bluuebunny/crossref_metadata_2025 huggingface dataset:
Modulatory Role of TPPP3 in Microtubule Organization and Its Impact on Alpha-Synuclein Pathology
Authors: Oláh Judit , Lehotzky Attila , Szénási Tibor , Berki Tímea , Ovádi Judit
Published: September 2022

Parkinson’s disease is characterized by locomotion deficits, dopaminergic neuronal loss and alpha-synuclein (SYN) aggregates; the Tubulin Polymerization Promoting Protein (TPPP/p25 or TPPP1) is also implicated in these processes. The moonlighting and chameleon TPPP1 modulates the dynamics/stability of the multifunctional microtubule network by promoting its acetylation and bundling. Previously, we identified the microtubule-associated TPPP3, a homologue of TPPP1 lacking its N-terminus; however, its involvement in physiological or pathological processes was not elucidated. In this work, we have shown the modulatory role of TPPP3, similarly to TPPP1, in microtubule organization, as well as its homo- and hetero-associations with TPPP1. TPPP3, in contrast to TPPP1, virtually does not bind to SYN; consequently, it does not promote SYN aggregation. Its anti-aggregative potency is achieved by counteracting the formation of the TPPP1–SYN pathological complex/aggregation leading to Parkinsonism. The interactions of TPPP3 have been determined and quantified in vitro with recombinant human proteins, cell extracts and in living human cells using different methods including bifunctional fluorescence complementation. The tight association of TPPP3 with TPPP1, but not with SYN, may ensure a unique mechanism for its inhibitory effect. TPPP3 or its selected fragments may become a leading agent for developing anti-Parkinson agents.
Immunogenic Cell Death: An Emerging Target in Gastrointestinal Cancers
Authors: Chiaravalli Marta , Spring Alexia , Agostini Antonio , Piro Geny , Carbone Carmine , Tortora Giampaolo
Published: September 2022

Immunogenic cell death (ICD) is a regulated form of cell death that induces the activation of both innate and adaptive immune responses through the release of damage-associated molecular patterns (DAMPs) and their subsequent recognition by pattern-recognition receptors (PRRs), generating specific CD8+ T lymphocytes. Thus, ICD inducers (such as certain chemotherapeutic agents, targeted therapies, radiation, and oncolytic viruses) could become a potential cancer treatment by providing antitumour immunity and cancer vaccination. Moreover, their combination with immunotherapy, especially with immune checkpoint inhibitors, could overcome the immunosuppressive tumour microenvironment that characterises certain cancers, including gastrointestinal cancers. This review will provide insights into the role of ICD induction in colorectal, gastric, pancreatic, and hepatocellular carcinomas. Specifically, we will discuss the main mechanisms involved in ICD, their potential application in gastrointestinal cancer treatment, and the latest clinical trial updates.
Characterization and Tribological Behavior of Electroless-Deposited Ni-P-PTFE Films on NBR Substrates for Dynamic Contact Applications
Authors: Vasconcelos Beatriz , Serra Ricardo , Oliveira João , Fonseca Carlos
Published: September 2022

The use of rubber in dynamic contacts often results in severe degradation and wear of the rubber surface, which is why dynamic rubber seal contacts are usually oil lubricated to ensure their functionality. However, the increasing demand for more convenient and environmentally friendly sealing solutions has prompted the development of dry low-friction rubber coatings. In this work, and for the first time, Ni-P and polytetrafluoroethylene (PTFE) particles were co-deposited by electroless plating on Nitrile Butadiene Rubber (NBR), as a low-cost solution to improve the NBR tribological behavior. A cationic surfactant, cetyltrimethylammonium bromide (CTAB), was added to the plating bath to ensure a homogeneous and efficient incorporation of PTFE into the Ni-P. The optimized PTFE incorporation reached 6.8%, and the composite coating adhesion to NBR was 20% higher than that of nickel-phosphorous (Ni-P) films. The tribological properties of the coatings evaluated by pin-on-disk tests showed a marginal decrease in the coefficient of friction (CoF) (10%, 1 N load), compared to that of Ni-P. However, the tested PTFE-based coatings displayed significantly smoother surfaces with less debris and cracks, clearly demonstrating the benefits of the PTFE in terms of wear resistance for loads up to 5 N.
Comparative Analysis of Raman Signal Amplifying Effectiveness of Silver Nanostructures with Different Morphology
Authors: Yakimchuk Dzmitry , Khubezhov Soslan , Prigodich Uladzislau , Tishkevich Daria , Trukhanov Sergei , Trukhanov Alex , Sivakov Vladimir , Kaniukov Egor
Published: September 2022

