bluuebunny/crossref_metadata_2025 huggingface dataset:
Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules
Authors: Bini Fabiano , Pica Andrada , Marinozzi Franco , Giusti Alessandro , Leoncini Andrea , Trimboli Pierpaolo
Published: October 2023

Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.
Anthropomorphic Characterization of Ankle Joint
Authors: Gundapaneni Dinesh , Tsatalis James T. , Laughlin Richard T. , Goswami Tarun
Published: October 2023

Even though total ankle replacement has emerged as an alternative treatment to arthrodesis, the long-term clinical results are unsatisfactory. Proper design of the ankle device is required to achieve successful arthroplasty results. Therefore, a quantitative knowledge of the ankle joint is necessary. In this pilot study, imaging data of 22 subjects (with both females and males and across three age groups) was used to measure the morphological parameters of the ankle joint. A total of 40 measurements were collected by creating sections in the sagittal and coronal planes for the tibia and talus. Statistical analyses were performed to compare genders, age groups, and image acquisition techniques used to generate 3D models. About 13 measurements derived for parameters (TiAL, SRTi, TaAL, SRTa, TiW, TaW, and TTL) that are very critical for the implant design showed significant differences (p-value < 0.05) between males and females. Young adults showed a significant difference (p-value < 0.05) compared to adults for 15 measurements related to critical tibial and talus parameters (TiAL, TiW, TML, TaAL, SRTa, TaW, and TTL), but no significant differences were observed between young adults and older adults, and between adults and older adults for most of the parameters. A positive correlation (r > 0.70) was observed between tibial and talar width values and between the sagittal radius values. When compared with morphological parameters obtained in this study, the sizes of current total ankle replacement devices can only fit a very limited group of people in this study. This pilot study contributes to the comprehensive understanding of the effects of gender and age group on ankle joint morphology and the relationship between tibial and talus parameters that can be used to plan and design ankle devices.
Gene Variants Implicated in Steatotic Liver Disease: Opportunities for Diagnostics and Therapeutics
Authors: Huang Gary , Wallace Daniel F. , Powell Elizabeth E. , Rahman Tony , Clark Paul J. , Subramaniam V. Nathan
Published: October 2023

Non-alcoholic fatty liver disease (NAFLD) describes a steatotic (or fatty) liver occurring as a consequence of a combination of metabolic, environmental, and genetic factors, in the absence of significant alcohol consumption and other liver diseases. NAFLD is a spectrum of conditions. Steatosis in the absence of inflammation is relatively benign, but the disease can progress into more severe forms like non-alcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma. NAFLD onset and progression are complex, as it is affected by many risk factors. The interaction between genetic predisposition and other factors partially explains the large variability of NAFLD phenotype and natural history. Numerous genes and variants have been identified through large-scale genome-wide association studies (GWAS) that are associated with NAFLD and one or more subtypes of the disease. Among them, the largest effect size and most consistent association have been patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and membrane-bound O-acyltransferase domain containing 7 (MBOAT7) genes. Extensive in vitro and in vivo studies have been conducted on these variants to validate these associations. The focus of this review is to highlight the genetics underpinning the molecular mechanisms driving the onset and progression of NAFLD and how they could potentially be used to improve genetic-based diagnostic testing of the disease and develop personalized, targeted therapeutics.
Blood Coagulation and Thrombotic Disorders following SARS-CoV-2 Infection and COVID-19 Vaccination
Authors: Sekulovski Metodija , Mileva Niya , Vasilev Georgi Vasilev , Miteva Dimitrina , Gulinac Milena , Peshevska-Sekulovska Monika , Chervenkov Lyubomir , Batselova Hristiana , Vasilev Georgi Hristov , Tomov Latchezar , Lazova Snezhina , Vassilev Dobrin , Velikova Tsvetelina
Published: October 2023

Although abundant data confirm the efficacy and safety profile of the developed vaccines against COVID-19, there are still some concerns regarding vaccination in high-risk populations. This is especially valid for patients susceptible to thrombotic or bleeding events and hesitant people due to the fear of thrombotic incidents following vaccination. This narrative review focuses on various inherited and acquired thrombotic and coagulation disorders and the possible pathophysiologic mechanisms interacting with the coagulation system during immunization in view of the currently available safety data regarding COVID-19 vaccines. Inherited blood coagulation disorders and inherited thrombotic disorders in the light of COVID-19, as well as blood coagulation and thrombotic disorders and bleeding complications following COVID-19 vaccines, along with the possible pathogenesis hypotheses, therapeutic interventions, and imaging for diagnosing are discussed in detail. Lastly, the lack of causality between the bleeding and thrombotic events and COVID-19 vaccines is debated, but still emphasizes the importance of vaccination against COVID-19, outweighing the minimal risk of potential rare adverse events associated with coagulation.
Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis
Authors: Fan Xuqing , Deng Sai , Wu Zhengxing , Fan Junfeng , Zhou Chao
Published: October 2023

Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.
The Effect of Beta-Carotene on Cognitive Function: A Systematic Review
Authors: Abrego-Guandique Diana Marisol , Bonet Maria Luisa , Caroleo Maria Cristina , Cannataro Roberto , Tucci Paola , Ribot Joan , Cione Erika
Published: October 2023

β-carotene is a powerful antioxidant and dietary precursor of vitamin A whose role in maintaining mental health and cognitive performance, either alone or in combination with other dietary compounds, has been a topic of recent research. However, its effectiveness is still unclear. This systematic review, conducted according to the PRISMA guideline and assisted by the MySLR platform, addressed this issue. A total of 16 eligible original research articles were identified. Dietary intake or β-carotene serum levels were associated with improved measures of cognitive function in 7 out of 10 epidemiological studies included. In intervention studies, β-carotene consumption alone did not promote better cognitive function in the short term, but only in a long-term intervention with a mean duration of 18 years. However, all but one intervention study suggested the beneficial effects of β-carotene supplementation at doses ranging from 6 mg to 50 mg per day in combination with a multicomplex such as vitamin E, vitamin C, zinc, or selenium for a period of 16 weeks to 20 years. Despite the current limitations, the available evidence suggests a potential association between β-carotene dietary/supplementary intake and the maintenance of cognitive function. The β-carotene most probably does not act alone but in synergy with other micronutrients.
Intravesical Therapy for Upper Urinary Tract Urothelial Carcinoma: A Comprehensive Review
Authors: Wang Zheng , Shi Haoqing , Xu Yifan , Fang Yu , Song Jiaao , Jiang Wentao , Xia Demeng , Wu Zhenjie , Wang Linhui
Published: October 2023

Upper tract urothelial carcinoma (UTUC) poses unique challenges in diagnosis and treatment. This comprehensive review focuses on prophylactic intravesical therapy for UTUC, summarizing key aspects of intravesical therapy in various clinical scenarios, including concurrent with or following radical nephroureterectomy, kidney-sparing surgery, ureteroscopy-guided biopsy. The incidence of intravesical recurrence in UTUC after surgical treatment is significant, necessitating effective preventive measures. Intravesical therapy plays a vital role in reducing the risk of bladder recurrence following UTUC surgery. Tailoring timing, drug selection, dosage, and frequency is vital in optimizing treatment outcomes and reducing intravesical recurrence risk in UTUC. This review provides a comprehensive summary of the history, clinical trials, guideline recommendations, and clinical applications of intravesical therapy for UTUC. It also discusses the future directions based on current clinical needs and ongoing trials. Future directions entail optimizing dosage, treatment duration, and drug selection, as well as exploring novel agents and combination therapies. Intravesical therapy holds tremendous potential in improving outcomes for UTUC patients and reducing the risk of bladder recurrence. Although advancements have been made in UTUC treatment research, further refinements are necessary to enhance efficacy and safety.
Impact of cfDNA Reference Materials on Clinical Performance of Liquid Biopsy NGS Assays
Authors: Hallermayr Ariane , Keßler Thomas , Fujera Moritz , Liesfeld Ben , Bernstein Samuel , von Ameln Simon , Schanze Denny , Steinke-Lange Verena , Pickl Julia M. A. , Neuhann Teresa M. , Holinski-Feder Elke
Published: October 2023

Background: Liquid biopsy enables the non-invasive analysis of genetic tumor variants in circulating free DNA (cfDNA) in plasma. Accurate analytical validation of liquid biopsy NGS assays is required to detect variants with low variant allele frequencies (VAFs). Methods: Six types of commercial cfDNA reference materials and 42 patient samples were analyzed using a duplex-sequencing-based liquid biopsy NGS assay. Results: We comprehensively evaluated the similarity of commercial cfDNA reference materials to native cfDNA. We observed significant differences between the reference materials in terms of wet-lab and sequencing quality as well as background noise. No reference material resembled native cfDNA in all performance metrics investigated. Based on our results, we established guidelines for the selection of appropriate reference materials for the different steps in performance evaluation. The use of inappropriate materials and cutoffs could eventually lead to a lower sensitivity for variant detection. Conclusion: Careful consideration of commercial reference materials is required for performance evaluation of liquid biopsy NGS assays. While the similarity to native cfDNA aids in the development of experimental protocols, reference materials with well-defined variants are preferable for determining sensitivity and precision, which are essential for accurate clinical interpretation.
Exploring the Relationship between Fusion Genes and MicroRNAs in Cancer
Authors: Panicker Saurav , Chengizkhan Gautham , Gor Ravi , Ramachandran Ilangovan , Ramalingam Satish
Published: October 2023

