bluuebunny/crossref_metadata_2025 huggingface dataset:
Satisfied with Life? The Protective Function of Life Satisfaction in the Relationship between Perceived Stress and Negative Mental Health Outcomes
Authors: Padmanabhanunni Anita , Pretorius Tyrone B. , Isaacs Serena Ann
Published: September 2023

Life satisfaction is a key index of well-being, yet few studies have examined its role as a protective factor in the context of the COVID-19 pandemic. The current study expands the research in this area through an examination of the role of life satisfaction in the relationship between perceived stress and negative indices of mental health. Participants were university students (N = 322) who completed the Perceived Stress Scale, the Satisfaction with Life Scale, and short forms of the trait scale of the Spielberger State-Trait Anxiety Inventory, the Center for Epidemiological Depression Scale, and the Beck Hopelessness Scale. The results indicate that life satisfaction had a health-sustaining effect on indices of well-being. It also moderated the relationship between perceived stress, on the one hand, and anxiety and hopelessness, on the other hand. Further, life satisfaction played a partial mediating role in the relationship between perceived stress and indices of mental health. The findings suggest that life satisfaction could be a protective factor in the context of stressful life events. Cultivating life satisfaction through mindfulness training and the enhancement of gratitude could potentially sustain mental health.
Investigation of a Hybrid LSTM + 1DCNN Approach to Predict In-Cylinder Pressure of Internal Combustion Engines
Authors: Ricci Federico , Petrucci Luca , Mariani Francesco , Grimaldi Carlo Nazareno
Published: September 2023

The control of internal combustion engines is becoming increasingly challenging to the customer’s requirements for growing performance and ever-stringent emission regulations. Therefore, significant computational efforts are required to manage the large amount of data coming from the field for engine optimization, leading to increased operating times and costs. Machine-learning techniques are being increasingly used in the automotive field as virtual sensors, fault detection systems, and performance-optimization applications for their real-time and low-cost implementation. Among them, the combination of long short-term memory (LSTM) together with one-dimensional convolutional neural networks (1DCNN), i.e., LSTM + 1DCNN, has proved to be a promising tool for signal analysis. The architecture exploits the CNN characteristic to combine feature classification and extraction, creating a single adaptive learning body with the ability of LSTM to follow the sequential nature of sensor measurements over time. The current research focus is on evaluating the possibility of integrating virtual sensors into the on-board control system. Specifically, the primary objective is to assess and harness the potential of advanced machine-learning technologies to replace physical sensors. In realizing this goal, the present work establishes the first step by evaluating the forecasting performance of a LSTM + 1DCNN architecture. Experimental data coming from a three-cylinder spark-ignition engine under different operating conditions are used to predict the engine’s in-cylinder pressure traces. Since using in-cylinder pressure transducers in road cars is not economically viable, adopting advanced machine-learning technologies becomes crucial to avoid structural modifications while preserving engine integrity. The results show that LSTM + 1DCNN is particularly suited for the prediction of signals characterized by a higher variability. In particular, it consistently outperforms other architectures utilized for comparative purposes, achieving average error percentages below 2%. As cycle-to-cycle variability increases, LSTM + 1DCNN reaches average error percentages below 1.5%, demonstrating the architecture’s potential for replacing physical sensors.
An Empirical Study of Deep Learning-Based SS7 Attack Detection
Authors: Guo Yuejun , Ermis Orhan , Tang Qiang , Trang Hoang , De Oliveira Alexandre
Published: September 2023

Signalling protocols are responsible for fundamental tasks such as initiating and terminating communication and identifying the state of the communication in telecommunication core networks. Signalling System No. 7 (SS7), Diameter, and GPRS Tunneling Protocol (GTP) are the main protocols used in 2G to 4G, while 5G uses standard Internet protocols for its signalling. Despite their distinct features, and especially their security guarantees, they are most vulnerable to attacks in roaming scenarios: the attacks that target the location update function call for subscribers who are located in a visiting network. The literature tells us that rule-based detection mechanisms are ineffective against such attacks, while the hope lies in deep learning (DL)-based solutions. In this paper, we provide a large-scale empirical study of state-of-the-art DL models, including eight supervised and five semi-supervised, to detect attacks in the roaming scenario. Our experiments use a real-world dataset and a simulated dataset for SS7, and they can be straightforwardly carried out for other signalling protocols upon the availability of corresponding datasets. The results show that semi-supervised DL models generally outperform supervised ones since they leverage both labeled and unlabeled data for training. Nevertheless, the ensemble-based supervised model NODE outperforms others in its category and some in the semi-supervised category. Among all, the semi-supervised model PReNet performs the best regarding the Recall and F1 metrics when all unlabeled data are used for training, and it is also the most stable one. Our experiment also shows that the performances of different semi-supervised models could differ a lot regarding the size of used unlabeled data in training.
Knowledge Graph Based Recommender for Automatic Playlist Continuation
Authors: Ivanovski Aleksandar , Jovanovik Milos , Stojanov Riste , Trajanov Dimitar
Published: September 2023