To increase the attractiveness of the practical application of molecular sensing methods, the experimental search for the optimal shape of silver nanostructures allowing to increase the Raman cross section by several orders of magnitude is of great interest. This paper presents a detailed study of spatially separated plasmon-active silver nanostructures grown in SiO2/Si template pores with crystallite, dendrite, and “sunflower-like” nanostructures shapes. Nile blue and 2-mercaptobenzothiazole were chosen as the model analytes for comparative evaluation of the Raman signal amplification efficiency using these structures. It was discussed the features of the structures for the enhancement of Raman intensity. Finally, we showed that silver crystals, dendrites, and “sunflower-like” nanostructures in SiO2/Si template could be used as the relevant materials for Raman signal amplification, but with different efficiency.
SrNi(VO4)(OH): The High-Temperature Hydrothermal Synthesis and Magnetic Properties of an Adelite-Descloizite-Type Structure
Authors: Sanjeewa Liurukara D. , Pellizzeri Tiffany M. Smith , McMillen Colin D. , Taddei Keith , Heitmann Thomas , Kaiser Helmut , Kolis Joseph W.
Published: September 2022

Single crystals of a new transition metal adelite-descloizite-type structure were synthesized using a high temperature (580 °C) high-pressure hydrothermal technique. Single crystal X-ray diffraction and energy dispersive X-ray analysis (EDX) were used to investigate the structure and elemental composition, respectively. SrNi(VO4)(OH) crystallizes in an acentric orthorhombic crystal system in the space group P212121 (no. 19); Z = 4, a = 5.9952(4) Å, b = 7.5844(4) Å, c = 9.2240(5) Å. The structure is comprised of a Ni–O–V framework where Sr2+ ions reside inside the channels. Single-crystal magnetic measurements display a significant anisotropy in both temperature- and field-dependent data. The temperature dependent magnetic measurement shows antiferromagnetic behavior at TN~8 K. Overall, the magnetic properties indicate the presence of competing antiferromagnetic and ferromagnetic interactions of SrNi(VO4)(OH).
Design of a Hollow-Core Photonic Crystal Fiber Based Edible Oil Sensor
Authors: Islam Md. Nazmul , Al-tabatabaie Kusay Faisal , Habib Md. Ahasan , Iqbal Sheikh Sharif , Qureshi Khurram Karim , Al-Mutairi Eid M.
Published: September 2022

This work proposes and statistically analyzes a hexagonal-shaped hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range. The suggested sensor’s performance was assessed by means of Comsol Multiphysics, a finite element method-based commercial tool. The simulation results demonstrate that the suggested sensor has more than 99% relative sensitivity for different types of edible oils at 1.6 THz under ideal geometric conditions. Furthermore, the suggested sensor exhibits low confinement loss, high numerical aperture and effective area at optimal geometry and operational conditions. The proposed sensor is realizable using conventional production procedures and its superior sensing qualities may make it a key component of real-world oil detection systems.
Interface Strengthening and Toughening Mechanism of Hot Rolled Multilayer TWIP/40Si2CrMo Steels
Authors: Dong Mingchao , Li Zhuoyu , Liu Baoxi , Chen Cuixin , Yu Wenxing , Yang Bo , Yin Fuxing
Published: September 2022

Layered metal composites play an increasingly important role in aerospace, automotive, and nuclear energy. Compared with a single metal or alloy, the layered metal composite exhibits an excellent strong-plastic matching effect. In this paper, multilayer TWIP/40Si2CrMo steels with different hot rolling reductions were successfully fabricated by the vacuum hot rolling. The results show that the multilayer steels can improve the lower yield strength of TWIP steel and lower the fracture elongation of 40Si2CrMo steel. In addition, with the increase of the hot rolling reduction, the mechanical properties and interfacial bonding strength of multilayer steels were improved, while the size and number of interfacial oxides decrease, and the fracture mode was also changed. This shows that a higher hot rolling reduction will promote the breakage of the interface oxides and make them appear dispersed, thereby improving the bonding strength of the interface, effectively suppressing the delamination and local necking of the multilayer steel, and making the multilayer steel show a higher ability of uniform plastic deformation. At the same time, under the dual action of layer thickness scale and interface strengthening effect, the brittle layer of multilayer steel presents a multiple tunnel crack mode. It was beneficial to alleviate the stress concentration and further improve the strengthening and toughening effect of multilayer steel.
Lung Metastatectomy: Can Laser-Assisted Surgery Make a Difference?
Authors: Grapatsas Konstantinos , Papaporfyriou Anastasia , Leivaditis Vasileios , Ehle Benjamin , Galanis Michail
Published: September 2022

