bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Assessment of Viscoelastic Skeletal Muscle Properties in Chronic Kidney Disease: Association with Physical Functioning
Authors: Wilkinson Thomas J. , Gore Ellie F. , Baker Luke A. , Smith Alice C.
Published: September 2023

Chronic kidney disease (CKD) is characterised by poor physical function. Mechanical muscle properties such as tone, elasticity, and stiffness influence the functional state of the muscle. Measuring these muscle mechanical properties is difficult and data on CKD are sparse. Using a novel myotonometer device, the aims of this study were to compare the viscoelastic muscle properties in CKD patients with previously reported data and to explore the association with muscle function. Non-dialysis-dependent CKD participants were recruited into a cross-sectional study conducted between 2018 and 2020. Muscle properties (tone, stiffness, elasticity) were assessed using a myotonometer (MyotonPRO). Muscle function was assessed using physical performance tests (sit-to-stand 5 and 60, timed up and go, short physical performance battery, gait speed, incremental shuttle walk, postural sway). General linear regression models were used to explore the association between muscle properties and physical function. Thirty-nine participants were included (age 64.2 (SD: 10.4) years; 51% male; eGFR 40.9 (SD: 20.0) mL/min/1.73 m2). Participants with CKD had reduced muscle tone, stiffness, and elasticity compared to previously reported studies. Muscle tone (B = −0.567, p = 0.003) and muscle stiffness (B = −0.368, p = 0.071) were greater in males than females. Increased BMI was associated with lower muscle tone (B = −0.528, p = 0.002) and muscle stiffness (B = −0.577, p = 0.002). No meaningful nor consistent associations were found between these properties and measures of muscle function and physical performance. In conclusion, using a novel handheld myotonometer, this study found that CKD patients exhibit a reduction in muscle tone, stiffness, and elasticity. In a passive state, these viscoelastic muscle properties showed no consistent associations with physical performance.
Advancing Knowledge of the Bio-Cultural Complexities of Low Energy Availability: The Value of Mixed-Methods Approaches
Authors: Schofield Katherine L. , Thorpe Holly , Sims Stacy T.
Published: September 2023

Low energy availability (LEA) is a complex health condition that most commonly affects female athletes. Research on LEA is weighted to quantitative approaches, and few studies have employed qualitative methods to understand the development of LEA. Current research fails to understand the complexity of LEA by largely operating within isolated research paradigms. This paper aims to demonstrate the value of integrating a mixed-methods research approach to understand the influence of sporting cultures on the physiological experiences of female athletes with LEA. The mixed-methods approach implemented the use of physiological (quantitative) and socio-psychological (qualitative) data obtained from elite female athletes in three sports: triathlons and running, (n = 11), rugby sevens (n = 9), and track cycling (n = 10). The physiological data consisted of energy availability, haematological analysis, bone health, and body composition. The socio-psychological data consisted of individual semi-structured interviews with topics covering nutrition, body image, the impact of the sporting environment, and experience with LEA. The interview data were thematically analysed. By bringing qualitative and quantitative data together, this paper illustrates the complex relationship between sporting culture and the physiology of LEA. First, endurance athletes categorised as having an LEA showed a positive correlation between the relative energy intake (EI) and serum ferritin, with the interviews revealing a focus on a low body weight and reducing the EI. Second, the interviews with the rugby players showed a strong but hierarchical team culture, with the experienced players monitoring and controlling the EI of novice players. Third, among the cyclists, the EI was reduced in those categorised as having an LEA, with the interviews revealing a coach–athlete power relationship impacting dietary behaviours. To conclude, this paper demonstrates how mixed methods are important for capturing how different sporting cultures impact athletes’ socio-psychological and physiological experiences of LEA.
Investigation of Curing Process and Thermal Behavior of Copolymers Based on Polypropylene Glycol Fumarate and Acrylic Acid Using the Methods of DSC and TGA
Authors: Burkeyeva Gulsym , Kovaleva Anna , Tazhbayev Yerkeblan , Ibrayeva Zhansaya , Zhaparova Lyazzat
Published: September 2023

