bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Diversity of Mycobacterium avium subsp. paratuberculosis in Four Dairy Goat Herds from Thuringia (Germany)
Authors: Pickrodt Chris , Köhler Heike , Moog Udo , Liebler-Tenorio Elisabeth M. , Möbius Petra
Published: November 2023

This study investigated the intra- and inter-herd diversity of Mycobacterium avium subsp. paratuberculosis (MAP) isolates from four goat herds in Thuringia (Germany) that were affected by paratuberculosis for several years. The main focus was on the characterization and distribution of genotypes among animals and the environment of goat herd 1. This study included 196 isolates from the feces of 121 infected goats, various tissues from 13 clinically diseased goats, 29 environmental samples from herd 1, and additionally, 22 isolates of different origin from herds 2 to 4. The isolates, sampled between 2018 and 2022, were genotyped using short-sequence-repeat (SSR) analysis, mycobacterial-interspersed repetitive units–variable-number tandem repeat (MIRU–VNTR) analysis, and a single nucleotide polymorphism (SNP)-based assay for phylogenetic grouping. All the isolates belonged to the MAP-C group. In herd 1, one predominant genotype was determined, while two other genotypes were identified very rarely and only in fecal and environmental samples. One of three further genotypes was found in each of herds 2 to 4. The assignment of genotypes to different phylogenetic clades suggested six different infection strains. The results indicated no epidemiological links between the examined herds. Based on the current MAP genotyping data from Germany, possible sources of infection are MAP-contaminated barns previously used by infected cattle and the purchase of sub-clinically infected goats.
Depletion of Labile Iron Induces Replication Stress and Enhances Responses to Chemoradiation in Non-Small-Cell Lung Cancer
Authors: Bayanbold Khaliunaa , Singhania Mekhla , Fath Melissa A. , Searby Charles C. , Stolwijk Jeffrey M. , Henrich John B. , Pulliam Casey F. , Schoenfeld Joshua D. , Mapuskar Kranti A. , Sho Sei , Caster Joseph M. , Allen Bryan G. , Buettner Garry R. , Spies Maria , Goswami Prabhat C. , Petronek Michael S. , Spitz Douglas R.
Published: November 2023

The intracellular redox-active labile iron pool (LIP) is weakly chelated and available for integration into the iron metalloproteins that are involved in diverse cellular processes, including cancer cell-specific metabolic oxidative stress. Abnormal iron metabolism and elevated LIP levels are linked to the poor survival of lung cancer patients, yet the underlying mechanisms remain unclear. Depletion of the LIP in non-small-cell lung cancer cell lines using the doxycycline-inducible overexpression of the ferritin heavy chain (Ft-H) (H1299 and H292), or treatment with deferoxamine (DFO) (H1299 and A549), inhibited cell growth and decreased clonogenic survival. The Ft-H overexpression-induced inhibition of H1299 and H292 cell growth was also accompanied by a significant delay in transit through the S-phase. In addition, both Ft-H overexpression and DFO in H1299 resulted in increased single- and double-strand DNA breaks, supporting the involvement of replication stress in the response to LIP depletion. The Ft-H and DFO treatment also sensitized H1299 to VE-821, an inhibitor of ataxia telangiectasis and Rad2-related (ATR) kinase, highlighting the potential of LIP depletion, combined with DNA damage response modifiers, to alter lung cancer cell responses. In contrast, only DFO treatment effectively reduced the LIP, clonogenic survival, cell growth, and sensitivity to VE-821 in A549 non-small-cell lung cancer cells. Importantly, the Ft-H and DFO sensitized both H1299 and A549 to chemoradiation in vitro, and Ft-H overexpression increased the efficacy of chemoradiation in vivo in H1299. These results support the hypothesis that the depletion of the LIP can induce genomic instability, cell death, and potentiate therapeutic responses to chemoradiation in NSCLC.
c-Abl Phosphorylates MFN2 to Regulate Mitochondrial Morphology in Cells under Endoplasmic Reticulum and Oxidative Stress, Impacting Cell Survival and Neurodegeneration
Authors: Martinez Alexis , Lamaizon Cristian M. , Valls Cristian , Llambi Fabien , Leal Nancy , Fitzgerald Patrick , Guy Cliff , Kamiński Marcin M. , Inestrosa Nibaldo C. , van Zundert Brigitte , Cancino Gonzalo I. , Dulcey Andrés E. , Zanlungo Silvana , Marugan Juan J. , Hetz Claudio , Green Douglas R. , Alvarez Alejandra R.
Published: November 2023

