bluuebunny/crossref_metadata_2025 huggingface dataset:
Geological Distribution of the Miocene Carbonate Platform in the Xisha Sea Area of the South China Sea, and Its Implications for Hydrocarbon Exploration
Authors: Yang Zhen , Zhang Guangxue , Wu Shiguo , Zhu Youhua , Wu Cong , Zhang Li , Liu Songfeng , Yan Wei , Sun Ming , Zhang Yaoming , Du Xuebin , Xu Chenlu
Published: November 2022

The newly collected seismic data and the existing drilling data provide a good opportunity to better understand the carbonate platform distribution characteristics and the hydrocarbon resource potential in the Xisha sea area of the South China Sea. Based on the seismic data and the reflection characteristics of the carbonate platform’s edge, three boundary indicators were established: abrupt lithological interfaces, fault interfaces, and tidal channels. Combined with the regional geological settings, its spatial and temporal distribution was clearly identified for the first time. The development of the Miocene carbonate platform in the Xisha sea area is divided into six phases, which are further assigned to three evolutionary stages: the bloom stage, the recession stage, and the submerged stage. The sedimentary facies belt of the carbonate platform in each stage is well developed, and the reefs are mainly distributed on the west and southwest edges of the platform. The analysis of the data indicates that the area of the reef and carbonate platform reached 80,000 km2 during the mature period, followed by a retreat period where the scale decreased with the platform’s decline. The Miocene carbonate rocks in the Xisha sea area are widely distributed. They have experienced multiple periods of exposure and infiltration, which further improved the quality of their physical properties for hydrocarbon reservoirs. According to the regional hydrocarbon geological conditions in this area—including the source rock, migration system and the capping layer—the hydrocarbon accumulation potential is preliminarily discussed in this paper. A reservoir model of the reef and carbonate platform is established, which is proposed as typical characteristics of “lower generation, upper accumulation”. It is pointed out that the carbonate platform in the Xisha sea area adjacent to the Huaguang Sag in the Qiongdongnan Basin and the northern Zhongjiannan Basin is a potential area for oil and gas exploration.
Research on Deep Coalbed Methane Localized Spotting and Efficient Permeability Enhancement Technology
Authors: Zhang Jiayong , Niu Yongzhen , Chen Jian , Guo Yanlei , Guo Liwen
Published: November 2022

To solve the bottleneck problem of low deep coal seam permeability and difficult coalbed methane (CBM) mining. Combining hydraulic splitting technology and directional drilling technology, a directional hydraulic splitting enhancement method of deep CBM mining was proposed. The selection equation for the directional hydraulic splitting of deep coalbed was constructed. The numerical simulation reveals the variation in coal fractures around different split angles. The split angle under the maximum coal damage effect was obtained. It was found that the combined effect of the double crack damage disturbance region led to reciprocal stress fluctuations during crack development and, eventually, the formation of a zigzag fracture. The larger the splitting angle, the larger the fissure development length and the larger the coal-damaged area. A double crack takes 25% less time to complete propagation than a single crack. When the splitting angle is 90°, the disturbed area occupies 2/3 of the area around the borehole, and the overall fracturing effect is the best. In the application process, the new directional hydraulic splitting technology can increase CBM mining by 5.08%, greatly improve CBM mining efficiency, and reduce the coal mining risk, which is of great significance to the project.
YOLO-T: Multitarget Intelligent Recognition Method for X-ray Images Based on the YOLO and Transformer Models
Authors: Wang Mingxun , Yang Baolu , Wang Xin , Yang Cheng , Xu Jie , Mu Baozhong , Xiong Kai , Li Yanyi
Published: November 2022

X-ray security inspection processes have a low degree of automation, long detection times, and are subject to misjudgment due to occlusion. To address these problems, this paper proposes a multi-objective intelligent recognition method for X-ray images based on the YOLO deep learning network and an optimized transformer structure (YOLO-T). We also construct the GDXray-Expanded X-ray detection dataset, which contains multiple types of dangerous goods. Using this dataset, we evaluated several versions of the YOLO deep learning network model and compared the results to those of the proposed YOLO-T model. The proposed YOLO-T method demonstrated higher accuracy for multitarget and hidden-target detection tasks. On the GDXray-Expanded dataset, the maximum mAP of the proposed YOLO-T model was 97.73%, which is 7.66%, 16.47%, and 7.11% higher than that obtained by the YOLO v2, YOLO v3, and YOLO v4 models, respectively. Thus, we believe that the proposed YOLO-T network has good application prospects in X-ray security inspection technologies. In all kinds of security detection scenarios using X-ray security detectors, the model proposed in this paper can quickly and accurately identify dangerous goods, which has broad application value.
Improving the Performance of an Ultrashort Soft X-ray Free-Electron Laser via Attosecond Afterburners
Authors: Tu Lingjun , Qi Zheng , Wang Zhen , Zhao Sheng , Lu Yujie , Fan Weijie , Sun Hao , Wang Xiaofan , Feng Chao , Zhao Zhentang
Published: November 2022

