bluuebunny/crossref_metadata_2025 huggingface dataset:
Eliciting indigenous knowledge to predict climate events for the food security of agro-pastoral households in North Benin
Authors: Egah Janvier , Yegbemey Rosaine N. , Idrissou Fadilath Abikè , Baco Mohamed Nasser
Published: April 2023

Populations are subjected to climatic uncertainties, the predictions of which are major challenges for the scientific community. This study analyzes indigenous climate event prediction and adaptation based on indigenous knowledge in North Benin. A sample of 80 holders of indigenous knowledge of prediction of climate events was selected using snowball sampling in the district of Gogonou. Data were collected during semi-structured interviews with the indigenous knowledge holders. Data included the profile of the knowledge holders, climate events and their prediction signs, and the planned responses in the face of climate events. Data were analyzed using discourse analysis and descriptive statistics. The results showed that the holders of indigenous knowledge of prediction of climate events are agropastoralists and some of their wives. They are over 60 years old and rooted in traditional religion. Climate events predicted were floods, pockets of drought, early cessation of rains, and invasion of fields by caterpillars. These climate events are predicted from animal, vegetable, and nature behaviors. The responses developed in the face of climate events are mainly the relocation of fields to firm land, the use of drought-tolerant varieties, the relocation of planting periods, and the planning of the suspension of children's schooling for spreading and planting operations. These findings demonstrate the necessity for policymakers to consider the value of indigenous knowledge to reduce the effects of climate change.
Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data
Authors: Park Taejin , Sim Sunhui
Published: April 2023

The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems.
Predicting prognosis and clinical efficacy of immune checkpoint blockade therapy via interferon-alpha response in muscle-invasive bladder cancer
Authors: Fan Bohan , Zheng Xin , Wang Yicun , Hu Xiaopeng
Published: April 2023

Background: Immune checkpoint blockade (ICB) can prompt durable and robust responses in multiple cancers, involving muscle-invasive bladder cancer (MIBC). However, only a limited fraction of patients received clinical benefit. Clarifying the determinants of response and exploring corresponding predictive biomarkers is key to improving outcomes.

Methods: Four independent formerly published cohorts consisting of 641 MIBC patients were enrolled in this study. We first analyzed the associations between various cancer hallmarks and ICB therapy response in two immunotherapeutic cohorts to identify the leading prognostic hallmark in MIBC. Furthermore, advanced machine learning methods were performed to select robust and promising predictors from genes functioning in the above leading pathway. The predictive ability of selected genes was also validated in multiple MIBC cohorts.

Results: We identified and verified IFNα response as the leading cancer hallmark indicating better treatment responses, favorable overall survival, and an inflamed tumor microenvironment with higher infiltration of immune effector cells in MIBC patients treated with ICB therapy. Subsequently, two commonly selected genes, CXCL10 and LAMP3, implied better therapy response and the CXCL10highLAMP3high patients would benefit more from ICB therapy, which was comprehensively validated from the perspective of gene expression, clinical response, patient survival and immune features.

Conclusion: Higher IFNα response primarily predicted better ICB therapeutic responses and reflected an inflamed microenvironment in MIBC. A composite of CXCL10 and LAMP3 expression could serve as promising predictive biomarkers for ICB therapeutic responses and be beneficial for clinical decision-making in MIBC.
Combined Reinforcement Learning and CPG Algorithm to Generate Terrain-Adaptive Gait of Hexapod Robots
Authors: Li Daxian , Wei Wu , Qiu Zhiying
Published: April 2023

Terrain adaptation research can significantly improve the motion performance of hexapod robots. In this paper, we propose a method that combines reinforcement learning with a central pattern generator (CPG) to enhance the terrain adaptation of hexapod robots in terms of gait planning. The hexapod robot’s complex task presents a high-dimensional observation and action space, which makes it challenging to directly apply reinforcement learning to robot control. Therefore, we utilize the CPG algorithm to generate the rhythmic gait while compressing the action space dimension of the agent. Additionally, the proposed method requires less internal sensor information, which exhibits strong applicability. Finally, we conduct experiments and deploy the proposed framework in the simulation environment. The results show that the terrain adaptation policy trained in our framework enables the hexapod robot to move more smoothly and efficiently on rugged terrain compared to the traditional CPG method.
Multi-Head Attention Network with Adaptive Feature Selection for RUL Predictions of Gradually Degrading Equipment
Authors: Nie Lei , Xu Shiyi , Zhang Lvfan
Published: April 2023

