bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Investigation of the Dynamic Response of a Sliding Bearing System under Different Oil Pressure Levels
Authors: Feng Zhiguo , Ding Qiang , Cai Yinhui , Sun Weipeng
Published: September 2022

A sliding bearing system performs a critical function in a rotating machinery system and provides support for the rotating components. In this paper, a single-rotor experimental rig and a dual-rotor sliding bearing experimental rig were independently established, and the dynamic response of the test systems are investigated. To study the effect of oil pressure levels on the dynamic response of the test systems under the single-rotor system and the dual-rotor system, the vibration response of single-rotor and dual-rotor systems ware analyzed by different oil inlet pressures to study the effect of oil pressure levels on the dynamic response of the test systems under the single-rotor system and the dual-rotor system. Among them, the single rotor system under a large oil pressure system is mostly periodic and more stable. While under different inlet oil pressure conditions applied in the experiments, the dynamic motion of the dual-rotor bearing system is relatively stable in both the subcritical and supercritical speed regions. However, the oil whirl and oil whip of the dual-rotor system were observed at the critical speed, which causes the system into unstable multiperiodic motions. In general, this paper can give some insight into the dynamic response of the sliding bearing system influenced by different oil pressure levels.
High-Speed Alumina Stereolithography
Authors: Spirrett Fiona , Ito Tatsuya , Kirihara Soshu
Published: September 2022

The additive manufacturing of ceramics offers a reliable and repeatable method for fabricating parts with complex geometries. To compete with conventional ceramic forming methods, the time and cost associated with material and process optimization for ceramic stereolithography should be improved. Computational analysis methods can be utilized to reduce the number of experimental steps required for material and process optimization. This work used the discrete element method and ray tracing analyses to predict suitable material parameters and processing conditions for ceramic stereolithography. The discrete element method was used to create alumina particle dispersion models to predict suitable paste compositions, and ray tracing was used to predict suitable laser power and scan speed to achieve a sufficient curing depth for stereolithography processing. The predicted conditions of paste composition and processing parameters were comparable to experimental values, reducing the number of experimental iterations required for process optimization. Furthermore, suitable processing parameters for high-speed fabrication by stereolithography was predicted, achieving a processing speed much faster than previously reported ceramic stereolithography. The reduction in process optimization timeline, and the increase in fabrication speed, could increase the appeal of ceramic stereolithography to industry.
Multi-Scale Feature Fusion for Interior Style Detection
Authors: Yaguchi Akitaka , Ono Keiko , Makihara Erina , Ikushima Naoya , Nakayama Tomomi
Published: September 2022

Text-based search engines can extract various types of information when a user enters an appropriate search query. However, a text-based search often fails in image retrieval when image understanding is needed. Deep learning (DL) is often used for image task problems, and various DL methods have successfully extracted visual features. However, as human perception differs for each individual, a dataset with an abundant number of images evaluated by human subjects is not available in many cases, although DL requires a considerable amount of data to estimate space ambiance, and the DL models that have been created are difficult to understand. In addition, it has been reported that texture is deeply related to space ambiance. Therefore, in this study, bag of visual words (BoVW) is used. By applying a hierarchical representation to BoVW, we propose a new interior style detection method using multi-scale features and boosting. The multi-scale features are created by combining global features from BoVW and local features that use object detection. Experiments on an image understanding task were conducted on a dataset consisting of room images with multiple styles. The results show that the proposed method improves the accuracy by 0.128 compared with the conventional method and by 0.021 compared with a residual network. Therefore, the proposed method can better detect interior style using multi-scale features.
Characterization of Tissue Equivalent Materials Using 3D Printing for Patient-Specific DQA in Radiation Therapy
Authors: Choi Yona , Jang Young Jae , Kim Kum Bae , Bahng Jungbae , Choi Sang Hyoun
Published: September 2022