The endoplasmic reticulum is a subcellular organelle key in the control of synthesis, folding, and sorting of proteins. Under endoplasmic reticulum stress, an adaptative unfolded protein response is activated; however, if this activation is prolonged, cells can undergo cell death, in part due to oxidative stress and mitochondrial fragmentation. Here, we report that endoplasmic reticulum stress activates c-Abl tyrosine kinase, inducing its translocation to mitochondria. We found that endoplasmic reticulum stress-activated c-Abl interacts with and phosphorylates the mitochondrial fusion protein MFN2, resulting in mitochondrial fragmentation and apoptosis. Moreover, the pharmacological or genetic inhibition of c-Abl prevents MFN2 phosphorylation, mitochondrial fragmentation, and apoptosis in cells under endoplasmic reticulum stress. Finally, in the amyotrophic lateral sclerosis mouse model, where endoplasmic reticulum and oxidative stress has been linked to neuronal cell death, we demonstrated that the administration of c-Abl inhibitor neurotinib delays the onset of symptoms. Our results uncovered a function of c-Abl in the crosstalk between endoplasmic reticulum stress and mitochondrial dynamics via MFN2 phosphorylation.
Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System
Authors: Park Jae-beom , Lee Han-sung , Cho Hyun-chong
Published: November 2023

Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further increase the size of the dataset. The results showed an accuracy of 0.8317 for T-stage 1 and T-stage 4 image classification and an accuracy of 0.8417 for early gastric cancer and normal image classification, indicating improvements of 7 and 9%, respectively. Furthermore, through the application of test-time augmentation to the early gastric cancer and normal image datasets, the image classification accuracy was improved by 5.8% to 0.9000. Overall, the results of this study demonstrate the effectiveness of the proposed augmentation methods for enhancing gastric cancer classification performance.
A New Approach of Well Productivity Evaluation for Fractured Buried Hill Gas Reservoirs Based on Imaging Logging Data
Authors: Bi Hongri , Chen Peng
Published: November 2023

Fractures function as storage spaces and effective seepage channels for metamorphic rock buried hill reservoirs. Their effectiveness and permeability govern the content and enrichment of oil and gas. Owing to the convoluted distribution patterns of fractures, it is arduous to gauge the effectiveness and permeability of fractures with precision, thus rendering well productivity prediction difficult. This article cites fractured gas reservoirs in a metamorphic rock buried hill as an example. Through comprehensive usage of core and imaging logging data to finely interpret fractures, calculate the fracture parameters that control productivity, including fracture density, fracture width, and fracture porosity. According to the evaluation index of fracture effectiveness, the method of constructing effectiveness index is proposed to quantitatively evaluate the effectiveness. Combined with the study of the law of influence of fracture parameters on reservoir permeability, the permeability index is established to reflect permeability. Productivity coefficients for fractured reservoirs with pollution factors have been established by using well-test interpretation data. To evaluate the well productivity of buried hill reservoirs, a productivity assessment chart is constructed by integrating the fracture effectiveness index, permeability index, and productivity coefficient. This chart enables prompt predictions of the buried hill reservoir’s productivity. In order to verify the reliability of the method, a comprehensive comparison is made through conventional, array acoustic logging data and test data. The results show that the method is well applied in the evaluation of metamorphic rock buried hill reservoirs and provides a new idea for the rapid prediction of well productivity.
Application of Response Surface Design for Optimization of Direct Red Dye Biosorption onto Cockleshells
Authors: Laggoun Zakaria , Khalfaoui Amel , Benalia Abderrezzaq , Ghomrani Amira Fadia , Bouchareb Raouf , Mahfouf Asma , Pizzi Antonio , Panico Antonio , Derbal Kerroum
Published: November 2023