A multi-head-attention-network-based method is proposed for effective information extraction from multidimensional data to accurately predict the remaining useful life (RUL) of gradually degrading equipment. The multidimensional features of the desired equipment were evaluated using a comprehensive evaluation index, constructed of discrete coefficients, based on correlation, monotonicity, and robustness. For information extraction, the optimal feature subset, determined by the adaptive feature selection method, was input into the multi-head temporal convolution network–bidirectional long short-term memory (TCN-BILSTM) network. Each feature was individually mined to avoid the loss of information. The effectiveness of our proposed RUL prediction method was verified using the NASA IMS bearings dataset and C-MAPSS aeroengines dataset. The results indicate the superiority of our method for the RUL prediction of gradually degrading equipment compared to other mainstream machine learning methods.
Hoist-Based Shape Memory Alloy Actuator with Multiple Wires for High-Displacement Applications
Authors: Ballester Carmen , Copaci Dorin , Arias Janeth , Moreno Luis , Blanco Dolores
Published: April 2023

Shape memory alloys (SMAs) are smart materials that change their crystalline structures when subjected to heat or tension, resulting in a macroscopic deformation. When applied to actuators, SMAs present a remarkable load–weight ratio and flexibility, making them suitable for diverse applications. However, challenges such as their energy consumption, nonlinear control, and low displacement must be considered. This paper presents a new strategy for improving the total displacement while adding neither supplementary SMA wires nor complex external devices. In addition, a novel control strategy is proposed to improve the nonlinearity of SMAs’ behavior. A hoist system was developed to linearly increase the displacement with the number of pulleys and wire turns used. The design also used parallel actuation to increase the load capacity. The actuator presented a high load capacity with reduced weight, lifting more than 100 times its own mass, with a low-cost and robust external system. The simplicity of the actuator’s control and production and its lightness make it a suitable option for a wide range of applications, including wearable exoskeletons.
Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
Authors: Si Haiping , Wang Yunpeng , Zhao Wenrui , Wang Ming , Song Jiazhen , Wan Li , Song Zhengdao , Li Yujie , Fernando Bacao , Sun Changxia
Published: April 2023

Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading.
Identification of Influential Factors in the Adoption of Irrigation Technologies through Neural Network Analysis: A Case Study with Oil Palm Growers
Authors: Martínez-Arteaga Diana , Arias Arias Nolver Atanacio , Darghan Aquiles E. , Barrios Dursun
Published: April 2023

Water is one of the most determining factors in obtaining high yields in oil palm crops. However, water scarcity is becoming a challenge for agricultural sustainability. Therefore, when the environmental supply of water is low, it is necessary to provide it to crops with the highest degree of efficiency. However, although irrigation technologies are available, for various reasons farmers continue to use inefficient irrigation systems, which causes resource losses. The objective of this study was to analyze the percentage of adoption of irrigation technologies for water management in oil palm crops and to classify the factors influencing their adoption by producers. The method for the classification of influential factors was based on multiple correspondence analysis and perceptron neural networks. The results showed that fewer than 15% of the producers adopt irrigation technologies, and the factors classified as influential in the adoption decision were the age of the palm growers, the size of the plantation, and the access to extension services. These results are the basis for the formulation of effective and focused extension strategies according to the characteristics of the producers and the local and technological specificity.
Prevalence of the SigB-Deficient Phenotype among Clinical Staphylococcus aureus Isolates Linked to Bovine Mastitis
Authors: Walzl Anna , Marbach Helene , Belikova Darya , Vogl Claus , Ehling-Schulz Monika , Heilbronner Simon , Grunert Tom
Published: April 2023

