bluuebunny/crossref_metadata_2025 huggingface dataset:
Diabetes and Its Cardiovascular Complications: Potential Role of the Acetyltransferase p300
Authors: Di Pietrantonio Nadia , Di Tomo Pamela , Mandatori Domitilla , Formoso Gloria , Pandolfi Assunta
Published: January 2023

Diabetes has been shown to accelerate vascular senescence, which is associated with chronic inflammation and oxidative stress, both implicated in the development of endothelial dysfunction. This condition represents the initial alteration linking diabetes to related cardiovascular (CV) complications. Recently, it has been hypothesised that the acetyltransferase, p300, may contribute to establishing an early vascular senescent phenotype, playing a relevant role in diabetes-associated inflammation and oxidative stress, which drive endothelial dysfunction. Specifically, p300 can modulate vascular inflammation through epigenetic mechanisms and transcription factors acetylation. Indeed, it regulates the inflammatory pathway by interacting with nuclear factor kappa-light-chain-enhancer of activated B cells p65 subunit (NF-κB p65) or by inducing its acetylation, suggesting a crucial role of p300 as a bridge between NF-κB p65 and the transcriptional machinery. Additionally, p300-mediated epigenetic modifications could be upstream of the activation of inflammatory cytokines, and they may induce oxidative stress by affecting the production of reactive oxygen species (ROS). Because several in vitro and in vivo studies shed light on the potential use of acetyltransferase inhibitors, a better understanding of the mechanisms underlying the role of p300 in diabetic vascular dysfunction could help in finding new strategies for the clinical management of CV diseases related to diabetes.
Fabric Electrode Monitoring of Dynamic and Static ECG Signal and Comfort Performance
Authors: Zhou Jinli , Zhang Yazhou , Yang Hongying , Liu Qingxia , Wang Ming , Xiong Fan , Chen Dongyi , Du Lixin
Published: January 2023

To monitor dynamic ECG for a long time, fabric electrodes must have excellent comfort and electrical properties. In addition, the quality of the collected ECG should be as free as possible from interference by motion artifacts due to dry skin and body movement. This study explores the comfort of four different materials and structures of silver-plated fabric electrodes, analyzing the acquisition effect of ECG signals under dynamic and static conditions. To obtain fabric electrodes with good comfort levels and stable ECG signal monitoring under dynamic and static conditions, four kinds of electroless silver-plated conductive fabrics were selected and assembled into fabric electrodes. Permeability, electrochemical impedance spectrum, static opening voltage, and dynamic static electrocardiogram were tested and evaluated for each of the four fabric electrodes; additionally, the comfort of the four fabric electrodes and the mass of ECG monitored under dynamic and static conditions were assessed. The results showed that the highly hygroscopic knitted fabric electrode showed better comfort than the other three samples. The electrochemical impedance spectrum curve of the highly hygroscopic knitted fabric electrode was relatively smooth and stable, and it had lower impedance than the other electrodes; moreover, the static open-circuit voltage changed more stably with the increase of processing time compared to the other samples. The four kinds of fabric electrodes all collected clear and stable ECG in the resting state. However, in dynamic conditions, only the highly hygroscopic knitted fabric electrodes collected stable ECG under the conditions of seven daily life actions, clearly distinguishing between the P-wave, QRS wave group, and T-wave. The knitted fabric electrode has a high correlation with the ECG measured by a disposable gel electrode, meeting the standards needed to monitor ECG during the human body’s daily activities.
Acknowledgment to the Reviewers of Coatings in 2022
Authors:   Coatings Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Protective Characteristics of TiO2 Sol-Gel Layer Deposited on Zn-Ni or Zn-Co Substrates
Authors: Boshkova Nelly , Stambolova Irina , Stoyanova Daniela , Simeonova Silviya , Grozev Nikolay , Avdeev Georgi , Shipochka Maria , Dimitrov Ognian , Bachvarov Vasil , Peshova Miglena , Boshkov Nikolai
Published: January 2023