Three-dimensional printing technology has the advantage of facilitating the construction of complex three-dimensional shapes. For this reason, it is widely used in medical and radiological fields. However, few materials with high electron density similar to that of bone exist for fabricating a human phantom. In this study, commercially available filament materials were used with an FDM 3D printer to perform delivery quality assurance (DQA) and were evaluated for medical use. For the bone filament material, BaSO4 was synthesized in five ratios of 2%, 4%, 6%, 8%, and 10% with 40% PBAT and 50~58% PLA. The electron density for the 3D printing material fabricated was obtained using kV energy CT and compared with the electron density of human organs and bones. The radiation beam properties of the 3D printed structures were analyzed as films for treatment using a linear accelerator. As a result, by changing the infill density of the material, it was possible to produce a material similar to the density of human organs, and a homogeneous bone material with HU values ranging from 371 ± 9 to 1013 ± 28 was produced. The 3D printing material developed in this study is expected to be usefully applied to the development of a patient-specific phantom to evaluate the accuracy of radiotherapy.
The Efficacy, Phytotoxicity, and Safety of Liquid Ethyl Formate Used to Control the Grape (Campbell Early) Quarantine Pest Pseudococcus comstocki
Authors: Lee Byung-Ho , Hong Ki-Jeong , Park Min-Goo
Published: September 2022

Mealybugs found on grapes during quarantine in Korea are fumigated with methyl bromide (MB). However, MB is an ozone-depleting, highly toxic pesticide; therefore, the International Plant Protection Convention, a representative organization involved in quarantine, recommends using MB alternatives. We evaluated the feasibility of using liquid ethyl formate (EF), a new EF formulation, to control mealybugs (Pseudococcus comstocki) on grapes (Campbell Early). Large-scale tests and the comparative evaluation of EF and MB desorption from grapes were conducted during the simulated 72-h post-fumigation period. Dose–response tests showed that the EF concentration and time product causing 99% mortality (LCt99) at 5 °C was 47.36 and 145.85 g h/m3 for adults and eggs, respectively. EF treatment at 70 g/m3 for 4 h at 5 °C with a loading ratio of 10% achieved an LCt99 of 145.85 g h/m3 on P. comstocki, confirming EF efficacy on mealybugs without phytotoxic effects on grapes. EF fumigation may also be safer because EF concentrations were maintained at less than 100 ppm, the specified exposure limit of EF; meanwhile, those of MB were higher than the exposure limit (1 ppm). Therefore, liquid EF can be used as a safer alternative to MB in phytosanitary treatments of grapes to control P. comstocki.
Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
Authors: Li Jialei , Zhou Chong , Xin Gongfeng , Long Guanxu , Zhang Wenliang , Li Chao , Zhuang Peizhi , Yao Zhanyong
Published: September 2022

This paper presents a numerical investigation on the improvement mechanism of dynamic compaction (DC) in saturated soil of a weak layer with high levels of groundwater, using an improved fluid–solid coupling method with a Drucker–Prager–Cap soil model. The numerical model is verified by comparing with published test data at first, which show that the dual-phase coupling method can approximately reflect the development law of excess pore water pressure and the improvement effect of layered saturated soil foundation under DC. Then, based on the numerical model, the influences of the thickness and depth of the weak layer as well as tamping energy on the development and dissipation of excess pore water pressure, effective stress, and the relative degree of reinforcement during DC were investigated. The results showed that the embedment depth and thickness of the weak interlayer may greatly affect the effective reinforcement depth of DC. Meanwhile, the tamping energy and the groundwater table also play a great role in the improvement of the layered saturated soil under DC. The groundwater table should be lowered by dewatering or adding a drainage layer to achieve a better compaction effect during DC.
GisaxStudio—An Open Platform for Analysis and Simulation of GISAXS from 3D Nanoparticle Lattices
Authors: Mekterović Igor , Svalina Gabrijela , Isaković Senad , Mičetić Maja
Published: September 2022

Grazing-incidence small-angle X-ray scattering (GISAXS) is a powerful method for the structural analysis of ordered arrays of nanoparticles, quantum dots, or similar objects. However, for the correct interpretation of the measured GISAXS intensity distributions, a proper data analysis, including a suitable model, is required. Here, we demonstrate a software platform, GisaxStudio, aimed at the analysis and simulation of 2D GISAXS intensity distributions from ordered lattices of different nanoparticles. It contains several models that satisfactorily describe the GISAXS from 3D lattices or crystals of nanoparticles prepared by the self-assembly processes, pre-pattering, or ion-beam interaction with the material within their tracks. It also supports different shapes of nanoparticles, including core-shell structure with the center of core possibly displaced from the center of the shell. The software is very useful for fast and accurate GISAXS data analysis.
SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain
Authors: Bai Rui , Li Li , Zhang Shanqing , Lu Jianfeng , Chang Chin-Chen
Published: September 2022

Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking.
Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information
Authors: Zhang Qinghui , Wu Meng , Lv Pengtao , Zhang Mengya , Lv Lei
Published: September 2022

Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models.
Career in Cloud Computing: Exploratory Analysis of In-Demand Competency Areas and Skill Sets
Authors: Ozyurt Ozcan , Gurcan Fatih , Dalveren Gonca Gokce Menekse , Derawi Mohammad
Published: September 2022

This study aims to investigate up-to-date career opportunities and in-demand competence areas and skill sets for cloud computing (CC), which plays a crucial role in the rapidly developing teleworking environments with the COVID-19 pandemic. In this paper, we conducted a semantic content analysis on 10,161 CC job postings using semi-automated text-mining and probabilistic topic-modeling procedures to discover the competency areas and skill sets as semantic topics. Our findings revealed 22 competency areas and 46 skills, which reflect the interdisciplinary background of CC jobs. The top five competency areas for CC were identified as “Engineering”, “Development”, “Security”, “Architecture”, and “Management”. Besides, the top three skills emerged as “Communication Skills”, “DevOps Tools”, and “Software Development”. Considering the findings, a competency-skill map was created that illustrates the correlations between CC competency areas and their related skills. Although there are many studies on CC, the competency areas and skill sets required to deal with cloud computing have not yet been empirically studied. Our findings can contribute to CC candidates and professionals, IT organizations, and academic institutions in understanding, evaluating, and developing the competencies and skills needed in the CC industry.
Energy Forecasting in a Public Building: A Benchmarking Analysis on Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) Networks
Authors: Huang Junhui , Algahtani Mohammed , Kaewunruen Sakdirat
Published: September 2022

A primary energy consumption and CO2 emission source stems from buildings and infrastructures due to rapid urbanisation and social development. An accurate method to forecast energy consumption in a building is thus critically needed to enable successful management of adaptive energy consumption and ease the level of CO2 emission. However, energy forecasting for buildings, especially residential buildings, has several challenges, such as significant variations in energy usage patterns due to unpredicted demands of the residences and some intricate factors, which can randomly affect the patterns. Traditional forecasting approaches require a tremendous number of inputs needed for building physic models and variations often exist between as-built and as-designed buildings in reality. Most recent studies have adopted only ambient weather conditions, building components, and the occupant’s behaviours. As a result, in order to take into account the complexity of factors that can affect the building energy model development and its computation, we develop advanced machine learning models driven by the inherent electricity consumption pattern associated with the day and time. In this study, we demonstrate benchmarking results derived from three different machine learning algorithms, namely SVR, XGBoost, and LSTM, trained by using 1-year datasets with sub-hourly (30 min) temporal granularity to determine the outperformed predictor. Ultimately, the machine learning model robustness and performance on a basis of the coefficient of variation (CV) obtained by the SVR is benchmarked across XGBoost and LSTM trained by the same datasets containing attributes related to the building type, data size, and temporal granularity. The insight stemming from this study indicates that the suitable choice of the machine learning models for building energy forecasts largely depends on the natural characteristics of building energy data. Hyperparameter tuning or mathematical modification within an algorithm may not be sufficient to attain the most accurate machine learning model for building energy forecast.
Enhancing 3D Reconstruction Model by Deep Learning and Its Application in Building Damage Assessment after Earthquake
Authors: Hong Zhonghua , Yang Yahui , Liu Jun , Jiang Shenlu , Pan Haiyan , Zhou Ruyan , Zhang Yun , Han Yanling , Wang Jing , Yang Shuhu , Zhong Changyue
Published: September 2022

