bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of the Effect of Eudragit RSPO on the Photostability of Venlafaxine in a Physical Mixture and in a Melt Form
Authors: Maślanka Anna , Szlósarczyk Marek , Talik Przemysław , Szafraniec-Szczęsny Joanna , Woyna-Orlewicz Krzysztof , Żmudzki Paweł , Hubicka Urszula
Published: August 2023

The effect of Eudragit RSPO on the photostability of venlafaxine in a physical mixture and as a melt was studied. A validated ultra-high-performance liquid chromatography mass spectrometry method was used to determine the content of venlafaxine alongside degradation products. The most likely structures of the degradation products were determined by mass spectrometry. The samples were evaluated before and after exposure to ultraviolet A by differential scanning calorimetry and scanning electron microscopy. The kinetic parameters of the decomposition of venlafaxine in the physical mixture and melt form were calculated and comparable to those of venlafaxine as an active pharmaceutical ingredient. The results indicate that the process of venlafaxine decomposition is more than three times slower in the venlafaxine–eudragit melt than in its physical mixture with eudragit and its pure form. The strong photoprotective nature of the tested polymer against venlafaxine in the melt form was also confirmed by differential scanning calorimetry and scanning electron microscopy.
Seismic Response Variance of Depositional Sequences: Implications for Reservoir Prediction in Lacustrine Basin
Authors: He Qiaolin , Yang Shuwen , He Wenxiang , Hu Yong , Wang Tong , Gao Xiaoyang
Published: August 2023

In recent years, lithologic oil and gas reservoirs have become an important target in continental hydrocarbon-bearing basins. Geophysical prospecting technology using seismic data is an indispensable tool for oil and gas exploration. However, while previous work has paid much attention to the seismic responses of reservoirs (sandstones), the seismic responses of depositional sequences composed of sandstone–mudstone cycles are not well understood in reservoir prediction. This problem seriously restricts efficient oil–gas exploration and development. The Cretaceous Baxigai Formation in the Yingmaili area, west of the Tabei Uplift, is an important exploration target for lithologic oil and gas reservoirs in the Tarim Basin. The Baxigai Formation is deeply buried with thin thickness. The Baxigai Formation in the study area is divided into a lower sandstone section and an upper mudstone section. Braided river delta sand bodies are developed in the lower sandstone section, and braided river delta sand bodies and beach bar sand bodies are developed in the upper mudstone section. According to the difference in the depositional sequences in different zones, five types of the vertical combination style of sandstone and mudstone were identified. Through seismic forward modeling, the seismic response variance of the five kinds of sequence models was established. Then, the amplitude attributes were extracted via wavelet decomposition to reflect the distribution of sandstone–mudstone in different zones. This could help predict the vertical and horizontal distributions of different depositional sequences and the sandstones in these sequences. During the sedimentary period of the upper mudstone section of the Baxigai Formation, the beach bar sand bodies were distributed along the northeast coast. The thin sand bodies pinched out along the up-dip direction to form favorable lithologic traps, which has important significance for lithologic reservoir exploration.
Design of Radial Flow Channel Proton Exchange Membrane Fuel Cell Based on Topology Optimization
Authors: Qu Cheng , Zhang Yifan , Zhang Zhihu , Zheng Minggang
Published: August 2023

In this paper, the flow channel of the radial proton exchange membrane fuel cell (PEMFC) is optimized by the topological optimization method. Using the SNOPT algorithm, a two-dimensional stable constant temperature model is freely constructed in the cyclic sector design domain. Topology optimization aims to maximize the efficiency of PEMFC and minimize the energy dissipation of reaction gas. We analyze radial topology flow channels’ mass transfer capacity and cell performance with different maximum volume constraints. The results show that under high current density, the performance of the optimized channel is significantly better than that of the traditional channel. Increasing the maximum volume constraint is beneficial for improving the mass transfer of PEMFC. At 0.6 V, the cell performance of Scheme 4 is 14.9% higher than the serpentine flow channel and 9.5% higher than the parallel flow channel. In addition, in the optimal selection, 3D simulation modeling is carried out for more accurate verification.
Mass and Heat Transfer of Pressure Swing Adsorption Oxygen Production Process with Small Adsorbent Particles
Authors: Sun Yuan , Zhang Chuanzhao , Zhu Xianqiang , Dong Liang , Sun Xianhang
Published: August 2023

