bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods
Authors: de la Vega Joaquín , Riba Jordi-Roger , Ortega-Redondo Juan Antonio
Published: April 2023

This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms.
Two-Dimensional Space Turntable Pitch Axis Trajectory Prediction Method Based on Sun Vector and CNN-LSTM Model
Authors: Dai Shuang , Song Ke-Fei , Wang Yan-Long , Zhang Pei-Jie
Published: April 2023

A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the dynamic model using traditional trajectory prediction, we propose a new method for predicting the pitch axis trajectory of the turntable based on the sun vector and a deep learning CNN-LSTM model. First, the ideal solar position of the pitch axis was calculated using the sun vector. Then, the ideal solar position was combined with the running turntable pitch axis motor speed, current, and solar position error signal as the CNN-LSTM model input data. The model parameters were trained and adjusted through test data simulation using Fengyun-3E satellite orbit data. Finally, the next position of the pitch axis was predicted. The test results showed that in the sun vector and CNN-LSTM model, the RMSE value was 0.623 and the MSE value was 0.388. It was better than the LSTM model or CNN model alone and could accurately predict the pitch axis position.
Development of a Two-Dimensional Hybrid Sediment-Transport Model
Authors: Zhang Yaoxin , Al-Hamdan Mohammad , Wren Daniel
Published: April 2023

This paper presents the development of a two-dimensional hydrodynamic sediment transport model using the finite volume method based on a collocated unstructured hybrid-mesh system consisting of triangular and quadrilateral cells. The model is a single-phase nonequilibrium sediment-transport model for nonuniform and noncohesive sediments in unsteady turbulent flows that considers multiple sediment-transport processes such as deposition, erosion, transport, and bed sorting. This model features a hybrid unstructured mesh system for easy mesh generation in complex domains. To avoid interpolation from vertices in conventional unstructured models, this model adopted a second-order accurate edge-gradient evaluation method to consider the mesh irregularities based on Taylor’s series expansion. In addition, the multipoint momentum interpolation corrections were integrated to avoid possible nonphysical oscillations during the wetting-and-drying process, common in unsteady sediment transport problems, to ensure both numerical stability and numerical accuracy. The developed sediment transport model was validated by a benchmark degradation case for the erosion process with armoring effects, a benchmark aggradation case for the deposition process, and a naturally meandering river for long-term unsteady sediment-transport processes. Finally, the model was successfully applied to simulate sediment transport in a reservoir that was significantly affected by typhoon events.
DB-Net: Detecting Vehicle Smoke with Deep Block Networks
Authors: Chen Junyao , Peng Xiaojiang
Published: April 2023

Vision-based vehicle smoke detection aims to locate the regions of vehicle smoke in video frames, which plays a vital role in intelligent surveillance. Existing methods mainly consider vehicle smoke detection as a problem of bounding-box-based detection or pixel-level semantic segmentation in the deep learning era, which struggle to address the trade-off of localization accuracy and speed. In addition, although various studies have been reported, there is no open benchmark available for real vehicle smoke detection. To address these issues, we made three contributions as follows: (i) We built a real-world vehicle smoke semantic segmentation dataset with 3962 polygon-based annotated vehicle smoke images, which will be released to the community. (ii) We regard vehicle smoke detection as a block-wise prediction problem and propose a conceptually new, yet simple deep block network model (DB-Net). It provides more accurate localization information than bounding-box-based ones and has a lower computational cost than semantic segmentation methods. (iii) We introduce a coarse-to-fine training strategy, where we first pre-train a model on bounding-box annotated data and then fine-tune it on pixel-wise labeled data. We compare our DB-Net to several advanced methods and evaluate them in several metrics. Extensive experiments demonstrate that our method is significantly superior to other methods.
Optimal Modeling for Dynamic Response of Energy Storage Systems
Authors: Lee Chen-Cheng , Hsin Yu-Min , Dai Shang-Chun , Kuo Cheng-Chien
Published: April 2023

Using energy storage systems with solar and wind energy can overcome the intermittence of these types of renewable energy. According to the regulations made by the utilities in each country, facilities that are connected to the power grid should be assessed on how they influence the power grid. Power system simulation software such as PSS/E, DIgSILENT, and DSATools are widely used by utilities. The default models in these kinds of software are defined by the Western Electricity Coordinating Council (WECC). Improper parameters will produce estimation results that are not informative, and the utilities may make wrong decisions. To avoid this problem, this study designed the process to find suitable parameters for real energy storage systems. Using models created by the WECC and reducing it according to the application condition decreased the burden of the optimization process. We used two algorithms and their improved versions to search for an appropriate value of variables that can represent a real energy storage system. We also compared the results between the BESS simplified model that we built and the original model from PSS/E.
An Update of Eyeglasses-Supported Nasal–Facial Prosthetic Rehabilitation of Cancer Patients with Post-Surgical Complications: A Case Report
Authors: Ciocca Leonardo , Emiliani Nicolas , Artuso Giulia , Breschi Lorenzo , Marcelli Emanuela , Cercenelli Laura
Published: April 2023

