bluuebunny/crossref_metadata_2025 huggingface dataset:
Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification
Authors: Babichev Sergii , Liakh Igor , Kalinina Irina
Published: October 2023

The importance of gene expression data processing in solving the classification task is determined by its ability to discern intricate patterns and relationships within genetic information, enabling the precise categorization and understanding of various gene expression profiles and their consequential impacts on biological processes and traits. In this study, we investigated various architectures and types of recurrent neural networks focusing on gene expression data. The effectiveness of the appropriate model was evaluated using various classification quality criteria based on type 1 and type 2 errors. Moreover, we calculated the integrated F1-score index using the Harrington desirability method, the value of which allowed us to improve the objectivity of the decision making when model effectiveness was evaluated. The final decision regarding model effectiveness was made based on a comprehensive classification quality criterion, which was calculated as the weighted sum of classification accuracy, integrated F1-score index, and loss function values. The simulation results show higher appeal of a single-layer GRU recurrent network with 75 neurons in the recurrent layer. We also compared convolutional and recurrent neural networks on gene expression data classification. Although convolutional neural networks showcase benefits in terms of loss function value and training time, a comparative analysis revealed that in terms of classification accuracy calculated on the test data subset, the GRU neural network model is slightly better than the CNN and LSTM models. The classification accuracy when using the GRU network was 97.2%; in other cases, it was 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 objects were correctly identified.
Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
Authors: Han Chansu , Choo Hyunseung , Jeong Jongpil
Published: October 2023

Studying domain adaptation is a recent research trend. Generally, many generative models that researchers have studied perform well on training data from a specific domain. However, their ability to be generalized to other domains might be limited. Therefore, a growing body of research has utilized domain adaptation techniques to address the problem of generative models being vulnerable to input from other domains. In this paper, we focused on generative models and representation learning. Generative models have received a lot of attention for their ability to generate various types of data such as images, music, and text. In particular, studies utilizing generative adversarial neural networks (GANs) and autoencoder structures have received a lot of attention. In this paper, we solved the domain adaptation problem by reconstructing real image data using an autoencoder structure. In particular, reconstructed image data, considered a type of noisy image data, are used as input data. How to reconstruct data by extracting features and selectively transforming them in order to reduce differences in characteristics between domains entails representative learning. Considering these research trends, this paper proposed a novel methodology combining bidirectional feature learning and generative networks to innovatively approach the domain adaptation problem. It could improve the adaptation ability by accurately simulating the real data distribution. The experimental results show that the proposed model outperforms the traditional DANN and ADDA. This demonstrates that combining bidirectional feature learning and generative networks is an effective solution in the field of domain adaptation. These results break new ground in the field of domain adaptation. They are expected to provide great inspiration for future research and applications. Finally, through various experiments and evaluations, we verify that the proposed approach outperforms the existing works. We conducted experiments for representative generative models and domain adaptation techniques and found that the proposed approach was effective in improving data and domain robustness. We hope to contribute to the development of domain-adaptive models that are robust to the domain.
A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
Authors: Zhou Chunye , Wei Chunlei , Yang Fan , Wei Jun
Published: October 2023

We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.
Preliminary Numerical Analysis of Mechanical Wave Propagation Due to Elastograph Measuring Head Application in Non-Invasive Liver Condition Assessment
Authors: Romanowska Katarzyna , Pyka Dariusz , Opieliński Krzysztof , Krawiec Karina , Śliwiński Robert , Jamroziak Krzysztof
Published: October 2023

The authors of this paper focused their attention on developing numerical models of mechanical wave propagation along human tissue as a result of the application of the measuring head of the FibroScan® elastograph. The FibroScan® diagnostic device is used for diagnostic testing of liver fibrosis and steatosis. This examination is carried out using an in vivo method by directly applying the surface of the ultrasonic measuring probe to the patient’s skin at the site of the liver. The authors’ idea is to use this apparatus for non-invasive testing on the liver used for transplantation. In order to do this, the measuring head cap should be modified so that its application to the liver does not result in damage as a result of mechanical wave excitation. The purpose of the manuscript was to build numerical models of the liver and the tissues surrounding the liver. Then, the corresponding numerical simulations were carried out, the results of which corresponded to the mechanical–acoustic properties of the physical models of the tissues. The obtained results were validated on a set of commercial calibrated phantoms. High agreement of the numerical models was obtained.
Meta-Learning-Based Incremental Nonlinear Dynamic Inversion Control for Quadrotors with Disturbances
Authors: Zhang Xinyue , Ran Maopeng
Published: October 2023