In this work, we present a state-of-the-art solution for automatic playlist continuation through a knowledge graph-based recommender system. By integrating representational learning with graph neural networks and fusing multiple data streams, the system effectively models user behavior, leading to accurate and personalized recommendations. We provide a systematic and thorough comparison of our results with existing solutions and approaches, demonstrating the remarkable potential of graph-based representation in improving recommender systems. Our experiments reveal substantial enhancements over existing approaches, further validating the efficacy of this novel approach. Additionally, through comprehensive evaluation, we highlight the robustness of our solution in handling dynamic user interactions and streaming data scenarios, showcasing its practical viability and promising prospects for next-generation recommender systems.
Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students
Authors: Sang Hongfeng , Ma Liyi , Ma Nan
Published: September 2023

A three-dimensional MOOC analysis framework was developed, focusing on platform design, organizational mechanisms, and course construction. This framework aims to investigate the current situation of big data MOOCs in the intelligent era, particularly from the perspective of improving the mental health of college students; moreover, the framework summarizes the construction experience and areas for improvement. The construction of 525 big data courses on 16 MOOC platforms is compared and analyzed from three aspects: the platform (including platform construction, resource quantity, and resource quality), organizational mechanism (including the course opening unit, teacher team, and learning norms), and course construction (including course objectives, teaching design, course content, teaching organization, implementation, teaching management, and evaluation). Drawing from the successful practices of international big data MOOCs and excellent Chinese big data MOOCs, and considering the requirements of authoritative government documents, such as the no. 8 document (J.G. [2019]), no. 3 document (J.G. [2015]), no. 1 document (J.G. [2022]), as well as the “Educational Information Technology Standard CELTS-22—Online Course Evaluation Standard”, recommendations about the platform, organizational mechanism, and course construction are provided for the future development of big data MOOCs in China.
Reinforcement Learning in Education: A Literature Review
Authors: Fahad Mon Bisni , Wasfi Asma , Hayajneh Mohammad , Slim Ahmad , Abu Ali Najah
Published: September 2023

The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.
The Spatial Distribution of Crimean–Congo Haemorrhagic Fever and Its Potential Vectors in Europe and Beyond
Authors: Messina Jane Paula , Wint G. R. William
Published: September 2023

Crimean–Congo haemorrhagic fever (CCHF) is considered to be spreading across the globe, with many countries reporting new human CCHF cases in recent decades including Georgia, Türkiye, Albania, and, most recently, Spain. We update a human CCHF distribution map produced in 2015 to include global disease occurrence records to June 2022, and we include the recent records for Europe. The predicted distributions are based on long-established spatial modelling methods and are extended to include all European countries and the surrounding areas. The map produced shows the environmental suitability for the disease, taking into account the distribution of the most important known and potential tick vectors Hyalomma marginatum and Hyalomma lusitanicum, without which the disease cannot occur. This limits the disease’s predicted distribution to the Iberian Peninsula, the Mediterranean seaboard, along with Türkiye and the Caucasus, with a more patchy suitability predicted for inland Greece, the southern Balkans, and extending north to north-west France and central Europe. These updated CCHF maps can be used to identify the areas with the highest probability of disease and to therefore target areas where mitigation measures should currently be focused.
The Relationship between Changes in MYBPC3 Single-Nucleotide Polymorphism-Associated Metabolites and Elite Athletes’ Adaptive Cardiac Function
Authors: Riguene Emna , Theodoridou Maria , Barrak Laila , Elrayess Mohamed A. , Nomikos Michail
Published: September 2023