In this work, the possibility of preparation of copolymers of three-dimensional crosslinked structure based on polypropylene glycol fumarate and acrylic acid is shown. The initial reagent polypropylene glycol fumarate has been synthesized by polycondensation reaction of fumaric acid and propylene glycol. The curing process of polypropylene glycol fumarate and acrylic acid at various mole concentrations was studied using DSC method at isothermal and dynamic regimens. Curing in isothermal condition was carried out at temperatures of 60 °C, 70 °C, and 80 °C. Residual reactivity was evaluated at a dynamic regimen within the temperature range from 30 °C to 200 °C at a constant heating rate. On the basis of calorimetric studies, the thermal effects and kinetic parameters of the reaction (conversion, reaction rate, activation energy) have been determined. Thermal behavior of cured samples of p-PGF-AA was estimated using dynamic thermogravimetry (TGA). According to TGA data, the process of decomposition of the studied copolymers proceeds in several stages. Based on the results obtained, the activation energies of thermal decomposition were calculated using the iso-conversional methods of Kissinger–Akakhira–Sunose and Friedman.
An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence
Authors: Baazeem Amani S. , Arif Muhammad Shoaib , Abodayeh Kamaleldin
Published: September 2023

Engineering and technological research groups are becoming interested in neural network techniques to improve productivity, business strategies, and societal development. In this paper, an explicit numerical scheme is given for both linear and nonlinear differential equations. The scheme is correct to second order. Additionally, the scheme’s consistency and stability are guaranteed. Backpropagation of Levenberg–Marquardt, the effect of including an induced magnetic field in a mathematical model for electrical boundary layer nanofluid flow on a flat plate, is quantitatively investigated using artificial neural networks. Later, the model is reduced into a set of boundary value problems, which are then resolved using the suggested scheme and a shooting strategy. The outcomes are also contrasted with earlier studies and the MATLAB solver bvp4c for validation purposes. In addition, neural networking is also employed for mapping input to outputs for velocity, temperature, and concentration profiles. These results prove that artificial neural networks can make precise forecasts and optimizations. Using a neural network to optimize the fluid flow in an electrical boundary layer while subjected to an induced magnetic field is a promising application of the suggested computational scheme. Fluid dynamics benefits greatly from combining numerical methods and artificial neural networks, which could lead to new developments in various fields. Results from this study may aid in optimizing fluid systems, leading to greater productivity and effectiveness in numerous technical fields.
The Three Dimensions of Buddhist Ecological Ethics Wisdom
Authors: Li Yuyong , Kong Yang , Tang Decai , Boamah Valentina
Published: September 2023

Buddhist ecological ethics wisdom is an important ideological resource for dealing with contemporary ecological environmental problems. Compared with Western eco-cultural pluralism and local Confucian and Taoist eco-ethical thinking, Buddhist ecological ethics wisdom is unique and profound regarding theoretical roots and core principles. In-depth study and grasp of the three major dimensions of Buddhist ecological ethics wisdom, namely, cherishing nature, equal mercy, and purifying minds and lands, will make it possible to face the ecological dilemmas in reality and realize the creative transformation and innovative development of its values.
A Case Study on the Convection Initiation Mechanisms of an Extreme Rainstorm over the Northern Slope of Kunlun Mountains, Xinjiang, Northwest China
Authors: Sun Qi , Abulikemu Abuduwaili , Yao Junqiang , Mamtimin Ali , Yang Lianmei , Zeng Yong , Li Ruqi , An Dawei , Li Zhiyi
Published: September 2023