Background: Resection of lung metastases with curative intention in selected patients is associated with prolonged survival. Laser–assisted resection of lung metastases results in complete resection of a high number of lung metastases, while preserving lung parenchyma. However, data concerning laser lung resections are scarce and contradictory. The aim of this study was to conduct a systematic review to evaluate the utility of laser-assisted pulmonary metastasectomy. Methods: An electronic search in MEDLINE (via PubMed), complemented by manual searches in article references, was conducted to identify eligible studies. Results: Fourteen studies with a total of 1196 patients were included in this metanalysis. Laser-assisted surgery (LAS) for lung metastases is a safe procedure with a postoperative morbidity up to 24.2% and almost zero mortality. LAS resulted in the resection of a high number of lung metastases with reduction of the lung parenchyma loss in comparison with conventional resection methods. Survival was similar between LAS and conventional resections. Conclusion: LAS allows radical lung-parenchyma saving resection of a high number of lung metastases with similar survival to conventional methods.
Effect of Wild Boar (Sus scrofa L.) on Forests, Agricultural Lands and Population Management in Lithuania
Authors: Tarvydas Arūnas , Belova Olgirda
Published: September 2022

The wild boar (Sus scrofa L., 1758) is one of the most common and widespread game species not only in the Baltic Sea region but also throughout its entire range. However, quantitative population management is required due to the danger of contagious diseases; the census is imprecise, and integrated management and monitoring are still missing in Lithuania. This study focused on the current condition of the wild boar population at the country level, species activity in forests and adjacent agricultural lands, the problem of damage to agriculture and forestry caused by wild boar, and methods for the management of the wild boar population. A methodology for the assessment of the effect of wild boar will help in reducing their impact. For the successful management of wild boar, an increase in the carrying capacity in conformity with animal density should be accomplished.
A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images
Authors: Ramamurthy Karthik , George Timothy Thomas , Shah Yash , Sasidhar Parasa
Published: September 2022

The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.
Intra- and Inter-Reader Variations in Lung Nodule Measurements: Influences of Nodule Size, Location, and Observers
Authors: Chen Hong , Huang Haozhe , Zhang Jianye , Wang Xuexue , Han Mengyang , Ding Chanjun , Wang Jinhong
Published: September 2022

(1) Background: Accurate measurement of lung-nodule size is necessary, but whether a three-dimensional volume measurement is better or more reliable than the one-dimensional method is still unclear. This study aimed to investigate the intra- and inter-reader variations according to nodule type, size, three-dimensional volume measurements, and one-dimensional linear measurements. (2) Methods: This retrospective study included computed tomography (CT) examinations of lung nodules and volume measurements performed from October to December 2016. Two radiologists independently performed all measurements. Intra-class correlation coefficients (ICC) and Bland-Altman plots were used for analysis. (3) Results: The overall variability in the calculated volume was larger than when using the semiautomatic volume measurement. Nodules <6 mm tended to have larger variability than nodules ≥6 mm in both one-dimensional and calculated volume measurements. The isolated type showed smaller variability in both intra- and inter-reader comparisons. The juxta-vascular type showed the largest variability in both one-dimensional and calculated volume measurements. The variability was decreased when using the 3D volume semiautomated software. (4) Conclusions: The present study suggests that 3D semiautomatic volume measurements showed lower variability than the calculated volume measurement. Nodule size and location influence measurement variability. The intra- and inter-reader variabilities in nodule volume measurement were considerable.
Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities
Authors: Apostolopoulos Ioannis D. , Papathanasiou Nikolaos D. , Papandrianos Nikolaos I. , Papageorgiou Elpiniki I. , Panayiotakis George S.
Published: September 2022

Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.
Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review
Authors: Gavriilidis Prodromos , Koole Michel , Annunziata Salvatore , Mottaghy Felix M. , Wierts Roel
Published: September 2022