Athletic performance is a multifactorial trait influenced by a complex interaction of environmental and genetic factors. Over the last decades, understanding and improving elite athletes’ endurance and performance has become a real challenge for scientists. Significant tools include but are not limited to the development of molecular methods for talent identification, personalized exercise training, dietary requirements, prevention of exercise-related diseases, as well as the recognition of the structure and function of the genome in elite athletes. Investigating the genetic markers and phenotypes has become critical for elite endurance surveillance. The identification of genetic variants contributing to a predisposition for excellence in certain types of athletic activities has been difficult despite the relatively high genetic inheritance of athlete status. Metabolomics can potentially represent a useful approach for gaining a thorough understanding of various physiological states and for clarifying disorders caused by strength–endurance physical exercise. Based on a previous GWAS study, this manuscript aims to discuss the association of specific single-nucleotide polymorphisms (SNPs) located in the MYBPC3 gene encoding for cardiac MyBP-C protein with endurance athlete status. MYBPC3 is linked to elite athlete heart remodeling during or after exercise, but it could also be linked to the phenotype of cardiac hypertrophy (HCM). To make the distinction between both phenotypes, specific metabolites that are influenced by variants in the MYBPC3 gene are analyzed in relation to elite athletic performance and HCM. These include theophylline, ursodeoxycholate, quinate, and decanoyl-carnitine. According to the analysis of effect size, theophylline, quinate, and decanoyl carnitine increase with endurance while decreasing with cardiovascular disease, whereas ursodeoxycholate increases with cardiovascular disease. In conclusion, and based on our metabolomics data, the specific effects on athletic performance for each MYBPC3 SNP-associated metabolite are discussed.
Association between Major Adverse Cardiovascular Events and the Liver Fibrosis Score in Patients with and without Coronary Artery Disease: From the FU-CCTA Registry
Authors: Ajimu Yusuke , Shiga Yuhei , Hirata Tetsuo , Tashiro Kohei , Higashi Sara , Kawahira Yuto , Suematsu Yasunori , Kato Yuta , Kuwano Takashi , Sugihara Makoto , Miura Shin-ichiro
Published: September 2023

The liver fibrosis score reflects the degree of hepatic scarring and has been reported to be associated with cardiovascular disease. Using a coronary artery computed tomography angiography registry at the Fukuoka University Hospital (FU-CCTA registry), we investigated the association between major adverse cardiovascular events (MACEs) and the liver fibrosis score (fibrosis-4 index (FIB-4I)) in 612 patients who underwent CCTA to screen for coronary artery disease and performed a prognosis survey for up to 5 years. The primary endpoint was MACEs (all-cause mortality, acute myocardial infarction, ischemic stroke, coronary revascularization). FIB-4I in all patients and in patients with hypertension (HTN) was significantly higher in the MACE group than in the non-MACE group. The event-free survival rate of MACEs targeting only patients with HTN was significantly lower in patients with a high risk of liver fibrosis (FIB-4I values of 2.67 or higher) than in those with a low or intermediate risk (less than 2.67). However, no significant difference was observed in all patients or in patients without HTN. Finally, FIB-4I and body mass index were independent factors associated with MACEs in patients with HTN. In conclusion, the liver fibrosis score may be an independent predictor of MACEs in hypertensive patients undergoing CCTA.
Advances in Targeted Therapy for the Treatment of Cervical Cancer
Authors: Watkins Dean E. , Craig Daniel J. , Vellani Shahnaz D. , Hegazi Ahmad , Fredrickson Kaylee J. , Walter Adam , Stanbery Laura , Nemunaitis John
Published: September 2023

Cervical cancer is an international public health crisis, affecting several hundred thousand women annually. While not universally protective due to other risk factors, many such cases are preventable with vaccination against high-risk serotypes of the human papilloma virus (HPV 6, 11, 16, 18, 31, 33, 45, 53, 58). Advanced-stage and recurrent cervical cancers are typically lethal and have been the focus in recent years of the integration of immune checkpoint inhibitors (CPIs) to improve survival. We have consolidated information regarding the role of the immune system in both disease progression and disease clearance with the aid of targeted therapies and immunotherapeutic agents. Additionally, we have characterized the treatment modalities currently indicated as the standard of care—such as bevacizumab and the immune CPIs—and those recently approved or in development, including Tivdak, Vigil, and chimeric antigen receptor (CAR) T-cells.
Evidence Mapping and Quality Analysis of Systematic Reviews on Various Aspects Related to Cleft Lip and Palate
Authors: Srivastav Sukeshana , Tewari Nitesh , Antonarakis Gregory S. , Duggal Ritu , Saji Seba , Lokade Amol Kumar , Yadav Rahul
Published: September 2023