Extreme precipitation events have been occurring frequently worldwide, and their causative factors and convection initiation (CI) mechanisms have been attracting more and more attention in recent years. As a comprehensive study on the CI mechanisms of extreme rainstorms over the northern slope of the Kunlun Mountains (KLM), Xinjiang, based on both observational and high tempo-spatial numerical simulation, the major findings of this work are as follows: A cold pool (CP) was formed in the northwestern Tarim Basin under the influence of early precipitation evaporation, and it moved towards the northern slope of the KLM several hours before the CI. With the movement of the CP, a significant vertical temperature gradient was formed close to the leading edge of the CP, thereby enhancing local convective instability (up to ~10 PVU). In addition, the vertical shear of the horizontal winds at the leading edge of the CP led to a notable increase in the baroclinic component of moist potential vorticity, thus reinforcing the local conditional symmetric instability (up to ~8 PVU), providing another important unstable energy for the CI. In addition, the combined effect of the convergent lifting of a boundary layer jet (BLJ, the maximum wind speed below 1 km exceeding 10 m s−1) and the significant frontogenetical forcing (up to ~100 × 10−8 K m−1 s−1) at the leading edge of the CP were the causes of the release of the unstable energies. Further analysis of the frontogenetical forcing associated with the CP indicates that the convergence (up to ~2 × 10−3 s−1), diabatic heating and slantwise terms (indicates the baroclinicity and inhomogeneity of the vertical momentum in horizontal direction) were the major contributors, whereas the deformation term at the leading edge of the CP provided a relatively weaker contribution.
Influence of Stochastic Modeling for Inter-Frequency Clock Biases on Multi-Frequency Precise Point Positioning
Authors: Lu Yangyang , Zhu Huizhong , Tang Longjiang , Li Bo , Li Jun , Xu Aigong
Published: September 2023

The incorporation of multi-frequency signals into global navigation satellite systems (GNSS) has presented new possibilities for precise positioning and rapid ambiguity resolution. Inter-frequency clock bias (IFCB) pertains to the time-varying biases among distinct frequencies within carrier phase observations in GNSS signals. The appropriate handling of IFCB is critical in enhancing the accuracy and convergence time of precise point positioning (PPP) solutions. The focus of this study is on the proper modeling of phase IFCB in multi-GNSS multi-frequency PPP. In this paper, the optimal IFCB power spectral density value of 0.6 m/sqrt(s) is first determined. To obtain the optimal stochastic model for IFCB, a thorough comparison and analysis of the product correction and parameter estimation methods is conducted. Additionally, experiments are conducted on the effect of IFCB modeling on the performance of undifferenced and uncombined PPP using data from 130 multi-GNSS experiment stations across the globe over a period of one week in January 2022. The study reveals that the optimal power spectral density for IFCB is within [60, 0.006] m/sqrt(s), modeling IFCB as a random walk is feasible, and the PPP is comparable for the three IFCB processing schemes of product correction, random walk, and white noise. Meanwhile, it is not reasonable to treat IFCB as a random constant or neglect it in the multi-GNSS multi-frequency PPP. In the absence of product correction or for users who require immediate and continuous positioning solutions, modeling IFCBs as random walks can lead to more reliable positioning results and improved convergence performance.
Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery
Authors: Luo Chengwei , Yang Yuli , Xin Zhiming , Li Junran , Jia Xiaoxiao , Fan Guangpeng , Zhu Junying , Song Jindui , Wang Zhou , Xiao Huijie
Published: September 2023

The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740–950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS–AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.
A Five-Component Decomposition Method with General Rotated Dihedral Scattering Model and Cross-Pol Power Assignment
Authors: Duan Yancui , Quan Sinong , Fan Hui , Xu Zhenhai , Xiao Shunping
Published: September 2023

The model-based polarimetric decomposition is extensively studied due to its simplicity and clear physical interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) data. Though there are many fine basic scattering models and well-designed decomposition methods, the overestimation of volume scattering (OVS) may still occur in highly oriented buildings, resulting in severe scattering mechanism ambiguity. It is well known that not only vegetation areas but also oriented buildings may cause intense cross-pol power. To improve the scattering mechanism ambiguity, an appropriate scattering model for oriented buildings and a feasible strategy to assign the cross-pol power between vegetation and oriented buildings are of equal importance. From this point of view, we propose a five-component decomposition method with a general rotated dihedral scattering model and an assignment strategy of cross-pol power. The general rotated dihedral scattering model is established to characterize the integral and internal cross-pol scattering from oriented buildings, while the assignment of cross-pol power between volume and rotated dihedral scattering is achieved by using an eigenvalue-based descriptor DOOB. In addition, a simple branch condition with explicit physical meaning is proposed for model parameters inversion. Experiments on spaceborne Radarsat−2 C band and airborne UAVSAR L band PolSAR datasets demonstrate the effectiveness and advantages of the proposed method in the quantitative characterization of scattering mechanisms, especially for highly oriented buildings.
Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images
Authors: Jiang Yan , Yin Guisheng
Published: September 2023