Rapid-cycle pressure swing adsorption (PSA) with small adsorbents particles is intended to improve mass transfer rate and productivity. However, the mass transfer mechanisms are changed with reduction of particle size during rapid-cycle adsorption process. A heat and mass transfer model of rapid-cycle PSA air separation process employing small LiLSX zeolite particles is developed and experimentally validated to numerically analyze the effects of mass transfer resistances on the characteristics of cyclic adsorption process. Multicomponent Langmuir model and linear driving force model are employed for characterizing the adsorption equilibrium and kinetic. The results of numerical analysis demonstrate that the dominant mass transfer resistance of small adsorbents particles is a combination of film resistance, axial dispersion effect and macropore diffusion resistance. The oxygen purity, recovery and productivity of the product are overestimated by ~2–4% when the effect of axial dispersion on mass transfer is ignored. As particle size decreases, the front of nitrogen-adsorbed concentration and gas temperature become sharp, which effectively improves the performance. However, the adverse effect of axial dispersion on the mass transfer becomes significant at very small particles conditions. It is nearly identical shapes of nitrogen concentration and gas temperature profiles after adsorption and desorption steps. The profiles are pushed forward near the production end with an increase in bed porosities. The optimal oxygen recovery and productivity are achieved with a particle diameter of 0.45 mm and bed porosity of 0.39 during the PSA process.
Preliminary Results of Innovative Two-Stage Torrefaction Technology Applied for Thermochemical Treatment of Sunflower Husk
Authors: Nebyvaev Artemy , Klimov Dmitry , Ryzhenkov Artem , Brulé Mathieu
Published: August 2023

This article proposes an innovative two-stage technology for biomass torrefaction generating high-quality biochar, more specifically biocoal, as solid fuel, and offering a promising solution to the challenges posed by the combustion of biomass. In particular, the higher quality of biochar as solid fuel reduces the build-up of unmanageable deposits on fired surfaces, as these deposits inhibit heat transfer and reduce the efficiency of biomass boilers. The proposed two-stage technology involves torrefaction in a hearth-type reactor at temperatures up to 250 °C, followed by a subsequent stage of cooling achieved through washing with water. The two-stage torrefaction technology is integrated within a vertical hearth reactor vessel composed of three superimposed trays serving for biomass input, torrefaction, and water washing combined with biomass cooling, respectively. Upon contact with torrefied biomass, cooling water turned into water vapor; hence, eliminating the requirement for subsequent biomass separation and drying. The system was tested on sunflower husk, and results showed a reduction in the content of problematic elements such as alkali metal chlorides or sulfur compounds in biochar ash, suggesting lower corrosion rates of convective heating surfaces of the boiler under ash sediments. It is hypothesized that, while water exited hot biomass in the form of water vapor instead of liquid water, as is typically the case in water-washing processes, a share of undesirable elements may still have been removed from biomass through vaporization, without necessitating any additional process for ash removal. Hence, the index values calculated according to the chemical analysis of biomass ash suggested that sunflower husk biochar (biocoal) resulting from the proposed two-stage torrefaction process may display fuel characteristics similar to biomasses whose combustion ash may form reduced levels of deposits on boiler surfaces. Therefore, the proposed technology holds the potential to improve solid fuel characteristics of biomass, targeting enhanced efficiency and sustainability of biomass-fired power plants.
Experimental Studies of Methane Adsorption on Activated Carbon and 3D Graphene Materials
Authors: Zhu Ziwen , Zhang Muyuan
Published: August 2023