In this study, we implement attosecond afterburners in an ultrashort soft X-ray free-electron laser (FEL) to improve the performance of generating attosecond pulses. In this scheme, the FEL pulse produced in the normal radiator section is dumped while the well bunched electron beam is reserved and reused in downstream afterburners. Subsequently, radiation in the afterburners gains rapidly as the bunching factor in the current spike is large, making the radiation pulse much shorter and cleaner than that from a normal radiator. Multi-shot simulations are carried out to demonstrate the performance and stability of the proposed technique.
Adaptive Feature Fusion for Small Object Detection
Authors: Zhang Qi , Zhang Hongying , Lu Xiuwen
Published: November 2022

In order to alleviate the situation that small objects are prone to missed detection and false detection in natural scenes, this paper proposed a small object detection algorithm for adaptive feature fusion, referred to as MMF-YOLO. First, aiming at the problem that small object pixels are easy to lose, a multi-branch cross-scale feature fusion module with fusion factor was proposed, where each fusion path has an adaptive fusion factor, which can allow the network to independently adjust the importance of features according to the learned weights. Then, aiming at the problem that small objects are similar to background information and small objects overlap in complex scenes, the M-CBAM attention mechanism was proposed, which was added to the feature reinforcement extraction module to reduce feature redundancy. Finally, in light of the problem of small object size and large size span, the size of the object detection head was modified to adapt to the small object size. Experiments on the VisDrone2019 dataset showed that the mAP of the proposed algorithm could reach 42.23%, and the parameter quantity was only 29.33 MB, which is 9.13% ± 0.07% higher than the benchmark network mAP, and the network model was reduced by 5.22 MB.
Balance Stability and Cervical Spine Range of Motion While Wearing a Custom-Made Mandibular Splint with Special Consideration of the Sex
Authors: Göttfert Florian , Herzog Johanna , Maurer-Grubinger Christian , Oremek Gerhard , Holzgreve Fabian , Groneberg David A. , Ohlendorf Daniela
Published: November 2022

Introduction: An altered dental occlusion can also affect balance stability or mobility. Thus, the aim of this study was to examine whether wearing a mandibular splint, which retains the occlusion close to the centric occlusion, can increase or decrease balance stability and the range of motion (ROM) of the cervical spine as opposed to the habitual occlusion, and if there is a difference between men and women. Material and methods: In this study, 41 male (34.7 ± 11.4 years) and 50 female subjects (29.3 ± 12.7 years) participated. Cervical spine ROM was recorded using the Zebris CMS 70P system. For balance stability, a pressure measuring platform integrated into the treadmill system (FDM-T) of the company Zebris® Medical GmbH was used. Here, the area of the ellipse and the length of the Center of Pressure (CoP) was recorded whilst in the bipedal and unipedal stance. Results: The sex comparison showed significant differences for the area of the ellipse of the right leg and ROM extension in the habitual occlusion: females showed a better balance stability and a larger ROM extension. When wearing the splint, only the CoP of the left leg was significant with a better balance stability in female subjects. Within the male subjects, the ellipse area in the bipedal and unipedal (left/right) stance showed mostly significant reductions, while the rotation left and right as well as the lateral flexion (left/right) improved when wearing the splint. Female subjects, when wearing the splint, showed a significant decrease of the ellipse area and the CoP length when standing on either leg. Flexion, rotation to the right and lateral flexion to the left/right, all increased significantly. Conclusion: Wearing a splint that keeps the jaw close to the centric relation improved balance stability and increased the ROM of the cervical spine for both male and female subjects. Women may have marginally different basic balance stability strategies than men, with regard to bipedal and unipedal standing. Nevertheless, there are scarcely any differences between the two sexes in the adaptation when wearing a splint. Changing the jaw relation in healthy adults can possibly support the release of movement potentials that simplify the performance of everyday activities or sports movements.
Multivehicle Point-to-Point Network Problem Formulation for UAM Operation Management Used with Dynamic Scheduling
Authors: Thu Zin Win , Kim Dasom , Lee Junseok , Won Woon-Jae , Lee Hyeon Jun , Ywet Nan Lao , Maw Aye Aye , Lee Jae-Woo
Published: November 2022

In this paper, we introduce a new formulation of the multivehicle point-to-point network problem to be utilised in urban air mobility (UAM) vertiport-to-vertiport network operations. Vehicle routing problems (VRPs) and their variants have previously been studied and applied in real-world situations, but these problems require additional depot locations, and not all the vehicles can travel to all the locations. In UAM operations, additional depot locations may not be required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to the management of UAMs. Therefore, we propose a new formulation for UAM vertiport-to-vertiport operation by introducing new constraints. In addition, we integrate dynamic scheduling with the flight mission by controlling cruise speed and waiting in each UAM at each vertiport location to generate an arrival and departure schedule for different vertiports that can avoid collisions and increase the number of vehicles. A computational experiment is conducted using an MILP model, and the results show that although our formulation satisfies the problem definition, the computation time increases exponentially with an increase in the problem size. A case study is conducted in the Seoul area involving five vertiports, with 10- and 15-vehicle scenarios studied. This case study shows that the cruise speed variable is active only for the lower and upper bounds under dynamic scheduling, whereas the waiting time variable can be controlled between user-defined limits that can be applied to the management of vertiport-to-vertiport UAM operations.
Effects of Surgically Assisted Rapid Palatal Expansion on Facial Soft Tissues: A Systematic Review
Authors: Vogiatzis Fotis , Roussos Petros , Doulis Ioannis , Palikaraki Georgia , Christopoulos Panagiotis , Sifakakis Iosif
Published: November 2022