A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage level of the buildings, which cannot provide the multi-view information of the damaged building, resulting in inaccurate assessment results. According to the knowledge of the authors, there is no related research using the deep-learning-based 3D reconstruction method for the evaluation of building damage. In this paper, we first applied the deep-learning-based MVS model to reconstruct the 3D model of the buildings after an earthquake using multi-view UAV images, to assist the building damage assessment task. The method contains three main steps. Firstly, the camera parameters are calculated. Then, 3D reconstruction is conducted based on CasMVSNet. Finally, a building damage assessment is performed based on the 3D reconstruction result. To evaluate the effectiveness of the proposed method, the method was tested in multi-view UAV aerial images of Yangbi County, Yunnan Province. The results indicate that: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, which can meet the timeliness requirement of post-earthquake rescue and damage assessment. In addition, the memory consumption of CasMVSNet is the lowest; (2) CasMVSNet exhibits the best 3D reconstruction result in both high and small buildings; (3) the proposed method can provide detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The results of the building damage assessment are very similar to the results of the field survey.
Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos
Authors: Döllinger Michael , Schraut Tobias , Henrich Lea A. , Chhetri Dinesh , Echternach Matthias , Johnson Aaron M. , Kunduk Melda , Maryn Youri , Patel Rita R. , Samlan Robin , Semmler Marion , Schützenberger Anne
Published: September 2022

Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold dynamics in the larynx are diverse and technically advancing. To consider resulting “concepts shifts” for neural network (NN)-based image processing, re-training of already trained and used NNs is necessary to allow for sufficiently accurate image processing for new recording modalities. We propose and discuss several re-training approaches for convolutional neural networks (CNN) being used for HSV image segmentation. Our baseline CNN was trained on the BAGLS data set (58,750 images). The new BAGLS-RT data set consists of additional 21,050 images from previously unused HSV systems, light sources, and different spatial resolutions. Results showed that increasing data diversity by means of preprocessing already improves the segmentation accuracy (mIoU + 6.35%). Subsequent re-training further increases segmentation performance (mIoU + 2.81%). For re-training, finetuning with dynamic knowledge distillation showed the most promising results. Data variety for training and additional re-training is a helpful tool to boost HSV image segmentation quality. However, when performing re-training, the phenomenon of catastrophic forgetting should be kept in mind, i.e., adaption to new data while forgetting already learned knowledge.
Large Scale Model Test Study of Foundation Pit Supported by Pile Anchors
Authors: Su Tiantao , Zhou Yong , Wang Zhengzhen , Ye Shuaihua
Published: September 2022

Due to the special time–space and environmental effects of the foundation pit, there are many unstable factors in the construction process of the field test. The indoor model test can avoid many uncertainties in the construction process due to its operability, which can reduce the interference with the test results and improve the accuracy of the test. In order to further discuss the force-bearing characteristics and deformation laws of loess pits’ support structure in Northwest China, a large model test of foundation pit supported by a pile anchor with a geometric similarity ratio of 1:10 was designed and completed. The force and deformation characteristics of the support structure were systematically studied by simulating the conditions of additional load at the pit edge, soil layered excavated, and anchors tensioned. The test results show that: for the pile-anchor support structure, the anchors have significant limiting effects on the displacement of the piles. Especially, when the position of the first row of anchors is closer to the pile top, the displacement of the pile is smaller. The stress state of the piles was changed by the prestressed anchor. The passive stress state of piles is changed from one side of tension and the other side of compression to the active stress state of “S” shape, which makes the distribution of the bending moment of piles more reasonable. The measured earth pressure in the process of soil unloading has a nonlinear distribution, which is different from the classical Rankine earth pressure distribution; specifically, the passive earth pressure in front of the pile is more obvious. In addition, the prestress applied to the anchors has a more significant effect on the internal forces of the other anchors. Compared with sequential tensioning, the prestress loss caused by interval hole tensioning is significantly reduced. The greater the number of spaced holes, the smaller the prestress loss and the better the anchoring effect of the anchor. The results of the study can provide reference for similar model tests, and also for related engineering applications.
Study on the Mechanical Properties of Loess Improved by Lignosulfonate and Its Mechanism Analysis and Prospects
Authors: Xu Ping , Lin Qingwei , Fang Lingyun
Published: September 2022