Phenotypic adaptation has been associated with persistent, therapy-resistant Staphylococcus aureus infections. Recently, we described within-host evolution towards a Sigma factor B (SigB)-deficient phenotype in a non-human host, a naturally infected dairy cow with chronic, persistent mastitis. However, to our knowledge, the prevalence of SigB deficiency among clinical S. aureus isolates remains unknown. In this study, we screened a collection of bovine mastitis isolates for phenotypic traits typical for SigB deficiency: decreased carotenoid pigmentation, increased proteolysis, secretion of α-hemolysin and exoproteins. Overall, 8 out of 77 (10.4%) isolates of our bovine mastitis collection exhibited the SigB-deficient phenotype. These isolates were assigned to various clonal complexes (CC8, CC9, CC97, CC151, CC3666). We further demonstrated a strong positive correlation between asp23-expression (a marker of SigB activity) and carotenoid pigmentation (r = 0.6359, p = 0.0008), underlining the role of pigmentation as a valuable predictor of the functional status of SigB. Sequencing of the sigB operon (mazEF-rsbUVW-sigB) indicated the phosphatase domain of the RsbU protein as a primary target of mutations leading to SigB deficiency. Indeed, by exchanging single nucleotides in rsbU, we could either induce SigB deficiency or restore the SigB phenotype, demonstrating the pivotal role of RsbU for SigB functionality. The data presented highlight the clinical relevance of SigB deficiency, and future studies are needed to exploit its role in staphylococcal infections.
Salvianolic Acid A Protects against Acetaminophen-Induced Hepatotoxicity via Regulation of the miR-485-3p/SIRT1 Pathway
Authors: Tang Fan , Wang Zhecheng , Zhou Junjun , Yao Jihong
Published: April 2023

The vast majority of drug-induced liver injury is mainly attributed to acetaminophen (APAP) overdose. Salvianolic acid A (Sal A), a powerful water-soluble compound obtained from Salvia miltiorrhiza, has been confirmed to exert hepatoprotective effects. However, the beneficial effects and the exact mechanisms of Sal A on APAP-induced hepatotoxicity remain unclear. In this study, APAP-induced liver injury with or without Sal A treatment was examined in vitro and in vivo. The results showed that Sal A could alleviate oxidative stress and inflammation by regulating Sirtuin 1 (SIRT1). Furthermore, miR-485-3p could target SIRT1 after APAP hepatotoxicity and was regulated by Sal A. Importantly, inhibiting miR-485-3p had a hepatoprotective effect similar to that of Sal A on APAP-exposed AML12 cells. These findings suggest that regulating the miR-485-3p/SIRT1 pathway can alleviate oxidative stress and inflammation induced by APAP in the context of Sal A treatment.
Promising Bioactivity of Vitamin B1-Au Nanocluster: Structure, Enhanced Antioxidant Behavior, and Serum Protein Interaction
Authors: Ungor Ditta , Gombár Gyöngyi , Juhász Ádám , Samu Gergely F. , Csapó Edit
Published: April 2023

In the current work, we first present a simple synthesis method for the preparation of novel Vitamin-B1-stabilized few-atomic gold nanoclusters with few atomic layers. The formed nanostructure contains ca. eight Au atoms and shows intensive blue emissions at 450 nm. The absolute quantum yield is 3%. The average lifetime is in the nanosecond range and three main components are separated and assigned to the metal–metal and ligand–metal charge transfers. Based on the structural characterization, the formed clusters contain Au in zero oxidation state, and Vitamin B1 stabilizes the metal cores via the coordination of pyrimidine-N. The antioxidant property of the Au nanoclusters is more prominent than that of the pure Vitamin B1, which is confirmed by two different colorimetric assays. For the investigation into their potential bioactivity, interactions with bovine serum albumin were carried out and quantified. The determined stoichiometry indicates a self-catalyzed binding, which is almost the same value based on the fluorometric and calorimetric measurements. The calculated thermodynamic parameters verify the spontaneous bond of the clusters along the protein chain by hydrogen bonds and electrostatic interactions.
Determination of a Time-Varying Point Source in Cauchy Problems for the Convection–Diffusion Equation
Authors: Georgiev Slavi , Vulkov Lubin
Published: April 2023

In this paper, we suggest a method for recovering the unknown time-dependent strength of a contaminant concentration source from measurements of the concentration inside an unbounded domain. This problem is formulated as a Cauchy parabolic inverse problem. For its efficient numerical processing, the problem is solved by reduction of the Cauchy problem to a Dirichet one on a bounded domain using the method of the fundamental (potential) solutions in combination with an adjoint equation technique. A numerical solution to this approach is explained. Next, by choosing the source strength in the form of a finite series of shape functions with unknown constant coefficients and using a linear-square method, the term concentration source is estimated. Computational simulations using model examples from water pollution are discussed.
Design, Development, and Experimental Verification of a Trajectory Algorithm of a Telepresence Robot
Authors: Altalbe Ali A. , Shahzad Aamir , Khan Muhammad Nasir
Published: April 2023