This case report aims to describe novel steps in the digital design/manufacturing of facial prostheses for cancer patients with wide inoperable residual defects, with a focus on a case of a mid-facial defect. A facial scanner was used to make an impression of the post-surgical residual defect and to digitalize it. The daughter’s face scan was used for reconstructing the missing anatomy. Using 3D printing technologies, try-in prototypes were produced in silicone material. The substructure was laser melted. The final prosthesis was relined directly onto the patient’s defect. The prosthesis resulted in a very low weight and a high elasticity of the external margins. The laser-melted substructure ensured the necessary rigidity with minimum thickness.
Industrial Internet Identity Resolution+5G Full Connection Digital Factory Research
Authors: Wang Dajiang , Jiang Renben , Sun Wenlei , Zhang Xuedong , Lu Cheng , Zou Yuxin
Published: April 2023

In this paper, in order to help improve the information capacity of modern factories, a full connection digital factory for industrial Internet identity resolution is built, and digital technology is used to perform the digital interconnection of the production equipment. The full connection digital factory is an advanced factory with extensive connection of production units, deep integration of IT–OT, full release of data value, and improvement of the total factor productivity. This is enabled by the full integration and use of the new generation technologies represented by 5G, edge computing, and identity resolution to reach new factory level, workshop level, and production line level production scenarios. Based on the integration of infrastructure, information technology, and platform service resources, a full connection digital factory architecture is constructed. The fusion application of 5G, edge computing, and mark resolution technology in transformer discrete manufacturing digital plant is then discussed. The challenges and development trends are summarized. Finally, it is demonstrated that the full connection digital factory of identity resolution is a possible future development direction.
Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
Authors: Lee Jaeeun , Kim Jongnam
Published: April 2023

Three-dimensional film images which are recently developed are seen as three-dimensional using the angle, amount, and viewing position of incident light rays. However, if the pixel contrast of the image is low or the patterns are cloudy, it does not look three-dimensional, and it is difficult to perform a quality inspection because its detection is not easy. In addition, the inspection method has not yet been developed since it is a recently developed product. To solve this problem, we propose a method to calculate the width of pixels for a specific height from the image histogram of a 3D film image and classify it based on a threshold. The proposed algorithm uses the feature that the widths of pixels by height in the image histogram of the good 3D film image are wider than the image histogram of the bad 3D film image. In the experiment, it was confirmed that the position of the height section of the image histogram has the highest classification accuracy. Through comparison tests with conventional algorithms, we showed excellent classification accuracy for 3D film image classification. We verified that it is possible with high accuracy even if the image’s contrast is low and the patterns in the image are not detected.
Conditional Knowledge Extraction Using Contextual Information Enhancement
Authors: Xu Zhangbiao , Zhang Botao , Gu Jinguang , Gao Feng
Published: April 2023

Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.
An Analysis of Artificial Intelligence Techniques in Surveillance Video Anomaly Detection: A Comprehensive Survey
Authors: Şengönül Erkan , Samet Refik , Abu Al-Haija Qasem , Alqahtani Ali , Alturki Badraddin , Alsulami Abdulaziz A.
Published: April 2023

Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using human power to monitor them for special security purposes. Abnormal events deviate from normal patterns and are considered rare. Furthermore, collecting or producing data on these rare events and modeling abnormal data are difficult. Therefore, there is a need to develop an intelligent approach to overcome this challenge. Many research studies have been conducted on detecting abnormal events using machine learning and deep learning techniques. This study focused on abnormal event detection, particularly for video surveillance applications, and included an up-to-date state-of-the-art that extends previous related works. The major objective of this survey was to examine the existing machine learning and deep learning techniques in the literature and the datasets used to detect abnormal events in surveillance videos to show their advantages and disadvantages and summarize the literature studies, highlighting the major challenges.
Microsporidiosis Causing Necrotic Changes in the Honeybee Intestine
Authors: Ptaszyńska Aneta A. , Gancarz Marek
Published: April 2023