Two kinds of slit pore carbon materials, namely activated carbon (AC) and 3D graphene materials (3D-GS), were purchased to examine their methane storage capabilities. The structural analysis and characterization of AC and 3D-GS were carried out using X-ray diffraction (XRD), scanning electron microscopy (SEM), the X-ray energy dispersive spectrum (EDS), and N2 adsorption/desorption isotherms. Additionally, a thermodynamic framework was employed in the Henry’s law region to evaluate the potential well between the adsorbed fluid and adsorbent. The adsorption behavior of methane on two materials at room temperature and high pressure was also investigated. The results show that the Toth equation is the most suitable model for predicting adsorption isotherms than the Langmuir and L-F equations and determines that the absolute uptake of methane storage on AC and 3D-GS are, respectively, 7.86 mmol·g−1 and 8.9 mmol·g−1 at 298 K and 35 bar. In the Henry’s law region, the isosteric heat of methane adsorption on 3D-GS is larger than that of AC. Meanwhile, the potential well between methane and carbon-based materials decreases as the temperature increases. This indicates that the capacity of methane uptake is enhanced at lower temperatures, which is consistent with the measurements of adsorption isotherms. The research concludes that the 3D-GS is more suitable as a material storage medium than AC. This study provides valuable theoretical guidance for exploring the potential of methane storage on slit pore carbon-based material.
Stability Analysis: Two-Area Power System with Wind Power Integration
Authors: Barrueto Guzmán Aldo , Chávez Oróstica Héctor , Barbosa Karina A.
Published: August 2023

This paper focuses on a comprehensive stability study of a two-area power system with wind power integration and synthetic inertia control in each area, considering the effects of varying the interconnection link. Normally, synthetic inertia proposals are analyzed in one-area systems, in which stability is tested without considering transmission system phenomena, such as coherency. As modern power systems are progressively becoming interconnected, the possibility of forming two or more non-coherent areas is likely, which poses a challenge to synthetic inertia control techniques that use system frequency as a main feedback signal. In this context, this work addresses a crucial gap in the existing literature and provides a valuable starting point for studying more complex interconnected power systems with wind power integration. Simulations were performed in Matlab-Simulink considering a data-driven frequency dynamics model of the Chilean Electric System, and a wind power model with synthetic inertia control H2 norm minimization in each area. The results showed that it is possible to find local optimal feedback gains, preserving the stability of the global system under significant variations in the interconnection link. RoCoF and Nadir indicators are provided, highlighting the benefits of synthetic inertia control, particularly in low-inertia situations.
Increased Activity in the Prefrontal Cortex Related to Planning during a Handwriting Task
Authors: Megumi Akiko , Shin Jungpil , Uchida Yuta , Yasumura Akira
Published: August 2023

We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters.
The Impact of a National Crisis on Research Collaborations: A Scientometric Analysis of Ukrainian Authors 2019–2022
Authors: Damaševičius Robertas , Zailskaitė-Jakštė Ligita
Published: August 2023

This paper analyzes the impact of the ongoing war in Ukraine on the productivity and collaboration networks of Ukrainian academics. As a case study, we analyze the publication patterns in open-access MDPI journals using bibliographic analysis methods and compare the research output published in 2022 with research papers published in the three preceding years (2019–2021) with at least one author having an Ukrainian affiliation. A total of 2365 publications were analyzed. The identified publication trends provide an interesting insight into the dynamics of the research network of Ukrainian researchers, which demonstrated a decline in diversity of international collaborations in 2022. The findings of this study emphasize the necessity of international research collaboration in a variety of fields in order to mitigate the detrimental effects of national crises and emergencies.
Religion upon the Mountains: From Christianisation to Social Actions against Summit Crosses in Italy
Authors: Rech Giovanna
Published: August 2023