This study aimed to present the differences in the corrosion properties and protective ability of two bi-layer systems obtained on low-carbon steel in a model corrosive medium of 5% NaCl solution. These newly developed systems consist of Zn-Co (3 wt.%) or Zn-Ni (10 wt.%) alloy coatings as under-layers and a very thin TiO2 sol-gel film as a top-layer. Scanning electron microscopy (SEM) is used for characterization of the surface morphology of the samples indicating that some quantitative differences appear as a result of the different composition of both zinc alloys. Surface topography is investigated by means of atomic force microscopy (AFM), and the hydrophobic properties are studied by contact angle (CA) measurements. These investigations demonstrate that both sample types possess grain nanometric surface morphology and that the contact angle decreases very slightly. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) are used for characterization of the chemical composition and electronic structure of the samples. The roughness Rq of the Zn-Ni/TiO2 is 49.5 nm, while for Zn-Co/TiO2, the Rq value is 53.4 nm. The water contact angels are 93.2 and 95.5 for the Zn-Ni/TiO2 and Zn-Co/TiO2 systems, respectively. These investigations also show that the co-deposition of Zn and Ni forms a coating consisting entirely of Ni2Zn11, while the other alloy contains Zn, Co and the intermetallic compound CoZn13. The corrosion resistance and protective ability are estimated by potentiodynamic polarization (PDP) curves, as well as polarization resistance (Rp) measurements for a prolonged test period (35 days). The results obtained are compared with the corrosion characteristics of ordinary zinc coating with an equal thickness. The experimental data presents the positive influence of the newly developed systems on the enhanced protective properties of low-carbon steel in a test environment causing a localized corrosion—lower corrosion current density of about one magnitude of order (~10−6 A.cm−2 for both systems and ~10−5 A.cm−2 for Zn) and an enhanced protective ability after 35 days (~10,000–17,000 ohms for the systems and ~900 ohms for Zn).
Single-Impact Failure of Multi-Layered Automotive Coatings: A Finite Element-Based Study
Authors: Chen Li , Zou Chenqi , Zang Mengyan , Chen Shunhua
Published: January 2023

Automotive coatings are a multi-layered polymer composite structure whose impact resistance is closely related to the appearance and safety of a vehicle. Since experimental methods are of high cost and poor repeatability, in our work, a finite element model is developed for the single-impact failure of automotive coatings. In this model, a multi-mechanism damage model and a large deformation cohesive zone model are employed to account for the polymer-ply and interlaminar failures of the coating, and some rate-dependent material models are adopted to capture the effect of impact velocity. The simulated results indicate that the proposed model can reproduce the failure patterns of automotive coatings well. In addition, the impact failure mechanisms of the coating are revealed. Numerical findings show that both brittle and ductile failures are found in the coating and there are three stages for the propagation of the delamination crack. Finally, we numerically investigate the effects of primer mechanical properties, i.e., Young’s modulus, yield strength, and re-hardening modulus, on the impact resistance of automotive coatings. Our work is helpful to the design of coating, which can improve the impact resistance of automotive coatings.
Virtual Local Area Network Performance Improvement Using Ad Hoc Routing Protocols in a Wireless Network
Authors: Nourildean Shayma Wail , Mohammed Yousra Abd , Attallah Hussein Ali
Published: January 2023

Wireless Communication has become one of the most popular types of communication networks because of the many services it provides; however, it has experienced several challenges in improving network performance. VLAN (Virtual Local Area Network) is a different approach which enables a network administrator to create a logical network from a physical network. By dividing a large network into smaller networks, VLAN technology improves network efficiency, management, and security. This study includes VLAN for wireless networks with mobile nodes integration. The network protection was improved by separating the connections and grouping them in a way that prevents any party from being able to contact unauthorized stations in another party using VLAN. VLAN demonstrated restricted access to private server data by managing traffic, improving security, and reducing levels of congestion. This paper investigates the virtual local area network in a wireless network with three ad hoc routing protocols in a number of different scenarios, using the Riverbed Modeler simulation, which was used as a simulation program in this study. It was found from the investigation process that adopting VLAN technology could reduce delay and data of the network and considerably lower throughput, which is a major drawback of VLAN. Ad hoc routing algorithms, including AODV (Ad Hoc On-Demand Distance Vector), DSR (Dynamic Source Routing), and OLSR (Optimized Link State Routing) routing protocols, were used to improve the delay and throughput of the network. Routing methods with VLAN were tested across the WLAN to obtain the best throughput gain performance. The findings also revealed that these ad hoc routing protocols improved the Wireless Sensor Network performance as an additional investigation for the improvement of any network’s delay and throughput.
Thickness Effects on Oxidation Behavior and Consequent γ’ Degradation of a High-Al Ni-Based Single Crystal Superalloy
Authors: Zhao Haigen , Guo Wenqi , Zhao Wenyue , Ru Yi , Wang Junwu , Pei Yanling , Gong Shengkai , Li Shusuo
Published: January 2023