This paper proposes an online meta-learning-based incremental nonlinear dynamic inversion (INDI) control method for quadrotors with disturbances. The quadrotor dynamic model is first transformed into linear form via an INDI control law. Since INDI largely depends on the accuracy of the control matrix, a method composed of meta-learning and adaptive control is proposed to estimate it online. The effectiveness of the proposed control framework is validated through simulation on a quadrotor with 3D wind disturbances.
On-Line Partial Discharge Monitoring System for Switchgears Based on the Detection of UHF Signals
Authors: Yan Xuewen , Cheng Chen , Zhang Chen , Bai Lei , Zhang Wenwen
Published: October 2023

Aiming at the phenomenon of partial discharge caused by the insulation fault of switchgears, an ultra-high frequency (UHF) partial discharge online monitoring system based on the Internet of Thing (IoT) is designed. The hardware of the system mainly consists of UHF sensors, signal conditioning circuits, and data acquisition circuits to realize the monitoring of the discharge signal. The monitoring data is uploaded to the cloud platform through the 4G DTU module, and the host computer software based on B/S architecture is designed on the server side, which can monitor the operation status of the switchgear anytime and anywhere. The experimental results show that the detection system is stable and reliable and can meet practical needs.
Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia
Authors: Mohd Yamin Muhammad Nadzree , Ab. Aziz Kamarulzaman , Gek Siang Tan , Ab. Aziz Nor Azlina
Published: October 2023

Emotion recognition systems (ERS) are an emerging technology with immense potential, exemplifying the innovative utilization of artificial intelligence (AI) within the context of the fourth industrial revolution (IR 4.0). Given that personalization is a key feature of the fifth industrial revolution (IR 5.0), ERS has the potential to serve as an enabler for IR 5.0. Furthermore, the COVID-19 pandemic has increased the relevance of this technology as work processes were adapted for social distancing and the use of face masks. Even in the post-pandemic era, many individuals continue to wear face masks. Therefore, ERS offers a technological solution to address communication challenges in a masked world. The existing body of knowledge on ERS primarily focuses on exploring modalities or modes for emotion recognition, system development, and the creation of applications utilizing emotion recognition functions. However, to enhance the development of impactful ERS, it is essential for researchers and innovators to understand the factors that influence its usage and adoption among the intended users. Therefore, this study presents a framework that combines technology adoption theories to identify the determinants of ERS adoption among Malaysian youth. Data for this study were collected through a survey involving 386 respondents. The findings revealed attitudes, subjective norms, perceived behavioral control, and awareness as significant determinants of ERS adoption. Additionally, the study found that technology aptitude plays a moderating role. These insights can inform the formulation of effective policies and programs to encourage and facilitate the development of innovative ERS solutions.
Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
Authors: Li Yanpeng , Wang Chuang , Li Zengqun , Ren Dawei , Xing Ziwen , Wu Dawei , Wu Huagen
Published: October 2023

The energy consumption in the cold store is growing day by day, 70% of which is consumed by the refrigeration system. Meanwhile, a significant amount of electricity generated by power plants is wasted during off-peak periods. Demand-side management (DSM) provides a viable solution for addressing the problem of the time and space inconsistency between energy supply and consumption, hence improving overall system efficiency. In this paper, an artificial intelligence model is developed for accurate cooling load forecasting. On this basis, a peak shifting control strategy with two optional modes combining temperature setpoint control and operation mode control is then proposed to realize cost reductions. Taking a large-scale cold store as a case study, the cooling capacity supply and temperature variation within two typical working days are investigated to illustrate the feasibility and applicability of the strategy. Detailed thermodynamic and thermo-economic analyses of the proposed strategy are then carried out to demonstrate the control effect. The results show that both modes have good peaking performances and the average cost reduction rate of the two modes reaches 40% and 13.4%, respectively.
Biomonitoring of Potentially Toxic Elements in an Abandoned Mining Region Using Taraxacum officinale: A Case Study on the “Tsar Asen” Mine in Bulgaria
Authors: Mihaylova Veronika , Yotova Galina , Marinova Kristina , Benderev Aleksey , Lyubomirova Valentina , Tsakovski Stefan
Published: October 2023