Surgically assisted rapid palatal expansion (SARPE) is a successful method for treating transverse discrepancies in adult patients. The relocation of maxillary segments may induce changes at the surrounding soft tissues as well. The aim of this systematic review was to examine the possible effects that SARPE may have in the soft tissues of the face. Our search strategy included electronic databases (Pubmed, Scopus, ProQuest, Google Scholar, Cochrane Library) and a hand search of the reference list of found reviews. A priori definition of inclusion and exclusion criteria was made. Finally, 15 articles were included in qualitative synthesis. Risk of bias was generally high among the included studies. Study outcomes included nasal, labial, nasolabial and other facial soft tissue measurements. The evaluation of the changes was two-dimensional in six studies, and three-dimensional in nine studies. Meta-analysis was unfeasible due to lack of standardization, important methodological limitations, and shortcomings of the studies. A post-surgical increase in the dimensions of the alar width and the alar base width was commonly reported among the included studies. However, the above should be considered with caution due to the high risk of bias and the inability for quantitative synthesis.
Improving User Intent Detection in Urdu Web Queries with Capsule Net Architectures
Authors: Shams Sana , Aslam Muhammad
Published: November 2022

Detecting the communicative intent behind user queries is critically required by search engines to understand a user’s search goal and retrieve the desired results. Due to increased web searching in local languages, there is an emerging need to support the language understanding for languages other than English. This article presents a distinctive, capsule neural network architecture for intent detection from search queries in Urdu, a widely spoken South Asian language. The proposed two-tiered capsule network utilizes LSTM cells and an iterative routing mechanism between the capsules to effectively discriminate diversely expressed search intents. Since no Urdu queries dataset is available, a benchmark intent-annotated dataset of 11,751 queries was developed, incorporating 11 query domains and annotated with Broder’s intent taxonomy (i.e., navigational, transactional and informational intents). Through rigorous experimentation, the proposed model attained the state of the art accuracy of 91.12%, significantly improving upon several alternate classification techniques and strong baselines. An error analysis revealed systematic error patterns owing to a class imbalance and large lexical variability in Urdu web queries.
Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review
Authors: Albalawi Farraj , Alamoud Khalid A.
Published: November 2022

Artificial intelligence (AI) is a new breakthrough in technological advancements based on the concept of simulating human intelligence. These emerging technologies highly influence the diagnostic process in the field of medical sciences, with enhanced accuracy in diagnosis. This review article intends to report on the trends and application of AI models designed for diagnosis and treatment planning in orthodontics. A data search for the original research articles that were published over the last 22 years (from 1 January 2000 until 31 August 2022) was carried out in the most renowned electronic databases, which mainly included PubMed, Google Scholar, Web of Science, Scopus, and Saudi Digital Library. A total of 56 articles that met the eligibility criteria were included. The research trend shows a rapid increase in articles over the last two years. In total: 17 articles have reported on AI models designed for the automated identification of cephalometric landmarks; 12 articles on the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs; two articles on palatal shape analysis; seven articles for determining the need for orthodontic tooth extractions; two articles for automated skeletal classification; and 16 articles for the diagnosis and planning of orthognathic surgeries. AI is a significant development that has been successfully implemented in a wide range of image-based applications. These applications can facilitate clinicians in diagnosing, treatment planning, and decision-making. AI applications are beneficial as they are reliable, with enhanced speed, and have the potential to automatically complete the task with an efficiency equivalent to experienced clinicians. These models can prove as an excellent guide for less experienced orthodontists.
Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning
Authors: Iqbal Saeed , Qureshi Adnan N. , Ullah Amin , Li Jianqiang , Mahmood Tariq
Published: November 2022

Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist’s aid tool.
Triple Phase Shift Control of Wireless Charging DAB LCC Resonant Converter for Unity Power Factor Operation with Optimized Rectifier AC Load Resistance
Authors: Cheong Yanni , Cao Shuyu , Naayagi Ramasamy Thaiyal , Lee Szesing
Published: November 2022