In Italy, the debate regarding the presence of crosses and crucifixes in public places is long-standing and involves their detractors, supporters and defenders. Over time, these conflicting positions have gained media resonance, becoming a sociopolitical controversy that has led to lawsuits at various levels, including the European Court of Human Rights. In the social sphere, the issue has oscillated between the recognition of the universal value of religious symbols and advocacy for secularism, even in open spaces such as mountaintops. During the last few decades, several initiatives have been undertaken in the Italian Alps, driven by ecological concerns and opposition to the presence of crosses on the mountains. These initiatives have resulted in collective actions against the positioning and erection of crosses, and there have even been attempts to diversify the Italian peaks. By providing a historical overview of the Christianisation of Italian mountaintops and focusing on the mobilisation against the presence of crosses, this article contributes to the understanding of the role of such symbols in Italian public opinion, which is intertwined with the vitality of the Catholic Church and the sociopolitical implications of these initiatives. The research questions will investigate the process of legitimisation and delegitimisation of Christian symbols. The cross on the mountaintop serves as an example of culturalised religion, where this cultural object can become a “passive religious symbol,” polarising claims for the defence of the natural environment and the sustainability of religion in the mountains.
Development of Wind Energy in EU Countries as an Alternative Resource to Fossil Fuels in the Years 2016–2022
Authors: Wolniak Radosław , Skotnicka-Zasadzień Bożena
Published: August 2023

The aim of this article is to present solutions related to wind energy in EU countries as an alternative to fossil fuels. This article is based on secondary information and statistical data regarding the development of wind power engineering in EU countries for the years 2016–2022. The main purpose of this paper is to analyze of the relations between the development of wind energy in European Union countries and GPD (gross domestic product) per capita and selected factors. The following hypotheses were formulated: H1—There is a statistically significant correlation between GDP per capita and the use of wind energy in European Union countries. H2—There is a relationship between the length of the coastline and the use of wind energy in European Union countries. H3—There is a statistically significant correlation between the attitude to uncertainty of the inhabitants of a given country and the use of wind energy in said country. The presented research results support all these hypotheses. The results of the research regarding H2 are as follows: in the case of northern European countries (Ireland and Finland) and the Iberian Peninsula (Spain and Portugal), the development of wind power engineering in the study period was faster than could be inferred from the length of the coastline in these countries. Regarding hypothesis H1, it was concluded on the basis of the analysis that the involvement of countries in the development of wind power engineering is correlated with their wealth. The novelty of this paper emerges from its innovative approach to analyzing wind power engineering, its incorporation of cultural factors, its quantitative assessment of correlations, and its actionable policy recommendations. These elements collectively contribute to a comprehensive and impactful study that advances our understanding of wind energy adoption in the European Union.
Pricing Multi-Event-Triggered Catastrophe Bonds Based on a Copula–POT Model
Authors: Tang Yifan , Wen Conghua , Ling Chengxiu , Zhang Yuqing
Published: August 2023

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.
A Spatial Data-Driven Approach for Mineral Prospectivity Mapping
Authors: Senanayake Indishe P. , Kiem Anthony S. , Hancock Gregory R. , Metelka Václav , Folkes Chris B. , Blevin Phillip L. , Budd Anthony R.
Published: August 2023

Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence points and existing mineral prospectivity maps developed using the weights-of-evidence (WofE) method. The ability to capture training points accurately and the simplicity of the proposed approach make it advantageous over complex mineral prospectivity mapping methods, to serve as a preliminary evaluation technique. The methodology can be modified with different datasets and algorithms, facilitating the investigations of mineral prospectivity in other regions and providing guidance for more detailed, high-resolution geological investigations.
Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region
Authors: Zou Zhenhua , Huang Chengquan , Lang Megan W. , Du Ling , McCarty Greg , Ingebritsen Jeffrey C. , Herold Nate , Griffin Rusty , Gong Weishu , Lu Jiaming
Published: August 2023

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance.
Telerehabilitation with ARC Intellicare to Cope with Motor and Respiratory Disabilities: Results about the Process, Usability, and Clinical Effect of the “Ricominciare” Pilot Study
Authors: Capecci Marianna , Cima Rossella , Barbini Filippo A. , Mantoan Alice , Sernissi Francesca , Lai Stefano , Fava Riccardo , Tagliapietra Luca , Ascari Luca , Izzo Roberto N. , Leombruni Maria Eleonora , Casoli Paola , Hibel Margherita , Ceravolo Maria Gabriella
Published: August 2023