Mining is a significant industrial and economic activity, but it can also result in ore waste that contains several potentially toxic elements (PTEs). Furthermore, the wide range of observed transfer and accumulation factors suggests that not only the soil element concentrations but also the soil characteristics are important for uptake, which is recognized as a serious problem with potential impacts on human health and ecosystems. The aim of this study was to perform a pollution risk assessment of a region near an abandoned mine using suitable biomonitoring followed by multivariate statistical treatment of the obtained results. The content of PTEs in the soil and plant samples was determined to exceed the background concentrations typical for Bulgaria. The observed concentrations of copper in this study’s soil samples exceeded 2 to 16 times the maximum permissible values. The Taraxacum officinale concentrations for PTEs, particularly for Cd (5.13 mg/kg), were higher than the background levels. The obtained results for PTEs show significant TFs for Cd and Sn, while the AFs outline a significant accumulation of Cd and Sb. The applied multivariate statistical approaches revealed differences between the sampling locations and relationships between the elemental transfer/accumulation factors and soil cation exchange capacity. The outcomes from the statistical analysis confirm that the usage of both element factors and soil properties in biomonitoring studies are essential for reliable risk assessment.
Cyclist Injuries from Tram Track Incidents: A Retrospective 10-Year Analysis
Authors: Schneider Friedemann , Kaiser Peter , Kuhl Anna Sophia , Dankl Lukas , Runer Armin , Arora Rohit
Published: October 2023

Trams are widely used for urban public transportation. The aim of this study was to identify the total number of accidents and common injury patterns of tram-system-related cycling injuries. All patients attending the traumatology emergency department of University Hospital of Innsbruck between January 2010 and December 2019 due to tram-track-related bike crashes were identified. Patient demographics; date, time, and mechanism of the accident; type of injury; exact diagnosis and subsequent treatment; as well as direct treatment costs were evaluated and analyzed. A total of 378 patients (56.1% female; mean age: 38.4 years) were identified. Bike tire wedging was the most frequent accident mechanism (95.2%). A total of 108 patients (28.7%) sustained a total number of 135 fractures, predominately of the upper limb. Twenty-six patients (7.0%) required surgical treatment. No patient in this collective died as a result of the injuries sustained in the accident. During the time of this analysis, the city government implemented several measures to increase cyclists’ safety. For the main injury site, a sharp reduction in injuries was observed. Urban tram tracks are a significant risk factor for cycling-related injuries. Injuries might be reduced with the implementation of cyclist-friendly safety measures.
The Impact of Ultrasound Pre-Treatment on Hot-Air-Drying Kinetics and Quality of Carrot Slices Assessed by Simulations and Experiments
Authors: Tran Thi Thu Hang , Nguyen Thi Thuy Dung , Kharaghani Abdolreza , Le Kieu Hiep
Published: October 2023

This study investigated experimentally and numerically the influence of ultrasound pre-treatment on the drying kinetics of sliced carrot samples. Drying experiments were performed under different conditions, including scenarios with and without ultrasound pre-treatment at drying temperatures of 30 °C, 40 °C, and 50 °C. A diffusion-based-drying model was developed to study the impact of ultrasound pre-treatment on drying kinetics. The effective moisture diffusivity of carrots was expressed as a function of moisture content and temperature. Given the complexity of the dehydration process in carrot slices, which depends on the spatiotemporal variations in moisture content and temperature, and is challenging to monitor experimentally, the effective moisture diffusivity is computed by minimizing the discrepancy between numerical predictions and experimental moisture-content changes over time. This study revealed that ultrasound pre-treatment significantly enhanced the moisture diffusivity of the samples, increasing it by 43% to 90% at drying temperatures of 40 °C and 50 °C, respectively. To apply this analysis of ultrasound pre-treatment in large-scale dryers where thousands of slices may be involved, the proposed diffusion model was simplified to a characteristic drying-curve model. Afterwards, this characteristic drying-curve model was incorporated into a belt-dryer model. The results indicated a 12% reduction in the length of the belt dryer when ultrasound pre-treatment was applied. Additionally, the color of carrot samples was preserved better with ultrasound pre-treatment. On the basis of these results, the application of ultrasound pre-treatment in the hot-air drying of carrot slices was favored, both in terms of improved drying kinetics and quality aspects.
Efficiency of Flange-Bonded CFRP Sheets in Relocation of Plastic Hinge in RC Beam–Column Joints
Authors: Hejazi Farzad , Azarm Ramin , Firoozi Ali Akbar
Published: October 2023