This work emphasizes the efficiency of the response surface design to optimize the parameters affecting the removal of a textile dye—Direct Red 81 (DR-81)—by biosorption on seafood waste, namely, cockleshells (CS). The adsorbent was characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analysis of surface and pH points of zero charge (pHpzc). A Box–Behnken design (BBD) with three factors was used to optimize the experimental conditions. After the experiment and data analysis, the optimal conditions found were 1 g of adsorbents, 10 mg/L of initial dye concentration, and a pH of 2 in the adsorbate solution, with the highest removal efficiency of 99.98%. The experimental results were analyzed by the ANOVA test, and they demonstrated the acceptability of the quadratic regression model. The adjusted determination coefficient R2 (adj) was equal to 98.82%, indicating an excellent relationship between the predicted and experimental responses. Langmuir isotherms were determined to be the best-fitting model, and the maximum adsorption capacity was 4.65 mg/g. The adsorption process was endothermic and fit the pseudo-second-order model. The negative values of ∆H and ∆S in the thermodynamic research showed that the bio-adsorption technique for the removal of Direct Red 81 is exothermic, spontaneous, and feasible. In addition, the negative value of ∆G indicates that the adsorption mechanism occurs at solid–liquid interfaces with an increasing number of species.
Standardizing Criteria for Calculating Urban Storm Drainage Flow Rates Using Basin Division Based on Aerial Photogrammetry: The Case Study of Culiacan, Mexico
Authors: Sánchez-Núñez Guadalupe Yuceli , Rentería-Guevara Sergio Arturo , Rangel-Peraza Jesús Gabriel , Monjardín-Armenta Sergio Alberto , Sanhouse-García Antonio Jesús , Mora-Felix Zuriel Dathan
Published: November 2023

Urban storm drainage is fundamental for the well-being of the population of cities with torrential rainfall regimes because it is essential for the rapid and safe disposal of stormwater runoff. However, it is not uncommon for hydrological studies to determine the design flow of storm drainage works carried out in the same urban basin using different criteria depending on the experience of the person performing them. This can represent a problem when integrating and reviewing the results of hydrological studies carried out by different hydrologists. To address this problem, we propose a methodology consisting of methods used by various authors to determine the design flow rate in urban hydrologic studies. We suggest using a novel method to delineate urban basins based on photogrammetry obtained through flights with unmanned aerial vehicles. Subsequently, the necessary parameters are obtained to define the intensity–duration–return period curves, the runoff coefficients, and finally the design flow rate. The contribution of this article is technological. In this sense, a new methodology is proposed that applies existing knowledge to solve a practical problem observed in the field of urban hydrology and storm drainage. The case study is a basin with frequent flooding located in Culiacan, Mexico.
The Effect of Ultrasound and Pulsed Electric Field on the Osmotic Dehydration Process of Strawberries
Authors: Pobiega Katarzyna , Matys Aleksandra , Trusinska Magdalena , Rybak Katarzyna , Witrowa-Rajchert Dorota , Nowacka Malgorzata
Published: November 2023