Gallium-68 (68Ga) is characterized by relatively high positron energy compared to Fluorine-18 (18F), causing substantial image quality degradation. Furthermore, the presence of statistical noise can further degrade image quality. The aim of this literature review is to identify the recently developed positron range correction techniques for 68Ga, as well as noise reduction methods to enhance the image quality of low count 68Ga PET imaging. The search engines PubMed and Scopus were employed, and we limited our research to published results from January 2010 until 1 August 2022. Positron range correction was achieved by using either deblurring or deep learning approaches. The proposed techniques improved the image quality and, in some cases, achieved an image quality comparable to 18F PET. However, none of these techniques was validated in clinical studies. PET denoising for 68Ga-labeled radiotracers was reported using either reconstruction-based techniques or deep learning approaches. It was demonstrated that both approaches can substantially enhance the image quality by reducing the noise levels of low count 68Ga PET imaging. The combination of 68Ga-specific positron range correction techniques and image denoising approaches may enable the application of low-count, high-quality 68Ga PET imaging in a clinical setting.
Liver Stiffness Is Associated with the Burden of Carotid and Systemic Atherosclerosis in an Unorganized Cohort of Patients 40–64 Years Old
Authors: Kuznetsova Alla , Dolgushina Anastasiya , Savochkina Albina , Pykhova Lubov , Sumerkina Veronika , Selyanina Anna , Kudrinskaya Yana , Genkel Vadim
Published: September 2022

Background: The aim of the study is to research the relationship between the severity of liver fibrosis and the burden of carotid and systemic atherosclerosis. Methods: The study includes 163 patients 40 to 64 years of age without atherosclerotic CVD or liver disease. All patients underwent duplex scanning of the carotid and lower limb arteries. All patients underwent transient liver elastometry using the FibroScan (Echosens, France). Results: Carotid plaque was detected in 110 (67.5%) patients. Based on the results of linear regression analysis, relationships between liver stiffness and carotid total plaque area (r = 0.21; p = 0.025) were found. Significant relationships were established between liver stiffness and atherosclerosis burden score based on the results of linear regression (r = 0.17; p = 0.029). Liver stiffness showed moderate diagnostic performance (AUC 0.666; p = 0.01) with regard to generalized atherosclerosis. An increase in liver stiffness >4.5 kPa was associated with an odds ratio of generalized atherosclerosis of 3.48 (95% CI 1.07–11.3; p = 0.038) after adjusting confounding factors. Conclusion: Among patients 40–64 years of age without established atherosclerotic CVD and liver disease, liver stiffness directly correlates with the burden of carotid and systemic atherosclerosis. Liver stiffness showed moderate diagnostic performance (AUC 0.666; p = 0.01) with regard to generalized atherosclerosis.
Providing Care: Intrinsic Human–Machine Teams and Data
Authors: Russell Stephen , Kumar Ashwin
Published: September 2022

Despite the many successes of artificial intelligence in healthcare applications where human–machine teaming is an intrinsic characteristic of the environment, there is little work that proposes methods for adapting quantitative health data-features with human expertise insights. A method for incorporating qualitative expert perspectives in machine learning training data is proposed. The method implements an entropy-based consensus construct that minimizes the challenges of qualitative-scale data such that they can be combined with quantitative measures in a critical clinical event (CCE) vector. Specifically, the CCE vector minimizes the effects where (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. The incorporation of human perspectives in machine learning training data provides encoding of human considerations in the subsequent machine learning model. This encoding provides a basis for increasing explainability, understandability, and ultimately trust in AI-based clinical decision support system (CDSS), thereby improving human–machine teaming concerns. A discussion of applying the CCE vector in a CDSS regime and implications for machine learning are also presented.
Agricultural Development’s Influence on Rural Poverty Alleviation in the North Buton Regency, Indonesia—The Mediating Role of Farmer Performance
Authors: Boni Yohanes
Published: September 2022

Low productivity and farmer business competitiveness are central issues for agricultural development and rural poverty alleviation. This study aimed to determine the influence of agricultural development on rural poverty alleviation. Data were obtained from farmers’ groups in the North Buton Regency in 2019 using questionnaires and analyzed using AMOS. The results showed that agricultural development improves farm business performance and influences rural poverty alleviation. Farm business performance leads to rural poverty alleviation. The influence of agricultural development on rural poverty alleviation was magnified when supported by improved farm enterprise performance. Therefore, agricultural development is the flagship program for poverty alleviation of rural farmers in the North Buton Regency.
Rewilding Play: Design Build Interventions
Authors: Herrington Susan , Lexa-French Ivana , Brussoni Mariana
Published: September 2022