Background: Management of cleft lip and palate is interdisciplinary. An evidence-mapping approach was envisaged to highlight the existing gaps in this field, using only the highest level of evidence. Objectives: To conduct evidence mapping and quality analysis of systematic reviews and meta-analyses related to any aspect of cleft lip and palate. Search Methods: The cleft lip and palate field was divided into 9 domains and 50 subdomains and a method of categorization of systematic reviews was established. A comprehensive search strategy was carried out in seven databases along with the search of gray literature and references of included articles. Selection criteria: Systematic reviews related to any aspect of cleft lip and palate, conducted by a minimum of two reviewers, with a comprehensive search strategy and adequate quality analysis were included. Data collection and analysis: A self-designed, pre-piloted data-extraction sheet was used to collect information that was analyzed through an expert group discussion. Quality analysis was performed using ROBIS-I, AMSTAR 2, and the PRISMA checklist. Results: A total of 144 systematic reviews published between 2008 and 2022 were included. The largest number of these could be categorized in the therapeutic domain (n = 58). A total of 27% of the studies were categorized as inconclusive, 40% as partially conclusive, and 33% as conclusive. As per ROBIS-I, 77% of reviews had high risk of bias while 58% were graded as critically low in quality as per AMSTAR 2. The majority of systematic reviews showed low reporting errors. Conclusions: The majority of systematic reviews related to cleft lip and palate relate to therapeutic and prognostic domains and show high risk of bias and critically low quality regardless of the source journal. The results of this paper might serve as a starting point encouraging authors to carry out high-quality research where evidence is lacking. Registration: A multidisciplinary expert-group formulated an a priori protocol, registered in Open Science Framework (DOI 10.17605/OSF.IO/NQDV2).
Predictors of 30-Day Postoperative Outcome after Elective Endovascular Abdominal Aortic Aneurysm Repair: A Tertiary Referral Center Experience
Authors: Ntalouka Maria P. , Nana Petroula , Brotis Alexandros , Chatzis Athanasios , Mermiri Maria , Stamoulis Konstantinos , Bareka Metaxia , Giannoukas Athanasios , Matsagkas Miltiadis , Arnaoutoglou Eleni
Published: September 2023

Background: We evaluated the 30-day postoperative outcome after elective endovascular aneurysm repair (EVAR) and the possible predictors for the 30-day postoperative outcome. Materials: Demographics, medical history, laboratory values, intensive care unit (ICU) admission and 30-day complications classified as major (major adverse cardiovascular events (MACEs), acute kidney injury (AKI) and death of any cause) and minor (postimplantation syndrome (PIS), postoperative delirium (POD), urinary tract infection (UTI) and technical graft failure) were documented (March 2016 to February 2019). Results: We included 322 patients. The majority were managed under general anesthesia (83%) with femoral cutdown (98.1%). Overall, 121 (37.5%) complications, mostly minor (n = 103, 31.9%), were recorded. In total, 11 patients (3.4%) developed MACEs, 5 (1.6%) experienced AKI and 2 (0.6%) died in the ICU. Moreover, 77 patients (23.9%) suffered from PIS, 11 from POD, 11 from UTI and 4 from technical graft failure. The multivariate logistic regression analysis revealed that aneurysm diameter (p = 0.01) and past smoking (p = 0.003) were predictors for complications. PAD was an independent predictor of MACEs (p = 0.003), preoperative neutrophil to lymphocyte ratio (NLR) of AKI (p = 0.003) and past smoking of PIS (p = 0.008), respectively. Conclusions: Our study showed that the 30-day morbidity after EVAR exceeded 35%. However, the majority of complications were minor, and the associated mortality was low. Aneurysm diameter and past smoking were independent predictors for postoperative outcome.
The TG/HDL-c Lipid Ratio as a Cardiovascular Risk Marker in a Mexican Urban Middle-Class Population: Do We Need a Risk Score Tailored for Mexicans?
Authors: Martínez-Marroquín Yolanda , Meaney Alejandra , Samaniego-Méndez Virginia , Nájera Nayelli , Ceballos Guillermo , Fernández-Barros Carlos , Meaney Eduardo
Published: September 2023

Introduction: Risk scores are essential in primary prevention to detect high-risk patients. The most common scores exclude hypertriglyceridemia and abdominal obesity in their risk assessment. We examined the triglyceride/HDL-cholesterol (TG/HDL-c) ratio as a cardiovascular (CV) risk marker in a middle-class urban Mexican population sample. Aim: Our aim was to test the concept of a scoring system reflecting Mexican population characteristics. Methods: A total of 2602 healthy adults from the Lindavista primary prevention program were considered, evaluating gender, age, blood pressure, smoking, body mass index, waist circumference, lipid profile, and fasting glucose. According to the abnormality, a score from −3 to +3 was assigned. Results: The summation of eleven variables yielded the Lindavista score (LS), which was calibrated versus the TG/HDL ratio and ACC ASCVD Risk Estimator Plus score to determine its correlation with risk categories. The TG/HDL-c ratio had a linear correlation with LS and high-risk ACC ASCVD categories. Conclusions: Compared with LS and TG/HDL-c, the ACC ASCVD system underestimates the high-risk category. The high prevalence of obesity and lipid triad in the Mexican population requires a scale that considers those traits. The TG/HDL-c ratio is a practical, easy, and economical instrument to categorize risk in Mexicans.
Nonlinear Associations between Physical Function, Physical Activity, Sleep, and Depressive Symptoms in Older Adults
Authors: Bae Seongryu , Jang Minwoo , Kim Gwon-Min , Yang Ja-Gyeong , Thapa Ngeemasara , Park Hye-Jin , Park Hyuntae
Published: September 2023