Fusion genes are key cancer driver genes that can be used as potential drug targets in precision therapies, and they can also serve as accurate diagnostic and prognostic biomarkers. The fusion genes can cause microRNA (miRNA/miR) aberrations in many types of cancer. Nevertheless, whether fusion genes incite miRNA aberrations as one of their many critical oncogenic functionalities for driving carcinogenesis needs further investigation. Recent discoveries of miRNA genes that are present within the regions of genomic rearrangements that initiate fusion gene-based intronic miRNA dysregulation have brought the fusion genes into the limelight and revealed their unexplored potential in the field of cancer biology. Fusion gene-based ‘promoter-switch’ event aberrantly activate the miRNA-related upstream regulatory signals, while fusion-based coding region alterations disrupt the original miRNA coding loci. Fusion genes can potentially regulate the miRNA aberrations regardless of the protein-coding capability of the resultant fusion transcript. Studies on out-of-frame fusion and nonrecurrent fusion genes that cause miRNA dysregulation have attracted the attention of researchers on fusion genes from an oncological perspective and therefore could have potential implications in cancer therapies. This review will provide insights into the role of fusion genes and miRNAs, and their possible interrelationships in cancer.
Clinical Cases and the Molecular Profiling of a Novel Childhood Encephalopathy-Causing GNAO1 Mutation P170R
Authors: Larasati Yonika A. , Solis Gonzalo P. , Koval Alexey , Griffiths Silja T. , Berentsen Ragnhild , Aukrust Ingvild , Lesca Gaetan , Chatron Nicolas , Ville Dorothée , Korff Christian M. , Katanaev Vladimir L.
Published: October 2023

De novo mutations in GNAO1, the gene encoding the major neuronal G protein Gαo, cause a spectrum of pediatric encephalopathies with seizures, motor dysfunction, and developmental delay. Of the >80 distinct missense pathogenic variants, many appear to uniformly destabilize the guanine nucleotide handling of the mutant protein, speeding up GTP uptake and deactivating GTP hydrolysis. Zinc supplementation emerges as a promising treatment option for this disease, as Zn2+ ions reactivate the GTP hydrolysis on the mutant Gαo and restore cellular interactions for some of the mutants studied earlier. The molecular etiology of GNAO1 encephalopathies needs further elucidation as a prerequisite for the development of efficient therapeutic approaches. In this work, we combine clinical and medical genetics analysis of a novel GNAO1 mutation with an in-depth molecular dissection of the resultant protein variant. We identify two unrelated patients from Norway and France with a previously unknown mutation in GNAO1, c.509C>G that results in the production of the Pro170Arg mutant Gαo, leading to severe developmental and epileptic encephalopathy. Molecular investigations of Pro170Arg identify this mutant as a unique representative of the pathogenic variants. Its 100-fold-accelerated GTP uptake is not accompanied by a loss in GTP hydrolysis; Zn2+ ions induce a previously unseen effect on the mutant, forcing it to lose the bound GTP. Our work combining clinical and molecular analyses discovers a novel, biochemically distinct pathogenic missense variant of GNAO1 laying the ground for personalized treatment development.
Effects of Melatonin on Liver of D-Galactose-Induced Aged Mouse Model
Authors: Lee Ran , Lee Won-Yong , Park Hyun-Jung
Published: October 2023

Melatonin, a hormone secreted by the pineal gland of vertebrates, regulates sleep, blood pressure, and circadian and seasonal rhythms, and acts as an antioxidant and anti-inflammatory agent. We investigated the protective effects of melatonin against markers of D-galactose (D-Gal)-induced hepatocellular aging, including liver inflammation, hepatocyte structural damage, and non-alcoholic fatty liver. Mice were divided into four groups: phosphate-buffered saline (PBS, control), D-Gal (200 mg/kg/day), melatonin (20 mg/kg), and D-Gal (200 mg/kg) and melatonin (20 mg) cotreatment. The treatments were administered once daily for eight consecutive weeks. Melatonin treatment alleviated D-Gal-induced hepatocyte impairment. The AST level was significantly increased in the D-Gal-treated groups compared to that in the control group, while the ALT level was decreased compared to the melatonin and D-Gal cotreated group. Inflammatory genes, such as IL1-β, NF-κB, IL-6, TNFα, and iNOS, were significantly increased in the D-Gal aging model, whereas the expression levels of these genes were low in the D-Gal and melatonin cotreated group. Interestingly, the expression levels of hepatic steatosis-related genes, such as LXRα, C/EBPα, PPARα, ACC, ACOX1, and CPT-1, were markedly decreased in the D-Gal and melatonin cotreated group. These results suggest that melatonin suppresses hepatic steatosis and inflammation in a mouse model of D-Gal-induced aging.
Neuroprotective Effects of Water Extract from Brown Algae Petalonia binghamiae in an Experimental Model of Focal Cerebral Ischemia In Vitro and In Vivo
Authors: Eom Sun Ho , Hong Geum-Lan , Kang Hyun Bae , Lee Nam-Seob , Kim Do Kyung , Jeong Young Gil , Kim Chun-Sung , Yoo Yung Choon , Lee Bong Ho , Jung Ju-Young , Kim Dong-Sub , Han Seung Yun
Published: October 2023