Background: “Ricominciare” is a single-center, prospective, pre-/post-intervention pilot study aimed at verifying the feasibility and safety of the ARC Intellicare (ARC) system (an artificial intelligence-powered and inertial motion unit-based mobile platform) in the home rehabilitation of people with disabilities due to respiratory or neurological diseases. Methods. People with Parkinson’s disease (pwPD) or post-COVID-19 condition (COV19) and an indication for exercise or home rehabilitation to optimize motor and respiratory function were enrolled. They underwent training for ARC usage and received an ARC unit to be used independently at home for 4 weeks, for 45 min 5 days/week sessions of respiratory and motor patient-tailored rehabilitation. ARC allows for exercise monitoring thanks to data from five IMU sensors, processed by an AI proprietary library to provide (i) patients with real-time feedback and (ii) therapists with information on patient adherence to the prescribed therapy. Usability (System Usability Scale, SUS), adherence, and adverse events were primary study outcomes. Modified Barthel Index (mBI), Barthel Dyspnea Index (BaDI), 2-Minute Walking Test (2MWT), Brief Fatigue Inventory (BFI), Beck Depression or Anxiety Inventory (BDI, BAI), and quality of life (EQ-5D) were also monitored pre- and post-treatment. Results. A total of 21 out of 23 eligible patients were enrolled and completed the study: 11 COV19 and 10 pwPD. The mean total SUS score was 77/100. The median patients’ adherence to exercise prescriptions was 80%. Clinical outcome measures (BaDI, 2MWT distance, BFI; BAI, BDI, and EQ-5D) improved significantly; no side effects were reported. Conclusion. ARC is usable and safe for home rehabilitation. Preliminary data suggest promising results on the effectiveness in subjects with post-COVID condition or Parkinson’s disease.
Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion
Authors: Deng Weichu , Wei Huanchun , Huang Teng , Cao Cong , Peng Yun , Hu Xuan
Published: August 2023

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.
The Godspeed Questionnaire Series in the Assessment of the Social Robot TIAGo by Older Individuals
Authors: Tobis Slawomir , Piasek-Skupna Joanna , Suwalska Aleksandra
Published: August 2023

(1) Background: A robot in care for older adults requires solid research confirming its acceptance. The aim of this study was to present the Polish version of the Godspeed Questionnaire Series (GQS) and assess the perception of the social robot TIAGo; (2) Methods: The study involved older individuals living in the community and care homes and measured perception after interaction with TIAGo using five series of GQS (S1: Anthropomorphism, S2: Animacy, S3: Likeability, S4: Perceived intelligence, and S5: Perceived safety); (3) Results: We studied 178 individuals (age: 75.2 ± 9.6 years, 103 women). Good internal consistency was found. Cronbach’s Alpha was 0.90 for the entire tool (from 0.75 to 0.94 for the individual series). Mean scores for S1 and S2 were comparable but lower than all others (p < 0.001). Average scores for S3 and S4 did not differ but were higher than those of S5. Age, gender and education did not impact the answers, as did the ease of use of technology and self-assessment of independence. Solely, the place of residence influenced the results of S3 and S5; people living in institutions scored higher (p < 0.05 and p < 0.001, respectively); (4) Conclusions: Acceptance does not go hand in hand with the perception of anthropomorphism and animacy.
A Hybrid Model for Vehicle Acceleration Prediction
Authors: Luo Haoxuan , Hu Xiao , Huang Linyu
Published: August 2023

Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multiple factors on acceleration, a single data model may not be suitable for various driving scenarios. Therefore, this paper proposes a hybrid approach for vehicle acceleration prediction by combining clustering and deep learning techniques. Based on historical data of vehicle speed, acceleration, and distance to the preceding vehicle, the proposed method first clusters the acceleration patterns of vehicles. Subsequently, different prediction models and parameters are applied to each cluster, aiming to improve the prediction accuracy. By considering the unique characteristics of each cluster, the proposed method can effectively capture the diverse acceleration patterns. Experimental results demonstrate the superiority of the proposed approach in terms of prediction accuracy compared to benchmarks. This paper contributes to the advancement of sensor data processing and artificial intelligence techniques in the field of vehicle acceleration prediction. The proposed hybrid method has the potential to enhance the accuracy and reliability of acceleration prediction, enabling applications in various domains, such as autonomous driving, traffic management, and vehicle control.
Sensors and Robotics for Digital Agriculture
Authors: Tagarakis Aristotelis C. , Bochtis Dionysis
Published: August 2023