The purpose of this study is to examine how physical function, physical activity, and sleep are related to depressive symptoms in older adults using a nonlinear model. The participants were 283 Korean older adults aged 65 and older who met the study inclusion criteria. Depressive symptoms were measured using the shortened version of the Geriatric Depression Scale in Korean (SGDS-K). Physical activity and sleep time were objectively quantified by continuously monitoring participants over 20 consecutive days using a triaxial accelerometer. Physical function was evaluated using five distinct measurements: grip strength, gait speed, the Timed Up and Go Test (TUG), the Six-Minute Walk Test (SMWT), and the Five Times Sit to Stand Test (FTSST). The SMWT, gait speed, and MVPA exhibited a nonlinear relationship with depressive symptoms. However, other physical functions showed linear relationships. Also, sleep time showed a U-shaped trend starting at approximately 390 min. After adjusting for age, sex, drinking, and smoking in the logistic regression model, SMWT, MVPA, and sleep time were significantly associated with depressive symptoms. The outcomes highlight the importance of considering multiple factors in understanding depression among the elderly, particularly the intricate interactions between these elements and biological rhythms.
A Qualitative Study on the Recovery Process and Its Associated Factors in Morita Therapy for Inpatients with Mood Disorders
Authors: Tanii Kazuo , Kubota Mikiko , Nakamura Kei , Nunomura Akihiko , Shigeta Masahiro
Published: September 2023

Morita therapy (MT) has been re-evaluated and has attracted much attention internationally to date. However, it is not known what kinds of experiences inpatients with mood disorders undergo during the process of recovery through MT. The purpose of this study was therefore to empirically clarify what subjective experiences influence the recovery from depression when it is treated with MT. Patients with mood disorders who were admitted to the Jikei University Center for Morita Therapy were included. Successive assessments of depression were performed using rating scales. Semi-structured interviews were conducted at the time of discharge regarding factors contributing to improvement, and were analyzed using qualitative data analysis methods to identify factors contributing to the recovery from depression among inpatients treated with MT. There were 24 subjects, 19 of whom completed treatment. The completers had significantly lower severity of depression severity upon discharge. Remarkably, qualitative analysis revealed that nine categories of experiences contributed to recovery from depression. In particular, experiences of “isolation bed-resting of MT”, “getting stuck in doing things one’s way”, “identifying maladaptive behavior patterns”, “modifying maladaptive behavior patterns”, “restoring self-evaluation”, and “change in negative emotions” were considered as the key experiences for recovery.
Biological Performance of Duplex PEO + CNT/PCL Coating on AZ31B Mg Alloy for Orthopedic and Dental Applications
Authors: Daavari Morteza , Atapour Masoud , Mohedano Marta , Matykina Endzhe , Arrabal Raul , Nesic Dobrila
Published: September 2023

To regulate the degradation rate and improve the surface biocompatibility of the AZ31B magnesium alloy, three different coating systems were produced via plasma electrolytic oxidation (PEO): simple PEO, PEO incorporating multi-walled carbon nanotubes (PEO + CNT), and a duplex coating that included a polycaprolactone top layer (PEO + CNT/PCL). Surfaces were characterized by chemical content, roughness, topography, and wettability. Biological properties analysis included cell metabolism and adhesion. PEO ± CNT resulted in an augmented surface roughness compared with the base material (BM), while PCL deposition produced the smoothest surface. All surfaces had a contact angle below 90°. The exposure of gFib-TERT and bmMSC to culture media collected after 3 or 24 h did not affect their metabolism. A decrease in metabolic activity of 9% and 14% for bmMSC and of 14% and 29% for gFib-TERT was observed after 3 and 7 days, respectively. All cells died after 7 days of exposure to BM and after 15 days of exposure to coated surfaces. Saos-2 and gFib-TERT adhered poorly to BM, in contrast to bmMSC. All cells on PEO anchored into the pores with filopodia, exhibited tiny adhesion protrusions on PEO + CNT, and presented a web-like spreading with lamellipodia on PEO + CNT/PCL. The smooth and homogenous surface of the duplex PEO + CNT/PCL coating decreased magnesium corrosion and led to better biological functionality.
Adhesion and Activation of Blood Platelets on Laser-Structured Surfaces of Biomedical Metal Alloys
Authors: Kamińska Marta , Jastrzębska Aleksandra , Walkowiak-Przybyło Magdalena , Walczyńska Marta , Komorowski Piotr , Walkowiak Bogdan
Published: September 2023