This paper presents a new triple phase shift (TPS) closed-loop control scheme of a dual active bridge (DAB) LCC resonant DC/DC converter to improve wireless charging power transfer efficiency. The primary side inverter phase shift angle regulates the battery charging current/voltage. The secondary side rectifier phase shift angle regulates the rectifier AC load resistance to match its optimized setting. The inverter-to-rectifier phase shift angle is set to achieve unity power factor operation of the DAB rectifier and inverter. The mathematical formulation of the TPS shift control is given for each phase shift angle. The analytical calculation, circuit simulation, and experimental test are carried out in a power scaled-down DAB LCC resonant wireless charging converter laboratory hardware setup to validate the proposed TPS close-loop control scheme. The PLECS circuit simulation shows that DAB LCC resonant SiC MOSFET operates at zero-voltage-switching (ZVS) with a unity power factor in emulated constant current (CC) mode battery charging. In constant voltage (CV) mode operation, one inverter/rectifier Leg does not operate at ZVS switching when Sic MOSFET is switched on near zero current. The experimental results show that the efficiency is greatly improved for CV mode charging with large DC load resistance connected if rectifier AC load resistance matching control is enabled. The measured efficiency matches well with the analytical calculation. The estimated efficiency improvement will be much more significant for EV applications in the kW power range with greater winding loss. The challenges and possible solutions to implement TPS PWM modulation in two separate inverter and rectifier control hardware are explained for future TPS control algorithm development in practical wireless charging products.
Separate Syntax and Semantics: Part-of-Speech-Guided Transformer for Image Captioning
Authors: Wang Dong , Liu Bing , Zhou Yong , Liu Mingming , Liu Peng , Yao Rui
Published: November 2022

Transformer-based image captioning models have recently achieved remarkable performance by using new fully attentive paradigms. However, existing models generally follow the conventional language model of predicting the next word conditioned on the visual features and partially generated words. They treat the predictions of visual and nonvisual words equally and usually tend to produce generic captions. To address these issues, we propose a novel part-of-speech-guided transformer (PoS-Transformer) framework for image captioning. Specifically, a self-attention part-of-speech prediction network is first presented to model the part-of-speech tag sequences for the corresponding image captions. Then, different attention mechanisms are constructed for the decoder to guide the caption generation by using the part-of-speech information. Benefiting from the part-of-speech guiding mechanisms, the proposed framework not only adaptively adjusts the weights between visual features and language signals for the word prediction, but also facilitates the generation of more fine-grained and grounded captions. Finally, a multitask learning is introduced to train the whole PoS-Transformer network in an end-to-end manner. Our model was trained and tested on the MSCOCO and Flickr30k datasets with the experimental evaluation standard CIDEr scores of 1.299 and 0.612, respectively. The qualitative experimental results indicated that the captions generated by our method conformed to the grammatical rules better.
Improving Thickness Uniformity of Amorphous Oxide Films Deposited on Large Substrates by Optical Flux Mapping
Authors: Tien Chuen-Lin , Cheng Kuan-Sheng
Published: November 2022

In this study, three amorphous oxide thin films are prepared by an electron beam evaporation combined with ion-assisted deposition technique. With the aid of optical flux mapping method, thin film thickness distribution with good uniformity can be obtained by appropriate coating masks. Three metal oxide single-layer thin films are SiO2, Ta2O5 and Nb2O5, respectively. These thin films were deposited on a substrate holder with a radius of 275 mm that was divided into five different segments. Based on the optical flux mapping method, we can effectively simulate the geometric dimensions of the coating mask and obtain the width of the coating mask at different segments. If the film thickness uniformity is a function of masking area and center angle, it is necessary to determine the thickness distribution of the different segments and use a surface profiler to accurately measure the film thickness. We analyzed the thickness uniformity of three oxide films deposited at five different segments. The experimental measurement results show that the deviation of thickness uniformity is 0.38% for SiO2, 0.36% for Ta2O5, and 0.15% for Nb2O5 thin films, respectively.
Mechanical Analysis of Frozen Wall with Trapezoidal Temperature Field Distribution Based on Unified Strength Theory
Authors: Wu Yangyong , Shi Hao , Long Wei , Shi Xin
Published: November 2022

In order to study the elastic–plastic stress field distribution of a double-row-pipe frozen wall, the temperature field of the double-row-pipe frozen wall is equivalent to a trapezoidal distribution, and the frozen wall is regarded as an elastic–plastic thick-walled cylinder with functionally gradient material (FGM) characteristics in the radial direction. Considering that the elastic modulus and cohesion of the frozen wall material change linearly with the radius, the elastic–plastic analysis of the frozen wall is carried out based on unified strength theory. The analytical solutions of the elastic–plastic stress field distribution, the elastic ultimate bearing capacity, the plastic ultimate bearing capacity, and the relative radius of the plastic zone of the frozen wall are derived. The analytical solution is calculated based on the engineering case and compared with the numerical solution obtained based on COMSOL. At the same time, the influence of strength theory parameters on the mechanical properties of heterogeneous and homogeneous frozen walls is analyzed. The results show that the analytical solution and the numerical solution are in good agreement, and their accuracy is mutually verified. The external load on the frozen wall of the selected layer is greater than its elastic ultimate bearing capacity and less than its plastic ultimate bearing capacity, which indicates that the frozen wall is in a safe state of stress. The radial stress increases with the increase in the strength theoretical parameter b and the relative radius r, the tangential stress increases with the increase in the strength theoretical parameter b, and first increases and then decreases with the increase in the relative radius r. The larger the strength theoretical parameter b, the smaller the relative radius of the plastic zone of the frozen wall. The strength theoretical parameter b increases from 0 to 1, the elastic ultimate bearing capacity and plastic ultimate bearing capacity of the heterogeneous frozen wall increase by 33.3% and 40.8%, respectively, and the elastic ultimate bearing capacity and plastic ultimate bearing capacity of the homogeneous frozen wall increase by 33.3% and 41.0%, respectively. Therefore, considering the influence of intermediate principal stress, the potential of materials can be fully exerted and the ultimate bearing capacity of frozen walls can be improved. This study can provide theoretical reference for the design and construction of frozen wall.
Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes
Authors: Togobickij Benjaminas , Povilaitis Mantas , Slavickas Andrius , Stora Thierry , Barozier Vincent , Stankūnas Gediminas
Published: November 2022