Background: Over the last few decades, telepresence robots (TRs) have drawn significant attention in academic and healthcare systems due to their enormous benefits, including safety improvement, remote access and economics, reduced traffic congestion, and greater mobility. COVID-19 and advancements in the military play a vital role in developing TRs. Since then, research on the advancement of robots has been attracting much attention. Methods: In critical areas, the placement and movement of humans are not safe, and researchers have started looking at the development of robots. Robot development includes many parameters to be analyzed, and trajectory planning and optimization are among them. The main objective of this study is to present a trajectory control and optimization algorithm for a cognitive architecture named auto-MERLIN. Optimization algorithms are developed for trajectory control. Results: The derived work empirically tests the solutions and provides execution details for creating the trajectory design. We develop the trajectory algorithm for the clockwise direction and another one for the clockwise and counterclockwise directions. Conclusions: Experimental results are drawn to support the proposed algorithm. Self-localization, self-driving, and right and left turn trajectories are drawn. All of the experimental results show that the designed TR works properly, with better accuracy and only a slight jitter in the orientation. The jitter is found due to the environmental factor caught by the sensors, which can be filtered easily. The results show that the proposed approach is less complex and provides better trajectory planning accuracy.
Experimental Study on Confined Compaction Deformation of Crushed Gangue under Different Water Content Conditions
Authors: Liang Pengfei , Yang Yu , Liu Jiashun , Xin Changhao , Sun Yidan
Published: April 2023

To study the deformation characteristics of crushed gangue under different water content conditions, the confined compaction test of crushed gangue was carried out under five different water content conditions by using a universal material testing machine and a compaction device. The strain–stress relationship in the compaction process of crushed gangue and the crushing law of gangue particles were obtained through the test, and the effects of the axial stress, water content, and relative crushing rate on the compaction deformation of crushed gangue were analyzed. The results show that the confined compaction deformation process of crushed gangue can be divided into three stages: the rapid compaction stage, crushing compaction stage, and stable compaction stage. The strain–stress relationship can be described by the parameter logarithmic function and Chapman–Richards function, respectively. The maximum change in the compression modulus under different water content states ranged from 9.9 to 19.3 MPa, and the influence of the water content on the compression modulus was more significant with increasing axial stress. There is a good correlation between particle crushing and compaction deformation. The relative crushing rate increases with the increase in the axial strain, and the increasing rate of the relative crushing rate also gradually increases. The change law of the relative crushing rate and axial strain is different in dry and water-containing conditions.
Improvement Effect of Membrane-Free Stem Cell Extract on Atopic Dermatitis in NC/Nga Mice
Authors: Pang Qi Qi , Noh Byeong Wook , Park Hye Sook , Kim Young Sil , Kim Ji-Hyun , Cho Eun Ju
Published: April 2023

Membrane-free stem cell extract (MFSCE) derived from adipose tissue has been reported to have anti-inflammatory activity. In the present study, we investigated the effects of MFSCE on atopic dermatitis (AD)-like skin inflammation using house-dust-mite-sensitized NC/Nga mice. Topical application of MFSCE significantly ameliorated AD-like clinical symptoms including erythema, dry skin, edema, excoriation, erosion, lichenification, and scratching. In addition, the levels of serum immunoglobulin E and inflammatory cytokines were decreased by MFSCE treatment. Furthermore, treatment with MFSCE inhibited the increase in epidermal thickness, infiltration of mast cells, expression of interleukin (IL)-4, IL-10, interferon-γ, tumor necrosis factor-α, thymus, and activation-regulated chemokines in the dorsal skin of NC/Nga mice. In conclusion, MFSCE effectively suppressed AD-like manifestations preclinically, systemically, and topically. Our study suggests that MFSCE may be a useful natural product for AD therapeutic strategies.
Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
Authors: Abdullah Abdullah A. , Hassan Masoud M. , Mustafa Yaseen T.
Published: April 2023