Beam–column connection zones are high regions of interest in reinforced concrete (RC) structures, which are expected to respond elastically to seismic loads. Using carbon fiber-reinforced polymers (CFRP) to improve these connections, performance is critical in retrofitting deficient RC frames because existing slabs may pose numerous limitations in the design and wrapping of CFRP sheets in joints. The main aim of this research is to develop a new design for flange-bonded CFRP retrofit of frames, including slabs, for the relocation of plastic hinges of the connection area toward the beam and to develop beam–column joint capacity and building stability in cases of subjection to dynamic loads. The performance of these proposed retrofittings was explored both experimentally and numerically. Two full-scale fabricated interior RC joints of a real moment-resisting frame with moderate ductility were subjected to monotonic loads before and after retrofitting, and the results were used to detail the numerical progress and verify of the beam–column connection. Moreover, a parametric study was conducted on CFRP sheets’ optimal thickness to examine its influence on plastic hinge relocation in the connection region. Results show that the retrofitting method can efficiently relocate the plastic hinge to the mid-span of the beam, which, in turn, leads to improved capacity and achievement of the RC frame and guarantees better structural safety a lower cost.
Cooperative Computing Offloading Scheme via Artificial Neural Networks for Underwater Sensor Networks
Authors: Liu Xin , Du Xiujuan , Zhang Shuailiang , Han Duoliang
Published: October 2023

Aiming at the problem of being unable to meet some high computing power, high-precision applications due to the limited capacity of underwater sensor nodes, and the difficulty of low computation power, in this paper, we introduce the edge servers, known as base stations for underwater sensor nodes, and propose a scheme to process the computational tasks based on coalition game theory. This scheme provides functions such as cooperation among different base stations within the coalition, the smart division of tasks, and efficient computational offloading. In order to reduce the complexity of the algorithm, the artificial neural network model is introduced into the method. Each task is divided into sub-parts and fed to an artificial neural network for training, testing, and validation. In addition, the scheme delivers the computed task from base stations back to sink nodes via a shortened path to enhance the service reliability. Due to the mobility of the base station in the ocean, our proposed scheme takes into account the dynamic environment at the same time. The simulation results show that, compared with the existing state-of-the-art methods, the success rate of our proposed approach improves by 30% compared with the Greedy method. The total service time of our proposed approach decreases by 12.6% compared with the Greedy method and 31.2% compared with the Always-Migrate method.
Experimental Study on the Mechanical Properties of Vertical Corrugated Pipe Grout Anchor Connection Joints
Authors: Liu Fenglin , Qian Hui , Zhang Zhizeng , Zhang Haidong
Published: October 2023

Prefabricated buildings’ quality, safety, and construction efficiency are closely related to the connections between various prefabricated components. This study optimizes the design of corrugated pipe-restrained grout anchor lap connections, further simplifying the construction methodology while ensuring reliable connection performance. The objective is to achieve cost savings and accelerate construction progress. A pull-out test of this new type of joint is conducted, generating two sets of 17 specimens each. The research examines the effects of grouting material strength, grout anchor steel bar anchorage length, and U-shaped stirrup spacing on the connection performance of corrugated pipe grout anchor connections. The results indicated that the specimens primarily experience two types of failure: anchorage failure with steel bar pull-out and steel bar tensile failure without anchorage failure. The strength of the grouting material and the anchorage length of the grout anchor steel bar positively correlate with bond anchorage performance. U-shaped stirrups contribute to restraint and enhance the bonding force between the corrugated pipe, grouting materials, and the grout anchor steel bar to a certain extent.
The Upgraded Planck System of Units That Reaches from the Known Planck Scale All the Way Down to Subatomic Scales
Authors: Christodoulou Dimitris M. , Kazanas Demosthenes
Published: October 2023