To enhance the production of short-lived isotopes, higher beam powers are sought, which require targets able to accommodate them. One such target prototype is a liquid metal target LIEBE, developed at CERN. In this paper, a simulation of the proton beam interaction with the LIEBE target is presented. Simulations were performed by a series of proton transport calculations using the MCNP Monte Carlo code. The latest LIEBE target MCNP input was created in high-fidelity geometry, and the FENDL-3.1 cross-section data library was used. Flux and dose rate maps in the LIEBE target obtained from the simulations are presented in the paper. The maximum obtained dose around the target is roughly 361 Sv/h for gamma rays and 214 Sv/h for neutrons. The 70 MeV–100 µA proton beam penetrates roughly 7 mm deep into the liquid eutectic lead–bismuth. Based on this, further required changes to the LIEBE target can be evaluated.
Knowledge Management for Injection Molding Defects by a Knowledge Graph
Authors: Zhou Zhe-Wei , Ting Yu-Hung , Jong Wen-Ren , Chiu Ming-Chien
Published: November 2022

Injection molding is a technique with a high knowledge content. However, most of the injection molding knowledge is stored in books, and it is difficult for personnel to clarify the influence of the different factors. This study applies the concept of a knowledge graph by using three types of nodes and edges to express the complex injection molding knowledge in the related literature, and also combines SBERT and search engine building to retrieve the graph. The search engine can follow different search logics, according to the types of nodes, then find the knowledge related to the node, classify it according to the search path, and visualize the search results to the user. Users can clarify the relationship between various process factors and product qualities in a different way. We also use multiple tests to show the actual search results and verify the performance of the search engine. The results show that the search engine can quickly and correctly find the relevant knowledge in the graph, and maintain its performance when the graph is expanded. At the same time, users can clarify the impact of various process factors on the product quality, according to the search results.
Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
Authors: Liu Haijun , Lei Dongxing , Yuan Jing , Yuan Guoming , Cui Chunjie , Wang Yali , Xue Wei
Published: November 2022

The prediction of the total electron content (TEC) in the ionosphere is of great significance for satellite communication, navigation and positioning. This paper presents a multiple-attention mechanism-based LSTM (multiple-attention Long Short-Term Memory, MA-LSTM) TEC prediction model. The main achievements of this paper are as follows: (1) adding an L1 constraint to the LSTM-based TEC prediction model—an L1 constraint prevents excessive attention to the input sequence during modelling and prevents overfitting; (2) adding multiple-attention mechanism modules to the TEC prediction model. By adding three parallel attention modules, respectively, we calculated the attention value of the output vector from the LSTM layer, and calculated its attention distribution through the softmax function. Then, the vector output by each LSTM layer was weighted and summed with the corresponding attention distribution so as to highlight and focus on important features. To verify our model’s performance, eight regions located in China were selected in the European Orbit Determination Center (CODE) TEC grid dataset. In these selected areas, comparative experiments were carried out with LSTM, GRU and Att-BiGRU. The results show that our proposed MA-LSTM model is obviously superior to the comparison models. This paper also discusses the prediction effect of the model in different months. The results show that the prediction effect of the model is best in July, August and September, with the R-square reaching above 0.99. In March, April and May, the R-square is slightly low, but even at the worst time, the fitting degree between the predicted value and the real value still reaches 0.965. We also discussed the influence of a magnetic quiet period and a magnetic storm period on the prediction performance. The results show that in the magnetic quiet period, our model fit very well. In the magnetic storm period, the R-square is lower than that of the magnetic quiet period, but it can also reach 0.989. The research in this paper provides a reliable method for the short-term prediction of ionospheric TEC.
Commissioning of the HITRAP Cooling Trap with Offline Ions
Authors: Rausch Simon , Horst Max , Andelkovic Zoran , Fedotova Svetlana , Geithner Wolfgang , Herfurth Frank , Neidherr Dennis , Nörtershäuser Wilfried , Stallkamp Nils , Trotsenko Sergiy , Vorobyev Gleb
Published: November 2022