Research on physical interventions installed in outdoor environments and their impacts on children’s play and development is a growing area of study. This paper focuses on the design and installation of outdoor interventions at early childhood education centres in Vancouver, Canada and the impact that theses interventions had on play affordances. With the aim of intervening with inexpensive natural materials and loose parts, graduate students designed, built, and installed interventions and using the Seven Cs evaluation form they scored the play spaces pre- and post-installation. Design methods included the Seven Cs design guidelines and the Two-Eyed Seeing model. Students also sought the insights of Early Childhood Educators, maintenance staff, licensing officers, the British Columbia Cancer Agency, and an Indigenous herbalist/educator. They also examined and addressed solar modifications to create dappled light. To understand the impacts of the student interventions researchers compared the pre- and post-intervention Seven Cs scores, which increased by 20 to 30 points. Researchers seeking to replicate this type of project in their own institutions should carefully consider the impact of climate change on construction timing and material selection, and sensitivity to the diversity of socio-cultural values embedded in the community and within design decisions and the interventions themselves.
Using the Plan–Teach–Reflect Cycle of the Refined Consensus Model of PCK to Improve Pre-Service Biology Teachers’ Personal PCK as Well as Their Motivational Orientations
Authors: Behling Franziska , Förtsch Christian , Neuhaus Birgit J.
Published: September 2022

In this article, we analyse how to improve pre-service biology teachers’ pPCK (personal Pedagogical Content Knowledge), professional values and motivational orientations in the field of academic and scientific language. On the basis of the theory of the Refined Consensus Model of PCK (RCM), we made a two-month quasi-experimental intervention study with 32 pre-service biology teachers. As a treatment, we trained the participants in the Plan–Teach–Reflect Cycle of enacted PCK in a school class, in the framework of a seminar. In the control group, the teaching of the cycle was replaced by presentations of their lesson plans. As dependent variables, we analysed participants’ pPCK, professional values and motivational orientations. Our results showed an increase in pre-service biology teachers’ pPCK (F(1,28) = 3.51, p = 0.04, part. η2 = 0.11, d = 0.70) and motivational orientations (F(1,23) = 29.68, p < 0.01, part. η2 = 0.56, d = 2.26) in both groups, but no effects on participants’ professional values. The teaching experience in a school class strengthened the effects both in participants’ pPCK (F(1,28) = 2.92, p = 0.04, part. η2 = 0.10, d = 0.67) and motivational orientations (F(1,23) = 7.64, p < 0.01, part. η2 = 0.25, d = 1.15). We recommend integrating the use of the Plan–Teach–Reflect Cycle of ePCK into science teacher education programmes.
Development of a Low-Cost Pulse Oximeter for Taking Medical-Scientific Parameters to Monitor Remote Patients
Authors: Viciano-Tudela Sandra , Sendra Sandra , Lloret Jaime , Tomas Jesus , Belda-Ramirez Jose
Published: September 2022

The development of low-cost systems for measuring medical parameters is currently an important issue since this type of system ensures that any sector of the population can access these technologies. Likewise, pandemic situations, such as the one experienced some months ago due to SARS-CoV-2, require the rapid availability of diagnostic devices. One of the devices, which has turned out to be the most relevant in the early detection of respiratory problems, is the finger pulse oximeter. However, the default information that these devices offer is limited. This paper presents the process carried out to analyze the data measured by a commercial pulse oximeter and takes advantage of them to extract relevant information about the vital parameters of the patient that is being monitored, such as peripheral oxygen saturation (SpO2), pulse rate (bpm) (PR), respiratory rate (RR/min), perfusion index (%) (Pi), plethysmography wave, plethysmographic variation index (%) (PVi), the shape of the dicrotic fissure, and the area under the curve. To do this, the Bluetooth frames generated by the device are analyzed through reverse engineering and processed to obtain the aforementioned parameters. Finally, an application for Android devices is developed in order to facilitate the collection and reading of the parameters. The system is tested with different patients whose results are validated by a physician.
An Interference-Resistant and Low-Consumption Lip Recognition Method
Authors: Jia Junwei , Wang Zhilu , Xu Lianghui , Dai Jiajia , Gu Mingyi , Huang Jing
Published: September 2022

Lip movements contain essential linguistic information. It is an important medium for studying the content of the dialogue. At present, there are many studies on how to improve the accuracy of lip language recognition models. However, there are few studies on the robustness and generalization performance of the model under various disturbances. Specific experiments show that the current state-of-the-art lip recognition model significantly drops in accuracy when disturbed and is particularly sensitive to adversarial examples. This paper substantially alleviates this problem by using Mixup training. Taking the model subjected to negative attacks generated by FGSM as an example, the model in this paper achieves 85.0% and 40.2% accuracy on the English dataset LRW and the Mandarin dataset LRW-1000, respectively. The correct recognition rates are improved by 9.8% and 8.3%, compared with the current advanced lip recognition models. The positive impact of Mixup training on the robustness and generalization of lip recognition models is demonstrated. In addition, the performance of the lip recognition classification model depends more on the training parameters, which increase the computational cost. The InvNet-18 network in this paper reduces the consumption of GPU resources and the training time while improving the model accuracy. Compared with the standard ResNet-18 network used in mainstream lip recognition models, the InvNet-18 network in this paper has more than three times lower GPU consumption and 32% fewer parameters. After detailed analysis and comparison in various aspects, it is demonstrated that the model in this paper can effectively improve the model’s anti-interference ability and reduce training resource consumption. At the same time, the accuracy is comparable with the current state-of-the-art results.
Quality Assessment of Virtual Human Assistants for Elder Users
Authors: Foukarakis Michalis , Karuzaki Effie , Adami Ilia , Ntoa Stavroula , Partarakis Nikolaos , Zabulis Xenophon , Stephanidis Constantine
Published: September 2022