Recent studies have shown that deep-learning-based models for processing Unmanned Aerial Vehicle (UAV) remote sensing images are vulnerable to artificially designed adversarial examples, which can lead to incorrect predictions of deep models when facing adversarial examples. Previous adversarial attack methods have mainly focused on the classification and detection of UAV remote sensing images, and there is still a lack of research on adversarial attacks for object tracking in UAV video. To address this challenge, we propose an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline. First, we employ an attention feature loss to make the original frame’s features dissimilar to those of the adversarial frame, and an attention confidence loss to either suppress or enhance different confidence scores. Additionally, by forcing the tracker to concentrate on the background information near the target, a background distraction loss is used to mismatch templates with subsequent frames. Finally, we add total variation loss to generate adversarial examples that appear natural to humans. We validate the effectiveness of our method against popular trackers such as SiamRPN, DaSiamRPN, and SiamRPN++ on the UAV123 remote sensing dataset. Experimental results verify the superior attack performance of our proposed method.
Regional Gravity Field Modeling Using Band-Limited SRBFs: A Case Study in Colorado
Authors: Ma Zhiwei , Yang Meng , Liu Jie
Published: September 2023

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly for data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good localization characteristics in the frequency domain, were taken as the main tool in this study. To determine the optimal expansion degree of SRBFs for gravity data, a residual and a priori accuracy comparative analysis method was proposed. Using this methodology, the expansion degrees of terrestrial and airborne data were determined to be 5200 and 1840, respectively, and then a high-resolution geoid model called ColSRBF2023 was constructed for use in Colorado. The results indicated that ColSRBF2023 had a standard deviation (STD) of 2.3 cm with respect to the GSVS17 validation data. This value was 2–6 mm lower than models obtained using different expansion degrees for gravity data and models from other institutions considered in this study. Furthermore, when comparing it with the validation geoid model on a 1′ × 1′ grid, ColSRBF2023 exhibited an STD value of 2.4 cm, which was also the best among the examined models. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly for constructing high-resolution gravity field models in rugged mountainous areas.
Real-Time EEG-Based Emotion Recognition
Authors: Yu Xiangkun , Li Zhengjie , Zang Zhibang , Liu Yinhua
Published: September 2023

Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.
Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis
Authors: Lee Haemi , Lee Yoonjae , Jo Minho , Nam Sanghoon , Jo Jeongdai , Lee Changwoo
Published: September 2023

Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.
Soft Sensing of LPG Processes Using Deep Learning
Authors: Sifakis Nikolaos , Sarantinoudis Nikolaos , Tsinarakis George , Politis Christos , Arampatzis George
Published: September 2023

This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.
Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals
Authors: Khare Smith K. , Bajaj Varun , Gaikwad Nikhil B. , Sinha G. R.
Published: September 2023

Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain–computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.
A Collaborative Governance Strategy for Power Quality in AC/DC Distribution Networks Considering the Coupling Characteristics of Both Sides
Authors: Wang Jiang , Qin Liang , Xiang Yang , Ren Penghui , Tang Xu , Ruan Jiangjun , Liu Kaipei
Published: September 2023

The interactions between power quality in the AC-DC distribution network segments contribute to the distributed propagation of power quality anomalies throughout the entire network. Focusing on the photovoltaic multifunctional grid-connected inverter (PVMFGCI), this study deeply explores a collaborative governance strategy for optimizing regional power quality. Initially, the analysis dissects the DC ripple generation mechanism corresponding to harmonics and asymmetry in AC subnetwork voltages. Subsequently, a strategy is proposed for partitioning comprehensive control regions for AC-side power quality, taking into account photovoltaic governance resources based on insights from photovoltaic control realms and power quality classifications. Further, a collaborative allocation model for governance resources incorporating active optimization potentials of grid-connected converters is established based on the governance capabilities and residual capacities of PVMFGCI. Finally, the effectiveness of the proposed approach is validated through a MATLAB-based example analysis.
Development and Testing of a Daily Activity Recognition System for Post-Stroke Rehabilitation
Authors: Proffitt Rachel , Ma Mengxuan , Skubic Marjorie
Published: September 2023