The laser surface modification of metallic implants presents a promising alternative to other surface modification techniques. A total of four alloyed metallic biomaterials were used for this study: medical steel (AISI 316L), cobalt–chromium–molybdenum alloy (CoCrMo) and titanium alloys (Ti6Al4V and Ti6Al7Nb). Samples of metallic biomaterials after machining were subjected to polishing or laser modification in two different versions. The results of surface modification were documented using SEM imaging and roughness measurement. After modification, the samples were sterilized with dry hot air, then exposed to citrate blood, washed with PBS buffer, fixed with glutaraldehyde, sputtered with a layer of gold and imaged using SEM to enable the quantification of adhered, activated and aggregated platelets on the surface of biomaterial samples. The average total number, counted in the field of view, of adhered platelets on the surfaces of the four tested biomaterials, regardless of the type of modification, did not differ statistically significantly (66 ± 81, 67 ± 75, 61 ± 70 and 57 ± 61 for AISI 316L, CoCrMo, Ti6Al4V and Ti6Al7Nb, respectively) and the average number of platelet aggregates was statistically significantly higher (p < 0.01) on the surfaces of AISI 316L medical steel (42 ± 53) and of the CoCrMo alloy (42 ± 52) compared to the surfaces of the titanium alloys Ti6Al4V (33 ± 39) and Ti6Al7Nb (32 ± 37). Remaining blood after contact was used to assess spontaneous platelet activation and aggregation in whole blood by flow cytometry. An in-depth analysis conducted on the obtained results as a function of the type of modification indicates small but statistically significant differences in the interaction of platelets with the tested surfaces of metallic biomaterials.
A Simple Denoising Algorithm for Real-World Noisy Camera Images
Authors: Hartbauer Manfred
Published: September 2023

The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an adaptive local averaging filter was executed for each pixel to remove moderate sensor noise while preserving fine image details and object contours. In the second step, image sharpness was enhanced by means of an unsharp mask filter to generate output images that are close to ground-truth images (multiple averages of static camera images). The performance of this denoising algorithm was compared with five popular denoising methods: bm3d, wavelet, non-local means (NL-means), total variation (TV) denoising and bilateral filter. Results show that the two-step filter had a performance that was similar to NL-means and TV filtering. Bm3d had the best denoising performance but sometimes led to blurry images. This novel two-step filter only depends on a single parameter that can be obtained from global image statistics. To reduce computation time, denoising was restricted to the Y channel of YUV-transformed images and four image segments were simultaneously processed in parallel on a multi-core processor.
Between Habitats: Transfer of Phytopathogenic Fungi along Transition Zones from Kettle Hole Edges to Wheat Ears
Authors: Gerling Marina , von der Waydbrink Grit , Verch Gernot , Büttner Carmen , Müller Marina E. H.
Published: September 2023

Kettle holes are able to increase the soil and air humidity around them. Therefore, they create a perfect habitat for phytopathogenic fungi of the genera Fusarium and Alternaria to develop, sporulate, and immigrate into neighboring agricultural fields. In our study, we establish transects from the edges of different kettle holes and field edges up to 50 m into the fields to analyze the abundance and diversity of pathogenic fungi in these transition zones by culture-dependent and culture-independent methods. However, in 2019 and 2020, low precipitation and higher temperatures compared to the long-time average were measured, which led to limited infections of weeds in the transition zones with Fusarium and Alternaria. Therefore, the hypothesized significantly higher infection of wheat plants next to the kettle holes by a strong spread of fungal spores was not detected. Infestation patterns of Fusarium and Alternaria fungi on weeds and wheat ears were spatially different. In total, 9 different Fusarium species were found in the transition zone. The species diversity at kettle holes differed from 0 to 6 species. The trend toward increased dryness in the northeast German agricultural landscape and its impact on the changing severity of fungal infections is discussed.
Osmotic Stress Responses, Cell Wall Integrity, and Conidiation Are Regulated by a Histidine Kinase Sensor in Trichoderma atroviride
Authors: Calcáneo-Hernández Gabriela , Landeros-Jaime Fidel , Cervantes-Chávez José Antonio , Mendoza-Mendoza Artemio , Esquivel-Naranjo Edgardo Ulises
Published: September 2023