Virtual humans (VHs) are gaining increasing attention in various fields, including games and cultural heritage and technological contexts including virtual reality and augmented reality. Recently, since VHs can simulate human-like behavior, VHs have been proposed as virtual assistants (VAs) for all sorts of education and training applications, including applications focused on the improvement of quality of life (QoL) and well-being. In this research work, we consider the quality and efficiency of VHs implemented as part of the MyHealthWatcher project, which focuses on the monitoring of health-related parameters of elder users to improve their QoL and self-management of chronic conditions. To validate our hypothesis that the increased quality of the VH has a positive effect on user satisfaction and user quality of interaction with the system, we developed and integrated into the MyHealthWatcher system two VH variations. The first was developed with mainstream technologies and the second was developed using a professional pipeline. The two variations developed were assessed by representative target users through a between-subject focus group study. The development and validation process of the two variations allowed us to draw valuable conclusions, which are discussed in this paper.
Robust Cyber-Physical System Enabled Smart Healthcare Unit Using Blockchain Technology
Authors: Ch Rupa , Srivastava Gautam , Nagasree Yarajarla Lakshmi Venkata , Ponugumati Akshitha , Ramachandran Sitharthan
Published: September 2022

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work’s main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN.
Modeling the Combined Effects of Cyclable Lithium Loss and Electrolyte Depletion on the Capacity and Power Fades of a Lithium-Ion Battery
Authors: Lee Dongcheul , Kim Byungmook , Shin Chee Burm , Oh Seung-Mi , Song Jinju , Jang Il-Chan , Woo Jung-Je
Published: September 2022

In this study, we present a modeling approach to estimate the combined effects of cyclable lithium loss and electrolyte depletion on the capacity and discharge power fades of lithium-ion batteries (LIBs). The LIB cell based on LiNi0.6Co0.2Mn0.2O2 (NCM622) was used to model the discharge behavior in the multiple degradation modes. The discharge voltages for nine different levels of cyclable lithium loss and electrolyte depletion were measured experimentally. When there was no cyclable lithium loss, the 50% of electrolyte depletion brought about 5% reduction in discharge capacity at 0.05 C discharge rate, while it resulted in 46% reduction when it was coupled with 30% of cyclable lithium loss. The 50% of electrolyte depletion with no cyclable lithium loss caused 1% reduction in discharge power during 0.5 C discharge at the state of charge (SOC) level of 0.8, while it resulted in 13% reduction when it was coupled with 30% of cyclable lithium loss. The modeling results obtained by using the one-dimensional finite element method were compared with the experimental data. The justification of the modeling methods is demonstrated by the high degree of concordance between the predicted and experimental values. Using the validated modeling methodology, the discharge capacity and usable discharge power can be estimated effectively under various combined degradation modes of cyclable lithium loss and electrolyte depletion in the LIB cell.
Simulation Analysis of Ejector Optimization for High Mass Entrainment under the Influence of Multiple Structural Parameters
Authors: Zheng Jiantao , Hou Yuyan , Tian Zhongwei , Jiang Hongkui , Chen Weixiong
Published: September 2022

As an energy-saving technology, the ejector is widely used in the heating, aerospace, and chemical industry. The ejector performance is closely related to its structure, so the structure of the ejector needs to be optimized. In the present study, single-factor optimization is first carried out, and the main structural parameters affecting the ejector performance are screened out. Then, the response surface method is used to analyze the combined effect of the multiple structural parameters of the ejector, find out the optimal structure, and analyze the flow field inside the ejector. This study shows that, through numerical simulation, the ejector performance obtained by the response surface method is better than that obtained by the single-factor optimization method or theoretically designed ejector, and the ejector performance is 35.4% higher than that of the theoretically designed ejector. Moreover, the optimal structure of the ejector obtained by the response surface method has high reliability, and the difference between the simulation result and the prediction result of the response surface method is 0.96%.
Analysis of Requirements for Achieving Carbon Neutrality in a Cold Dense City Using GIS
Authors: Hisato Osawa , Mori Taro , Shinagawa Kouichi , Nakayama Satoshi , Hosobuchi Hayato , Mushtaha Emad
Published: September 2022