As an organic material with large reserves and environmental protection, the application of lignin in loess improvement can greatly improve the engineering properties of loess, such as water loss disintegration and water collapsibility. This paper summarizes the main achievements of lignosulfonate in improving loess over the past five years and discusses and analyzes its microstructure, mechanical properties, and mechanism of action. The microscopic results show that the incorporation of lignosulfonate produces a three-dimensional network structure, which can effectively cement loess particles and improve soil strength and impermeability, and the Quantity of this network structure depends on the amount of lignosulfonate. An excessive amount of lignosulfonate preferentially combines with itself due to its cohesive and chelating properties, resulting in an increase in soil porosity and a decrease in the improvement effect. Based on the macroscopic and microscopic tests, it was concluded that the optimal dosage reference range of lignosulfonate-improved loess is mostly between 1% and 1.5%. However, considering the limited number of references in this paper, the differences in physical properties of the experimental loess used in the literature, and the different curing ages, the optimal dosage needs to be further studied. The discussion and analysis in this paper provide a reference for research on lignosulfonate-improved soil, as well as new knowledge and information for more efficient engineering applications of lignin-improved loess.
K-Essence Lagrangians of Polytropic and Logotropic Unified Dark Matter and Dark Energy Models
Authors: Chavanis Pierre-Henri
Published: September 2022

We determine the k-essence Lagrangian of a relativistic barotropic fluid. The equation of state of the fluid can be specified in different manners depending on whether the pressure is expressed in terms of the energy density (model I), the rest-mass density (model II), or the pseudo rest-mass density for a complex scalar field in the Thomas-Fermi approximation (model III). In the nonrelativistic limit, these three formulations coincide. In the relativistic regime, they lead to different models that we study exhaustively. We provide general results valid for an arbitrary equation of state and show how the different models are connected to each other. For illustration, we specifically consider polytropic and logotropic dark fluids that have been proposed as unified dark matter and dark energy models. We recover the Born-Infeld action of the Chaplygin gas in models I and III and obtain the explicit expression of the reduced action of the logotropic dark fluid in models II and III. We also derive the two-fluid representation of the Chaplygin and logotropic models. Our general formalism can be applied to many other situations such as Bose-Einstein condensates with a |φ|4 (or more general) self-interaction, dark matter superfluids, and mixed models.
Effects of Supplementary Irrigation on Soil Respiration of Millet Farmland in a Semi-Arid Region in China
Authors: Gao Xiaoli , Zhao Nan , Lu Yuhui , Han Xuan , Yang Zhiping
Published: September 2022

Carbon dioxide (CO2) is recognized as key part of evaluating the soil environment, and the soil respiration rate is an effective indicator of CO2 emission. To explore the influence and coupling mechanism of irrigation on the soil respiration of millet farmland in the Northern Shanxi Province in China, conventional rainfed (CK) and supplementary irrigation (W1) at the late jointing stage were conducted. The soil respiration rate and carbon emission flux in millet farmland under different treatments were observed. The relationship between soil respiration rate and soil physical–chemical properties and the crop growth index was further analyzed. The result showed that the soil respiration rate and carbon emission flux of W1 were higher than those of CK treatment. The comparison of the linear regression correlation between soil respiration rate and soil physical–chemical properties revealed that the major regulating factors of the soil respiration rate were soil moisture (<10.6%) followed by soil pH, soil moisture (>10.6%), soil temperature, and finally soil organic matter content. There are uncertainties regarding the soil moisture content variation range in soil respiration. Moreover, supplementary irrigation promoted the growth indexes, yield, and irrigation water use efficiency in millet farmland. Further research with less irrigation treatment is necessary for exploring an optimization model of water use efficiency and low carbon dioxide emissions in millet fields, which would be helpful to realize agricultural water utilization and a “carbon peak” in the sense of farmland.
Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique
Authors: Sukor Abdul Syafiq Abdull , Cheik Goh Chew , Kamarudin Latifah Munirah , Mao Xiaoyang , Nishizaki Hiromitsu , Zakaria Ammar , Syed Zakaria Syed Muhammad Mamduh
Published: September 2022