Natural systems of units {Ui} need to be overhauled to include the dimensionless coupling constants {αUi} of the natural forces. Otherwise, they cannot quantify all the forces of nature in a unified manner. Thus, each force must furnish a system of units with at least one dimensional and one dimensionless constant. We revisit three natural systems of units (atomic, cosmological, and Planck). The Planck system is easier to rectify, and we do so in this work. The atomic system discounts {G,αG}, thus it cannot account for gravitation. The cosmological system discounts {h,αh}, thus it cannot account for quantum physics. Here, the symbols have their usual meanings; in particular, αG is the gravitational coupling constant and αh is Dirac’s fine-structure constant. The speed of light c and the impedance of free space Z0 are resistive properties imposed by the vacuum itself; thus, they must be present in all systems of units. The upgraded Planck system with fundamental units UPS:={c,Z0,G,αG,h,αh,…} describes all physical scales in the universe—it is nature’s system of units. As such, it reveals a number of properties, most of which have been encountered previously in seemingly disjoint parts of physics and some of which have been designated as mere coincidences. Based on the UPS results, which relate (sub)atomic scales to the Planck scale and the fine-structure constant to the Higgs field, we can state with confidence that no observed or measured physical properties are coincidental in this universe. Furthermore, we derive from first principles Koide’s K=2/3 enigmatic constant and additional analogous quark and vector boson constants. These are formal mathematical proofs that justify a posteriori the use of geometric means in deriving the quark/boson mass ladder. This ladder allows us to also calculate the Higgs couplings to the vector bosons and the Weinberg angle in terms of K only, and many of the “free” parameters of the Standard Model of particle physics were previously expected to be determined only from experiments.
The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning
Authors: Avgoustoglou Euripides , Shtivelman Alon , Khain Pavel , Marsigli Chiara , Levi Yoav , Cerenzia Ines
Published: October 2023

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.
Atmospheric CO2 Isotopic Variations, with Estimation of Ocean and Plant Source Contributions
Authors: Quirk Tom , Asten Michael
Published: October 2023

This analysis uses both atmospheric CO2 concentrations and the accompanying δ13C isotopic measurements of CO2 over 40 years from 1978 to 2015 observed at ten different latitudes from 90° S to 82 °N. Atmospheric CO2 is separated into two components of CO2 attributable to deep ocean and to plant (including fossil fuel) sources. The isotopic values assigned to the two components are δ13C = 0‰ and −26‰, respectively. The latitude variations in residual source component CO2 show the ocean source component peaking at the equator. This contrasts with the residual plant source component that peaks in the Arctic Circle region. Seasonal comparisons show no change in the ocean component peaking at the equator and no significant changes in its variation with latitude, while the plant component shows seasonal changes of the order of 15 ppm at high latitudes. The ocean component shows clear anomalous behavior in the three years following the 1989 Pacific Ocean regime shift (a shift independently identified from the changed biological time series). By contrast, the residual plant component shows a correlation in the timing of maxima in its annual variations with the timing of El Nino events over the time span of 1985–2015. It also shows a discontinuity in annual variation coinciding with the 1995 AMO phase change. We conclude that the ocean and plant components of atmospheric CO2 relate to independent sources of atmospheric CO2 and have approximately equal magnitudes. The observations are consistent with a hypothesis that variations in the ocean components have an origin from upwelling water from deep ocean currents, and variations in plant components are dominated by a combination of fossil fuel CO2, phytoplankton productivity, and forest and peat fires, which primarily occur in the northern hemisphere.
Semiarid Lakes of Southwestern Siberia as Sentinels of On-Going Climate Change: Hydrochemistry, the Carbon Cycle, and Modern Carbonate Mineral Formation
Authors: Novoselov Andrey , Konstantinov Alexandr , Konstantinova Elizaveta , Simakova Yulia , Lim Artem , Kurasova Alina , Loiko Sergey , Pokrovsky Oleg S.
Published: October 2023