Focal cerebral ischemia (fCI) can result in brain injury and sensorimotor deficits. Brown algae are currently garnering scientific attention as potential therapeutic candidates for fCI. This study investigated the therapeutic effects of the hot water extract of Petalonia binghamiae (wPB), a brown alga, in in vitro and in vivo models of fCI. The neuroprotective efficacy of wPB was evaluated in an in vitro excitotoxicity model established using HT-22 cells challenged with glutamate. Afterward, C57/BL6 mice were administered wPB for 7 days (10 or 100 mg/kg, intragastric) and subjected to middle cerebral artery occlusion and reperfusion (MCAO/R) operation, which was used as an in vivo fCI model. wPB co-incubation significantly inhibited cell death, oxidative stress, and apoptosis, as well as stimulated the expression of heme oxygenase-1 (HO-1), an antioxidant enzyme, and the nuclear translocation of its upstream regulator, nuclear factor erythroid 2-related factor 2 (Nrf2) in HT-22 cells challenged with glutamate-induced excitotoxicity. Pretreatment with either dose of wPB significantly attenuated infarction volume, neuronal death, and sensorimotor deficits in an in vivo fCI model. Furthermore, the attenuation of oxidative stress and apoptosis in the ischemic lesion accompanied the wPB-associated protection. This study suggests that wPB can counteract fCI via an antioxidative effect, upregulating the Nrf2/HO-1 pathway.
Large Independent Sets on Random d-Regular Graphs with Fixed Degree d
Authors: Marino Raffaele , Kirkpatrick Scott
Published: October 2023

The maximum independent set problem is a classic and fundamental combinatorial challenge, where the objective is to find the largest subset of vertices in a graph such that no two vertices are adjacent. In this paper, we introduce a novel linear prioritized local algorithm tailored to address this problem on random d-regular graphs with a small and fixed degree d. Through exhaustive numerical simulations, we empirically investigated the independence ratio, i.e., the ratio between the cardinality of the independent set found and the order of the graph, which was achieved by our algorithm across random d-regular graphs with degree d ranging from 5 to 100. Remarkably, for every d within this range, our results surpassed the existing lower bounds determined by theoretical methods. Consequently, our findings suggest new conjectured lower bounds for the MIS problem on such graph structures. This finding has been obtained using a prioritized local algorithm. This algorithm is termed ‘prioritized’ because it strategically assigns priority in vertex selection, thereby iteratively adding them to the independent set.
Housing Status and COVID-19 Prevention Recommendations among People Who Use Drugs
Authors: Zaidi Izza , Jordan Ashly E. , Jessell Lauren , Dominguez Gomez Leonardo , Harocopos Alex , Nolan Michelle
Published: October 2023

Housing conditions can increase health risks for people who use opioids (PWUO). Little research documents the influence of housing on PWUO’s ability to practice disease prevention methods. This study examines associations between housing status of PWUO in NYC and their ability to practice COVID-19 prevention recommendations during the initial wave of the pandemic. Participants were recruited via convenience sampling and administered a survey. Eligibility required age ≥18 years, using opioids ≥3times in the prior 30 days, and accessing a health-related service in the prior year. Descriptive and bivariate statistics assessed relationships between housing and the ability to practice social distancing, access soap and running water or hand sanitizer (soap), and access face masks. Multivariable Poisson regression was used to examine relationships between housing and the potential to practice COVID-19 prevention recommendations. The 329 participants were grouped into stable housing (34.3%), unstable/shelter housing (31.9%), and street homeless (33.7%) categories. Street homeless PWUO were significantly less likely to have access to soap and face masks than those stably housed. There were no significant differences between PWUO experiencing unstable and stable housing. PWUO experiencing street homelessness may have had increased vulnerability to COVID-19. Providing low-threshold health-related resources and increasing linkages to housing opportunities could promote health of PWUO experiencing street homelessness during future emergencies.
Ethnomedicinal Uses, Phytochemistry, and Pharmacological Activity of the Irvingia Species
Authors: Nguena-Dongue Branly-Natalien , Pone Kamdem Boniface , Lunga Paul Keilah , Fekam Boyom Fabrice
Published: October 2023