High temperature oxidation is considered to play an essential role in the thickness debit effect on the creep rupture life of Ni-based single crystal (SC) superalloys. In order to clarify thickness effects, thin-walled specimens of different thickness (t = 0.1, 0.3, 1.0 mm) were prepared. Cyclic and isothermal oxidation tests of a high-Al Ni-based SC superalloy IC21 were carried out at 900 °C and 1100 °C in order to study the thickness effects on the oxidation behavior and consequent microstructural degradation. Thin-walled specimens of S01-N exhibited a good oxidation resistance when the protective scale-forming elements were enough in the matrix. Specimen thickness tuned the oxidation kinetics by changing the spallation behavior but had few influences on the thermodynamics. The easier stress relief via creep deformation in thin specimens is the main reason behind this phenomenon. Moreover, the obvious temperature effects on the multilayer oxide scales caused the different thickness-related spallation behaviors. Weaker microstructural degradation appeared and was further mitigated by reduced specimen thickness. Sufficient Al content is considered to be indispensable for the formation of protective α-Al2O3 scale with less degradation of matrix during the high temperature oxidation of thin-walled Ni-based SC superalloy castings.
Artificial Intelligence for Cancer Detection—A Bibliometric Analysis and Avenues for Future Research
Authors: Karger Erik , Kureljusic Marko
Published: January 2023

After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.
Monitoring of Dactylorhiza sambucina (L.) Soó (Orchidaceae)—Variation in Flowering, Flower Colour Morph Frequencies, and Erratic Population Census Trends
Authors: Kropf Matthias , Kriechbaum Monika
Published: January 2023

Central questions of reproductive research on Dactylorhiza sambucina (Orchidaceae) are, if and how pollinator-mediated negative frequency dependent selection might maintain its flower colour polymorphism. As this hypothesis was based on artificial populations, it needs to be verified under natural conditions. Therefore, we demonstrate and discuss spatial-temporal variation of flowering, flower colour morph frequencies and population fluctuations in D. sambucina as prerequisites for understanding its population and reproduction dynamics. Knowledge of these dynamics is also crucial for the species’ conservation. We investigated colour morph frequencies for ten Austrian populations of D. sambucina over maximum time series of 18 consecutive years. We report repeated changes in the dominance of red- or yellow-flowering morphs in at least three populations during these time series. Even though being basically erratic (e.g., switches in different years), we identify smaller populations as being more prone to changes of flower colour dominance. Conversely, constant morph frequencies and the dominance of one flower colour morph is demonstrated for individual-poor and large populations. As previous large-scale (continental) analyses failed to identify environmental factors explaining the distribution of differing morph frequencies across Europe, we strongly argue for local approaches by investigating such factors at the micro-habitat scale.
Spatiotemporal Patterns of Wolves, and Sympatric Predators and Prey Relative to Human Disturbance in Northwestern Greece
Authors: Petridou Maria , Benson John F. , Gimenez Olivier , Kati Vassiliki
Published: January 2023

In an era of increasing human pressure on nature, understanding the spatiotemporal patterns of wildlife relative to human disturbance can inform conservation efforts, especially for large carnivores. We examined the temporal activity and spatial patterns of wolves and eight sympatric mammals at 71 camera trap stations in Greece. Grey wolves temporally overlapped the most with wild boars (Δ = 0.84) and medium-sized mammals (Δ > 0.75), moderately with brown bears (Δ = 0.70), and least with roe deer (Δ = 0.46). All wild mammals were mainly nocturnal and exhibited low temporal overlap with human disturbance (humans, vehicles, livestock, and dogs; Δ = 0.18–0.36), apart from roe deer, which were more diurnal (Δ = 0.80). Six out of nine species increased their nocturnality at sites of high human disturbance, particularly roe deer and wolves. The detection of wolves was negatively associated with paved roads, the detection of roe deer was negatively associated with human disturbance, and the detection of wild boars was negatively associated with dogs. The detection of bears, boars, and foxes increased closer to settlements. Our study has applied implications for wolf conservation and human–wildlife coexistence.
Development of Thresholds to Predict Grazing Behaviour of Dairy Cows from Motion Sensor Data and Application in a Pasture-Based Automatic Milking System
Authors: Cullen Brendan , Li Zelin , Talukder Saranika , Cheng Long , Jongman Ellen C.
Published: January 2023

