovered that the Z. schreberi-derived extract and its constituents significantly affected two key enzymes, AChE and GST (IC50 < 6 µM), which were associated with the phenotypic observations of T. urticae females. The evaluation of alkaloid-rich botanicals showcases promising potential as a relevant biotechnological strategy in addressing mite-related concerns, offering a pathway toward innovative and sustainable pest management solutions.
Boosting Solvent-Free Aerobic Oxidation of Benzylic Compounds into Ketones over Au-Pd Nanoparticles Supported by Porous Carbon
Authors: Sun Shanshan , Peng Xiaoyu , Guo Xingcui , Chen Xiufang , Liu Di
Published: February 2024

The exploitation of highly efficient solvent-free catalytic systems for the selective aerobic oxidation of benzylic compounds to produce corresponding ketones with molecular oxygen under mild conditions remains a great challenge in the chemical industry. In this work, Au-Pd nanoparticles supported on porous carbon catalysts were fabricated by the borax-mediated hydrothermal carbonization method and the chemical reduction method. The physicochemical properties of Au-Pd bimetallic samples were examined by XRD, N2 sorption, SEM, TEM, and XPS techniques. The Au-Pd nanoparticles have successfully immobilized on the spherical carbon support with a porous structure and large surface area. A solvent-free catalytic oxidation system was constructed to selectively convert indane into indanone with Au-Pd nanocatalysts and O2. In contrast with a monometallic Au or Pd catalyst, the resulting bimetallic Au-Pd catalyst could effectively activate O2 and exhibit improved catalytic activity in the controlled oxidation of indane into indanone under 1 bar O2. A total of 78% conversion and >99% selectivity toward indanone can be achieved under optimized conditions. The synergistic effect of Au and Pd and porous carbon support contributed to the high catalytic activity for aerobic benzylic compound oxidation. This work offers a promising application prospect of efficient and recyclable Au-Pd nanocatalysts in functional benzylic ketone production.
Understanding and Breaking the Intergenerational Cycle of Abuse in Families Enrolled in Routine Mental Health and Welfare Services by Investigating the Feasibility and Effectiveness of a Mentalization-Based Early Intervention Program (UBICA-II Study): Study Protocol for a Non-Randomized, Open-Label, Single-Arm Feasibility Study
Authors: Dittmann Denise , Dempfle Astrid , Nießen Anke , Puchert Ira , Konrad Kerstin , Herpertz-Dahlmann Beate
Published: February 2024

Although home visiting programs have generally shown small overall effects on the prevention of child maltreatment, at-risk families with severe strain do not seem to benefit sufficiently from this support. A crucial factor for success seems to be the quality of the service system. The aim of the current study is to evaluate the effects of mentalization-based team supervision on the already existing welfare service of a German early prevention program (EPP). This will be a non-randomized, open-label, single-arm feasibility study. The EPP staff will be trained according to the mentalization-based team approach (MB-TA) and regularly receive MFT supervision by a trained and experienced child and adolescent psychiatrist. A minimum of eighty-four families with defined risk factors with children below 24 months of age and pregnant women in the third trimester will be included. Assessments will take place at T0 (after inclusion in the study), at T1 (after family care ends, as an intermediate assessment,) and at T2 (as a follow-up). We hypothesize that the risk of maltreatment can be reduced by strengthening the skills and capacities of the primary care system. This will be evaluated at the end of the follow-up period by comparing the Parental Stress Index (PSI) scores of all participants pre- and postintervention. Stress levels and mentalization abilities will be assessed as feasibility endpoints for the participating EPP teams.
A U-Net Architecture for Inpainting Lightstage Normal Maps
Authors: Zuo Hancheng , Tiddeman Bernard
Published: February 2024

In this paper, we investigate the inpainting of normal maps that were captured from a lightstage. Occlusion of parts of the face during performance capture can be caused by the movement of, e.g., arms, hair, or props. Inpainting is the process of interpolating missing areas of an image with plausible data. We build on previous works about general image inpainting that use generative adversarial networks (GANs). We extend our previous work on normal map inpainting to use a U-Net structured generator network. Our method takes into account the nature of the normal map data and so requires modification of the loss function. We use a cosine loss rather than the more common mean squared error loss when training the generator. Due to the small amount of training data available, even when using synthetic datasets, we require significant augmentation, which also needs to take account of the particular nature of the input data. Image flipping and inplane rotations need to properly flip and rotate the normal vectors. During training, we monitor key performance metrics including the average loss, structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) of the generator, alongside the average loss and accuracy of the discriminator. Our analysis reveals that the proposed model generates high-quality, realistic inpainted normal maps, demonstrating the potential for application to performance capture. The results of this investigation provide a baseline on which future researchers can build with more advanced networks and comparison with inpainting of the source images used to generate the normal maps.
Stratifying Disease Severity in Pediatric COVID-19: A Correlative Study of Serum Biomarkers and Lung Ultrasound—A Retrospective Observational Dual-Center Study
Authors: Stoicescu Emil Robert , Iacob Roxana , Ilie Adrian Cosmin , Iacob Emil Radu , Susa Septimiu Radu , Ghenciu Laura Andreea , Constantinescu Amalia , Cocolea Daiana Marina , Ciornei-Hoffman Andreea , Oancea Cristian , Manolescu Diana Luminita
Published: February 2024