Background: Microsporidia from the Nosema (Vairimorpha) genus are pathogenic fungi that complete their life cycle in the honeybee intestine. Therefore, the aim of this study was to determine the impact of the course of infection on the viability of honeybee intestine cells. Methods and Results: Intestines isolated from healthy and N. ceranae-infected honeybees were stained using two dyes, SYTO 9 and propidium iodide, and analyzed under an Axiovert 200M fluorescence microscope immediately after the isolation of the intestines. The ImageJ program was used for the quantitative analysis of the cell structure parameters. Our study demonstrated for the first time that healthy bees have a higher number of live cells in their intestines than infected bees, and that the intestines of N. ceranae-infected honeybees contain dead cells concentrated in spots. The results obtained for these two cases differed significantly, and were confirmed by statistical tests. Conclusions: The intestines of infected honeybees contain dead cells concentrated in red/dead spots, which can lead to necrotic changes, the interruption of the host’s intestinal continuity, intestinal leaking and the increased mortality of the host.
An Advanced Fitness Function Optimization Algorithm for Anomaly Intrusion Detection Using Feature Selection
Authors: Hong Sung-Sam , Lee Eun-joo , Kim Hwayoung
Published: April 2023

Cyber-security systems collect information from multiple security sensors to detect network intrusions and their models. As attacks become more complex and security systems diversify, the data used by intrusion-detection systems becomes more dimensional and large-scale. Intrusion detection based on intelligent anomaly detection detects attacks based on machine-learning classification models, soft computing, and rule sets. Feature-selection methods are used for efficient intrusion detection and solving high-dimensional problems. Optimized feature selection can maximize the detection model performance; thus, a fitness function design is required. We proposed an optimization algorithm-based feature-selection algorithm to improve anomaly-detection performance. We used a genetic algorithm and proposed an advanced fitness function that finds the most relevant feature set, increasing the detection rate, reducing the error rate, and enhancing analysis speed. An improved fitness function for the selection of optimized features is proposed; this function can address overfitting by solving the problem of anomaly-detection performance from imbalanced security datasets. The proposed algorithm outperformed other feature-selection algorithms. It outperformed the PCA and wrapper-DR methods, with 0.99564 at 10%, 0.996455 at 15%, and 0.996679 at 20%. It performed higher than wrapper-DR by 0.95% and PCA by 3.76%, showing higher differences in performance than in detection rates.
Boron Carbonitride Films with Tunable Composition: LPCVD and PECVD Synthesis Using Trimethylamine Borane and Nitrogen Mixture and Characterization
Authors: Belaya Svetlana V. , Maksimovsky Eugene A. , Shayapov Vladimir R. , Shapovalova Aleksandra A. , Kolodin Alexey N. , Saraev Andrey A. , Asanov Igor P. , Khomyakov Maxim N. , Yushina Irina V. , Plekhanov Aleksandr G. , Sulyaeva Veronica S. , Kosinova Marina L.
Published: April 2023

This study reports the chemical vapor deposition of amorphous boron carbonitride films on Si(100) and SiO2 substrates using a trimethylamine borane and nitrogen mixture. BCxNy films with different compositions were produced via variations in substrate temperature and type of gas-phase activation. The low-pressure chemical vapor deposition (LPCVD) and plasma-enhanced chemical vapor deposition (PECVD) methods were used. The “elemental composition—chemical bonding state—properties” relationship of synthesized BCxNy was systematically studied. The hydrophilicity, mechanical, and optical properties of the films are discussed in detail. The composition of films deposited by the LPCVD method at temperatures ranging from 673 to 973 K was close to that of boron carbide with a low nitrogen content (BCxNy). The refractive index of these films changed in the range from 2.43 to 2.56 and increased with temperature. The transparency of these films achieved 85%. LPCVD films were hydrophilic and the water contact angles varied between 53 and 63°; the surface free energy was 42–48 mN/m. The microhardness, Young’s modulus and elastic recovery of LPCVD films ranged within 24–28 GPa, 220–247 GPa, and 70–74%, respectively. The structure of the PECVD films was close to that of hexagonal boron nitride, and their composition can be described by the BCxNyOz:H formula. In case of the PECVD process, the smooth films were only produced at low deposition temperatures (373–523 K). The refractive index of these films ranged from 1.51 to 1.67. The transparency of these films achieved 95%; the optical band gap was evaluated as 4.92–5.28 eV. Unlike LPCVD films, they were very soft, and their microhardness, Young’s modulus and elastic recovery were 0.8–1.4 GPa, 25–26 GPa, and 19–28%, respectively. A set of optimized process parameters to fabricate LPCVD BCxNy films with improved mechanical and PECVD films with high transparency is suggested.
Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data
Authors: Kim Changmin , Lee Woobeom
Published: April 2023