In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers’ conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97.
Potential of Pseudarthrobacter chlorophenolicus BF2P4-5 as a Biofertilizer for the Growth Promotion of Tomato Plants
Authors: Issifu Muazu , Songoro Edinah K. , Onguso Justus , Ateka Elijah Miinda , Ngumi Victoria Wambui
Published: September 2022

BF2P4-5 was isolated from the rhizosphere soil of tomato plants, and its potential to promote plant growth was investigated in tomato plants. An in vitro test revealed that the strain could fix nitrogen, solubilize phosphate and potassium, and synthesize indole acetic acid. The bacterial strain was identified and characterized as a kind of Pseudarthrobacter chlorophenolicus based on the analysis of culture characteristics, physiological and biochemical characteristics, and 16S rRNA gene sequence (GenBank accession number OP135548.1). pH 7.0, 15% NaCl, and 35 °C temperature were ideal for optimal strain growth under culture conditions. Tomato plants grown on a cocopeat substrate were inoculated with BF2P4-5 suspension (OD600 2.0). Positive control plants were inoculated with Nitrogen Phosphorus Potassium (NPK) fertilizer. This BF2P4-5 strain and NPK treatments were complemented with a negative control, in which only tap water was applied to tomato roots, thus, establishing three distinct treatment modalities with five replications each. Two months of greenhouse trials of inoculated tomato plants improved growth parameters. Interestingly, in most of the growth metrics evaluated, tomato plants treated with strain BF2P4-5 showed little to no variation with NPK fertilizer treatment, including plant height, stem length, girth, leaf number per plant, and chlorophyll content, when compared to uninoculated plants. Furthermore, the conditions for the cocopeat plants, including pH, EC, and moisture, were within acceptable limits. Furthermore, inoculation with BF2P4-5 increased the nitrogen, phosphorus, and potassium content available in the cocopeat medium. The results showed that the strain exhibited traits for the promotion of plant growth and could be deployed as an eco-friendly microbial biofertilizer for tomatoes and probably other essential crops.
Supporting Meteorologists in Data Analysis through Knowledge-Based Recommendations
Authors: Reis Thoralf , Funke Tim , Bruchhaus Sebastian , Freund Florian , Bornschlegl Marco X. , Hemmje Matthias L.
Published: September 2022

Climate change means coping directly or indirectly with extreme weather conditions for everybody. Therefore, analyzing meteorological data to create precise models is gaining more importance and might become inevitable. Meteorologists have extensive domain knowledge about meteorological data yet lack practical data analysis skills. This paper presents a method to bridge this gap by empowering the data knowledge carriers to analyze the data. The proposed system utilizes symbolic AI, a knowledge base created by experts, and a recommendation expert system to offer suiting data analysis methods or data pre-processing to meteorologists. This paper systematically analyzes the target user group of meteorologists and practical use cases to arrive at a conceptual and technical system design implemented in the CAMeRI prototype. The concepts in this paper are aligned with the AI2VIS4BigData Reference Model and comprise a novel first-order logic knowledge base that represents analysis methods and related pre-processings. The prototype implementation was qualitatively and quantitatively evaluated. This evaluation included recommendation validation for real-world data, a cognitive walkthrough, and measuring computation timings of the different system components.
An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews
Authors: Shaddeli Aitak , Soleimanian Gharehchopogh Farhad , Masdari Mohammad , Solouk Vahid
Published: September 2022

The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.
Effects of Different Titanium Surfaces Created by 3D Printing Methods, Particle Sizes, and Acid Etching on Protein Adsorption and Cell Adhesion, Proliferation, and Differentiation
Authors: Jin Max , Chung Haseung , Kwon Patrick , Akkouch Adil
Published: September 2022