The monitoring and measurement of animal behaviour may be valuable for improving animal production and welfare. This study was designed to develop thresholds to predict the grazing, standing, walking, and lying behaviour of dairy cows from motion sensor (IceTag) output. The experiment included 29 lactating cows grazed in a pasture-based dairy production system with voluntary cow movement in northern Victoria, Australia. Sensors recorded motion data at 1 min intervals. A total of 5818 min of cow observations were used. Two approaches were developed using (1) the IceTag lying index and steps only and (2) the IceTag lying index, steps, and motion index for each behaviour. Grazing behaviour was best predicted by the second approach, which had a sensitivity of 92% and specificity of 60%. The thresholds were then used to predict cow behaviour during two periods. On average, across both time periods, cows spent 38% of the day grazing, 38% lying, 19% standing, and 5% walking. Predicted individual cow grazing time was positively correlated with both milk production and milking frequency. The thresholds developed were effective at predicting cow behaviours and can be applied to measure behaviour in pasture-based dairy production.
Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: A Dataset of Frequently and Rarely Identified Diseases
Authors: Panchal Sachin , Naik Ankita , Kokare Manesh , Pachade Samiksha , Naigaonkar Rushikesh , Phadnis Prerana , Bhange Archana
Published: January 2023

Irreversible vision loss is a worldwide threat. Developing a computer-aided diagnosis system to detect retinal fundus diseases is extremely useful and serviceable to ophthalmologists. Early detection, diagnosis, and correct treatment could save the eye’s vision. Nevertheless, an eye may be afflicted with several diseases if proper care is not taken. A single retinal fundus image might be linked to one or more diseases. Age-related macular degeneration, cataracts, diabetic retinopathy, Glaucoma, and uncorrected refractive errors are the leading causes of visual impairment. Our research team at the center of excellence lab has generated a new dataset called the Retinal Fundus Multi-Disease Image Dataset 2.0 (RFMiD2.0). This dataset includes around 860 retinal fundus images, annotated by three eye specialists, and is a multiclass, multilabel dataset. We gathered images from a research facility in Jalna and Nanded, where patients across Maharashtra come for preventative and therapeutic eye care. Our dataset would be the second publicly available dataset consisting of the most frequent diseases, along with some rarely identified diseases. This dataset is auxiliary to the previously published RFMiD dataset. This dataset would be significant for the research and development of artificial intelligence in ophthalmology.
Clinical Evidence of Circulating Tumor DNA Application in Aggressive Breast Cancer
Authors: El Hejjioui Brahim , Bouguenouch Laila , Melhouf Moulay Abdelilah , El Mouhi Hind , Bennis Sanae
Published: January 2023

Breast cancer is clinically and biologically heterogeneous and is classified into different subtypes according to the molecular landscape of the tumor. Triple-negative breast cancer is a subtype associated with higher tumor aggressiveness, poor prognosis, and poor response to treatment. In metastatic breast cancer, approximately 6% to 10% of new breast cancer cases are initially staged IV (de novo metastatic disease). The number of metastatic recurrences is estimated to be 20–30% of all existing breast tumor cases, whereby the need to develop specific genetic markers to improve the prognosis of patients suffering from these deadly forms of breast cancer. As an alternative, liquid biopsy methods can minutely identify the molecular architecture of breast cancer, including aggressive forms, which provides new perspectives for more precise diagnosis and more effective therapeutics. This review aimed to summarize the current clinical evidence for the application of circulating tumor DNA in managing breast cancer by detailing the increased usefulness of this biomarker as a diagnostic, prognostic, monitoring, and surveillance marker for breast cancer.
Diagnostic Value of Whole-Blood and Plasma Samples in Epstein–Barr Virus Infections
Authors: Rzepka Mateusz , Depka Dagmara , Gospodarek-Komkowska Eugenia , Bogiel Tomasz
Published: January 2023