Highly charged heavy ions at rest offer a wide spectrum of precision measurements. The GSI Helmholtzzentrum für Schwerionenforschung GmbH is able to deliver ions up to U92+. As the production of these heavy, highly charged ions requires high kinetic energies, it is necessary to decelerate these ions for ultimate precision. The broad energy distribution, which results from the deceleration in the HITRAP linear decelerator, needs to be reduced to allow for further transportation and experiments. The HITRAP cooling trap is designed to cool, i.e., reduce, this energy spread by utilizing electron cooling. The commissioning of this trap is done with Ar16+-ions from a local EBIT ion source. By analyzing the signal of stored ions after ejection, properties such as ion lifetime, charge exchange, and ion motions can be observed. Here, we provide an overview of the recent results of the commissioning process and discuss future experiments.
Social Simulation Model of the Spread and Prevention of the Omicron SARS-CoV-2 Variant
Authors: Su Ya , Pan Lihu , Yan Huimin , Zhang Guoyou , Zhang Rui
Published: November 2022

The enhanced virulence and infectiousness of the Omicron variant of SARS-CoV-2 is having more significant impacts on certain socioeconomic areas, and rapidly suppressing the spread of the epidemic remains a priority for maintaining public health security throughout the world. Thus, we applied multi-agent modeling theory to create a social simulation model of Omicron variant transmission and prevention and control in order to analyze the virus transmission status in complex urban systems and its changing trends under different interventions. By considering the six municipal districts under the jurisdiction of Taiyuan City as examples, we developed state transition rules between five types of resident agents, mobility and contact behavior rules, and rules for patient admission behavior by hospital agents. We then conducted multi-scenario simulation experiments based on single measures of pharmacological and non-pharmacological interventions under non-governmental control as well as multiple interventions in combination to evaluate the effects of different measures on rapidly suppressing the spread of the epidemic. The experimental results demonstrated the utility of the model and the multi-agent modeling method effectively analyzed the transmission trends for the Omicron variant, thereby allowing a comprehensive diagnosis of the future urban epidemic situation and providing an important scientific basis for exploring more accurate normalized prevention and control measures.
Cultivation and Imaging of S. latissima Embryo Monolayered Cell Sheets Inside Microfluidic Devices
Authors: Clerc Thomas , Boscq Samuel , Attia Rafaele , Kaminski Schierle Gabriele S. , Charrier Bénédicte , Läubli Nino F.
Published: November 2022

The culturing and investigation of individual marine specimens in lab environments is crucial to further our understanding of this highly complex ecosystem. However, the obtained results and their relevance are often limited by a lack of suitable experimental setups enabling controlled specimen growth in a natural environment while allowing for precise monitoring and in-depth observations. In this work, we explore the viability of a microfluidic device for the investigation of the growth of the alga Saccharina latissima to enable high-resolution imaging by confining the samples, which usually grow in 3D, to a single 2D plane. We evaluate the specimen’s health based on various factors such as its growth rate, cell shape, and major developmental steps with regard to the device’s operating parameters and flow conditions before demonstrating its compatibility with state-of-the-art microscopy imaging technologies such as the skeletonisation of the specimen through calcofluor white-based vital staining of its cell contours as well as the immunolocalisation of the specimen’s cell wall. Furthermore, by making use of the on-chip characterisation capabilities, we investigate the influence of altered environmental illuminations on the embryonic development using blue and red light. Finally, live tracking of fluorescent microspheres deposited on the surface of the embryo permits the quantitative characterisation of growth at various locations of the organism.
The Gut Microbiome and Female Health
Authors: Siddiqui Ruqaiyyah , Makhlouf Zinb , Alharbi Ahmad M. , Alfahemi Hasan , Khan Naveed Ahmed
Published: November 2022

The possession of two X chromosomes may come with the risk of various illnesses, females are more likely to be affected by osteoarthritis, heart disease, and anxiety. Given the reported correlations between gut microbiome dysbiosis and various illnesses, the female gut microbiome is worthy of exploration. Herein, we discuss the composition of the female gut microbiota and its dysbiosis in pathologies affecting the female population. Using PubMed, we performed a literature search, using key terms, namely: “gut microbiome”, “estrogen”, “menopause”, “polycystic ovarian syndrome”, “pregnancy”, and “menstruation”. In polycystic ovarian syndrome (PCOS), the abundance of Bacteroides vulgatus, Firmicutes, Streptococcus, and the ratio of Escherichia/Shigella was found to be increased while that of Tenericutes ML615J-28, Tenericutes 124-7, Akkermansia, Ruminococcaceae, and Bacteroidetes S24-7 was reduced. In breast cancer, the abundance of Clostridiales was enhanced, while in cervical cancer, Prevotella, Porphyromonas, and Dialister were enhanced but Bacteroides, Alistipes, and members of Lachnospiracea, were decreased. In ovarian cancer, Prevotella abundance was increased. Interestingly, the administration of Lactobacillus acidophilus, Bifidobacterium bifidum, Lactobacillus reuteri, and Lactobacillus fermentum ameliorated PCOS symptoms while that of a mix of Bifidobacterium lactis W51, Bifidobacterium bifidum W23, Lactobacillus brevis W63, Bifidobacterium lactis W52, Lactobacillus salivarius W24, Lactobacillus acidophilus W37, Lactococcus lactis W19, Lactobacillus casei W56, and Lactococcus lactis W58 alleviated vascular malfunction and arterial stiffness in obese postmenopausal women, and finally, while further research is needed, Prevotella maybe protective against postmenopausal bone mass loss. As several studies report the therapeutic potential of probiotics and since the gut microbiota of certain female pathological states has been relatively characterized, we speculate that the administration of certain bacterial species as probiotics is warranted, as novel independent or adjunct therapies for various female pathologies.
Proteomic Characterization of Drosophila melanogaster Proboscis
Authors: Aruçi Enisa , Saliou Jean-Michel , Ferveur Jean-François , Briand Loïc
Published: November 2022