Photovoltaics (PV) can make a significant contribution to achieving carbon neutrality in buildings. Geographic information systems (GIS) make it easy to model cities and simulate PV with high accuracy. However, the computational burden from 3D urban models remains high. In this study, the complex geometry of a city was represented in 2D images, and we propose a method for performing multiple PV simulations in a short amount of time by using these 2D images. The ratio of electricity generation to primary energy consumption (EG/E) was calculated for 415 office buildings in Sapporo, Japan, and conditions for achieving carbon neutrality in these buildings were explored. For buildings with a small gross floor area, EG/E can be higher than one, and carbon neutrality can be achieved by improving the performance of the exterior walls and windows and by increasing the area of the PV installed. However, to improve EG/E in buildings with a large gross floor area, it is necessary to consider improving the performance of the equipment used and the efficient operation of the HVAC system. The method is useful for considering ways to achieve carbon neutrality in various cities.
A Game Changer: Microfluidic Technology for Enhancing Biohydrogen Production—Small Size for Great Performance
Authors: Šalić Anita , Zelić Bruno
Published: September 2022

One of the approaches widely used today to intensify processes is their miniaturization. Small, compact, portable devices that can be used directly in the field will become popular in the near future. The use of microstructured devices is becoming more widespread in diagnostics, analytics, and production, so there is no doubt that the same approach is being applied to energy production. The question is whether it is possible to create an energy production system that has all the external characteristics of a miniaturized device but is sustainable, durable, environmentally friendly, based on renewable sources, and cost-effective. The first challenge is to choose a production route, an energy source that has the required characteristics, and then to adapt this production on a microscale. Among the different energy sources, biohydrogen meets most of the requirements. The carbon emissions of biohydrogen are much lower, and its production is less energy-intensive than conventional hydrogen production. Moreover, it can be produced from renewable energy sources. The challenge today is to make this process sustainable due to the low substrate conversion, production rate, and yield. Microfluidic systems are one of the technologies that could address the above shortcomings of the current biohydrogen production processes. The combination of microdevices and biohydrogen production opens up new possibilities for energy production. Although this area of research is growing, the focus of this review is on the possibility of using microfluidics for biohydrogen production.
Multi-Objective Non-Dominated Sorting Genetic Algorithm Optimization for Optimal Hybrid (Wind and Grid)-Hydrogen Energy System Modelling
Authors: Mukoni Esmeralda , Garner Karen S.
Published: September 2022

In this paper, an optimal hybrid (wind and grid)-hydrogen energy system (H-HES) is proposed using multi-objective non-dominated sorting algorithm (NSGA-II) optimization. The H-HES consists of the main energy system; wind-energy system (W-ES), which supplies a proton exchange membrane (PEM) electrolyzer via an energy management system (EMS) and a rectifier. In addition, the grid-energy system (G-ES) is available to support the W-ES to meet the PEM electrolyzer’s energy demand, and the EMS facilitates control between the W-ES and G-ES. The W-ES is modelled using wind data from Wind Atlas South Africa (WASA) for six Renewable Energy Development Zones (REDZs) in South Africa and their appropriate wind turbine models. The selection of appropriate wind turbine models is guided by the optimal wind turbine variables obtained from NSGA-II corresponding to the optimal H-HES model. The optimal H-HES model is developed using two objective functions: cost of electricity and efficiency, which are minimized and maximized respectively and evaluated using NSGA-II available in Pymoo framework. NSGA-II successfully converges to a Pareto front, and the best solution for the H-HES cost of electricity and efficiency for each wind REDZ is determined by compromise programming; a multi-criteria decision-making technique available in Pymoo. From the optimal cost of electricity and efficiency solutions, optimal variables are successfully obtained for optimal modelling of the H-HES for each wind REDZ.
How to Train an Artificial Neural Network to Predict Higher Heating Values of Biofuel
Authors: Matveeva Anna , Bychkov Aleksey
Published: September 2022