Plants belonging to the genus Irvingia are widespread across the African and Southeast Asian regions of the world. Irvingia gabonensis, Irvingia malayana, and Irvingia grandifolia are among the commonly used species in ethnomedicine, especially in Africa. Fever, scabies, toothache, inflammation, and liver and gastrointestinal disorders are among the pathological conditions that are reverted by Irvingia plants upon traditional preparations. Modern pharmacological investigations have substantiated the ethnomedicinal uses of Irvingia spp. Reports on the phytochemical analysis of Irvingia plants have revealed the presence of a number of secondary metabolites such as flavonoids, phenolic compounds, tannins, saponins, and alkaloids. Based on the foregoing, the present study provides a comprehensive evaluation of reports on the ethnomedicinal use, phytochemistry, pharmacology, and toxicity of plants from the genus Irvingia. Relevant information on Irvingia plants was mostly obtained from major scientific databases from their inception until July 2023. As a result, more than forty compounds have been identified in Irvingia spp., proving the abundance of secondary metabolites in these plants. Reports have pointed out modern pharmacological activities such as antiprotozoal, antimicrobial, antioxidant, antidiabetic, anti-inflammatory, and hepatoprotective activities. The present study provides more insights for the successful utilization of Irvingia plants and may guide further research on their therapeutic potential in the treatment of various diseases.
Hyaline and Cystic Degeneration of Uterine Leiomyomas: CT and MR Imaging with Histopathological Sample Analyses
Authors: Sotomayor Camilo G. , Parra Carla , Miranda Maximiliano , Casas Juan Salvador , Cárdenas Gonzalo , Sanhueza Álvaro , Araya Francisca , Gallegos Iván , Yévenes Sebastián
Published: October 2023

Leiomyomas are the most common solid benign uterine neoplasms; they are usually asymptomatic and are identified incidentally. Yet, responsive to stimulation by estrogens, leiomyomas may expand, potentially outgrowing their blood supply to undergo hemorrhage, fibrosis, calcification, and atrophy. These pathologic mechanisms commonly lead to leiomyomas degeneration, i.e., red, hyaline, cystic, or myxoid. Magnetic resonance (MR) imaging is the most accurate imaging technique for the characterization of leiomyomas. In cases of degeneration, variable features on T2-weighted and contrast-enhanced images can be found. With no recent radiologic pathologic correlation literature available on this matter, herewith, we provide computed tomography (CT)/MR imaging along with histopathological specimens of two young women who were diagnosed with hyaline or hyaline and cyst degeneration of uterine leiomyomas at our university hospital. We report on the imaging features of uterine leiomyomas using CT and MR imaging and discuss the available literature on imaging signs that may be suggestive of hyaline or cyst degeneration using either of the imaging examination methods.
Optimizing Finite-Blocklength Nested Linear Secrecy Codes: Using the Worst Code to Find the Best Code
Authors: Shoushtari Morteza , Harrison Willie
Published: October 2023

Nested linear coding is a widely used technique in wireless communication systems for improving both security and reliability. Some parameters, such as the relative generalized Hamming weight and the relative dimension/length profile, can be used to characterize the performance of nested linear codes. In addition, the rank properties of generator and parity-check matrices can also precisely characterize their security performance. Despite this, finding optimal nested linear secrecy codes remains a challenge in the finite-blocklength regime, often requiring brute-force search methods. This paper investigates the properties of nested linear codes, introduces a new representation of the relative generalized Hamming weight, and proposes a novel method for finding the best nested linear secrecy code for the binary erasure wiretap channel by working from the worst nested linear secrecy code in the dual space. We demonstrate that our algorithm significantly outperforms the brute-force technique in terms of speed and efficiency.
Synchronization of Complex Dynamical Networks with Stochastic Links Dynamics
Authors: Zhao Juanxia , Wang Yinhe , Gao Peitao , Li Shengping , Peng Yi
Published: October 2023