Drosophila melanogaster flies use their proboscis to taste and distinguish edible compounds from toxic compounds. With their proboscis, flies can detect sex pheromones at a close distance or by contact. Most of the known proteins associated with probosci’s detection belong to gustatory receptor families. To extend our knowledge of the proboscis-taste proteins involved in chemo-detection, we used a proteomic approach to identify soluble proteins from Drosophila females and males. This investigation, performed with hundreds of dissected proboscises, was initiated by the chromatographic separation of tryptic peptides, followed by tandem mass spectrometry, allowing for femtomole detection sensitivity. We found 586 proteins, including enzymes, that are involved in intermediary metabolism and proteins dedicated to various functions, such as nucleic acid metabolism, ion transport, immunity, digestion, and organ development. Among 60 proteins potentially involved in chemosensory detection, we identified two odorant-binding proteins (OBPs), i.e., OBP56d (which showed much higher expression in females than in males) and OBP19d. Because OBP56d was also reported to be more highly expressed in the antennae of females, this protein can be involved in the detection of both volatile and contact male pheromone(s). Our proteomic study paves the way to better understand the complex role of Drosophila proboscis in the chemical detection of food and pheromonal compounds.
New Insights into Microglial Mechanisms of Memory Impairment in Alzheimer’s Disease
Authors: Li Na , Deng Mingru , Hu Gonghui , Li Nan , Yuan Haicheng , Zhou Yu
Published: November 2022

Alzheimer’s disease (AD) is the most common progressive and irreversible neurodegeneration characterized by the impairment of memory and cognition. Despite years of studies, no effective treatment and prevention strategies are available yet. Identifying new AD therapeutic targets is crucial for better elucidating the pathogenesis and establishing a valid treatment of AD. Growing evidence suggests that microglia play a critical role in AD. Microglia are resident macrophages in the central nervous system (CNS), and their core properties supporting main biological functions include surveillance, phagocytosis, and the release of soluble factors. Activated microglia not only directly mediate the central immune response, but also participate in the pathological changes of AD, including amyloid-beta (Aβ) aggregation, tau protein phosphorylation, synaptic dissection, neuron loss, memory function decline, etc. Based on these recent findings, we provide a new framework to summarize the role of microglia in AD memory impairment. This evidence suggests that microglia have the potential to become new targets for AD therapy.
Adjuvant Chemoradiotherapy Associated with Improved Overall Survival in Resected Esophageal Squamous Cell Carcinoma after Neoadjuvant Chemoradiotherapy in Intensity-Modulated Radiotherapy Era
Authors: Yap Wing-Keen , Shih Ming-Chieh , Chang Yu-Chen , Lin Chia-Hsin , Huang Shih-Ming , Tsai Tsung-You , Chang Ching-Fu , Hsu Chih-Chung , Tseng Chen-Kan , Chen Miao-Fen , Tsan Din-Li , Liau Chi-Ting , Hou Ming-Mo , Chao Yin-Kai , Chiu Chien-Hung , Hung Tsung-Min
Published: November 2022