Currently, the demands of consumers are growing, and they expect safe and natural products of higher quality compared to products processed using thermal methods. Thermal treatment influences the sensory as well as quality and nutritional value of processed plant material. This results in the development of innovative, non-thermal methods of food preservation and processing. Hence, the study was conducted to examine how ultrasound (US) and pulsed electric field (PEF) affect the osmotic dehydration process of strawberries. An US treatment with a power of 400 W and a frequency of 24 kHz for 30 and 90 s and a PEF treatment were used, adopting the appropriate energy consumption of 1 and 2.5 kJ/kg. Then, strawberries after both processes were osmotically dehydrated in 0.5; 1, and 2 h at 30 °C. Dehydration was carried out in a 50% sucrose solution. Research findings have indicated that the pretreatment positively enhanced the efficiency of osmotic dehydration. An improvement in the dry weight gain rate was noted. Strawberries dehydrated with the use of pretreatment had similar or lower color values and the content of bioactive components compared to strawberries subjected to dehydration only. The material treated with the PEF turned out to be the softest. Significant differences in sugar content were noted in fruits after pretreatment. Sucrose levels increased, glucose levels decreased, and fructose remained at a comparable level.
Fault Localization Using TrustRank Algorithm
Authors: Fan Xin , Wu Kaisheng , Zhang Shuqing , Yu Li , Zheng Wei , Ge Yun
Published: November 2023

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.
miGAP: miRNA–Gene Association Prediction Method Based on Deep Learning Model
Authors: Yoon Seungwon , Hwang Inwoo , Cho Jaeeun , Yoon Hyewon , Lee Kyuchul
Published: November 2023

MicroRNAs (miRNAs) are small RNA molecules consisting of approximately 22 nucleotides; they regulate gene expression and are employed in the development of therapeutics for intractable diseases. Predicting the association between miRNAs and genes is crucial for understanding their roles in molecular processes. miRNA–gene associations have been studied using deep learning methods, but these methods present various constraints. Through addressing the limitations of previous methods, this study aimed to achieve better performance than the state-of-the-art (SOTA) methods for studying miRNA–gene associations. We constructed the most extensive embedded dataset to date, comprising 717,728 miRNA–gene pairs, specifically designed for our deep learning model. Further, we applied an embedding method used for protein embedding for transforming our gene sequence data. Moreover, we constructed a sophisticated negative dataset based on three distance criteria, unlike most studies that randomly designate negative data. Leveraging the data and insights from these approaches, we built a deep learning model with the best performance among SOTA miRNA–gene studies (area under the receiver operating characteristic curve = 0.9834). In addition, we conducted a case study using the learned model to predict potential positive data. We also aimed to identify miRNAs closely associated with a gene linked to various cancers.
Autonomous Robotic Navigation Approach Using Deep Q-Network Late Fusion and People Detection-Based Collision Avoidance
Authors: de Sousa Bezerra Carlos Daniel , Teles Vieira Flávio Henrique , Queiroz Carneiro Daniel Porto
Published: November 2023

In this work, we propose an approach for the autonomous navigation of mobile robots using fusion the of sensor data by a Double Deep Q-Network with collision avoidance by detecting moving people via computer vision techniques. We evaluate two data fusion methods for the proposed autonomous navigation approach: Interactive and Late Fusion strategy. Both are used to integrate mobile robot sensors through the following sensors: GPS, IMU, and an RGB-D camera. The proposed collision avoidance module is implemented along with the sensor fusion architecture in order to prevent the autonomous mobile robot from colliding with moving people. The simulation results indicate a significant impact on the success of completing the proposed mission by the mobile robot with the fusion of sensors, indicating a performance increase (success rate) of ≈27% in relation to navigation without sensor fusion. With the addition of moving people in the environment, deploying the people detection and collision avoidance security module has improved about the success rate by 14% when compared to that of the autonomous navigation approach without the security module.
Experimental Evaluation of ND: YAG Laser Parameters and Sample Preparation Methods for Texturing Thin AISI 316L Steel Samples
Authors: Kozłowska Ewa , Grabska-Zielińska Sylwia
Published: November 2023