Epstein–Barr virus (EBV) is an oncogenic virus classified by the World Health Organization as a class 1 carcinogen. Post-transplant lymphoproliferative disorders are believed to be strongly related to an EBV infection. Monitoring of EBV DNAemia is recommended to assess the risk of reactivation of latent infection and to assess the effectiveness of therapy. Currently, various types of clinical specimens are used for this purpose. The aim of the study was to assess a reliable method of EBV viral load investigation depending on the clinical material used: whole blood or plasma samples. We found that of 134 EBV-DNA-positive whole-blood samples derived from 51 patients (mostly hemato-oncology or post-transplantation), only 43 (32.1%) were plasma-positive. Of these, 37 (86.0%) had lower plasma DNAemia compared to the corresponding whole-blood samples. We conclude that whole-blood samples have a higher sensitivity than plasma samples in EBV DNA detection. The clinical utility of the tests is unclear, but our results suggest that either whole blood or plasma should be used consistently for EBV viral load monitoring.
Long COVID Syndrome and Cardiovascular Manifestations: A Systematic Review and Meta-Analysis
Authors: Shrestha Abhigan Babu , Mehta Aashna , Pokharel Pashupati , Mishra Aakash , Adhikari Lukash , Shrestha Sajina , Yadav Randhir Sagar , Khanal Surakshya , Sah Ranjit , Nowrouzi-Kia Behdin , Padhi Bijaya Kumar , Chattu Vijay Kumar
Published: January 2023

(1) Background: Long COVID syndrome is a significant cause of morbidity in COVID-19 patients who remain symptomatic with varied clinical presentations beyond three weeks. Furthermore, the relevance of considering cardiovascular outcomes in post-COVID-19 syndrome is important in the current COVID-19 pandemic; (2) Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for this systematic review and meta-analysis. Systematic searches were conducted from multiple databases without language restrictions until October 8, 2022, to find studies evaluating cardiovascular outcomes such as arrhythmias, myocardium and pericardium diseases, coronary vessel disease, and thromboembolic disorders in post-COVID cases. The pooled odds ratio (OR), and standard mean difference (SMD) with their corresponding 95% confidence intervals (CI) were computed to find the association; (3) Results: Altogether, seven studies with a total of 8,126,462 (cases: 1,321,305; controls: 6,805,157) participants were included in the meta-analysis. Pooled odds ratios of cardiovascular outcomes were significantly higher in post-COVID cases (OR > 1, p < 0.05) than in controls. However, the mortality (OR: 4.76, p = 0.13), and heart rate variability (SMD: −0.06, p = 0.91) between cases and controls were not statistically significant; (4) Conclusions: Significant cardiovascular sequelae in long COVID syndrome highlight the importance of careful cardiac monitoring of COVID-19 patients in the post-COVID phase to address cardiovascular complications as soon as possible; larger-scale prospective studies are required for accurate estimation.
Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques
Authors: Elseddik Marwa , Mostafa Reham R. , Elashry Ahmed , El-Rashidy Nora , El-Sappagh Shaker , Elgamal Shimaa , Aboelfetouh Ahmed , El-Bakry Hazem
Published: January 2023

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.
An Isolated Intestinal Juvenile Polyp Diagnosed by Abdominal Ultrasonography and Resected by Double-Balloon Endoscopy: A Case Report and Literature Review
Authors: Nagata Masumi , Jimbo Keisuke , Arai Nobuyasu , Kashiwagi Kosuke , Tokushima Kaori , Suzuki Mitsuyoshi , Kudo Takahiro , Shimizu Toshiaki
Published: January 2023

Juvenile polyps, typically localized in the rectum and sigmoid colon, are a common cause of pediatric bloody stool. An isolated small intestinal juvenile polyp is uncommon and generally difficult to diagnose. The first case of an isolated juvenile polyp diagnosed by abdominal ultrasonography before acute abdomen had developed and resected by double-balloon endoscopy is presented along with a review of previous reports including this case. A two-year-old Japanese boy was referred to our institute for further evaluation of anemia persisting from one year of age. Laboratory findings showed mild iron deficiency anemia and elevated fecal human hemoglobin (Hb) and fecal calprotectin values. Upper and lower endoscopic findings showed no abnormalities. Because the abdominal ultrasonography performed one year later demonstrated a 15 mm jejunal polyp, combined with a similar finding on small intestinal capsule endoscopy, this was diagnosed as an isolated lesion. The lesion was resected by cautery with double-balloon endoscopy and diagnosed as a juvenile polyp pathologically. All clinical symptoms disappeared, and all laboratory data improved after treatment, without recurrence for more than one year after the procedure. Abdominal ultrasonographic screening and the fecal calprotectin value led to the diagnosis and non-surgical invasive treatment of an isolated small intestinal juvenile polyp.
Spectral Photon-Counting CT Imaging of Gold Nanoparticle Labelled Monocytes for Detection of Atherosclerosis: A Preclinical Study
Authors: Moghiseh Mahdieh , Searle Emily , Dixit Devyani , Kim Johoon , Dong Yuxi C. , Cormode David P. , Butler Anthony , Gieseg Steven P. ,   MARS Bioimaging Ltd.
Published: January 2023