Background: The prognosis of patients with resected esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy is particularly poor in those who were staged as ypT3/T4 and/or ypN+. This study investigated whether adjuvant chemoradiotherapy was associated with improved clinical outcomes in these patients. Methods: we identified patients with esophageal squamous cell carcinoma who were staged as ypT3/T4 and/or ypN+ after being treated with neoadjuvant chemoradiotherapy followed by esophagectomy between the years 2013 and 2019. Patients were divided into two groups based on whether they received adjuvant chemoradiotherapy. The Kaplan-Meier method and Cox regression modeling were performed for survival analyses and multivariable analysis, respectively. Results: 76 eligible patients were included in the analyses. The median follow-up for the study cohort was 43.4 months. On Kaplan-Meier analyses of the overall population, adjuvant chemoradiotherapy was associated with significantly improved median overall survival (31.7 months vs. 16.3 months, p = 0.036). On Kaplan-Meier analyses of the 35 matched pairs generated by propensity score matching, adjuvant chemoradiotherapy was associated with significantly longer median overall survival (31.7 months vs. 14.3 months; p = 0.004) and median recurrence-free survival (18.9 months vs. 11.7 months; p = 0.020). In multivariable analysis, adjuvant chemoradiotherapy was independently associated with a 60% reduction in mortality (p = 0.003) and a 48% reduction in risk of recurrence (p = 0.035) after adjusting for putative confounders. In addition, microscopic positive resection margin and Mandard tumor regression grade 3–4 were independently associated with increased mortality and risk of recurrence. While a greater number of lymph nodes dissected was independently associated with significantly improved overall survival, the number of positive lymph nodes was independently associated with significantly worse overall survival and a trend (p = 0.058) towards worse recurrence-free survival. Conclusions: This study demonstrated that adjuvant CRT was independently associated with a significantly improved survival and lower risk of recurrence than observation in esophageal squamous cell carcinoma patients staged as ypT3 and/or ypN+ after receiving neoadjuvant chemoradiotherapy and radical surgery. The results of this study have implications for the design of future clinical trials and may improve treatment outcomes of patients in this setting who cannot afford or are without access to adjuvant nivolumab.
Anti-Inflammatory Activity of Olive Oil Polyphenols—The Role of Oleacein and Its Metabolites
Authors: Costa Vânia , Costa Marlene , Videira Romeu António , Andrade Paula Branquinho , Paiva-Martins Fátima
Published: November 2022

The anti-inflammatory potential of oleacein, the main polyphenolic compound found in olive oil, and its main metabolites were characterized by their effects on RAW 264.7 macrophages challenged with lipopolysaccharide (LPS), and by their ability to inhibit enzymes of the arachidonic acid metabolism with a key role in the synthesis of pro-inflammatory lipid mediators. Oleacein at 12.5 µM significantly decreased the amount of L-citrulline and ●NO generated by LPS-stimulated macrophages. Hydroxytyrosol, hydroxytyrosol acetate and hydroxytyrosol acetate sulfate were also able to reduce the cellular amount of ●NO, although to a lesser extent. In contrast, hydroxytyrosol glucuronide and sulfate did not show detectable effects. Oleacein was also able to inhibit the coupled PLA2 + 5-LOX enzyme system (IC50 = 16.11 µM), as well as the 5-LOX enzyme (IC50 = 45.02 µM). Although with lower activity, both hydroxytyrosol and hydroxytyrosol acetate were also capable of inhibiting these enzymes at a concentration of 100 µM. None of the other tested metabolites showed a capacity to inhibit these enzymes. In contrast, all compounds, including glucuronides and sulfate metabolites, showed a remarkable capacity to inhibit both cyclooxygenase isoforms, COX-1 and COX-2, with IC50 values lower than 3 µM. Therefore, oleacein and its metabolites have the ability to modulate ●NO- and arachidonic acid-dependent inflammatory cascades, contributing to the anti-inflammatory activity associated with olive oil polyphenols.
An Adapted Deep Convolutional Neural Network for Automatic Measurement of Pancreatic Fat and Pancreatic Volume in Clinical Multi-Protocol Magnetic Resonance Images: A Retrospective Study with Multi-Ethnic External Validation
Authors: Yang John Zhiyong , Zhao Jichao , Nemati Reza , Yin Xavier , He Kevin Haokun , Plank Lindsay , Murphy Rinki , Lu Jun
Published: November 2022

Pancreatic volume and fat fraction are critical prognoses for metabolic diseases like type 2 diabetes (T2D). Magnetic Resonance Imaging (MRI) is a required non-invasive quantification method for the pancreatic fat fraction. The dramatic development of deep learning has enabled the automatic measurement of MR images. Therefore, based on MRI, we intend to develop a deep convolutional neural network (DCNN) that can accurately segment and measure pancreatic volume and fat fraction. This retrospective study involved abdominal MR images from 148 diabetic patients and 246 healthy normoglycemic participants. We randomly separated them into training and testing sets according to the proportion of 80:20. There were 2364 recognizable pancreas images labeled and pre-treated by an upgraded superpixel algorithm for a discernible pancreatic boundary. We then applied them to the novel DCNN model, mimicking the most accurate and latest manual pancreatic segmentation process. Fat phantom and erosion algorithms were employed to increase the accuracy. The results were evaluated by dice similarity coefficient (DSC). External validation datasets included 240 MR images from 10 additional patients. We assessed the pancreas and pancreatic fat volume using the DCNN and compared them with those of specialists. This DCNN employed the cutting-edge idea of manual pancreas segmentation and achieved the highest DSC (91.2%) compared with any reported models. It is the first framework to measure intra-pancreatic fat volume and fat deposition. Performance validation reflected by regression R2 value between manual operation and trained DCNN segmentation on the pancreas and pancreatic fat volume were 0.9764 and 0.9675, respectively. The performance of the novel DCNN enables accurate pancreas segmentation, pancreatic fat volume, fraction measurement, and calculation. It achieves the same segmentation level of experts. With further training, it may well surpass any expert and provide accurate measurements, which may have significant clinical relevance.