Convolutional neural networks (CNNs) have become a popular choice for various image classification applications. However, the multi-layer perceptron mixer (MLP-Mixer) architecture has been proposed as a promising alternative, particularly for large datasets. Despite its advantages in handling large datasets and models, MLP-Mixer models have limitations when dealing with small datasets. This study aimed to quantify and evaluate the uncertainty associated with MLP-Mixer models for small datasets using Bayesian deep learning (BDL) methods to quantify uncertainty and compare the results to existing CNN models. In particular, we examined the use of variational inference and Monte Carlo dropout methods. The results indicated that BDL can improve the performance of MLP-Mixer models by 9.2 to 17.4% in term of accuracy across different mixer models. On the other hand, the results suggest that CNN models tend to have limited improvement or even decreased performance in some cases when using BDL. These findings suggest that BDL is a promising approach to improve the performance of MLP-Mixer models, especially for small datasets.
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
Authors: Tan Kian Long , Lee Chin Poo , Lim Kian Ming
Published: April 2023

Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field.
Enhancing the Accuracy of Water-Level Forecasting with a New Parameter-Inversion Model for Estimating Bed Roughness in Hydrodynamic Models
Authors: Chen Yifan , Cao Feifeng , Cheng Weiping , Liu Bin
Published: April 2023

The accurate and efficient estimation of bed roughness using limited historical observational data is well-established. This paper presents a new parameter-inversion model for estimating bed roughness in hydrodynamic models that constrains the roughness distribution between river sections. The impact of various factors on the accuracy of inversed roughness was analyzed through a numerical experiment with the number of measurement stations, observed data amount, initial bed roughness, observational noise, and the weight of the regularization term. The results indicate that increasing the number of measurement stations and the amount of observed data significantly improves the robustness of the model, with an optimal parameter setting of 3 stations and 30 observed data. The initial roughness had little impact on the model, and the model showed good noise resistance capacity, with the error significantly reduced by controlling the smoothness level of inversed roughness using a small weight of the regularization term (i.e., 100). An experiment conducted on a real river using the calibrated model parameters shows a forecasted water level RMSE of 0.041 m, 31% less than that from the Federal Emergency Management Agency. The proposed model provides a new approach to estimating bed roughness parameters in hydrodynamic models and can help in improving the accuracy of water-level forecasting.
Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics
Authors: Pan Chaofeng , Zhang Chi , Wang Jian , Liu Qian
Published: April 2023

This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle’s speed and the workshop’s relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system.
COVIX—An Index Allowing for the Assessment of the Pandemic Situation Based on Infections and Hospitalisation Data
Authors: Kschonnek Michel , Dobrovolska Iryna , Protzer Ulrike , Zagst Rudi
Published: April 2023

Monitoring and assessing the severity of the pandemic situation is one of the key challenges that public officials faced during the COVID-19 pandemic. Daily new infections may lead to flawed assessments, as infected individuals lead to different constraints imposed on the health care system amid varying pandemic determinants. On the other hand, hospitalisations or hospital bed occupancy may lead to outdated assessments, as the corresponding data are only observable with considerable delay. In this study, we introduce a hospital beds model, which relates the three quantities of daily new infections, daily hospitalisation rates, and daily hospital bed occupancy in the context of the COVID-19 pandemic. Using this model, we develop COVIX—a severity index that assesses the impact of a pandemic in comparison to a specified reference date while taking infection and disease risks into account. The developed methodology and its implications are illustrated on data for the German federal state of Bavaria.
The Influence of Bearing Clearance on the Load Capacity of Gas Polymer Bearings
Authors: Schilling Gregor , Liebich Robert
Published: April 2023