A key process in the development of atherosclerotic plaques is the recruitment of monocytes into the artery wall. Using spectral photon-counting computed tomography we examine whether monocyte deposition within the artery wall of ApoE-/- mouse can be detected. Primary mouse monocytes were labelled by incubating them with 15 nm gold nanoparticles coated with 11-mercaptoundecanoic acid The monocyte uptake of the particle was confirmed by electron microscopy of the cells before injection into 6-week-old apolipoprotein E deficient (ApoE-/-) mouse that had been fed with the Western diet for 10 weeks. Four days following injection, the mouse was sacrificed and imaged using a MARS spectral photon counting computed tomography scanner with a spectral range of 7 to 120 KeV with five energy bins. Imaging analysis showed the presence of X-ray dense material within the mouse aortic arch which was consistent with the spectral characteristic of gold rather than calcium. The imaging is interpreted as showing the deposition of gold nanoparticles containing monocytes within the mouse aorta. The results of our study determined that spectral photon-counting computed tomography could provide quantitative information about gold nanoparticles labelled monocytes in voxels of 90 × 90 × 90 µm3. The imaging was consistent with previous micro-CT and electron microscopy of mice using the same nanoparticles. This study demonstrates that spectral photon-counting computed tomography, using a MARS small bore scanner, can detect a fundamental atherogenic process within mouse models of atherogenesis. The present study demonstrates the feasibility of spectral photon-counting computed tomography as an emerging molecular imaging modality to detect atherosclerotic disease.
Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials
Authors: Shanarova Nadezhda , Pronina Marina , Lipkovich Mikhail , Ponomarev Valery , Müller Andreas , Kropotov Juri
Published: January 2023

Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.
An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles
Authors: Jin Qingeng , Hu Qingwu , Zhao Pengcheng , Wang Shaohua , Ai Mingyao
Published: January 2023

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.
Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers
Authors: Gibril Mohamed Barakat A. , Shafri Helmi Zulhaidi Mohd , Al-Ruzouq Rami , Shanableh Abdallah , Nahas Faten , Al Mansoori Saeed
Published: January 2023

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.
Low Computational Coding-Efficient Distributed Video Coding: Adding a Decision Mode to Limit Channel Coding Load
Authors: Khursheed Shahzad , Badruddin Nasreen , Jeoti Varun , Vukobratovic Dejan , Hashmani Manzoor Ahmed
Published: January 2023

Distributed video coding (DVC) is based on distributed source coding (DSC) concepts in which video statistics are used partially or completely at the decoder rather than the encoder. The rate-distortion (RD) performance of distributed video codecs substantially lags the conventional predictive video coding. Several techniques and methods are employed in DVC to overcome this performance gap and achieve high coding efficiency while maintaining low encoder computational complexity. However, it is still challenging to achieve coding efficiency and limit the computational complexity of the encoding and decoding process. The deployment of distributed residual video coding (DRVC) improves coding efficiency, but significant enhancements are still required to reduce these gaps. This paper proposes the QUAntized Transform ResIdual Decision (QUATRID) scheme that improves the coding efficiency by deploying the Quantized Transform Decision Mode (QUAM) at the encoder. The proposed QUATRID scheme’s main contribution is a design and integration of a novel QUAM method into DRVC that effectively skips the zero quantized transform (QT) blocks, thus limiting the number of input bit planes to be channel encoded and consequently reducing both the channel encoding and decoding computational complexity. Moreover, an online correlation noise model (CNM) is specifically designed for the QUATRID scheme and implemented at its decoder. This online CNM improves the channel decoding process and contributes to the bit rate reduction. Finally, a methodology for the reconstruction of the residual frame (R^) is developed that utilizes the decision mode information passed by the encoder, decoded quantized bin, and transformed estimated residual frame. The Bjøntegaard delta analysis of experimental results shows that the QUATRID achieves better performance over the DISCOVER by attaining the PSNR between 0.06 dB and 0.32 dB and coding efficiency, which varies from 5.4 to 10.48 percent. In addition to this, results determine that for all types of motion videos, the proposed QUATRID scheme outperforms the DISCOVER in terms of reducing the number of input bit-planes to be channel encoded and the entire encoder’s computational complexity. The number of bit plane reduction exceeds 97%, while the entire Wyner-Ziv encoder and channel coding computational complexity reduce more than nine-fold and 34-fold, respectively.
Trajectory Tracking Control Method for Omnidirectional Mobile Robot Based on Self-Organizing Fuzzy Neural Network and Preview Strategy
Authors: Zhao Tao , Qin Peng , Zhong Yuzhong
Published: January 2023