In mechanical and material engineering, the effect of laser texturing depends on many factors besides device specification, primarily the properties of the materials being processed, and, secondly, the preparation of the sample. Laser texturing of thin (<5 mm) samples is mostly performed utilizing short-pulse lasers, but depending on the power of the laser beam, the process can also be performed by using continuous operation lasers. When using a laser beam to modify the surface layer, special attention should be paid to the surface preparation process. Engraving a shiny metal surface can lead to laser beam dispersion and energy loss. Some materials require special preparation and surface darkening in order to be effectively engraved. In the case of engraving, maximizing the efficiency and repeatability of the process is the key to obtaining the desired properties. The aim of the conducted study was to establish satisfying parameters and a sample preparation method for texturing thin AISI 316L samples. Appropriately selected laser parameters added to proper sample preparation. The sanding, etching, and darkening of the surface layer improved the quality of the weld and eliminated problems such as deformation and spark formation that often occur with raw samples during the texturing process.
Compiling Requirements from Models for Early Phase Scope Estimation in Agile Software Development Projects
Authors: Bisikirskienė Lina , Čeponienė Lina , Jurgelaitis Mantas , Ablonskis Linas , Grigonytė Eglė
Published: November 2023

Inadequate early scope estimation is a common problem in software projects, leading to failures in meeting project requirements. Agile projects usually do not concentrate on a comprehensive requirements analysis and specification before the start of the project, making scope assessment difficult. This paper presents the methodology for facilitating a more accurate early estimation of project scope, based on requirements information gathered in various forms (requirements models and textual descriptions) during the requirements workshop. The requirements from different sources are compiled into one list and reconciled, since they are prepared by a number of participants in the requirements workshop using different notations (UML diagrams, SysML models, Story map) and may have differences in the vocabulary. Reconciliation encompasses the unification of vocabulary, as well as the identification and the removal of overlaps in requirements. The final list of requirements is used to estimate the scope of the project in story points. The estimate can be presented to the client and used as a basis for the project contract. A case study on the application of the proposed methodology is presented, using the animal shelter information system as a development project. It demonstrates that the methodology is viable and can facilitate the gathering of a more extensive set of requirements, thus ensuring a more detailed scope estimation.
Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
Authors: Cheng Feng , Gao Wei , Jia Shuchun
Published: November 2023

To enhance the lane-changing safety of autonomous vehicles, it is crucial to accurately identify the driving styles of human drivers in scenarios involving the coexistence of autonomous and human-driven vehicles, aiming to avoid encountering vehicles exhibiting hazardous driving patterns. In this study, based on the real traffic flow data from the Next Generation Simulation (NGSIM) dataset in the United States, 301 lane-changing vehicles that meet the criteria are selected. Six evaluation parameters are chosen, and principal component analysis (PCA) is employed for dimensionality reduction in the data. The K-means algorithm is then utilized to cluster the driving styles, classifying them into three categories. Finally, ant colony optimization (ACO) of a backpropagation (BP) neural network model was constructed, utilizing the dimensionality reduction results as inputs and the clustering results as outputs for the purpose of driving style recognition. Simulation experiments are conducted using MATLAB Version 9.10 (R2021a) for comparative analysis. The results indicate that the constructed ACO-BP model achieved an overall recognition accuracy of 96.7%, significantly higher than the recognition accuracies of the BP, artificial neural network (ANN), and gradient boosting machine (GBM) models. The ACO-BP model also exhibited the fastest recognition speed among the four models. Moreover, the ACO-BP model shows varied improvements in recognition accuracy for each of the three driving styles, with an increase of 13.7%, 4.4%, and 4.3%, respectively, compared to the BP model. The simulation results validate the high accuracy, real-time capability, and classification effectiveness of this model in driving style recognition, providing new insights for this field.
Architectural Cultural Heritage Conservation: Fire Risk Assessment of Ancient Vernacular Residences Based on FAHP and EWM
Authors: Li Guokai , Yuan Hongping , Shan Yi , Lin Guiye , Xie Guojie , Giordano Andrea
Published: November 2023