The COVID-19 pandemic, caused by SARS-CoV-2, has manifested distinct impacts on infants and children. This study delves into the intricate connection between lung ultrasound (LUS) findings and serum biomarkers in neonates and infants with COVID-19. Exploring factors contributing to the mild symptoms in this demographic, including immune responses and pre-existing immunity, the study spans 3 years and 9 months, involving 42 patients. Respiratory and gastrointestinal symptoms predominate, and LUS emerges as a vital, non-irradiating tool for evaluating pulmonary abnormalities. Serum biomarkers like CRP, procalcitonin, and cytokines provide key insights into the pathophysiology. Correlations reveal nuanced links between LUS score and clinical parameters, unveiling associations with hospitalization duration (rho = 0.49), oxygen saturation (rho = −0.88), and inflammatory markers, like ferritin (rho = 0.62), LDH (rho = 0.73), and D-dimer (rho = 0.73) with significance level (p < 0.05). The absence of large consolidations in LUS suggests unique pulmonary characteristics. The novelty of these findings lies in the comprehensive integration of LUS with serum biomarkers to assess and monitor the severity of lung involvement in neonates and infants affected by SARS-CoV-2. This approach offers valuable insights into disease severity, biomarker levels, the duration of hospitalization, and oxygen saturation, providing a multifaceted understanding of COVID-19’s impact on this vulnerable population.
An Innovative and Efficient Diagnostic Prediction Flow for Head and Neck Cancer: A Deep Learning Approach for Multi-Modal Survival Analysis Prediction Based on Text and Multi-Center PET/CT Images
Authors: Wang Zhaonian , Zheng Chundan , Han Xu , Chen Wufan , Lu Lijun
Published: February 2024

Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image–text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.
Relationship between Skeletal Malocclusion and Radiomorphometric Indices of the Mandible in Long Face Patients
Authors: D’Amato Giuseppe , Tofangchiha Maryam , Sheikhdavoodi Nima , Mohammadi Zahra , Ranjbaran Mehdi , Jabbarian Razieh , Patini Romeo
Published: February 2024

This study aimed to assess the relationship between skeletal malocclusion and radiomorphometric indices of the mandible in long face patients. This cross-sectional study evaluated 174 lateral cephalograms and panoramic radiographs of long face patients between the ages of 17 and 30 presenting at the Orthodontics Department of Qazvin Dental School. The gonial angle, antegonial angle, type of antegonial notch, and depth of antegonial notch were measured bilaterally on panoramic radiographs. The correlation between the radiomorphometric parameters and the type of occlusion was analyzed using one-way ANOVA, independent t-test, Chi-square test, and Fisher’s exact test (alpha = 0.05). The mean size of gonial angle was significantly different among the three classes of occlusion (p = 0.046), while the difference was not significant regarding the antegonial angle size and antegonial notch depth (p > 0.05). An independent t-test showed that the mean sizes of gonial angle (p = 0.026) and antegonial angle (p = 0.036), and the antegonial notch depth (p = 0.046) in males, were significantly greater than the values in females. According to the Chi-square and Fisher’s exact test, the right antegonial notch type was significantly different among the three classes of malocclusion (p = 0.006), while this difference was not significant in the left side (p = 0.318). The right antegonial notch type II was more common in males, while the right antegonial notch type I was more common in females (p = 0.014). According to the results, the indices of gonial angle and type of antegonial notch can be clinically useful for predicting the growth rate of the mandible and designing the appropriate treatment in long face patients.
Editorial for the Special Issue “Cardiothoracic Imaging: Recent Techniques and Applications in Diagnostics”
Authors: Sica Giacomo , Rea Gaetano , Scaglione Mariano
Published: February 2024