This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In particular, since the structure of traditional approximation network is preset, it will cause problems such as input constraints and rule redundancy, resulting in low adaptability of the controller. Therefore, a self-organizing algorithm including rule growth and local access is designed according to the tracking control requirements of omnidirectional mobile robots. In addition, a preview strategy (PS) based on Bezier curve trajectory re-planning is proposed to solve the problem of tracking curve instability caused by the lag of tracking starting point. Finally, the simulation verifies the effectiveness of this method in tracking and trajectory starting point optimization.
Detecting Non-Overlapping Signals with Dynamic Programming
Authors: Roth Mordechai , Painsky Amichai , Bendory Tamir
Published: January 2023

This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained likelihood optimization problem and design a computationally efficient dynamic program that attains its optimal solution. Our proposed framework is scalable, simple to implement, and robust to model uncertainties. We show by extensive numerical experiments that our algorithm accurately estimates the locations in dense and noisy environments, and outperforms alternative methods.
Exploring the Online and Blended Modes of Learning for Post-COVID-19: A Study of Higher Education Institutions
Authors: Sharma Lavina , Shree Sonal
Published: January 2023

The advent of the COVID-19 pandemic brought a tectonic shift in the otherwise traditional education sector. The classroom teaching approach, which has been followed in educational institutions for a long time, suddenly shifted to include e-learning through virtual platforms. Technological savviness is no longer a choice. As institutions were contemplating opening their campuses to students, they faced a dilemma to include a mode of learning that could impact the students positively and increase their overall learning effectiveness. At the same time, a total online mode of learning was not inclusive enough for the learners without access to seamless internet connectivity. In this context, the present study aims to draw a comparison between the three modes of learning: face-to-face education, online learning, and blended learning. The difference in the three modes of learning was assessed on the content of the course, facilitation, perceived value, and learning effectiveness. The data for the study were collected from 119 students studying in Higher Education Institutions (HEIs). The study’s findings suggest a significant difference between the three modes of learning in terms of facilitation, perceived value, and learning effectiveness. However, the results also suggest that there was no significant difference between the three modes in terms of content. While content creates a more substantial impact through some parameters in one particular mode of learning, it does not create a similar impact through others. The study also delves into a pairwise comparison of each construct’s learning modes that have shown a significant difference. The results can guide institutions towards choosing a suitable mode of learning for the students by considering a holistic approach.
Acute Relaxation Response Induced by Tibetan Singing Bowl Sounds: A Randomized Controlled Trial
Authors: Rio-Alamos Cristobal , Montefusco-Siegmund Rodrigo , Cañete Toni , Sotomayor Joaquín , Fernandez-Teruel Alberto
Published: January 2023

The prevalence of anxiety has increased dramatically due to COVID-19, so effective preventive interventions are welcome. The main objective of our study was to compare the acute relaxation response (RR) induced by Tibetan singing bowl (TSB) sound-based treatment against progressive muscle relaxation (PMR) and a control waiting list group (CWL) in a single treatment session in an adult nonclinical anxious population. In this cross-sectional randomized control trial, 50 participants selected based on high state anxiety were randomly assigned to one of the experimental groups. Pre/post self-reported anxiety, electroencephalographic activity (EEG), and heart rate variability (HRV) were recorded at baseline (T1), minute 15 (T2), minute 30 (T3), and minute 45 (T4). The TSB group showed significant reductions in alpha power (from T2 to T4) and increased HRV (from T3 to T4) compared with the PMR and CWL groups. Moreover, TSB and PMR both showed significant reductions in self-reported anxiety compared with CWL, with this effect being more evident in the TSB group. We concluded that a single session of TSB treatment was able to induce a more evident psychological/physiological relaxation response compared with PMR and CWL. TSB could be a relevant acute intervention in stressful situations or crisis intervention and while waiting for conventional interventions.
Transfer Functions of Ammonia and Partly Cracked Ammonia Swirl Flames
Authors: Shohdy Nader N. , Alicherif Mhedine , Lacoste Deanna A.
Published: January 2023