The surfaces of 3D printed titanium prostheses have major impacts on the clinical performance of the prostheses. To investigate the surface effects of the products generated by 3D printed titanium on osseointegration, six surface types of titanium discs produced by the direct metal laser sintering (DMLS) and electron beam melting (EBM) methods, with two sizes of titanium particles and post-printing acid etching, were used to examine the surface topography and to explore the protein adsorption, pro-inflammatory cytokine gene expressions, and MC3T3-E1 cell adhesion, proliferation, and differentiation. The EBM-printed disc showed a stripy and smooth surface without evidence of the particles used, while the DMLS surface contained many particles. After acid etching, small particles on the DMLS surface were removed, whereas the large particles were left. Moreover, distinct proteins with low molecular weights were attached to the 3D printed titanium discs but not to the pre-printing titanium particles. The small titanium particles stimulated the highest TNF-α and IL-6 gene expressions at 24 h. The alizarin red content and osteocalcin gene expression at day 21 were the highest in the groups of acid-etched discs printed by DMLS with the small particles and by EBM. Therefore, the acid-treated surfaces without particles favor osteogenic differentiation. The surface design of 3D printed titanium prostheses should be based on their clinical applications.
Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review
Authors: Issa Julien , Abou Chaar Mazen , Kempisty Bartosz , Gasiorowski Lukasz , Olszewski Raphael , Mozdziak Paul , Dyszkiewicz-Konwińska Marta
Published: September 2022

This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.
Checkpoint Inhibitors Immunotherapy in Metastatic Melanoma: When to Stop Treatment?
Authors: De Risi Ivana , Sciacovelli Angela Monica , Guida Michele
Published: September 2022

Background: Immune checkpoint inhibition (ICI) has significantly improved the survival of metastatic melanoma (MM) with a significant proportion of patients obtaining long-lasting responses. However, ICI also exposes patients to new, heavy, and sometimes irreversible toxicities. Thus, identifying the minimal amount of treatment time is extremely urgent. Methods: We researched English peer-reviewed literature from electronic databases (MEDLINE and PubMed) until July 2022 with the aim of evaluating the clinical outcomes after the cessation of ICI therapy due to elective study plans, clinician–patient sharing, and adverse events. Results: Although most of the data are from retrospective studies, considering that most patients with major responses maintain it after treatment cessation, it is proposed that for complete response (CR)/near CR, a further six months of therapy after best response may be considered enough. For partial response (PR) or stable disease (SD), treatment must be continued for at least 2 years and, in some cases, indefinitely, based on residual disease, the patient’s will, and the toxic profile. Of note, in spite of the best response, 25–30% of patients relapsed, and, when retreated, responded far less than in front-line treatment. Conclusions: Most of the data being from retrospective and heterogeneous experiences, their grade of evidence is limited and no consensus has been reached on the optimal treatment duration. Controlled prospective studies are needed.
Application of Plant-Derived Nanoparticles (PDNP) in Food-Producing Animals as a Bio-Control Agent against Antimicrobial-Resistant Pathogens
Authors: Ajose Daniel Jesuwenu , Abolarinwa Tesleem Olatunde , Oluwarinde Bukola Opeyemi , Montso Peter Kotsoana , Fayemi Omolola Esther , Aremu Adeyemi Oladapo , Ateba Collins Njie
Published: September 2022

Antibiotics are regularly used in animal husbandry to treat diseases. This practice is beneficial to animals’ health and helps ensure food security. However, the misuse of antibiotics, especially in food-producing animals, has resulted in the advent of antimicrobial resistance (AMR) and its dissemination among foodborne pathogens. The occurrence of AMR in bacteria pathogens that cause infections in animals and those associated with food spoilage is now considered a global health concern affecting humans, animals and the environment. The search for alternative antimicrobial agents has kindled the interest of many researchers. Among the alternatives, using plant-derived nanoparticles (PDNPs) for treating microbial dysfunctions in food-producing animals has gained significant attention. In traditional medicine, plant extracts are considered as safe, efficient and natural antibacterial agents for various animal diseases. Given the complexity of the AMR and concerns about issues at the interface of human health, animal health and the environment, it is important to emphasize the role of a One Health approach in addressing this problem. This review examines the potential of PDNPs as bio-control agents in food-producing animals, intending to provide consumers with microbiologically safe food while ensuring food safety and security, better health for animals and humans and a safe environment.
Depletion of CD4 and CD8 T Cells Reduces Acute Disease and Is Not Associated with Hearing Loss in ML29-Infected STAT1-/- Mice
Authors: Reyna Rachel A. , Maruyama Junki , Mantlo Emily K. , Manning John T. , Taniguchi Satoshi , Makishima Tomoko , Lukashevich Igor S. , Paessler Slobodan
Published: September 2022