The architectural relics of ancient vernacular residences and villages with brick–timber structures are at great risk of fire; if one occurs, they cannot be recovered. To protect this cultural heritage, this study takes a southern Guangdong He Xinwu building complex as a case study. It focuses on four indicator systems: human factors, facility factors, environmental factors, and social management factors, and 20 sub-indicators to establish an assessment system for fire risk in vernacular residences. Combining triangular fuzzy hierarchical analysis and the entropy weight method to determine weight values reduces evaluation subjectivity, allowing for both qualitative and quantitative measurements to derive the safety level and determine key fire risk factors. The results showed that human misconduct and social management of fire had the greatest impact on fire risk (29% and 25.8%, respectively). The most important secondary indicators were the ability to fight fires early on, the fire resistance level of building materials, fire rescue capability, fire load, and electricity use by villagers. Moreover, comparing differences in protection between ancient houses and high-rise buildings provided targeted policy recommendations that offer a new perspective for protecting architectural heritage from fires.
Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements
Authors: Kashpruk Nataliia , Piskor-Ignatowicz Cezary , Baranowski Jerzy
Published: November 2023

Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today’s data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0.
A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE
Authors: Li Jialong , Guo Zhonghua , He Jiahao , Ma Xiaoyan , Ma Jing
Published: November 2023

Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.
Cementing Quality Prediction in the Shunbei Block Based on Genetic Algorithm and Support Vector Regression
Authors: Wei Juntao , Zheng Shuangjin , Han Jiafan , Bai Kai
Published: November 2023

There are a number of factors that can affect the quality of cementing, and they constrain each other. Current cementing quality prediction methods are still in the stage of development, and it is difficult to establish an analytical model for cementing quality prediction that meets the strict requirements of cementing design. In order to accurately predict the cementing quality in the Shunbei block of the Northwest Oilfield, in this study, we established a cementing quality prediction model based on support vector regression (SVR) and optimized the penalty parameter and kernel parameter by using grid search (GS), a Bayesian optimization algorithm (BOA), and a genetic algorithm (GA), which improve the prediction accuracy of SVR. The results show that the smallest root-mean-square error and average relative error (2.318% and 7.30%, respectively) and the highest accuracy are achieved when using GA–SVR as compared to SVR, GS–SVR, and BOA–SVR. Therefore, GA–SVR is suitable for cementing quality prediction in the Shunbei block.
A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions
Authors: Umar Abdullah , Khalid Zubair , Ali Mohammed , Abazeed Mohammed , Alqahtani Ali , Ullah Rahat , Safdar Hashim
Published: November 2023

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.
The Intrapelvic Pressure during Retrograde Intrarenal Surgery in the Setting of Ureteral Access Sheath Size: Experimental Study on 3D Printed Model
Authors: Balawender Krzysztof , Pliszka Anna , Oleksy Mariusz
Published: November 2023

Introduction: There is no standardised, universal method to assess physical conditions such as pressure in the pelvicalyceal system in real time during RIRS (retrograde intrarenal surgery). Therefore, the problem of increased pressure in the upper urinary tract during the procedure is underestimated. Moreover, it can potentially cause micro-damage and longer postoperative recovery. The aim of this study was to evaluate intrapelvic pressure (IPP) during RIRS procedures. Materials and Methods: The 3D printed models of the pelvicalyceal system were printed based on a CT scan of the real patient. They were used to perform 50 RIRS procedures with laser lithotripsy of artificially synthesised kidney stones with two different sizes of ureteral access sheath-UAS (10/12Fr vs. 12/14Fr) together with different energy settings generated by the holmium:YAG laser. IPP monitoring during RIRS was performed with the use of the PressureWire X Guidewire compatible with the CoroFlow system. Results and Conclusions: The results showed that a high IPP of up to 400 cmH2O would be achieved using a 10/12Fr UAS, while the use of a 12/14Fr UAS would significantly reduce the peak pressure to approximately 100 cmH2O; hence, the size of the UAS is a pivotal factor of the IPP generated during the procedure.
Multiple Watermarking Algorithms for Vector Geographic Data Based on Multiple Quantization Index Modulation
Authors: Wang Yingying , Yang Chengsong , Ding Kaimeng
Published: November 2023