In this paper, the influence of a nominal bearing clearance on the lift-off behaviour and the load-carrying capacity of gas polymer bearings (GPBs) is investigated. For this purpose, an experimental study with five different bearing gap configurations and a range of static loads is carried out. Furthermore, the test rig for the clearance and friction torque measurements is illustrated and discussed from a practical point of view. The experimental gap measurement results of all the bearing configurations and the method used are explained and discussed. Finally, the friction torque during the lift-off and run-up phases is measured for a range of static loads. A method that uses the time derivative of the measured friction torque during the run-up tests as a criterion for the lift-off detection is explained and applied to the measured data. The results of this study indicate that there are large differences between the most studied bump-type and gas polymer bearings in terms of the dependence of the load-carrying capacity on the bearing gap. Furthermore, the recorded effects are described, and possible explanations are given. Finally, the differences in characteristics from those of bump-type bearings are described and their importance for the design process of rotors supported in GPBs is explained.
Aggregated Rankings of Top Leagues’ Football Teams: Application and Comparison of Different Ranking Methods
Authors: Gyarmati László , Orbán-Mihálykó Éva , Mihálykó Csaba , Vathy-Fogarassy Ágnes
Published: April 2023

In this study, the effectiveness and characteristics of three ranking methods were investigated based on their performance in ranking European football teams. The investigated methods were the Thurstone method with ties, the analytic hierarchy process with logarithmic least squares method, and the RankNet neural network. The methods were analyzed in both complete and incomplete comparison tasks. The ranking based on complete comparison was performed on match results of national leagues, where each team had match results against all the other teams. In the incomplete comparison case, in addition to the national league results, only a few match results from international cups were available to determine the aggregated ranking of the teams playing in the top five European leagues. The rankings produced by the ranking methods were compared with each other, with the official national rankings, and with the UEFA club coefficient rankings. In addition, the correlation between the aggregated rankings and the Transfermarkt financial ranking was also examined for the sake of interest.
Analysis of Dynamic Characteristics of Low-Floor Train Passing Switch in Facing Direction with Bad Alignment Irregularity Ahead of the Turnout
Authors: Jia Xiaohong , Yang Xinwen , Shi Guangtian
Published: April 2023

A low-floor train–turnout coupling dynamic model considering bad alignment irregularity ahead of the turnout was established on the basis of a field test in which the dynamic performances of a low-floor train passing through a no. 6 turnout in a rail transit depot in China had been measured, as well as the line alignment and wheel and rail profiles. There are two types of wheelsets in the low-floor train: integral wheelset and independent wheelset. The model fully considers the coupling effect between the vehicle and the ballast components, and the simulated results are in consistence with measured results in the field test. Based on this, the influences of train speed, the wheel–rail friction coefficient, and the track alignment optimization of the dynamic performance of low-floor trains passing switch in facing direction are analyzed. The main results indicate that eliminating alignment irregularities before the switch can effectively improve the dynamic performance of the integrated wheelset in the switch and the lateral forces of the outer wheels of these two vehicles are reduced by about 80%, the derailment coefficients are reduced by about 70%, the vertical force is reduced by about 10–20% and wheel load is reduced by approximately 30%.
Reconfigurable Supply Chain Selection: Literature Review, Research Roadmap and New Trends
Authors: Zidi Slim , Kermad Lyes , Hamani Nadia , Zidi Hedi
Published: April 2023

The COVID-19 pandemic revealed weaknesses in the global supply chain management. With stock-outs, transportation problems and the bullwhip effect caused by ever-changing demand, it is necessary for decision-makers to review their supply chain configuration. The latter must adapt to new market changes and respond quickly and cost-effectively to customer requirements. In fact, the selection criteria of the most reconfigurable configuration, i.e., the configuration that adapts its structure and its functions to the new market changes, must respond to this problem. The aim of this paper is to conduct a literature review of the criteria and methods for configuration selection in reconfigurable manufacturing systems and reconfigurable supply chains. This study allows to define a roadmap for the configuration selection in reconfigurable supply chains based on the most efficient reconfigurability criteria. We provide new trends to consider in this selection problem. This paper allows managers to choose the most appropriate criteria to implement a reconfigurable supply chain.
Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
Authors: Chavarro Adrian F. , Renza Diego , Ballesteros Dora M.
Published: April 2023

Most of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops.
Evaluating the Mandibular Trabecular Structure in Patients with and without Bruxism: A Cross-Sectional Study
Authors: Valletta Alessandra , Tavakoli Tafti Kioumars , Baghaei Kimia , Moaddabi Amirhossein , Soltani Parisa , Spagnuolo Gianrico , Chaurasia Akhilanand
Published: April 2023