Lassa virus (LASV) is a zoonotic virus endemic to western Africa that can cause a potentially lethal and hemorrhagic disease, Lassa fever (LF). Survivors suffer a myriad of sequelae, most notably sudden onset sensorineural hearing loss (SNHL), the mechanism of which remains unclear. Unfortunately, studies aiming to identify the mechanism of these sequelae are limited due to the biosafety level 4 (BSL4) requirements of LASV itself. ML29, a reassortant virus proposed as an experimental vaccine candidate against LASV, is potentially an ideal surrogate model of LF in STAT1-/- mice due to similar phenotype in these animals. We intended to better characterize ML29 pathogenesis and potential sequelae in this animal model. Our results indicate that while both CD4 and CD8 T cells are responsible for acute disease in ML29 infection, ML29 induces significant hearing loss in a mechanism independent of either CD4 or CD8 T cells. We believe that this model could provide valuable information for viral-associated hearing loss in general.
A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction
Authors: K. Anitha , S. Radhika , C. Kavitha , Lai Wen-Cheng , Srividhya S. R. , K. Naresh
Published: September 2022

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.
Enhanced Response for Foodborne Pathogens Detection by Au Nanoparticles Decorated ZnO Nanosheets Gas Sensor
Authors: Zhao Cheng , Xu Shanshan , Wei Jing , Xie Siqi , Wei Jinlei , Han Jingting , Zhang Zhaohuan , Liu Haiquan , Cheng Jinsheng , Zhao Yong , Zhu Yongheng
Published: September 2022

Listeria monocytogenes is a hazardous foodborne pathogen that is able to cause acute meningitis, encephalitis, and sepsis to humans. The efficient detection of 3-hydroxy-2-butanone, which has been verified as a biomarker for the exhalation of Listeria monocytogenes, can feasibly evaluate whether the bacteria are contained in food. Herein, we developed an outstanding 3-hydroxy-2-butanone gas sensor based on the microelectromechanical systems using Au/ZnO NS as a sensing material. In this work, ZnO nanosheets were synthesized by a hydrothermal reaction, and Au nanoparticles (~5.5 nm) were prepared via an oleylamine reduction method. Then, an ultrasonic treatment was carried out to modified Au nanoparticles onto ZnO nanosheets. The XRD, BET, TEM, and XPS were used to characterize their morphology, microstructure, catalytic structure, specific surface area, and chemical composition. The response of the 1.0% Au/ZnO NS sensors vs. 25 ppm 3-hydroxy-2-butanone was up to 174.04 at 230 °C. Moreover, these sensors presented fast response/recovery time (6 s/7 s), great selectivity, and an outstanding limit of detection (lower than 0.5 ppm). This work is full of promise for developing a nondestructive, rapid and practical sensor, which would improve Listeria monocytogenes evaluation in foods.
Precision Medicine in Parkinson’s Disease: From Genetic Risk Signals to Personalized Therapy
Authors: Straccia Giulia , Colucci Fabiana , Eleopra Roberto , Cilia Roberto
Published: September 2022

Understanding the pathophysiology and genetic background of Parkinson’s disease (PD) increases the likelihood of developing effective disease-modifying therapeutic strategies. In particular, the discovery of genetic variants causing or increasing the risk for PD has contributed to refining the clinical, biological, and molecular classification of the disease and has offered new insights into sporadic forms. It is even more evident that specific genetic mutations can show different responses to pharmacological and device-aided therapies. To date, several agents acting on multiple PD-causing pathogenic pathways have been tested as disease-modifying strategies, with disappointing results. This may be caused by the recruitment of PD populations whose underlying molecular pathophysiology is heterogeneous. We believe that an effective model of personalized medicine must be prioritized in the near future. Here, we review the current therapeutic options under clinical and preclinical development for PD and discuss the key pending questions and challenges to face for successful clinical trials. Furthermore, we provide some insights into the role of genetics in guiding the decision-making process on symptomatic and device-aided therapies for PD in daily clinical practice.