HAR technology uses computer and machine vision to analyze human activity and gestures by processing sensor data. The 3-axis acceleration and gyro sensor data are particularly effective in measuring human activity as they can calculate movement speed, direction, and angle. Our paper emphasizes the importance of developing a method to expand the recognition range of human activity due to the many types of activities and similar movements that can result in misrecognition. The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy, particularly for similar activities. The method involves converting the sensor data into an image format, removing noise using time series features, generating visual patterns of waveforms, and standardizing geometric patterns. The resulting data (1D, 2D, and 3D) can simultaneously process each type by extracting pattern features using parallel convolution layers and performing classification by applying two fully connected layers in parallel to the merged data from the output data of three convolution layers. The proposed neural network model achieved 98.1% accuracy and recognized 18 types of activities, three times more than previous studies, with a shallower layer structure due to the enhanced input data features.
Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Signals
Authors: Vendrame Alessandra , Cappelletto Cristina , Chiovati Paola , Vinante Lorenzo , Parvej Masud , Caroli Angela , Pirrone Giovanni , Barresi Loredana , Drigo Annalisa , Avanzo Michele
Published: April 2023

Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Methods: The respiratory traces from 36 patients who underwent DIBH RT were collected. The patients’ RT treatment plans were generated for both DIBH and free-breathing (FB) modalities. The patients were divided into two classes (patient eligible or not), based on the decrease of maximum dose to the left anterior descending (LAD) artery achieved with DIBH, compared to that achieved with FB and ΔDL. Patients with ΔDL > median value of ΔDL within the patient cohort were assumed to be those selected for DIBH. A BLSTM-RNN was trained for classification of patients eligible for DIBH by analysis of their respiratory signals, as acquired during acquisition of the pre-treatment computed tomography (CT), for selecting the window for DIBH. The dataset was split into training (60%) and test groups (40%), and the hyper-parameters, including the number of hidden layers, the optimizer, the learning rate, and the number of epochs, were selected for optimising model performance. The BLSTM included 2 layers of 100 neural units, each followed by a dropout layer with 20% dropout, and was trained in 35 epochs using the Adam optimizer, with an initial learning rate of 0.0003. Results: The system achieved accuracy, specificity, and sensitivity of, F1 score and area under the receiving operating characteristic curve (AUC) of 71.4%, 66.7%, 80.1%, 72.4%, and 69.4% in the test dataset, respectively. Conclusions: The proposed BLSTM-RNN classified patients in the test set eligible for DIBH with good accuracy. These results look promising for building an accurate and robust decision system to provide automated assistance to the radiotherapy team in assigning patients to DIBH.
Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
Authors: Xiao Qiao , Lee Khuan , Mokhtar Siti Aisah , Ismail Iskasymar , Pauzi Ahmad Luqman bin Md , Zhang Qiuxia , Lim Poh Ying
Published: April 2023

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.
Assessing the COVID-19 Impact of Projects under Construction with Monte Carlo Simulation
Authors: Chen Yih-Tzoo , Yang Yee-Yen , Chen Yi-Hua
Published: April 2023

The outbreak of the novel coronavirus pneumonia (COVID-19) in 2019 and the 2022 war in Ukraine have had profound global impacts on travel and logistics, disrupted the material supply chain, significantly influenced the cost and progress of construction projects, and further impacted the operational effectiveness of firms. Despite some existing studies providing valuable insights into the impact of COVID-19 on the construction industry, there remain research gaps that need to be addressed. Prior studies have mainly focused on the immediate impact factors of the pandemic, such as supply chain disruptions and workforce shortages, and strategies for effectively reducing or eliminating these risks. However, there is a need for research that delves into the long-term implications of these disruptions. So far, no relevant research has quantified the broader impact of the epidemic. Thus, this study aims to analyze the effects of the pandemic and the war on 136 construction industry professionals, their projects, and firms through literature review, questionnaire surveys, and expert interviews. The study compiles a list of significant risk factors for construction projects between 2019–2022, including their probability of occurrence, impact over time, and overall cost. The study also analyzes and discusses the impact of these high-risk factors as of 2022. To quantify the impact, cost, and level of exposure to these risks suffered by actual construction projects over this period, the Monte Carlo simulation method is introduced. This approach provides contractors with early prediction of risks and appropriate responses to mitigate risks.
Ephemeral Icons: Construction and Representation of Temporary Votive Chapels in Old Russian Religious Rituals
Authors: Leahy Emma Louise
Published: April 2023