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants’ homes over three months. Given the extensive amount of data, only a portion of the participants’ data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional–de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients.
Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices
Authors: Xu Weidong , He Jingke , Li Weihua , He Yi , Wan Haiyang , Qin Wu , Chen Zhuyun
Published: September 2023

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers’ health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers’ health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.
Natural-Language-Driven Multimodal Representation Learning for Audio-Visual Scene-Aware Dialog System
Authors: Heo Yoonseok , Kang Sangwoo , Seo Jungyun
Published: September 2023

With the development of multimedia systems in wireless environments, the rising need for artificial intelligence is to design a system that can properly communicate with humans with a comprehensive understanding of various types of information in a human-like manner. Therefore, this paper addresses an audio-visual scene-aware dialog system that can communicate with users about audio-visual scenes. It is essential to understand not only visual and textual information but also audio information in a comprehensive way. Despite the substantial progress in multimodal representation learning with language and visual modalities, there are still two caveats: ineffective use of auditory information and the lack of interpretability of the deep learning systems’ reasoning. To address these issues, we propose a novel audio-visual scene-aware dialog system that utilizes a set of explicit information from each modality as a form of natural language, which can be fused into a language model in a natural way. It leverages a transformer-based decoder to generate a coherent and correct response based on multimodal knowledge in a multitask learning setting. In addition, we also address the way of interpreting the model with a response-driven temporal moment localization method to verify how the system generates the response. The system itself provides the user with the evidence referred to in the system response process as a form of the timestamp of the scene. We show the superiority of the proposed model in all quantitative and qualitative measurements compared to the baseline. In particular, the proposed model achieved robust performance even in environments using all three modalities, including audio. We also conducted extensive experiments to investigate the proposed model. In addition, we obtained state-of-the-art performance in the system response reasoning task.
Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning
Authors: Piccoli Flavio , Barbato Mirko Paolo , Peracchi Marco , Napoletano Paolo
Published: September 2023

In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of R2, by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.
Role of Internet of Things and Deep Learning Techniques in Plant Disease Detection and Classification: A Focused Review
Authors: Dhaka Vijaypal Singh , Kundu Nidhi , Rani Geeta , Zumpano Ester , Vocaturo Eugenio
Published: September 2023

The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.
Comprehensive Profiling of Terpenes and Terpenoids in Different Cannabis Strains Using GC × GC-TOFMS
Authors: Kaur Jasmeen , Sun Ning , Hill Jane E.
Published: September 2023

Cannabis contains a wide range of terpenes and terpenoids that are mainly responsible for their distinctive aroma and flavor. These compounds have also demonstrated therapeutic effects either alone and/or as synergistic compounds with other terpenes, terpenoids, and/or cannabinoids. Several studies have attempted to fully characterize terpenes and terpenoids in cannabis; however, most of these studies used one-dimensional gas chromatography, which often results in the co-elution of the compounds. In the present study, we analyzed terpenes and terpenoids in the dried flowers of six cannabis strains using a two-dimensional gas chromatograph time-of-flight mass spectrometer (GC × GC-TOFMS). A total of 146 terpenes and terpenoids were detected across all six cannabis strains with an enhanced separation of 16 terpenes and terpenoids in the second dimension. Additionally, we achieved enhanced separation of four terpenes and terpenoids from a standard mixture in the second dimension. Chemical differences were observed in the number and relative abundance of monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids in all six strains. We were also able to identify four new terpenoids in cannabis, which are reported here for the first time.
Application of Performance and Efficiency Indicators in Measuring the Level of Success of Public Universities in Poland
Authors: Multan Ewa , Wójcik-Augustyniak Marzena , Sobotka Bartosz , Bis Jakub
Published: September 2023