Towards a better understanding of factors controlling carbon (C) exchange between inland waters and atmosphere, we addressed the inorganic carbon cycle in semiarid lakes of Central Eurasia, subjected to the strong impact of on-going climate change. As such, we assessed the hydrochemical variability and quantified its control on the formation of authigenic carbonate minerals, occurring within the upper layer of sediments in 43 semiarid lakes located in the southwest of Western Siberia (Central Eurasia). Based on measurements of pH, total dissolved solids (TDS), cationic and anionic composition, dissolved organic and inorganic C, as well as textural and mineralogical characterization of bottom sediments using X-ray diffraction and scanning electron microscopy, we demonstrate that lake water pH and TDS are primarily controlled by both the lithological and climatic context of the lake watershed. We have not revealed any direct relationships between lake morphology and water chemistry. The most common authigenic carbonates scavenging atmospheric CO2 in the form of insoluble minerals in lake sediments were calcite, aragonite, Mg-calcite, dolomite and hydromagnesite. The calcite was the most common component, aragonite mainly appears in lakes with sediments enriched in gastropod shells or artemia cysts, while hydromagnesite was most common in lakes with high Mg/Ca molar ratios, as well as at high DIC concentrations. The relationships between mineral formation and water chemistry established in this study can be generalized to a wide suite of arid and semiarid lakes in order to characterize the current status of the inorganic C cycle and predict its possible modification under on-going climate warming such as a rise water temperature and a change in hydrological connectivity, primary productivity and nutrient regime.
Ability of the GRAPES Ensemble Forecast Product to Forecast Extreme Temperatures over the Tibetan Plateau
Authors: Wang Ruixin , Liang Yuxi , Cai Hongke , Zheng Jiawen
Published: October 2023

Due to climate change, extreme temperature events are receiving increased attention. Based on the climate state deviation and threat score (TS), the ability of the Global/Regional Assimilation and Prediction System (GRAPES) ensemble model to forecast extreme temperature events was examined. The “optimal” Extreme Forecast Index (EFI) was derived for plateau forecasting, and its predictability was examined based on the receiver operating characteristic (ROC) curve method. Meanwhile, the applicability of the Shift of Tails (SOT) index to extreme temperature forecasting was analyzed using extreme temperature cases. Results showed that the GRAPES model has a warm bias for both summer extreme high temperature and winter extreme low temperature, and the warm bias decreases slightly with an increase in the forecasting lead time. The ensemble mean and median forecasts are less effective, and the maximum value is more predictable. However, for the ensemble forecast model, the extreme information in its forecast is more unstable, and the limitation of the extreme temperature forecast in the plateau region is higher. With different forecast lead times, the TS tends to increase and then decrease with an increase in the EFI threshold, which means that there is an optimal EFI. The optimal EFI thresholds for summer extreme high-temperature forecasts are all less than −0.5, while for winter extreme low-temperature forecasts, they are almost all less than 0. From the ROC curves, the EFI has a certain level of predictability for summer extreme high temperatures but poorer forecasting effects. Furthermore, the EFI has some predictability for extreme summer high temperatures, but the prediction effect is poor. For the extremely low temperatures in winter, which are poorly predicted by the model itself, post-processing of the extreme information predicted by the model with the EFI can improve the forecasting effect of the model. Through analysis of individual cases, it was found that the extreme intensity reflected by the SOT_+ (0.9) index of the model was closer to reality for the prediction of extremely high temperatures, whereas for the prediction of extremely low temperatures, the extreme intensity indicated by the SOT_− (0.1) index of the model was weaker. Therefore, the SOT index can play an important auxiliary role in the prediction of the intensity of extreme events based on the EFI.
Relationship between the Silk Road and Circumglobal Teleconnection Patterns on the Interannual and Interdecadal Timescales
Authors: Liu Yong
Published: October 2023

The Silk Road pattern (SRP) and circumglobal teleconnection pattern (CGT) are two well-known teleconnection patterns, representing the summer circulation variations of the Northern Hemisphere mid-latitudes, which have different definitions but are often regarded as one teleconnection pattern. In view of the distinct features of the SRP/CGT on the interannual (IA) and interdecadal (ID) timescales, the present study investigates the linkages and differences between the SRP and CGT on the two timescales, respectively. On the IA timescale, both the SRP and CGT feature a similar circumglobal wave train structure with strong and significant centers over Eurasia but show clear independence. Specifically, the SRP and CGT illustrate largely the mid-/high-latitude-related and tropics-related parts of the Northern Hemisphere upper tropospheric circulation variations, respectively. Also, the CGT shows a stronger connection to the Indian summer monsoon (ISM) heating and El Niño–Southern Oscillation than the SRP, which makes the CGT more like a tropical forcing-driven atmospheric mode and the SRP more like an internal atmospheric mode. The linkages and differences between them are associated with their asymmetrical relationship during their positive and negative phases, which are attributed mainly to the asymmetrical impact of the ISM heating/cooling on the Eurasian circulations. On the ID timescale, the SRP and CGT are characterized by a coherent two-wave train structure over Eurasia and feature a similar teleconnection pattern over Eurasia, which is associated with the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation. The present findings on their linkages and differences are helpful in understanding the variability and prediction of the SRP and CGT.
Assessing the Influence of Polymer-Based Anti-Drift Adjuvants on the Photolysis, Volatilization, and Secondary Drift of Pesticides after Application
Authors: Katzman Doron , Zivan Ohad , Dubowski Yael
Published: October 2023