The collective ritual of building one-day votive churches (obydennye khramy) was practiced in the European north of Russia between the late 14th and 17th centuries. The product of a syncretism between Orthodox Christianity and native folklore, the ritual’s purpose was to deliver the community from epidemic disease. One-day churches were built of freshly cut logs, on virgin ground, in a prominent place, such as a town square or crossroads. According to local belief, votive objects made from natural materials were simultaneously temporary and eternal; this paper interrogates how one-day churches fit this model. Obydennye khramy were ephemeral structurally, processually, and circumstantially. These were simple, rudimentary votive structures, not built to last nor substitute established churches. By condensing into a single day all of the traditional steps of church-building, the ritual prevented the church from growing old before completion, ensuring its purity through its newness. Built under threat of pestilence, obydennye khramy had the function of realigning the progression of time, putting an end to the period of disease, and thereby allowing humans to fleetingly triumph over natural forces. Obydennye khramy were enduring as objects of intercession, as governance instruments, and in their subsequent representations in the written word and urban topography. Votive churches were spatial icons, mediating between humans and the cosmos and returning to nature as they decayed. The ritual itself, led by religious and secular authorities, performatively reinforced social hierarchies. Obydennye khramy were immortalised in chronicle narratives and occasionally replaced with stone churches, some of which survive today.
Runoff Response to Climate in Two River Basins Supplied by Small Glacier Meltwater in Southern and Northern Tibetan Plateau
Authors: Yao Ruzhen , Li Shenghai , Chen Deliang
Published: April 2023

The Tibetan Plateau (TP) has experienced amplified warming in recent decades, causing glaciers to melt and affecting river runoff. It is well established that the southern and northern areas of the TP have responded to climate changes differently, with the north dominated by a westerly climate and the south by the Indian monsoon. While there are more glaciers in the TP than in any other region outside the polar areas, most of these glaciers are tiny, and only a limited number of them have been monitored to study mass balance and downward runoff. This study used the mass balance measured at two glaciers along with in situ and satellite data to drive a hydrological model called the Alpine Runoff Predictor that includes glacier melt to simulate glacial melting and the accompanying hydrological processes of the two glacierized basins, analyze their contributions to the river runoffs, and investigate their responses to local climate changes. The results show that the glacier meltwater in both river basins showed an increasing trend, with values of 0.001 × 108 m3 a−1 in the Kyanjing River basin and 0.0095 × 108 m3 a−1 in the Tuole River basin. However, their multi-year average contributions to the runoff were 12.5% and 5.6%, respectively. In contrast to the Tuole River basin, where runoff is increasing (0.0617 × 108 m3 a−1), the Kyanjing River basin has decreasing runoff (−0.0216 × 108 m3 a−1) as a result of decreasing precipitation. This result highlights the dominant role played by precipitation changes in the two basins under study, which are characterized by small glacier meltwater contributions.
Paroxysmal Positional Nystagmus in Acoustic Neuroma Patients
Authors: Comacchio Francesco , Magnavita Paola , Bellemo Barbara
Published: April 2023

The association between acoustic neuroma and positional vertigo with paroxysmal positional nystagmus is relatively rare, but, when present, it certainly represents a challenge for the otoneurologist. There are few reports in the literature on this particular issue, and some questions are still unanswered, particularly regarding the characteristics of positional nystagmus that may distinguish between a true benign paroxysmal vertigo and a positional nystagmus associated with the tumor. We present the videonystagmographic patterns of seven patients with acoustic tumor who had paroxysmal positional nystagmus and analyzed its features. A concomitant true benign paroxysmal positional vertigo may be present during the follow-up of a non-treated patient, as the paroxysmal positional vertigo may be the first symptom of the tumor, and it may show characteristics that are very similar to a posterior semicircular canal canalolithiasis or a horizontal canal “heavy or light cupula”. The possible mechanisms are discussed.
Intrusive and Impact Modes of a Falling Drop Coalescence with a Target Fluid at Rest
Authors: Chashechkin Yuli D. , Ilinykh Andrey Yu.
Published: April 2023