Multiple digital watermarking is an important and challenging task in geographic information science and data security. Vector geographic data are a basic data format for digital geographic data storage, and the security protection of these data involves copyright protection and tracking. As part of the solution, existing digital watermarking algorithms have made contributions to vector geographic data protection. However, when vector geographic data flow through multiple units, they need to be marked to ensure that the original data are not destroyed during data processing. Existing single or multiple data watermarking algorithms often fail in the presence of data processing because the new watermarks overlay the old ones. Consequently, a multiple digital watermarking algorithm based on multiple QIM (quantization index modulation) is proposed. First, based on traditional quantization index modulation (QIM), a multiple QIM is proposed. Unlike traditional QIM, in multiple QIM, the process of quantization is executed multiple times depending on the number of watermarks. Then, the vertices are quantized into different quantization intervals according to the multiple QIM. Finally, multiple watermarks are embedded into different quantization intervals to reduce the interference among multiple watermarks, and the original watermarks are not needed in the process of watermark detection. We then conducted experiments to test the multiple watermark method’s robustness and capacity, with an emphasis on datasets with a lower data volume. The experimental results show that the proposed algorithm achieves good performance in terms of its robustness against common issues, such as vertices addition, data simplification, data cropping, and feature deletion; this holds true for both normal and small amounts of data. Additionally, it has a high multiple watermark capacity.
Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
Authors: Li Xunchang , Ran Yutong , Wang Kang , Shi Zhengzheng
Published: November 2023

Anti-sliding short piles, a novel technique for slope stabilization, have been applied in engineering practices. Nonetheless, a mature structural calculation theory for these piles is still lacking. In this paper, the study presents an internal force solution model for anti-sliding short piles using the finite difference method. By extending the Euler–Bernoulli beam theory and defining boundary conditions, this study develops a set of finite difference equations for computing the structural forces of anti-sliding short piles. Furthermore, this study conducted laboratory model tests on soil landslide cases reinforced with anti-sliding short piles. By comparing the internal forces and deformations of these piles, the test validates the proposed calculation model for anti-sliding short piles. The results suggest that treating the load-bearing and embedded sections as a unified entity during the calculation process, instead of applying continuity conditions separately at the sliding surface as performed in traditional methods, simplifies the complex solving procedure. Moreover, under identical loading conditions, the displacement, bending moment, and shear force data obtained through the finite difference method closely coincide with the measurements from the model tests, confirming the reliability of the anti-sliding short pile calculation model. Additionally, this study demonstrates that reducing the spacing between nodes along the entire anti-sliding short pile, i.e., decreasing the value of the differential segment length ‘h’, results in more precise computational outcomes. This research offers valuable insights and references for sustainable solutions in the realm of geological disaster prevention and control.
An Efficient Steganographic Protocol for WebP Files
Authors: Koptyra Katarzyna , Ogiela Marek R.
Published: November 2023

In this paper, several ideas of data hiding in WebP images are presented. WebP is a long-known, but not very poplar file format that provides lossy or lossless compression of data, in the form of a still image or an animation. A great number of WebP features are optional, so the structure of the image offers great opportunities for data hiding. The article describes distinct approaches to steganography divided into two categories: format-based and data-based. Among format-based methods, we name simple injection, multi-secret steganography that uses thumbnails, hiding a message in metadata or in a specific data chunk. Data-based methods achieve secret concealment with the use of a transparent, WebP-specific algorithm that embeds bits by choosing proper prediction modes and alteration of the color indexing transform. The capacity of presented techniques varies. It may be unlimited for injection, up to a few hundred megabytes for other format-based algorithms, or be content-dependent in data-based techniques. These methods fit into the container modification branch of steganography. We also present a container selection technique which benefits from available WebP compression parameters. Images generated with the described methods were tested with three applications, including the Firefox web browser, GNU Image Manipulation Program, and ImageMagick. Some of the presented techniques can be combined in order to conceal more than one message in a single carrier.
Investigating Forkhead Box O Transcription Factor 1 Gene’s Relation to Immunoglobulin E in House Dust Mite-Allergic Asthma Patients
Authors: Mohamed Rania A. , Fakhr Ahmed ElSadek , Baioumy Shereen A.
Published: November 2023