(1) Background: Fractal analysis has been used as a mathematical method for studying the complexity of fractal structures such as trabecular bone that look similar at different scales. Bruxism is a disorder involving nonfunctional grinding and clenching of the teeth that leads to bone resorption and fractal dimension reduction. This study aimed to evaluate the trabecular pattern of the mandibular condyle, angle, and dental region in panoramic radiographs of individuals with and without bruxism using fractal analysis and a larger sample size. (2) Methods: A total of 365 panoramic radiographs belonging to two groups consisting of bruxism and non-bruxism individuals were assessed using fractal analysis. Fractal dimension (FD) values were calculated on each side for the three regions of interest (ROIs): the mandibular condylar head, mandibular angle, and interdental region between the second premolar and first molar. Statistical analysis was performed using binominal and chi-square tests, the Shapiro–Wilk test, and the Mann–Whitney test (α = 0.05). (3) Results: No significant differences were observed between the FD values of the ROIs in the two groups (p > 0.05). (4) Conclusions: No significant differences existed in the FD values of the ROIs in patients with and without bruxism. This result shows that fractal analysis of panoramic radiographs cannot be useful in detecting patients with bruxism.
New Perspectives on Old Pasts? Diversity in Popular Digital Games with Historical Settings
Authors: Schwarz Angela , Weber Milan
Published: April 2023

With their increasing popularity, digital games have come to stage notions of history and the past for ever broader circles of recipients, thereby shaping what is understood, interpreted, and negotiated as history in popular contexts. Digital games with historical settings not only adopt already successfully popularized and widely mediated images of history. They also integrate current social debates into the historical worlds they construct and recreate. Using three highly popular representatives of the medium as examples, this article examines how the debates about diversity and the representation of People of Color, which have intensified in recent years, inscribe a particular social self-image into the mediated staging of history and thus offer new perspectives on the past.
Towards a Weak Avant-Garde, Re-Shaping the Canon
Authors: Majewska Ewa
Published: April 2023

Feminist discussions in art history usually focus on the exclusion of women artists and the reification of women’s bodies. A growing constellation of queer and feminist theorists today, however, analyse failure and cuteness as aspects of contemporary feminist avant-garde practices, suggesting that alternative currents in art practice and theory are emerging that challenge traditional notions and modes of art production. In my article, I discuss these debates in relation to “weak universalism” and weak messianism in order to reshape our understanding of avant-garde theory and practice from a feminist perspective. I argue that feminist analysis transforms what was previously thematized as conditions of exclusion into an important part of artistic legacy in works made by previously excluded groups. I propose that the concept of a weak avant-garde might help to theorize these shifts.
Evolution of the Human Role in Manufacturing Systems: On the Route from Digitalization and Cybernation to Cognitization
Authors: Hozdić Elvis , Makovec Igor
Published: April 2023

Modern society is living at a time of revolutionary changes in all areas of human life. For example, the field of industrial manufacturing has greatly influenced the role of human beings during the past 30 years. Modern manufacturing systems are in a phase of transition, in accordance with the concept of the fourth industrial revolution (Industry 4.0). A new manufacturing paradigm based on the principles of Industry 4.0 is presented by Smart Manufacturing Systems (SMS). A basic building block of SMS is cyber-physical production systems (CPPS), which together with innovative-management principles of emergence, self-organization, learning, open innovation, collaboration and the networking of people and organizations are the key principles of Industry 4.0. The three key enablers of Industry 4.0, i.e., the connectivity, the digitization and the cybernation of work processes in manufacturing systems, have paved the way for a new industrial revolution, i.e., Industry 5.0 concept that is bringing about a new paradigm in the field of manufacturing systems, the so-called Adaptive Cognitive Manufacturing Systems (ACMS). A fundamental building block of ACMS is the new generation of manufacturing systems called Cognitive Cyber-Physical Production Systems (C-CPPS), which are based on CPPS concepts and incorporate cognitive technologies and artificial intelligence. This paper presents the revolutionary development of manufacturing and manufacturing systems through the industrial revolutions and the evolution of the role of humans in manufacturing systems towards Industry 5.0.
Natural Peptide Toxins as an Option for Renewed Treatment of Type 2 Vasopressin Receptor-Related Diseases
Authors: Gilles Nicolas
Published: April 2023