The evolution of the falling drop substance transfer in a target fluid at rest was traced by high-speed video techniques. Two flow modes were studied: slow intrusive flow, when the KE of the drop was comparable or less than the available potential energy (APSE), and a fast impact flow, at a relatively high drop contact velocity. For the substance transfer visualization, a drop of alizarin ink solution at various concentrations was used. The use of transparent partially colored fluid allows tracing the drop matter motion in the bulk and on the fluid free surface. The traditional side and frontal view of flow patterns were registered and analyzed. In both flow modes, the substance of the drop partially remained on the free surface and partially went into the target fluid bulk, where it was distributed non-uniformly. In the intrusive mode, the drop substance partially remained on the surface, while the main mass of the drop flowed into the thickness of the target fluid, forming the lenticular colored domain. The intrusion was gradually transformed into an annular vortex. In the impact mode, the drop broke up into individual fibers during the coalescence, creating linear and reticular structures on the surface of the cavity and the crown. The flow patterns composed of individual fibers were rapidly rebuilt as the flow evolved and the splash emerged and decayed. The sizes of cavities and colored fluid domains were compared in different flow regimes as well. The total energy transfer and transformation impact on the flow structure formation and dynamics was revealed.
Non-Parametric Regression and Riesz Estimators
Authors: Kountzakis Christos , Tsachouridou-Papadatou Vasileia
Published: April 2023

In this paper, we consider a non-parametric regression model relying on Riesz estimators. This linear regression model is similar to the usual linear regression model since they both rely on projection operators. We indicate that Riesz estimator regression relies on the positive basis elements of the finite-dimensional sub-lattice generated by the rows of some design matrix. A strong motivation for using the Riesz estimator model is that the data of explanatory variables may come from categorical variables. Calculations related to Riesz estimator regression are very easy since they arise from the measurability in finite-dimensional probability spaces. Moreover, we show that the fitted model of Riesz estimators is an ordinary least squares model. Any vector of some Euclidean space is supposed to be a rendom variable under the objective probability values, being used in expected utility theory and its applications. Finally, the reader may notice that goodness-of-fit measures are similar to those defined for the usual linear regression. Due to the fact that this model is non-parametric, it may include samples relevant to finance and actuarial science variables.
Determining Tipping Points and Responses of Macroinvertebrate Traits to Abiotic Factors in Support of River Management
Authors: Forio Marie Anne Eurie , Goethals Peter L. M. , Lock Koen , Nguyen Thi Hanh Tien , Damanik-Ambarita Minar Naomi , Dominguez-Granda Luis , Thas Olivier
Published: April 2023

Although the trait concept is increasingly used in research, quantitative relations that can support in determining ecological tipping points and serve as a basis for environmental standards are lacking. This study determines changes in trait abundance along a gradient of flow velocity, turbidity and elevation, and develops trait–response curves, which facilitate the identification of ecological tipping points. Aquatic macroinvertebrates and abiotic conditions were determined at 88 different locations in the streams of the Guayas basin. After trait information collection, a set of trait diversity metrics were calculated. Negative binomial regression and linear regression were applied to relate the abundance of each trait and trait diversity metrics, respectively, to flow velocity, turbidity and elevation. Tipping points for each environmental variable in relation to traits were identified using the segmented regression method. The abundance of most traits increased with increasing velocity, while they decreased with increasing turbidity. The negative binomial regression models revealed that from a flow velocity higher than 0.5 m/s, a substantial increase in abundance occurs for several traits, and this is even more substantially noticed at values higher than 1 m/s. Furthermore, significant tipping points were also identified for elevation, wherein an abrupt decline in trait richness was observed below 22 m a.s.l., implying the need to focus water management in these altitudinal regions. Turbidity is potentially caused by erosion; thus, measures that can reduce or limit erosion within the basin should be implemented. Our findings suggest that measures mitigating the issues related to turbidity and flow velocity may lead to better aquatic ecosystem functioning. This quantitative information related to flow velocity might serve as a good basis to determine ecological flow requirements and illustrates the major impacts that hydropower dams can have in fast-running river systems. These quantitative relations between invertebrate traits and environmental conditions, as well as related tipping points, provide a basis to determine critical targets for aquatic ecosystem management, achieve improved ecosystem functioning and warrant trait diversity.
From Non-Alcoholic Fatty Liver to Hepatocellular Carcinoma: A Story of (Mal)Adapted Mitochondria
Authors: Amorim Ricardo , Magalhães Carina C. , Borges Fernanda , Oliveira Paulo J. , Teixeira José
Published: April 2023