Trichoderma atroviride responds to various environmental stressors through the mitogen-activated protein kinase (MAPK) Tmk3 and MAPK-kinase Pbs2 signaling pathways. In fungi, orthologues to Tmk3 are regulated by a histidine kinase (HK) sensor. However, the role of T. atroviride HKs remains unknown. In this regard, the function of the T. atroviride HK Nik1 was analyzed in response to stressors regulated by Tmk3. The growth of the Δnik1 mutant strains was compromised under hyperosmotic stress; mycelia were less resistant to lysing enzymes than the WT strain, while conidia of Δnik1 were more sensitive to Congo red; however, ∆pbs2 and ∆tmk3 strains showed a more drastic defect in cell wall stability. Light-regulated blu1 and grg2 gene expression was induced upon an osmotic shock through Pbs2-Tmk3 but was independent of Nik1. The encoding chitin synthases chs1 and chs2 genes were downregulated after an osmotic shock in the WT, but chs1 and chs3 expression were enhanced in ∆nik1, ∆pbs2, and ∆tmk3. The vegetative growth and conidiation by light decreased in ∆nik1, although Nik1 was unrequired to activate the light-responsive genes by Tmk3. Altogether, Nik1 regulates responses related to the Pbs2-Tmk3 pathway and suggests the participation of additional HKs to respond to stress.
Effects of Tax Incentive Policies for Land Use on Local Socioeconomic Conditions: A Case of Tax Policies for Urban Regeneration Projects in Republic of Korea
Authors: Chung JiSun , Yoon SungMan
Published: September 2023

South Korea has implemented several housing urban regeneration projects (URPs) and offers various tax incentives to support these efforts. However, the reality is that there is insufficient empirical evidence to determine whether these incentives have a socioeconomic impact on URPs. The aim of this study is to examine the effects of tax incentives related to urban regeneration projects (URPTAX) on local socioeconomic factors, such as Gross Regional Domestic Product (GRDP), aging housing reduction, and housing prices. Fixed-effect models and cost–benefit analysis methods were employed in the study. The results demonstrate the following outcomes. Firstly, URPTAX positively contributes to an increase in GRDP, a decrease in the proportion of aging housing, and an increase in housing price fluctuations. However, the effectiveness of these effects varies between metropolitan and non-metropolitan areas. Therefore, this study suggests the significance of providing stronger tax incentives for housing URPs in non-metropolitan areas to encourage active projects and foster balanced regional development in South Korea.
The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings
Authors: Yan Jinlong , Yin Chaohui , An Zihao , Mu Bo , Wen Qian , Li Yingchao , Zhang Yali , Chen Weiqiang , Wang Ling , Song Yang
Published: September 2023

Urban form plays a critical role in shaping the spatial differentiation of land surface temperature (LST). However, limited research has investigated the underlying driving forces and interactions of multidimensional urban form, specifically considering two-dimensional (2D) urban land use and three-dimensional (3D) buildings, on LST. Furthermore, their multi-scale outcomes remain unclear. Taking the main urban area of Wuhan City as an example, a total of nine indicators—the proportion of administration land (PA), the proportion of commercial land (PB), the proportion of industrial land (PM), the proportion of residential land (PR), the proportion of water area (PE), the building density (BD), the building height (BH), the floor area ratio (FAR), and the sky view factor (SVF)—were selected; this paper used the geographic detector model to investigate the driving force of LST spatial differentiation in winter and summer, as well as the interaction of various influencing factors from a multi-scale perspective. The results showed that (1) the average LST in industrial land was higher than that in commercial land, both in summer and winter. The LST in administration land was higher than that in residential land, while in winter, it is the opposite. (2) The spatial differentiation of summer LST was mainly dominated by 3D buildings, while the spatial differentiation of winter LST was mainly dominated by 2D land use. (3) BD was the leading driving force of LST spatial differentiation in summer, and the interaction between BD and any other indicator showed the most significant explanatory power, which is the same for PM in winter. (4) As scale increased, the explanatory power of 2D urban land use for LST spatial differentiation gradually increased both in winter and summer, while the explanatory power of PE on LST spatial differentiation decreased. The explanatory power of BD, FAR, and SVF on LST spatial differentiation remains basically unchanged. The explanatory power of BH on summer LST spatial differentiation decreases with increasing scale, while the explanatory power of BH on winter LST spatial differentiation remains in a stable state. (5) The interaction among all urban form factors primarily increases as the scale increases, except for the interaction between BH and 2D urban land use in summer, and the interaction between PE and PR in winter. The research can provide scientific decision-making support for the collaborative optimization of multiscale urban forms to improve the urban thermal environment.
A New Experimental Methodology to Study Convective Heat Transfer in Oil Jet Lubricated Gear Units
Authors: Torres Thibaut , Changenet Christophe , Touret Thomas , Guilbert Bérengère
Published: September 2023