Technology is making giant strides and is increasingly improving the diagnostic imaging of both frequent and rare acute and chronic diseases [...]
Comparative Study of Eclipse and RayStation Multi-Criteria Optimization-Based Prostate Radiotherapy Treatment Planning Quality
Authors: Wong John Y. K. , Leung Vincent W. S. , Hung Rico H. M. , Ng Curtise K. C.
Published: February 2024

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: −0.89% (PTV76D98%) and −2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and −1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.
Clinical and Biological Validation of an Allogeneous Cancellous Bone Block for Alveolar Maxillary Ridge Reconstruction: A Case Series
Authors: Perez Alexandre , Pierantozzi Elena , Di Felice Roberto , Lombardi Tommaso
Published: February 2024

This exploratory case series clinically and histologically investigated the performance of allogeneic cancellous freeze-dried bone allograft (FDBA) bone blocks (Maxgraft®) for the lateral augmentation of local alveolar defects in the posterior maxilla as part of two-staged implant therapy. Five patients receiving eight implants 5 months after block augmentation with a follow-up period of up to 3 years were documented and analyzed. Horizontal alveolar dimensions before and 5 months after block augmentation were quantified using CBCT. Radiographic marginal bone level changes were quantified at implant placement, loading, and 1 year post-placement. Graft integration and resorption were histologically qualitatively evaluated from core biopsies retrieved at implant placement. Block augmentations resulted in a pronounced horizontal median bone gain of 7.0 (5.5 to 7.8) mm. Marginal implant bone levels in block-augmented bone remained constant over the 1 year follow-up period. Block grafts appeared histologically well integrated. Histologic analysis also revealed signs of progressive resorption and new bone formation at the lateral aspects of the grafts. The results of this case series support using Maxgraft® cancellous FDBA blocks as suitable materials for the lateral augmentation of local alveolar defects.
Not All Probability Density Functions Are Tomograms
Authors: Markovich Liubov A. , Urbanetz Justus , Man’ko Vladimir I.
Published: February 2024

This paper delves into the significance of the tomographic probability density function (pdf) representation of quantum states, shedding light on the special classes of pdfs that can be tomograms. Instead of using wave functions or density operators on Hilbert spaces, tomograms, which are the true pdfs, are used to completely describe the states of quantum systems. Unlike quasi-pdfs, like the Wigner function, tomograms can be analysed using all the tools of classical probability theory for pdf estimation, which can allow a better quality of state reconstruction. This is particularly useful when dealing with non-Gaussian states where the pdfs are multi-mode. The knowledge of the family of distributions plays an important role in the application of both parametric and nonparametric density estimation methods. We show that not all pdfs can play the role of tomograms of quantum states and introduce the conditions that must be fulfilled by pdfs to be “quantum”.
Design and Evaluation of Open-Source Soft-Core Processors
Authors: Gazziro Mario , Junior Jecel Mattos de Assumpção , Junior Oswaldo Hideo Ando , Cavallari Marco Roberto , Carmo João Paulo
Published: February 2024

The advantage of FPGAs lies in their ability to implement a fully hardware solution for interfacing with various input/output (I/O) devices. Each block can work in parallel with all the others, simplifying the satisfaction of timing constraints. However, this hardware utilization consumes FPGA resources that could otherwise be allocated to the primary project. An alternative involves employing a small “soft-core” processor to implement I/O in software. With the goal of designing and evaluating a new tiny soft-core processor optimized for FPGA resources in I/O, a novel processor named Baby8 is developed. It is an 8-bit CISC soft-core processor optimized for reduced FPGA resources, including program size for 8-bit applications. The number of instructions is not large, but any instruction can access arbitrary memory locations. The performance and resource utilization of the newly designed processor are evaluated and compared with a variety of other soft-core processors. The results demonstrate its competitive performance, achieving an average maximum clock frequency of approximately 57 MHz and a power consumption of around 2 mW. Furthermore, it conserves nearly half of the FPGA resources in implementation.
A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G
Authors: Huang Jia , Chen Zhen , Liu Shengzheng , Long Haixia
Published: February 2024

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.
High-Frequency Modelling of Electrical Machines for EMC Analysis
Authors: Moreno Yerai , Egea Aritz , Almandoz Gaizka , Ugalde Gaizka , Urdangarin Ander , Moreno Roberto
Published: February 2024