Plant biomass is one of the most promising and easy-to-use sources of renewable energy. Direct determination of higher heating values of fuel in an adiabatic calorimeter is too expensive and time-consuming to be used as a routine analysis. Indirect calculation of higher heating values using the data from the ultimate and proximate analyses is a more rapid and less equipment-intensive method. This study assessed the fitting performance of a multilayer perceptron as an artificial neural network for estimating higher heating values of biomass. The analysis was conducted using a specially gathered large and heterogeneous dataset (720 biomass samples) that included the experimental data of ultimate and proximate analysis on grass plants, peat, husks and shells, organic residues, municipal solid wastes, sludge, straw, and untreated wood. The quantity and preprocessing of data (namely, rejection of dependent and noisy variables; dataset centralization) were shown to make a major contribution to prediction accuracy improvement. In particular, it was demonstrated that 550 samples are sufficient to ensure convergence of the algorithm; carbon and hydrogen contents are sufficient ultimate analysis data; and volatile matters can be excluded from proximate analysis. The minimal required complexity of neural network is ~50 neurons.
Mathematical Model of Air Dryer Heat Pump Exchangers
Authors: Mołczan Tomasz , Cyklis Piotr
Published: September 2022

This paper presents a mathematical model of heat pump exchangers and their thermal interaction with a fan for an air dryer. The calculation algorithm developed for the finned heat exchangers is based on the ε-NTU method, allowing the determination of air side and refrigerant side heat transfer coefficients, evaporator and condenser heat capacity and air parameters at the dehumidifier outlet with known exchanger geometries, initial air parameters and mass flow rate. The model was verified on an industrial dehumidifier test bench. This enabled the heat transfer coefficients for the exchanger to be calculated as a function of the speed and, therefore, the power of the fan’s drive motor. An increase in fan performance on the one hand results in an increase in the heat transfer rate, but, on the other hand, it causes an increase in the total energy consumption of the motor. Thus, while it causes an increase in drying capacity, it also causes an increase in the energy consumption of the dehumidifier. In order to optimise the unit in terms of energy consumption, it is therefore necessary to determine a function that relates the amount of heat exchanged to the efficiency of the fan.
The Investigation of the Bubble Behaviors on the Vertical Heat Exchange Tube
Authors: Tian Yongsheng , Xu Pengfei , Zhang Linhua , Yang Luopeng
Published: September 2022

In the boiling process, the growth, separation, and movement of bubbles are expeditious. The visualization experiment of nucleate boiling was carried out with the help of high-speed photography. The evolution of the entire bubble life cycle is clearly observed at the nucleation site without interference from the leading and neighboring bubbles. Bubble behavior at the local heating surface has strong randomness due to the influence of the wall micro-structure, convection intensity, heating surface geometry configuration, heat flux density, and so on, but bubble behavior also has a certain regularity. In this paper, the behavior characteristics of bubbles were analyzed, with a particular focus on the evolution of bubbles. Under lower load (ΔTsat = 8~9 °C) in study conditions, nucleation sites have a long enough time interval. In addition, the bubble separation and rising velocity obviously increase due to the change of pool boiling flow characteristics in the restricted space. The setting of confined space increases the bubble escape velocity and the rising velocity, and decreases the diameter of bubbles escaping from the wall. The results will provide some help for the understanding of bubble behavior mechanisms and numerical research.
Safety Evaluation and Energy Consumption Analysis of Deep Foundation Pit Excavation through Numerical Simulation and In-Site Monitoring
Authors: Chen Ji , Xu Qi , Luo Xinyu , Tian Angran , Xu Sujing , Tang Qiang
Published: September 2022

Foundation pit excavation is common in urban construction, while safety evaluation is always significant in every specified project. The soil material properties, groundwater level, excavation method, supporting structure, monitoring points’ arrangement, and so on distinguish from one site from another. Thus, many studies have looked into the safety and reliability of designated projects. This paper was based on the co-construction underground tunnel project of a deep foundation pit excavation in Suzhou, China. This paper aimed to perform a safety evaluation on this foundation pit by means of numerical simulation for parameter influence analysis, as well as scientific comparison with in-site monitoring data. To minimize the energy consumption and contribute to the carbon neutrality, a brief energy consumption analysis was also conducted. The results indicated that the maximum deformation of the foundation pit bottom is 4.5 cm and the deformation of the foundation pit is within the allowable range. The maximum horizontal displacement of each excavation is approximately at 10 m to 12 m of the diaphragm wall and the largest deformation is 28 mm. The maximum ground settlement is less than 16 mm, which confirmed the safety during excavation. It is ideal that the above deformation law will provide a reference for similar projects. Furthermore, this research simulated and monitored the whole cycle of foundation pit excavation, and contributes to savings in energy consumption and limiting of carbon emissions.
Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters
Authors: Hossain Md Jakir , Naeini Mia
Published: September 2022

Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.