Non-alcoholic fatty liver disease (NAFLD) is a global pandemic affecting 25% of the world’s population and is a serious health and economic concern worldwide. NAFLD is mainly the result of unhealthy dietary habits combined with sedentary lifestyle, although some genetic contributions to NAFLD have been documented. NAFLD is characterized by the excessive accumulation of triglycerides (TGs) in hepatocytes and encompasses a spectrum of chronic liver abnormalities, ranging from simple steatosis (NAFL) to steatohepatitis (NASH), significant liver fibrosis, cirrhosis, and hepatocellular carcinoma. Although the molecular mechanisms that cause the progression of steatosis to severe liver damage are not fully understood, metabolic-dysfunction-associated fatty liver disease is strong evidence that mitochondrial dysfunction plays a significant role in the development and progression of NAFLD. Mitochondria are highly dynamic organelles that undergo functional and structural adaptations to meet the metabolic requirements of the cell. Alterations in nutrient availability or cellular energy needs can modify mitochondria formation through biogenesis or the opposite processes of fission and fusion and fragmentation. In NAFL, simple steatosis can be seen as an adaptive response to storing lipotoxic free fatty acids (FFAs) as inert TGs due to chronic perturbation in lipid metabolism and lipotoxic insults. However, when liver hepatocytes’ adaptive mechanisms are overburdened, lipotoxicity occurs, contributing to reactive oxygen species (ROS) formation, mitochondrial dysfunction, and endoplasmic reticulum (ER) stress. Impaired mitochondrial fatty acid oxidation, reduction in mitochondrial quality, and disrupted mitochondrial function are associated with a decrease in the energy levels and impaired redox balance and negatively affect mitochondria hepatocyte tolerance towards damaging hits. However, the sequence of events underlying mitochondrial failure from steatosis to hepatocarcinoma is still yet to be fully clarified. This review provides an overview of our understanding of mitochondrial adaptation in initial NAFLD stages and highlights how hepatic mitochondrial dysfunction and heterogeneity contribute to disease pathophysiology progression, from steatosis to hepatocellular carcinoma. Improving our understanding of different aspects of hepatocytes’ mitochondrial physiology in the context of disease development and progression is crucial to improving diagnosis, management, and therapy of NAFLD/NASH.
Mitochondrial Dysfunction in CD4+ T Effector Memory RA+ Cells
Authors: Strickland Marie , Lee Salanne , Neo Shi Yong , Balachander Akhila , Low Ivy , Mustafah Seri , Goh Wah Ing , Wright Graham D. , Larbi Anis , Pender Sylvia L. F.
Published: April 2023

Human ageing is accompanied by poor responses to infection and decreased vaccine efficacy. While the causes of this can be attributed to defects in the immune system that increase with age, it is unknown whether mitochondrial dysfunction may also contribute to these phenomena. This study aims to assess mitochondrial dysfunction in CD4+ terminal effector memory T cells re-expressing CD45RA (TEMRA) cells and other CD4+ memory T cell subtypes, which are increased in number in the elderly population, with respect to how their metabolic responses to stimulation are altered compared to CD4+ naïve T cells. In this study, we show that CD4+ TEMRA cells exhibit altered mitochondrial dynamics compared to CD4+ naïve cells and CD4+ central and effector memory cells, with a 25% reduction in OPA1 expression. CD4+ TEMRA and memory cells show increased upregulation of Glucose transporter 1 following stimulation and higher levels of mitochondrial mass compared to CD4+ naïve T cells. Additionally, TEMRA cells exhibit a decrease in mitochondrial membrane potential compared to other CD4+ memory cell subsets by up to 50%. By comparing young to aged individuals, more significant mitochondria mass and lower membrane potential were observed in CD4+ TEMRA of young individuals. In conclusion, we suggest that CD4+ TEMRA cells may be impaired with respect to their metabolic response to stimulation, possibly contributing to impaired responses to infection and vaccination.
Me, Myself and My Insula: An Oasis in the Forefront of Self-Consciousness
Authors: Tisserand Alice , Philippi Nathalie , Botzung Anne , Blanc Frédéric
Published: April 2023

The insula is a multiconnected brain region that centralizes a wide range of information, from the most internal bodily states, such as interoception, to high-order processes, such as knowledge about oneself. Therefore, the insula would be a core region involved in the self networks. Over the past decades, the question of the self has been extensively explored, highlighting differences in the descriptions of the various components but also similarities in the global structure of the self. Indeed, most of the researchers consider that the self comprises a phenomenological part and a conceptual part, in the present moment or extending over time. However, the anatomical substrates of the self, and more specifically the link between the insula and the self, remain unclear. We conducted a narrative review to better understand the relationship between the insula and the self and how anatomical and functional damages to the insular cortex can impact the self in various conditions. Our work revealed that the insula is involved in the most primitive levels of the present self and could consequently impact the self extended in time, namely autobiographical memory. Across different pathologies, we propose that insular damage could engender a global collapse of the self.
Advances Focusing on the Application of Decellularized Extracellular Matrix in Periodontal Regeneration
Authors: Liang Chao , Liao Li , Tian Weidong
Published: April 2023