The mean square synchronization problem of the complex dynamical network (CDN) with the stochastic link dynamics is investigated. In contrast to previous literature, the CDN considered in this paper can be viewed as consisting of two subsystems coupled to each other. One subsystem consists of all nodes, referred to as the nodes subsystem, and the other consists of all links, referred to as the network topology subsystem, where the weighted values can quantitatively reflect changes in the network’s topology. Based on the above understanding of CDN, two vector stochastic differential equations with Brownian motion are used to model the dynamic behaviors of nodes and links, respectively. The control strategy incorporates not only the controller in the nodes but also the coupling term in the links, through which the CDN is synchronized in the mean-square sense. Meanwhile, the dynamic stochastic signal is proposed in this paper, which is regarded as the auxiliary reference tracking target of links, such that the links can track the reference target asymptotically when synchronization occurs in nodes. This implies that the eventual topological structure of CDN is stochastic. Finally, a comparison simulation example confirms the superiority of the control strategy in this paper.
An Investigation of Learning Needs in the Mining Industry
Authors: Miranda Sergio , Marzano Antonio , Vegliante Rosa
Published: October 2023

Mining operations are risky and often dangerous, with a high potential for accidents. Many of these accidents can be prevented by implementing safety measures. It is essential that mining companies take these measures seriously to protect the safety and wellbeing of their workers and ensure the sustainability of the industry. Among these measures, those related to training are addressed in this paper in relation to the ERASMUS+ project entitled DigiRescueMe, which aims at developing courses to increase the knowledge and level of awareness of miners, rescue members, and mining engineers and, consequently, reduce the death rate in mine accidents. For this goal, semi-structured interviews and surveys were implemented, and the collected data were analyzed. The mining industry is a wide domain connected to other sectors like universities, vocational schools, rescue centers, and agencies. For this reason, the investigations carried out herein engaged people from all these sectors to identify firstly the themes, secondly, the topics, and finally the knowledge levels corresponding to those themes and topics in order to determine the learning needs and translate them into requirements for the courses that will be developed during the project activities.
University–Business Collaboration for the Design, Development, and Delivery of Critical Thinking Blended Apprenticeships Curricula: Lessons Learned from a Three-Year Project
Authors: Rebelo Hugo , Christodoulou Panagiota , Payan-Carreira Rita , Dumitru Daniela , Mäkiö Elena , Mäkiö Juho , Pnevmatikos Dimitrios
Published: October 2023

University–Business partnership for collaborative curriculum design, development, and delivery is an important dimension of University–Business Collaboration (UBC), but scarce information exists on how to enhance partnership for the design, development, and implementation of new curricula. With these questions in focus, this article intends to present and discuss the perceived experience during a three-year European funded project, namely, Think4Jobs. This project exemplifies the significance and benefits of UBC in the design, development, and delivery of curricula that meet the evolving demands of the labour market while promoting Critical Thinking (CT) as a foundational 21st century skill to contribute to graduates’ employability. Think4Jobs project brought together a multidisciplinary team of researchers and business organisations from five European countries (Germany, Greece, Lithuania, Portugal, and Romania) with interests in promoting and developing CT and mitigating eventual competence gaps. The project’s success was attributed to key practices, including defining a common conceptualization of CT, employing Participatory Co-Design, and providing common training for university and business partners. Clear objectives, explicit roles, effective communication, and ongoing evaluation further enhanced the collaboration. Experiential learning, real-work problems, and case studies reinforced the curricula, bridging the gap between academia and the labour market. By embracing these insights, future UBC initiatives can empower graduates with the necessary skills to stand out in an ever-changing labour market, contributing to enhanced education and successful careers.
Examining Relationships between Cognitive Flexibility, Exercise Perceptions, and Cardiovascular Disease Risk Factors
Authors: Buechner Bryan M. , Traylor Miranda K. , Feldman Rachel I. , Overstreet Kaitlyn F. , Hill Benjamin D. , Keller Joshua L.
Published: October 2023

Adults do not engage in enough physical activity. Investigating cognitive and physiological factors related to improving this behavior—and reducing health risks—remains a public health priority. Our objective was to assess whether cognitive flexibility influenced perceptions and choice of exercise programs and whether flexibility was associated with cardiovascular disease (CVD) risk factors. Independent sample groups of college-aged adults (18–24 yrs) participated in two studies. Data were collected on individuals’ degree of cognitive flexibility (both self-reported and objectively measured), perceptions and choice of exercise programs, and health status markers known to be associated with CVD (vascular function, muscular strength, and body composition). Vascular function was assessed with a near-infrared spectroscopy device, strength was defined as handgrip, and body composition was estimated via digital circumferences. Self-reported flexibility reliably predicted individuals’ choice of exercise program and perceptions of effort required for success on an exercise program. The relationships among CVD risk factors and objectively measured cognitive flexibility were not significant, demonstrating that identifying a healthy individual’s degree of performance-based cognitive flexibility does not predict health status. Furthermore, although greater self-reported trait flexibility (rigidity) is known to predict higher (lower) likelihood of physical activity, this finding should not be extrapolated to also assume that flexibility (rigidity), as measured by objective cognitive tests, is associated with reduced CVD risk in healthy adults. We posit a rationale for how understanding cognitive flexibility and rigidity can play an impactful role in improving adherence to exercise prescriptions targeted to reducing risks.
IoT Intrusion Detection System Based on Machine Learning
Authors: Xu Bayi , Sun Lei , Mao Xiuqing , Ding Ruiyang , Liu Chengwei
Published: October 2023