One practice to reduce spray drift during pesticide application is the addition of certain chemical adjuvants to spraying solutions, which change their physicochemical properties and result in larger droplets. The environmental impact of these agrochemicals continues however also after application, depending on surface processes occurring upon treated surfaces. While the impact of anti-drift adjuvants has been studied regarding spray drift, their impact on the fate of deposited pesticides has received little attention. Here, the effect of a polymer-based adjuvant (polyacrylamide) on the photolysis and evaporation rates of pyrimethanil (common fungicide) from dry films were investigated under controlled laboratory conditions and during two field studies. The laboratory results indicate that the adjuvant enhances the volatilization and photolysis rate both on hydrophobic lemon leaves and hydrophilic glass substrates. These results can be attributed to an increase in the geometrical area of residual film and a widening of its circumference rim, where solutes are likely to concentrate, when generated from adjuvant-containing droplets. Such morphological differences may enhance the exposure of deposited pesticides to interact with the overlaying atmosphere and incident radiation. The field data was less conclusive, suggesting a small impact of the anti-drift adjuvant on the fungicide’s secondary drift from crops and an even lower effect on volatilization from bare soil.
Prediction of Gas Emission in the Working Face Based on LASSO-WOA-XGBoost
Authors: Song Weihua , Han Xiaowei , Qi Jifei
Published: October 2023

In order to improve the prediction accuracy of gas emission in the mining face, a method combining least absolute value convergence and selection operator (LASSO), whale optimization algorithm (WOA), and extreme gradient boosting (XGBoost) was proposed, along with the LASSO-WOA-XGBoost gas emission prediction model. Aiming at the monitoring data of gas emission in Qianjiaying mine, LASSO is used to perform feature selection on 13 factors that affect gas emission, and 9 factors that have a high impact on gas emission are screened out. The three main parameters of n_estimators, learning_rate, and max_depth in XGBoost are optimized through WOA, which solves the problem of difficult parameter adjustment due to the large number of parameters in the XGBoost algorithm and improves the prediction effect of the XGBoost algorithm. "When comparing PCA-BP, PCA-SVM, LASSO-XGBoost, and PCA-WOA-XGBoost prediction models, the results indicate that utilizing LASSO for feature selection is more effective in enhancing model prediction accuracy than employing principal component analysis (PCA) for dimensionality reduction." The average absolute error of the LASSO-WOA-XGBoost model is 0.1775, and the root mean square error is 0.2697, which is the same as other models. Compared with the four prediction models, the LASSO-WOA-XGBoost prediction model reduced the mean absolute error by 7.43%, 8.81%, 4.16%, and 9.92%, respectively, and the root mean square error was reduced by 0.24%, 1.13%, 5.81%, and 8.78%. It provides a new method for predicting the gas emission from the mining face in actual mine production.
Flash Drought and Its Characteristics in Northeastern South America during 2004–2022 Using Satellite-Based Products
Authors: Barbosa Humberto Alves
Published: October 2023

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation.
The Michigan–Ontario Ozone Source Experiment (MOOSE): An Overview
Authors: Olaguer Eduardo P. , Su Yushan , Stroud Craig A. , Healy Robert M. , Batterman Stuart A. , Yacovitch Tara I. , Chai Jiajue , Huang Yaoxian , Parsons Matthew T.
Published: October 2023