The type 2 vasopressin receptor (V2R) is expressed in the kidneys, and it is the keystone of water homeostasis. Under the control of the antidiuretic hormone vasopressin, the V2R ensures vital functions, and any disturbance has dramatic consequences. Despite decades of research to develop drugs capable of activating or blocking V2R function to meet real medical needs, only one agonist and one antagonist are virtually used today. These two drugs cover only a small portion of patients’ needs, leaving millions of patients without treatment. Natural peptide toxins known to act selectively and at low doses on their receptor target could offer new therapeutic options.
Correction: Li et al. Identification and Functional Analysis of the Cell Proliferation Regulator, Insulin-like Growth Factor 1 (IGF1) in Freshwater Pearl Mussel (Hyriopsis cumingii). Biology 2022, 11, 1369
Authors: Li Xuenan , Feng Shangle , Wang He , Shen Xiaoya , Chen Yige , Xuan Xingrong , Fu Yuanshuai , Bai Zhiyi , Li Wenjuan
Published: April 2023

In the original publication [...]
One Scaffold, Two Conformations: The Ring-Flip of the Messenger InsP8 Occurs under Cytosolic Conditions
Authors: Kurz Leonie , Schmieder Peter , Veiga Nicolás , Fiedler Dorothea
Published: April 2023

Inositol poly- and pyrophosphates (InsPs and PP-InsPs) are central eukaryotic messengers. These very highly phosphorylated molecules can exist in two distinct conformations, a canonical one with five phosphoryl groups in equatorial positions, and a “flipped” conformation with five axial substituents. Using 13C-labeled InsPs/PP-InsPs, the behavior of these molecules was investigated by 2D-NMR under solution conditions reminiscent of a cytosolic environment. Remarkably, the most highly phosphorylated messenger 1,5(PP)2-InsP4 (also termed InsP8) readily adopts both conformations at physiological conditions. Environmental factors—such as pH, metal cation composition, and temperature—strongly influence the conformational equilibrium. Thermodynamic data revealed that the transition of InsP8 from the equatorial to the axial conformation is, in fact, an exothermic process. The speciation of InsPs and PP-InsPs also affects their interaction with protein binding partners; addition of Mg2+ decreased the binding constant Kd of InsP8 to an SPX protein domain. The results illustrate that PP-InsP speciation reacts very sensitively to solution conditions, suggesting it might act as an environment-responsive molecular switch.
Somatic Copy Number Alteration in Circulating Tumor DNA for Monitoring of Pediatric Patients with Cancer
Authors: Ruas Juliana Silveira , Silva Felipe Luz Torres , Euzébio Mayara Ferreira , Biazon Tássia Oliveira , Daiggi Camila Maia Martin , Nava Daniel , Franco Mayra Troiani , Cardinalli Izilda Aparecida , Cassone Alejandro Enzo , Pereira Luiz Henrique , Seidinger Ana Luiza , Maschietto Mariana , Jotta Patricia Yoshioka
Published: April 2023

Pediatric tumors share few recurrent mutations and are instead characterized by copy number alterations (CNAs). The cell-free DNA (cfDNA) is a prominent source for the detection of cancer-specific biomarkers in plasma. We profiled CNAs in the tumor tissues for further evaluation of alterations in 1q, MYCN and 17p in the circulating tumor DNA (ctDNA) in the peripheral blood at diagnosis and follow-up using digital PCR. We report that among the different kinds of tumors (neuroblastoma, Wilms tumor, Ewing sarcoma, rhabdomyosarcoma, leiomyosarcoma, osteosarcoma and benign teratoma), neuroblastoma presented the greatest amount of cfDNA, in correlation with tumor volume. Considering all tumors, cfDNA levels correlated with tumor stage, metastasis at diagnosis and metastasis developed during therapy. In the tumor tissue, at least one CNA (at CRABP2, TP53, surrogate markers for 1q and 17p, respectively, and MYCN) was observed in 89% of patients. At diagnosis, CNAs levels were concordant between tumor and ctDNA in 56% of the cases, and for the remaining 44%, 91.4% of the CNAs were present only in cfDNA and 8.6% only in the tumor. Within the cfDNA, we observed that 46% and 23% of the patients had MYCN and 1q gain, respectively. The use of specific CNAs as targets for liquid biopsy in pediatric patients with cancer can improve diagnosis and should be considered for monitoring of the disease response.