With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.
Deep Learning Neural Network-Based Detection of Wafer Marking Character Recognition in Complex Backgrounds
Authors: Zhao Yufan , Xie Jun , He Peiyu
Published: October 2023

Wafer characters are used to record the transfer of important information in industrial production and inspection. Wafer character recognition is usually used in the traditional template matching method. However, the accuracy and robustness of the template matching method for detecting complex images are low, which affects production efficiency. An improved model based on YOLO v7-Tiny is proposed for wafer character recognition in complex backgrounds to enhance detection accuracy. In order to improve the robustness of the detection system, the images required for model training and testing are augmented by brightness, rotation, blurring, and cropping. Several improvements were adopted in the improved YOLO model, including an optimized spatial channel attention model (CBAM-L) for better feature extraction capability, improved neck structure based on BiFPN to enhance the feature fusion capability, and the addition of angle parameter to adapt to tilted character detection. The experimental results showed that the model had a value of 99.44% for mAP@0.5 and an F1 score of 0.97. In addition, the proposed model with very few parameters was suitable for embedded industrial devices with small memory, which was crucial for reducing the hardware cost. The results showed that the comprehensive performance of the improved model was better than several existing state-of-the-art detection models.
Relative Jitter Measurement Methodology and Comparison of Clocking Resources Jitter in Artix 7 FPGA
Authors: Wojciechowski Andrzej A. , Marcinek Krzysztof , Pleskacz Witold A.
Published: October 2023

Phase jitter is one of the crucial factors in modern digital electronics, determining the reliability of a design. This paper presents a novel approach to designing a jitter comparison system and methodology for FPGA chips using a Tapped Delay Line (TDL)—commonly used to implement a Time-to-Digital Converter (TDC). The design and its revision utilizing latches replacing some of the flip-flops are presented and discussed, with potential further improvements. A minimal temperature influence is verified and presented. The methodology of automated relative jitter measurements is discussed. Multiple different FPGA clock signal path configurations are measured, and the results are presented. The influence of clock routing is identified as critical when MMCM or PLL modules are omitted. It is demonstrated that with careful resource and routing allocation, the clock signal’s jitter performance does not have to be deteriorated by the absence of jitter filtering blocks. The proposed technique was implemented and verified and relative jitter performance was measured in the AMD/Xilinx Artix 7 35T FPGA platform.
Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
Authors: Vasani Vatsal , Bairwa Amit Kumar , Joshi Sandeep , Pljonkin Anton , Kaur Manjit , Amoon Mohammed
Published: October 2023

In this paper, we explore how incident handling procedures are currently being implemented to efficiently mitigate malicious software. Additionally, it aims to provide a contextual understanding of diverse malcodes and their operational processes. This study also compares various ways of detecting adware against a selection of anti-virus software. Moreover, this paper meticulously examines the evolution of hacking, covering the methods employed and the actors involved. A comparative analysis of three prominent malware detection tools, Google Rapid Response (GRR), Wireshark, and VirusTotal, is also conducted, aiding in informed decision-making for enhancing application security. This paper reaches its conclusion by conducting an exhaustive analysis of two case studies, offering valuable insights into a diverse range of potential leaks and virus attacks that may pose threats to various conglomerates. In essence, this article provides a comprehensive overview that spans incident handling procedures, the historical development of hacking, and the diverse spectrum of tools accessible for achieving effective malware detection.
Leveraging Feature Extraction and Context Information for Image Relighting
Authors: Fang Chenrong , Wang Ju , Chen Kan , Su Ran , Lai Chi-Fu , Sun Qian
Published: October 2023

Example-based image relighting aims to relight an input image to follow the lighting settings of another target example image. Deep learning-based methods for such tasks have become highly popular. However, they are often limited by the geometric priors or suffer from shadow reconstruction and lack of texture details. In this paper, we propose an image-to-image translation network called DGATRN to tackle this problem by enhancing feature extraction and unveiling context information to achieve visually plausible example-based image relighting. Specifically, the proposed DGATRN consists of a scene extraction, a shadow calibration, and a rendering network, and our key contribution lies in the first two networks. We propose an up- and downsampling approach to improve the feature extraction capability to capture scene and texture details better. We also introduce a feature attention downsampling block and a knowledge transfer to utilize the attention impact and underlying knowledge connection between scene and shadow. Experiments were conducted to evaluate the usefulness and effectiveness of the proposed method.
La0.6Sr0.4MnO3-Based Fuel Electrode Materials for Solid Oxide Electrolysis Cells Operating under Steam, CO2, and Co-Electrolysis Conditions
Authors: Vibhu Vaibhav , Vinke Izaak C. , Eichel Rüdiger-A. , de Haart L. G. J. (Bert)
Published: October 2023