Success can be interpreted and assessed in various ways. This article proposes evaluating university success through performance and efficiency indicators, inspired by the Positioning School of Strategy and New Public Management. This approach faces challenges, such as limited economic data for Polish public universities. The article aimed to identify factors and success levels for public universities in the higher education sector. The research question, “What are the measures and levels of success of public universities?”, was divided into three specific inquiries: key success factors, performance and efficiency indicators, and success levels for selected Polish public universities. The study involved analysing international and Polish university rankings, interviewing 53 public university experts in Poland, and examining efficiency indicators for 10 selected public universities. Critical success factors (research excellence, education excellence, international cooperation) and critical performance indicators were identified. Efficiency indicators demonstrated the good financial condition of selected institutions. Critical success factors and critical performance indicators were defined, and performance and efficiency measures were used to assess the success of Polish public higher education institutions. The authors acknowledge the need for parametric and non-parametric methods to fully evaluate success. The proposed performance-oriented measurement tool can assist public university leaders in making strategic decisions.
Evolution of Physical and Mechanical Properties of Granite after Thermal Treatment under Cyclic Uniaxial Compression
Authors: Hu Bo , Hu Xiangqi , Lin Chenggeng , Du Guangzhen , Ma Tianxing , Li Kaihui
Published: September 2023

The combined effects of thermal and cyclic loading result in complex mechanical behavior in engineering rock masses. The study of the physical and mechanical properties of these rock masses is of great importance for improving the stability and sustainability of structures built on thermally treated rock masses. In order to understand the failure mechanism, uniaxial compression tests and cyclic loading and unloading tests were conducted on granite specimens that had undergone thermal treatment at various temperatures. The test results indicate that the density and P-wave velocity of the specimens decrease while the degree of damage increases after thermal treatment. The compressive strength and elastic modulus of the specimens generally decrease as a result of thermal treatment, although thermal hardening does occur within the temperature range of 200–400 °C. The dilatancy characteristics of the specimens change with the treatment temperature, and they are more prone to shear dilation under external loading. Furthermore, the failure mode of the specimens transitions from brittle to ductile failure as the treatment temperature increases. The combination of thermal treatment and cyclic loading causes the rock fragments to become looser and finer following specimen failure.
The Effect of Idiosyncratic Deals on Coworkers’ Knowledge Hiding: A Moderated Serial Mediation Model
Authors: Ding Chen , Ai Tian , Zhao Shuming , Zhang Gaoqi
Published: September 2023

The essence of knowledge management involves the personalized management of talented employees who possess tacit knowledge. Unfortunately, non-standardized practices can lead to negative knowledge behaviors among coworkers, which can hamper beneficial knowledge interactions. This study aims to explore the underlying mechanism of idiosyncratic deals (i-deals) on knowledge hiding from the bystander perspective. We conducted a two-wave on-site survey of 321 knowledge-based employees in Kunshan Industrial Park, China. During the first wave, employees provided information regarding their perceptions of others’ i-deals and ethical leadership. Two weeks later, employees reported their effort–reward imbalance (ERI), psychological distress, and knowledge hiding behaviors. To test our hypothesis, we used a hierarchical regression analysis with SPSS 26.0 and a path analysis with Mplus 7.4. The results indicate that (a) coworkers’ perceptions of other employees’ idiosyncratic deals (CPOEID) have an indirect effect on knowledge hiding via ERI and psychological distress; (b) ERI and psychological distress serially mediate the relationship between CPOEID and knowledge hiding; and (c) ethical leadership not only reduces the positive effect of CPOEID on ERI but also weakens the serial mediation effects of ERI and psychological distress between CPOEID and knowledge hiding. Our investigation, by using a cognitive-emotional processing system framework, provides a new theoretical perspective on the potential dysfunctionality of differentiated human resource management practices. Furthermore, our findings offer evidence for the compatibility of leadership and policy factors, as ethical leadership lessens the destructive effects of management practices.
Do Green Energy and Information Technology Influence Greenhouse Gas Emitting Countries to Attain Sustainable Development?
Authors: Cui Ningning , Nketiah Emmanuel , Ma Xiaoyu
Published: September 2023