The Michigan–Ontario Ozone Source Experiment (MOOSE) is an international air quality field study that took place at the US–Canada Border region in the ozone seasons of 2021 and 2022. MOOSE addressed binational air quality issues stemming from lake breeze phenomena and transboundary transport, as well as local emissions in southeast Michigan and southern Ontario. State-of-the-art scientific techniques applied during MOOSE included the use of multiple advanced mobile laboratories equipped with real-time instrumentation; high-resolution meteorological and air quality models at regional, urban, and neighborhood scales; daily real-time meteorological and air quality forecasts; ground-based and airborne remote sensing; instrumented Unmanned Aerial Vehicles (UAVs); isotopic measurements of reactive nitrogen species; chemical fingerprinting; and fine-scale inverse modeling of emission sources. Major results include characterization of southeast Michigan as VOC-limited for local ozone formation; discovery of significant and unaccounted formaldehyde emissions from industrial sources; quantification of methane emissions from landfills and leaking natural gas pipelines; evaluation of solvent emission impacts on local and regional ozone; characterization of the sources of reactive nitrogen and PM2.5; and improvements to modeling practices for meteorological, receptor, and chemical transport models.
Mobile Laboratory Investigations of Industrial Point Source Emissions during the MOOSE Field Campaign
Authors: Yacovitch Tara I. , Lerner Brian M. , Canagaratna Manjula R. , Daube Conner , Healy Robert M. , Wang Jonathan M. , Fortner Edward C. , Majluf Francesca , Claflin Megan S. , Roscioli Joseph R. , Lunny Elizabeth M. , Herndon Scott C.
Published: October 2023

Industrial emissions of trace gases and VOCs can be an important contributor to air quality in cities. Disentangling different point sources from each other and characterizing their emissions can be particularly challenging in dense industrial areas, such as Detroit, Dearborn and surrounding areas in Southeast Michigan (SEMI). Here, we leverage mobile measurements of trace gases and speciated volatile organic compounds (VOCs) to identify emitting sites. We characterize their complicated emissions fingerprints based on a core set of chemical ratios. We report chemical ratios for 7 source types including automakers, steel manufacturers, chemical refineries, industrial chemical use (cleaning; coatings; etc.), chemical waste sites, compressor stations, and more. The source dataset includes visits to over 85 distinct point sources. As expected, we find similarities between the different types of facilities, but observe variability between them and even at individual facilities day-to-day. Certain larger sites are better thought of as a collection of individual point sources. These results demonstrate the power of mobile laboratories over stationary sampling in dense industrial areas.
Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite
Authors: Li Yalan , Yuan Jing , Cao Jie , Liu Yaohui , Huang Jianping , Li Bin , Wang Qiao , Zhang Zhourong , Zhao Zhixing , Han Ying , Liu Haijun , Han Jinsheng , Shen Xuhui , Wang Yali
Published: October 2023

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s.
Addressing Glaucoma in Myopic Eyes: Diagnostic and Surgical Challenges
Authors: Vinod Kateki , Salim Sarwat
Published: October 2023

Epidemiological and genetic studies provide strong evidence supporting an association between myopia and glaucoma. The accurate detection of glaucoma in myopic eyes, especially those with high myopia, remains clinically challenging due to characteristic morphologic features of the myopic optic nerve in addition to limitations of current optic nerve imaging modalities. Distinguishing glaucoma from myopia is further complicated by overlapping perimetric findings. Therefore, longitudinal follow-up is essential to differentiate progressive structural and functional abnormalities indicative of glaucoma from defects that may result from myopia alone. Highly myopic eyes are at increased risk of complications from traditional incisional glaucoma surgery and may benefit from newer microinvasive glaucoma surgeries in select cases.
Chorioamnionitis: An Update on Diagnostic Evaluation
Authors: Carter Sean W. D. , Neubronner Samantha , Su Lin Lin , Dashraath Pradip , Mattar Citra , Illanes Sebastián E. , Choolani Mahesh A. , Kemp Matthew W.
Published: October 2023

Chorioamnionitis remains a major cause of preterm birth and maternal and neonatal morbidity. We reviewed the current evidence for the diagnostic tests of chorioamnionitis and how this relates to clinical practice today. A comprehensive literature search and review was conducted on chorioamnionitis and intra-uterine inflammation. Data from randomized control trials and systematic reviews were prioritized. This review highlights that sterile inflammation plays an important role in chorioamnionitis and that the current tests for chorioamnionitis including clinical criteria, maternal plasma and vaginal biomarkers lack diagnostic accuracy. Concerningly, these tests often rely on detecting an inflammatory response after damage has occurred to the fetus. Care should be taken when interpreting current investigations for the diagnosis of chorioamnionitis and how they guide obstetric/neonatal management. There is an urgent need for further validation of current diagnostic tests and the development of novel, accurate, minimally invasive tests that detect subclinical intra-uterine inflammation.
Application of Machine Learning Based on Structured Medical Data in Gastroenterology
Authors: Kim Hye-Jin , Gong Eun-Jeong , Bang Chang-Seok
Published: October 2023

The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.