The replacement of hydrocarbon fuels by ammonia in industrial systems is challenging due to its low burning velocity, its narrow flammability range, and a large production of nitric oxide and nitrogen dioxide when burned close to stoichiometric conditions. Cracking a fraction of ammonia into hydrogen and nitrogen prior to injection in the combustion chamber is considered a promising strategy to overcome these issues. This paper focuses on evaluating how different levels of ammonia cracking affect the overall burning velocity, the lean blow-off limit, the concentration of nitric oxide and nitrogen dioxide, and the flame response to acoustic perturbations. Swirl stabilized premixed flames of pure ammonia–air and ammonia–hydrogen–nitrogen–air mixtures mimicking 10%, 20%, and 28% of cracking are experimentally investigated. The results show that even though ammonia cracking is beneficial for enhancing the lean blow-off limit and the overall burning velocity, its impact on pollutant emissions and flame stability is detrimental for a percentage of cracking as low as 20%. Based on an analysis of the flame dynamics, reasons for these results are proposed.
Dependence Analysis for the Energy Sector Based on Energy ETFs
Authors: Kuziak Katarzyna , Górka Joanna
Published: January 2023

This study investigates the effects of crude oil and natural gas future returns on energy stock portfolios. We consider returns of portfolios of energy companies approximated by energy ETFs and returns of Brent crude oil and natural gas contracts listed on the US market from January 2015 to September 2022. To study the relationship between Brent crude oil, natural gas, and ETFs, we apply Granger causality in mean and variance, Dynamic Conditional Correlation and the tail dependence-focused copula approach. The research hypothesis regarding the dependence between energy ETFs and the underlying energy risk factors—crude oil and natural gas, and therefore, the existence of hedging or diversification opportunities, was verified. Our empirical findings indicate that crude oil has a medium effect on energy ETFs, and for natural gas it is even lower in the analyzed period, so hedging opportunities are weak, but opportunities for diversification arise.
Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects
Authors: Fujimoto Yu , Kaneko Akihisa , Iino Yutaka , Ishii Hideo , Hayashi Yasuhiro
Published: January 2023

The widespread introduction of functionally-smart inverters will be an indispensable factor for the large-scale penetration of distributed energy resources (DERs) via the power system. On the other hand, further smartization based on the data-centric operation of smart inverters (S-INVs) is required to cost-effectively achieve the same level of power system operational performance as before under circumstances where the spatio-temporal behavior of power flow is becoming significantly complex due to the penetration of DERs. This review provides an overview of current ambitious efforts toward smartization of operational management of DER inverters, clarifies the expected contribution of machine learning technology to the smart operation of DER inverters, and attempts to identify the issues currently open and areas where research is expected to be promoted in the future.
Nonimaging High Concentrating Photovoltaic System Using Trough
Authors: Iqbal Waseem , Ullah Irfan , Shin Seoyong
Published: January 2023

Solar energy is a long-established technology, which has zero CO2 emissions, and provides low-cost energy for a given area of land. The concentrator photovoltaic (CPV) has been given preference over the photovoltaic due to its high efficiency. In a CPV system, most of the solar cell area has been replaced with an optical concentrator. Various parabolic trough based CPV systems have been presented where a concentration of <300 is achieved. In the current research, a design is presented to achieve a high concentration of 622×. The design consists of two stages of concentration including parabolic trough as a main concentrator and nonimaging reflective grooves as a secondary concentrator. The trough reflects the incident light towards the secondary reflector where the light is redirected over the solar cell. Design of the two-stage concentrator, ray-tracing simulation, and results are presented. The system achieved an optical efficiency of 79%. The system would also be highly acceptable in solar thermal applications owing to its high concentration.