Transitioning from traditional energy sources to green and sustainable energy sources can potentially reduce environmental problems. Many countries are gradually recording increasing greenhouse gas (GHG) emissions as they develop their economies. As a result, this study aims to use top GHG-emitting countries in its analysis to establish the role green energy and information technology play in reducing their pollution levels. Data from 11 GHG-emitting countries from 1990–2020 were utilized. The Fully Modified Ordinary Least squares (FMOLS), Dynamic ordinary least squares (DOLS), and Granger causality are used for the analysis. The empirical results revealed that an increase in non-renewable energy usage of 1% increases GHG gas emissions by 0.6960% (FMOLS) and 0.6119% (DOLS). On the impact of renewable energy, a 1% increase reduces GHG emissions by 0.1145% (FMOLS) and 0.1957% (DOLS). Also, a 1% increase in information technology increases GHG emissions by 0.0459% (FMOLS) and 0.0429% (DOLS) under the specifications of FMOLS and DOLS. The directional causalities are established in the study as well. In light of this, using “abundant” renewable energy sources is the gateway to reducing GHG emissions alongside their tremendous economic growth and I.T. development. Other policy implications are outlined for future research and policymakers.
The Association between Climate Change Exposure and Climate Change Worry among Israeli Adults: The Interplay of Risk Appraisal, Collective Efficacy, Age, and Gender
Authors: Shinan-Altman Shiri , Hamama-Raz Yaira
Published: September 2023

Guided by the transactional model of stress and coping, we examined the association between climate change exposure and climate change worry among Israeli adults, with the interplay of risk appraisal, collective efficacy, age, and gender. Using an online survey with 402 participants, we found moderate levels of climate change worry. Higher climate change exposure, increased risk appraisal, and greater collective efficacy were associated with higher worry levels. Climate change risk appraisal mediated the relationship between climate change exposure and worry, whereas gender moderated the association between collective efficacy and worry. This study highlights the significant impact of climate change exposure on worry, emphasizing the roles of risk appraisal and collective efficacy, particularly among women, and underscores the need for tailored interventions to address emotional responses to climate change.
MSCF: Multi-Scale Canny Filter to Recognize Cells in Microscopic Images
Authors: Mbaidin Almoutaz , Cernadas Eva , Al-Tarawneh Zakaria A. , Fernández-Delgado Manuel , Domínguez-Petit Rosario , Rábade-Uberos Sonia , Hassanat Ahmad
Published: September 2023

Fish fecundity is one of the most relevant parameters for the estimation of the reproductive potential of fish stocks, used to assess the stock status to guarantee sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. A new segmentation technique, named the multi-scale Canny filter (MSCF), is proposed to recognize the boundaries of cells (oocytes), based on the Canny edge detector. Our results show the superior performance of MSCF on five fish species compared to five other state-of-the-art segmentation methods. It provides the highest F1 score in four out of five fish species, with values between 70% and 80%, and the highest percentage of correctly recognized cells, between 52% and 64%. This type of research aids in the promotion of sustainable fisheries management and conservation efforts, decreases research’s environmental impact and gives important insights into the health of fish populations and marine ecosystems.
Characteristics of Energy Dissipation in T-Shaped Fractured Rocks under Different Loading Rates
Authors: Zhang Yong , Zhong Lijia , Pang Fan , Li Peng , Liu Fengyin
Published: September 2023

T-shaped fractured rocks in the engineering rock mass with different inclination angles, quantities, and cross patterns will cause slope landslides, cavern collapse, roof fall, and other disasters under the action of external forces. Deformation evolution of the T-shaped fractured rock is also significant for monitoring the stability of rock engineering structures. In this paper, the compression test of T-shaped fracture specimens was carried out under different loading rates. By modulating both the fracture inclination angle and the loading rate, the attributes pertaining to energy dissipation in the T-shaped fractured specimen were scrupulously scrutinized and subsequently expounded upon. The difference in the energy characteristics between fractured rock and intact rock was investigated to understand the deformation evolution of T-shaped fractured rock samples. The results show that when the fracture angle is 45° and 90°, the elastic strain energy and dissipated energy decrease as the secondary fracture angle increases. At the peak point, as the secondary fracture angle increases from 0°, the total absorbed energy, elastic strain energy, and dissipated energy of the T-shaped fractured rock increase, the ratio Ue/U of elastic strain energy to total energy increases, and the ratio Ud/U of dissipated energy to total energy decreases. The increase in loading rate leads to an increase in Ue/U and a decrease in Ud/U at the peak point of the T-shaped fractured rock specimen. The increase in loading rate leads to an increase in the total absorbed energy and elastic energy at the peak point of the T-shaped fractured rock, while the dissipated energy decreases. Investigative endeavors into the mechanics and energetic attributes of T-shaped fractured rocks bestow pragmatic and directive significance upon the safety assessment and stability prognostication of sundry geological undertakings.