The trend towards electrification in mobility has led to the increased use of silicon carbide (SiC) semiconductors. These semiconductors are more efficient but also present challenges related to electromagnetic interference (EMI) due to their higher voltage derivatives. This paper introduces a new high-frequency impedance model for electrical machines. The proposed model distinguishes itself from existing approaches by being entirely derived from Finite Element Method (FEM) simulations, which include capacitances in the magnetic simulation. This approach achieves a balance between computational efficiency and high accuracy across the entire frequency spectrum, ranging from 100 Hz to 50 MHz. The model provides valuable insights during the design phase and was rigorously validated using data from 28 samples of an industrial machine.
Few-Shot Learning for Misinformation Detection Based on Contrastive Models
Authors: Zheng Peng , Chen Hao , Hu Shu , Zhu Bin , Hu Jinrong , Lin Ching-Sheng , Wu Xi , Lyu Siwei , Huang Guo , Wang Xin
Published: February 2024

With the development of social media, the amount of fake news has risen significantly and had a great impact on both individuals and society. The restrictions imposed by censors make the objective reporting of news difficult. Most studies use supervised methods, relying on a large amount of labeled data for fake news detection, which hinders the effectiveness of the detection. Meanwhile, the focus of these studies is on the detection of fake news in a single modality, either text or images, but actual fake news is more often in the form of text–image pairs. In this paper, we introduce a self-supervised model grounded in contrastive learning. This model facilitates simultaneous feature extraction for both text and images by employing dot product graphic matching. Through contrastive learning, it augments the extraction capability of image features, leading to a robust visual feature extraction ability with reduced training data requirements. The model’s effectiveness was assessed against the baseline using the COSMOS fake news dataset. The experiments reveal that, when detecting fake news with mismatched text–image pairs, only approximately 3% of the data are used for training. The model achieves an accuracy of 80%, equivalent to 95% of the original model’s performance using full-size data for training. Notably, replacing the text encoding layer enhances experimental stability, providing a substantial advantage over the original model, specifically on the COSMOS dataset.
Hybrid FSO/RF Communications in Space–Air–Ground Integrated Networks: A Reduced Overhead Link Selection Policy
Authors: Bithas Petros S. , Nistazakis Hector E. , Katsis Athanassios , Yang Liang
Published: February 2024

Space–air–ground integrated network (SAGIN) is considered an enabler for sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seems to be a quite promising solution to provide reliable connectivity everywhere and all the time. Its availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high-altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors, and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.
Decentralized Predictor Stabilization for Interconnected Networked Control Systems with Large Uncertain Delays and Event-Triggered Input
Authors: Zhu Yang , Zhang Meng , Jiang Qiang
Published: February 2024

In this article, we propose a control scheme with predictors in a decentralized manner for coupled networked control systems (NCSs) under uncertain, large time-delays and event-triggered inputs. The network-induced delays are handled via the prediction; thus, the delay value is allowed to be large, and the burden of the network is relieved by the event-triggered input. Two methods are employed to deal with the large delay issue: the state and output feedback. When the state of each subsystem is measurable, full-state feedback is used, whereas when the plant state cannot be measured, output feedback is employed with the help of an observer, which is more common in practice. Instead of treating the interactive plants like a global system, the exponential stability of the coupled systems, under decentralized predictors with asynchronous sampled-data feedback, is analyzed in a decentralized way. Finally, the proposed methods are verified via an example of three interconnected cart–pendulum systems, while such systems would not be stabilizable by the traditional approach when the network-induced delays are relatively large.
Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation
Authors: Wang Chuanjiang , Li Yuepeng , Wei Guohui , Hou Xiankai , Sun Xiujuan
Published: February 2024

The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.
Three-Dimensional Double Random-Phase Encryption for Simultaneous Two-Primary Data
Authors: Jang Jae-Young , Cho Myungjin
Published: February 2024

In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first (“LENA” text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively.
Machine Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative Perspective of Cybersecurity and Cyberdefense in Colombia
Authors: Ortiz-Ruiz Emanuel , Bermejo Juan Ramón , Sicilia Juan Antonio , Bermejo Javier
Published: February 2024

This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective machine learning methods for mitigating and preventing such threats. The study evaluates the performance of logistic regression, naïve Bayes, perceptron, and k-nearest neighbors algorithms in the context of cyberattack prevention. Results reveal the strengths and weaknesses of these techniques in addressing the unique challenges posed by cyberattackers in Colombia’s IoT infrastructure. The findings provide valuable insights for enhancing cybersecurity measures in the region and contribute to the broader field of IoT security.
Dynamic Voltage and Frequency Scaling as a Method for Reducing Energy Consumption in Ultra-Low-Power Embedded Systems
Authors: Zidar Josip , Matić Tomislav , Aleksi Ivan , Hocenski Željko
Published: February 2024

Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%.
An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques
Authors: Mofokeng Olga D. , Adelabu Samuel A. , Jackson Colbert M.
Published: February 2024

Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.