The conventional Ni–YSZ (yttria-stabilized zirconia) fuel electrode experiences severe degradation due to Ni- agglomeration and migration away from the electrolyte. Therefore, herein, we have considered Ni free electrodes, i.e., La0.6Sr0.4MnO3-δ (LSM)-based perovskite oxides as fuel electrodes. The LSM perovskite phase transforms into a Ruddlesden–Popper LSM (RP-LSM) phase with exsolved MnOx under reducing atmospheres. The RP-LSM is mainly interesting due to its good electrical conductivity, redox stability, and acceptable electrochemical behaviour. In this work, we synthesized the LSM powder and characterized it using several methods including X-ray diffraction (XRD), thermogravimetry analyses (TGA), four-probe conductivity, and scanning electron microscope with energy-dispersive X-ray spectroscopy (SEM-EDX). Finally, the electrolyte-supported single cells were fabricated and electrochemically characterized using AC and DC techniques under electrolysis conditions. In addition to pure LSM fuel electrodes, we have also investigated the electrochemical behaviour of LSM + YSZ (50:50) and LSM + GDC (50:50) composite fuel electrodes. The single cells containing LSM and LSM + GDC fuel electrodes show higher cell performance than LSM + YSZ. For instance, current densities of 1, 1.03, and 0.51 A·cm−2 at 1.5 V are obtained for LSM, LSM + GDC, and LSM + YSZ fuel electrodes containing single cells, respectively, with a 50% N2 and 50% H2O feed gas mixture. Moreover, the performance of the cell was also investigated under co-electrolysis with 50% CO2 and 50% H2O and under direct CO2 electrolysis conditions with 100% CO2 fuel gas.
A Stochastic Model of Anomalously Fast Transport of Heat Energy in Crystalline Bodies
Authors: Stępień Łukasz , Łagodowski Zbigniew A.
Published: October 2023

In this work, a new method for constructing the infinite-dimensional Ornstein–Uhlenbeck stochastic process is introduced. The constructed process is used to perturb the harmonic system in order to model anomalously fast heat transport in one-dimensional nanomaterials. The introduced method made it possible to obtain a transition probability function that allows for a different approach to the analysis of equations with such a disturbance. This creates the opportunity to relax assumptions about temporal correlations for such a process, which may lead to a qualitatively different model of energy transport through vibrations of the crystal lattice and, as a result, to obtain the superdiffusion equation on a macroscopic scale with an order of the fractional Laplacian different from the value of 3/4 obtained so far in stochastic models. Simulations confirming these predictions are presented and discussed.
Smart Energy Planning in the Midst of a Technological and Political Change towards a 100% Renewable System in Mexico by 2050
Authors: Icaza-Alvarez Daniel , Galan-Hernandez Nestor Daniel , Orozco-Guillen Eber Enrique , Jurado Francisco
Published: October 2023

This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points.
Model-Free HVAC Control in Buildings: A Review
Authors: Michailidis Panagiotis , Michailidis Iakovos , Vamvakas Dimitrios , Kosmatopoulos Elias
Published: October 2023

The efficient control of HVAC devices in building structures is mandatory for achieving energy savings and comfort. To balance these objectives efficiently, it is essential to incorporate adequate advanced control strategies to adapt to varying environmental conditions and occupant preferences. Model-free control approaches for building HVAC systems have gained significant interest due to their flexibility and ability to adapt to complex, dynamic systems without relying on explicit mathematical models. The current review presents the recent advancements in HVAC control, with an emphasis on reinforcement learning, artificial neural networks, fuzzy logic control, and their hybrid integration with other model-free algorithms. The main focus of this study is a literature review of the most notable research from 2015 to 2023, highlighting the most highly cited applications and their contributions to the field. After analyzing the concept of each work according to its control strategy, a detailed evaluation across different thematic areas is conducted. To this end, the prevalence of methodologies, utilization of different HVAC equipment, and diverse testbed features, such as building zoning and utilization, are further discussed considering the entire body of work to identify different patterns and trends in the field of model-free HVAC control. Last but not least, based on a detailed evaluation of the research in the field, the current work provides future directions for model-free HVAC control considering different aspects and thematic areas.