The purpose of this study is to generate some experimental data associated with the thermal heat exchange between an oil jet flow and a rotating gear. To this end, a specific test bench was designed. The principle of this test bench is to inject oil heated to a temperature of about 80 °C onto a rotating test sample at ambient temperature. Temperature measurements of the oil via injection nozzles and the rotating component allow the determination of the heat flow between these elements using a numerical method developed to this end. This test rig enables the study of the parameters that may affect heat exchange, such as oil flow rate and injection temperature, nozzle geometry and position, gear rotational speed and tooth geometry, or oil characteristics. In this study, three of these parameters were investigated, namely the test sample rotational speed, the oil flow rate, and the oil jet velocity. The experiments were conducted on an aluminum disc and spur gear. Subsequently, the experimental results were compared with existing models that represent the convective exchanges between oil and a gear. Some discrepancies between existing models and experimental results appear at high rotational speeds, underlining that the convective heat transfer does not always increase with this parameter.
Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising
Authors: Jiao Ziquan , Yang Tongshuai , Gao Xingyu , Chen Shanben , Liu Wenjing
Published: September 2023

The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification.
The Updated Scope of Machines
Authors: Cardoso Antonio J. Marques
Published: September 2023

Our journal Machines (https://www [...]
A Novel ANN-Based Radial Basis Function Collocation Method for Solving Elliptic Boundary Value Problems
Authors: Liu Chih-Yu , Ku Cheng-Yu
Published: September 2023

Elliptic boundary value problems (BVPs) are widely used in various scientific and engineering disciplines that involve finding solutions to elliptic partial differential equations subject to certain boundary conditions. This article introduces a novel approach for solving elliptic BVPs using an artificial neural network (ANN)-based radial basis function (RBF) collocation method. In this study, the backpropagation neural network is employed, enabling learning from training data and enhancing accuracy. The training data consist of given boundary data from exact solutions and the radial distances between exterior fictitious sources and boundary points, which are used to construct RBFs, such as multiquadric and inverse multiquadric RBFs. The distinctive feature of this approach is that it avoids the discretization of the governing equation of elliptic BVPs. Consequently, the proposed ANN-based RBF collocation method offers simplicity in solving elliptic BVPs with only given boundary data and RBFs. To validate the model, it is applied to solve two- and three-dimensional elliptic BVPs. The results of the study highlight the effectiveness and efficiency of the proposed method, demonstrating its capability to deliver accurate solutions with minimal data input for solving elliptic BVPs while relying solely on given boundary data and RBFs.
Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks
Authors: Asyaev Grigorii , Sokolov Alexander , Ruchay Alexey
Published: September 2023

This paper considers the main approaches to building algorithms for the decision support systems of information protection strategies against cyberattacks in the networks of automated process control systems (the so-called recommender systems). The advantages and disadvantages of each of the considered algorithms are revealed, and their applicability to the processing of the information security events of the UNSW-NB 15 dataset is analyzed. The dataset used contains raw network packets collected using the IXIA PerfectStorm software in the CyberRange laboratory of the Australian Cyber Security Centre (Canberra) in order to create a hybrid of the simulation of the real actions and the synthetic behavior of the network traffic generated during attacks. The possibility of applying four semantic proximity algorithms to partition process the data into clusters based on attack type in a distribution control system (DCS) is analyzed. The percentage of homogeneous records belonging to a particular type of attack is used as the metric that determines the optimal method of cluster partitioning. This metric was chosen under the assumption that cyberattacks located “closer” to each other in the multidimensional space have similar defense strategies. A hypothesis is formulated about the possibility of transferring knowledge about attacks from the vector feature space into a semantic form using semantic proximity methods. The percentage of homogeneous entries was maximal when the cosine proximity measure was used, which confirmed the hypothesis about the possibility of applying the corresponding algorithm in the recommender system.
Large-Signal Stability of the Quadratic Boost Converter Using a Disturbance Observer-Based Sliding-Mode Control
Authors: Chincholkar Satyajit , Tariq Mohd , Urooj Shabana
Published: September 2023

The quadratic boost (QB) converter is a fourth-order system with a dc gain that is higher than the traditional second-order step-up configuration. The modern controllers that control these high-order dc–dc converters often only guarantee local stability around a steady-state equilibrium point, which is one of their primary drawbacks. In this article, a non-linear robust control law design to attain large-signal stability in this single switch QB converter is presented. In the presence of an unpredictable load, the control objective is to maintain the regulation of an output voltage. The Brunovsky canonical model of the converter was derived first, and the non-linear disturbance observer-based sliding-mode (SM) control law is designed based on it. An observer variable precisely estimates the output disturbances. The detailed process for deriving the control signal is described in this paper and the large-signal stability of the closed-loop converter system is ensured via the Lyapunov function. Finally, some simulation results are shown to validate the usefulness of the given controller.