The latest advances in innovative sensing and data technologies have led to an increasing implementation of autonomous systems in agricultural production processes [...]
Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm
Authors: Babbar Himanshi , Rani Shalli , Sah Dipak Kumar , AlQahtani Salman A. , Kashif Bashir Ali
Published: August 2023

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.
MMNet: A Mixing Module Network for Polyp Segmentation
Authors: Ghimire Raman , Lee Sang-Woong
Published: August 2023

Traditional encoder–decoder networks like U-Net have been extensively used for polyp segmentation. However, such networks have demonstrated limitations in explicitly modeling long-range dependencies. In such networks, local patterns are emphasized over the global context, as each convolutional kernel focuses on only a local subset of pixels in the entire image. Several recent transformer-based networks have been shown to overcome such limitations. Such networks encode long-range dependencies using self-attention methods and thus learn highly expressive representations. However, due to the computational complexity of modeling the whole image, self-attention is expensive to compute, as there is a quadratic increment in cost with the increase in pixels in the image. Thus, patch embedding has been utilized, which groups small regions of the image into single input features. Nevertheless, these transformers still lack inductive bias, even with the image as a 1D sequence of visual tokens. This results in the inability to generalize to local contexts due to limited low-level features. We introduce a hybrid transformer combined with a convolutional mixing network to overcome computational and long-range dependency issues. A pretrained transformer network is introduced as a feature-extracting encoder, and a mixing module network (MMNet) is introduced to capture the long-range dependencies with a reduced computational cost. Precisely, in the mixing module network, we use depth-wise and 1 × 1 convolution to model long-range dependencies to establish spatial and cross-channel correlation, respectively. The proposed approach is evaluated qualitatively and quantitatively on five challenging polyp datasets across six metrics. Our MMNet outperforms the previous best polyp segmentation methods.
An Efficient Image Deblurring Network with a Hybrid Architecture
Authors: Chen Mingju , Yi Sihang , Lan Zhongxiao , Duan Zhengxu
Published: August 2023

Blurring is one of the main degradation factors in image degradation, so image deblurring is of great interest as a fundamental problem in low-level computer vision. Because of the limited receptive field, traditional CNNs lack global fuzzy region modeling, and do not make full use of rich context information between features. Recently, a transformer-based neural network structure has performed well in natural language tasks, inspiring rapid development in the field of defuzzification. Therefore, in this paper, a hybrid architecture based on CNN and transformers is used for image deblurring. Specifically, we first extract the shallow features of the blurred images using a cross-layer feature fusion block that emphasizes the contextual information of each feature extraction layer. Secondly, an efficient transformer module for extracting deep features is designed, which fully aggregates feature information at medium and long distances using vertical and horizontal intra- and inter-strip attention layers, and a dual gating mechanism is used as a feedforward neural network, which effectively reduces redundant features. Finally, the cross-layer feature fusion block is used to complement the feature information to obtain the deblurred image. Extensive experimental results on publicly available benchmark datasets GoPro, HIDE, and the real dataset RealBlur show that the proposed method outperforms the current mainstream deblurring algorithms and recovers the edge contours and texture details of the images more clearly.
Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices
Authors: Alhartomi Mohammed A. , Salh Adeeb , Audah Lukman , Alzahrani Saeed , Alzahmi Ahmed , Altimania Mohammad R. , Alotaibi Abdulaziz , Alsulami Ruwaybih , Al-Hartomy Omar
Published: August 2023