The decellularized extracellular matrix (dECM) is capable of promoting stem cell proliferation, migration, adhesion, and differentiation. It is a promising biomaterial for application and clinical translation in the field of periodontal tissue engineering as it most effectively preserves the complex array of ECM components as they are in native tissue, providing ideal cues for regeneration and repair of damaged periodontal tissue. dECMs of different origins have different advantages and characteristics in promoting the regeneration of periodontal tissue. dECM can be used directly or dissolved in liquid for better flowability. Multiple ways were developed to improve the mechanical strength of dECM, such as functionalized scaffolds with cells that harvest scaffold-supported dECM through decellularization or crosslinked soluble dECM that can form injectable hydrogels for periodontal tissue repair. dECM has found recent success in many periodontal regeneration and repair therapies. This review focuses on the repairing effect of dECM in periodontal tissue engineering, with variations in cell/tissue sources, and specifically discusses the future trend of periodontal regeneration and the future role of soluble dECM in entire periodontal tissue regeneration.
Combined In Vivo Microdialysis and PET Studies to Validate [11C]Yohimbine Binding as a Marker of Noradrenaline Release
Authors: Landau Anne Marlene , Jakobsen Steen , Thomsen Majken Borup , Alstrup Aage Kristian Olsen , Orlowski Dariusz , Jacobsen Jan , Wegener Gregers , Mørk Arne , Sørensen Jens Christian Hedemann , Doudet Doris J.
Published: April 2023

The noradrenaline system attracts attention for its role in mood disorders and neurodegenerative diseases but the lack of well-validated methods impairs our understanding when assessing its function and release in vivo. This study combines simultaneous positron emission tomography (PET) and microdialysis to explore if [11C]yohimbine, a selective antagonist radioligand of the α2 adrenoceptors, may be used to assess in vivo changes in synaptic noradrenaline during acute pharmacological challenges. Anesthetised Göttingen minipigs were positioned in a head holder in a PET/CT device. Microdialysis probes were placed in the thalamus, striatum and cortex and dialysis samples were collected every 10 min. Three 90 min [11C]yohimbine scans were acquired: at baseline and at two timepoints after the administration of amphetamine (1–10 mg/kg), a non-specific releaser of dopamine and noradrenaline, or nisoxetine (1 mg/kg), a specific noradrenaline transporter inhibitor. [11C]yohimbine volumes of distribution (VT) were obtained using the Logan kinetic model. Both challenges induced a significant decrease in yohimbine VT, with time courses reflecting their different mechanisms of action. Dialysis samples revealed a significant increase in noradrenaline extracellular concentrations after challenge and an inverse correlation with changes in yohimbine VT. These data suggest that [11C]yohimbine can be used to evaluate acute variations in synaptic noradrenaline concentrations after pharmacological challenges.
Plasma Cytokeratin-18 Fragment Level Reflects the Metabolic Phenotype in Obesity
Authors: Goralska Joanna , Razny Urszula , Gruca Anna , Zdzienicka Anna , Micek Agnieszka , Dembinska-Kiec Aldona , Solnica Bogdan , Malczewska-Malec Malgorzata
Published: April 2023

There is growing interest in the non-invasive identification and monitoring of the outcome of liver damage in obese patients. Plasma cytokeratin-18 (CK-18) fragment levels correlate with the magnitude of hepatocyte apoptosis and have recently been proposed to independently predict the presence of non-alcoholic steatohepatitis (NASH). The aim of the study was to analyze the associations of CK-18 with obesity and related complications: insulin resistance, impaired lipid metabolism and the secretion of hepatokines, adipokines and pro-inflammatory cytokines. The study involved 151 overweight and obese patients (BMI 25–40), without diabetes, dyslipidemia or apparent liver disease. Liver function was assessed based on alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and the fatty liver index (FLI). CK-18 M30 plasma levels, FGF-21, FGF-19 and cytokines were determined by ELISA. CK-18 values >150 U/l were accompanied by high ALT, GGT and FLI, insulin resistance, postprandial hypertriglyceridemia, elevated FGF-21 and MCP-1 and decreased adiponectin. ALT activity was the strongest independent factor influencing high CK-18 plasma levels, even after an adjustment for age, sex and BMI [β coefficient (95%CI): 0.40 (0.19–0.61)]. In conclusion, the applied CK-18 cut-off point at 150 U/l allows to distinguish between two metabolic phenotypes in obesity.