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability. The provision of sustainable Resource Allocation (RA) and ensure real-time data-processing interaction between Internet of Things (IoT) devices and edge servers depends on a balance between system latency and Energy Consumption (EC) based on the proposed DT-empowered Deep Reinforcement Learning (Deep-RL) agent. The Deep-RL agent evaluates the performance action based on RA actions in DT to distribute its bandwidth resources to IoT devices based on iteration and the actions taken to generate the best policy and enhance learning efficiency at every step. The simulation results show that the proposed Deep-RL-agent-based DT is able to exploit the best policy, select 47.5% of computing activities that are to be carried out locally with 1 MHz bandwidth and minimize the weighted cost of the transmission policy of edge-computing strategies.
Profiling Citizens in the Smart City: A Quantitative Study in Wallonia
Authors: Schelings Clémentine , Defays Aurore , Elsen Catherine
Published: August 2023

Based on the assumption that citizens can participate in smart city development, this paper aims to capture the diversity of their profiles and their positioning towards smart city dynamics. The article starts with a literature review of some models of citizens to better understand how they could be portrayed in the smart city era. Considering that there is no “general citizen” and that usual typologies remain restrictive, we construct tailor-made personas, i.e., fictitious profiles based on real data. To this end, we present the results of a large-scale survey distributed to highly educated Walloon people in the framework of a general public exhibition. The profiling focuses on three aspects: (1) perception of smart city dimensions, (2) intended behavior regarding smart city solutions, and (3) favorite participatory methods. The collected answers were first analyzed with descriptive and nonparametric statistics, then classified with a k-means cluster analysis. The main results are five personas, which highlight the coexistence of different citizen groups that think and behave in a specific way. This process of profiling citizens’ priorities, behaviors, and participatory preferences can help professional designers and local governments to consider various citizens’ perspectives in the design of future smart solutions and participatory processes.
Development of a Maturity Model for Assessing Smart Cities: A Focus Area Maturity Model
Authors: Aljowder Thajba , Ali Mazen , Kurnia Sherah
Published: August 2023

The concept of smart cities has gained significant attention due to the potential of smart cities to optimize city services and enhance citizens’ quality of life. Cities are investing in digital transformation to become smarter, sustainable, and resilient. Therefore, there is a need to build a comprehensive and holistic model to assess smart city initiatives. This paper aims to develop a model that can capture the maturity of smart city adoption across various functional domains. These domains are divided into focus areas that capture different dimensions of a smart city and grouped into seven groups: ICT, economy, environment, social, resources, services, and governance. Each focus area has a set of maturity levels that describe the capabilities and outcomes of the city at different stages of development. To develop the model, the focus areas were extracted from the literature based on 16 models that have been reviewed. Assessing these models helped in identifying gaps and building the foundation of the model. Using the information extracted from the literature, a focus area model was designed and developed. The model development included seven main phases, which were: scope, design, populate, test, deploy, and maintain. The current paper validates the proposed model using the Delphi method, which involves the participation of a panel of sixty field experts. The experts evaluated the model’s correctness and completeness based on their experience and provided feedback. This feedback was used to revise and finalize the model. The smart city maturity model provides a framework for benchmarking, planning, and improving smart city initiatives. Cities can use the model to measure their performance and evaluate their weaknesses and strengths. The model is also the most comprehensive in terms of the scope of the focus areas included, and the results show that the model has a high level of accuracy and consistency and can effectively assess smart city adoption.
Criteria for Assessing the Environmental Quality of Soils in a Mediterranean Region for Different Land Use
Authors: Jordán Vidal Manuel Miguel
Published: August 2023

Since the 1980s, there has been a notable increase in environmental sensitivity, which has decisively contributed to an improved perception of the role of soil in ecosystems. European (and especially Mediterranean) soils have a long tradition of use, which places them among the three Earth soils that have been affected the most by anthropic pressure. The definition of soil quality identifies and recognizes the soil’s main functions regarding productivity, environmental quality, and human health. Interpreting the criteria for assessing soil quality requires continuous information on its state. Therefore, certain measurable characteristics and properties of the soil are useful, as they can be affected by processes that impact its quality, and analyzing its variation can reflect or show that impact. The parameters used to measure a soil’s state are called indicators. Indicators are useful because they provide summarized and simplified information on the state of a process, but with a meaning that goes beyond an association with an individual parameter. There is an urgent need for consensus among soil scientists and institutions on the concept of soil quality and the applicable environmental quality indicators, as well as establishing interpretative guides for the selected indicators. Soil quality can be analyzed and assessed using several scales with different analysis objectives, information requirements, soil data, implications, and consequences for appropriate soil management. Spanish soil scientists developed a methodological proposal to assess the environmental quality of soil, its environmental impact, and plan and organize land use in the scope of a Mediterranean region. This manuscript is a contribution to the knowledge of the state-of-the-art research in the field of assessing the environmental quality of soils, providing the vision of numerous authors and a methodological proposal for an assessment on a regional scale that may be of interest in other regions or fields of study.
Spinal Muscle Thickness and Activation during Abdominal Hollowing and Bracing in CrossFit® Athletes
Authors: Tsartsapakis Ioannis , Pantazi Georgia-Andriana , Konstantinidou Agapi , Zafeiroudi Aglaia , Kellis Eleftherios
Published: August 2023

Exercises that improve muscle activation are essential for maintaining spinal stability and preventing low back pain. The purpose of this study was to compare the effects of abdominal hollowing and bracing on the activation of the core muscles in CrossFit® participants using ultrasound and electromyography (EMG). Twenty-four healthy adults aged 21 to 42 years old with at least two years of CrossFit® experience performed three core stability exercises (plank, side plank, bridge) with abdominal hollowing and bracing. We measured the ultrasound relative thickness of the local core muscles (transversus abdominis, internal oblique, and lumbar multifidus), and the EMG percentage of maximal voluntary contraction (MVC) of the global core muscles (rectus abdominis, external oblique, and iliocostalis lumborum). Analysis of variance tests showed that the relative thickness of the local core muscles was greater (p = 0.016) during hollowing (range from 26.8 ± 5.33 to 88.4 ± 11.9% of rest) than bracing (range from 15.9 ± 3.54 to 61.2 ± 15.9% of rest), while the EMG of the global muscles was greater (p = 0.001) in bracing (range from 24.4 ± 7.30 to 72.5 ± 9.17% of MVC) than hollowing (range from 16.4 ± 3.70 to 56.6 ± 7.65% of MVC). These results indicate that the recruitment of spinal muscles during popular exercises is achieved with both hollowing and bracing. Nevertheless, it appears that hollowing tends to recruit more of the local muscles, whilst bracing recruits more of the global muscles. The grading of the exercises varied between muscles and varied between maneuvers, especially for the surface abdominals and lumbar muscles. CrossFit® practitioners can choose to use either hollowing or bracing to activate their core muscles more selectively or more appropriately, depending on the goal and purpose of the exercise.
Comparing Different Strategies for Cr(VI) Bioremediation: Bioaugmentation, Biostimulation, and Bioenhancement
Authors: Yan Xiao , Yan Zhenghao , Zhu Xuezhe , Zhou Yupin , Ma Guoying , Li Shuangquan , Liu Xingyu , Zhang Mingjiang
Published: August 2023

Unchecked releases of industrial waste, including chromium smelting slag (CSS), have resulted in disastrous effects on the environment for human use. Considering the problems of environment, efficiency, and sustainability, the present research was designed to evaluate the potential feasibility of Cr(VI) bioremediation by different strategies of natural attenuation (NA), bioaugmentation (BA), biostimulation (BS), and bioenhancement (BE). Results showed the BE was the best strategy for Cr(VI) removal and reached 86.2% in 84 days, followed by the BA, BS, and NA. The variation of Eh values indicated all systems translated the oxidation state into reduction continuously except for NA and BS during the bioremediation process. After bioremediation, the Tessier sequential extraction analyzed in the BE showed stable chromium levels up to 97%, followed by BA (89~93%), BS (75~78%), and NA (68%), respectively. Moreover, High-throughput sequencing was also used to assist in revealing the differences in microbial community structure between the different strategies. Stenotrophomonas, Ochrobactrum, and Azomonas, as the bioremediation microbes, were enriched in the BE in comparison with the others. This provided a new enhancement strategy for bioremediation microbes colonized in a new environment to achieve sustainable removal of Cr(VI).