House dust mite (HDM)-allergic asthma is an abnormal immune response to extrinsic aeroallergens found in human vicinities. Studying the role of the associated immunity biomarkers and their interplay helps in discovering novel therapeutic strategies that can be used in adjunct with effective long-term immunotherapy. This study investigates the total serum IgE, FoxO1, and Sirtuin 1 (SIRT1) gene expressions in HDM-allergic asthma patients. We enrolled 40 patients for each of the following three groups: an HV group of healthy volunteers and HDM/AA and HDM/SCIT groups of HDM-allergic asthma patients who did not and who did receive immunotherapy before recruitment in this study, respectively. The results elucidated that total IgE was strikingly elevated in the HDM/AA group and showed little decline in the HDM/SCIT group. Both FoxO1 and SIRT1 gene expressions showed the highest levels in the HDM/SCIT group. There was a negative correlation between total IgE and both FoxO1 and SIRT1 in the HDM/AA group while there was a positive correlation with SIRT1 in the HDM/SCIT group. In conclusion, the interplay of the three immunity biomarkers related to HDM-allergic asthma after the course of immunotherapy treatment suggests further, broader studies on the feasibility of their role as immunity biomarkers in the control and remission of HDM-allergic asthma.
Numerical Simulation on Wind Speed Amplification of High-Rise Buildings with Openings
Authors: Gu Ziqi , Chen Fubin , Zhu Yuzhe , Mei Yu , Wang Zhanli , Xu Linfeng , Li Yi
Published: November 2023

To explore the influence of openings on wind loads and wind speeds in high-rise buildings, the wind flow around three-dimensional (3D) square cylinders with a breadth/height aspect ratio of 1:6 was numerically simulated using the large eddy simulation (LES) method via the Fluent 15.0 platform. The opening measures in the X-direction, Y-direction and both directions were all taken into consideration. Firstly, the inflow turbulence synthesis method and parameter settings for LES were verified by comparing the simulation results of standard square cylinders with those of wind tunnel experiments, and the optimal boundary conditions were determined. Then, the wind speed was extracted and compared with the mean wind speed of incoming flow at the same height to analyze the influence of different opening measures on the wind speed of incoming flow by setting monitoring points in the open holes. Finally, the mechanism underlying the effect of the opening form on wind loads and wind speeds was analyzed from the perspective of time-averaged and transient flow field. The results show that the X-direction openings affect the magnitude and distribution of the surface wind pressures by changing the flow separation and flow reattachment. The narrow tube effect can significantly increase the wind speed, while the Y-direction openings have no obvious improvement effect on the surface wind pressures of the structure. The wind speeds in the open holes are greatly reduced due to the shielding effect, and the wind pressures are also reduced for the Y-direction openings. In the X-direction opening holes, the wind speed at the monitoring point increases, while it decreases in the crosswind open holes. In general, the measure of openings in the X-direction can greatly improve the wind load of the structure compared to openings in the Y-direction, and it can provide a good reference for wind power generation in high-rise buildings.
Log-Lattices for Atmospheric Flows
Authors: Pikeroen Quentin , Barral Amaury , Costa Guillaume , Dubrulle Bérengère
Published: November 2023

We discuss how the projection of geophysical equations of motion onto an exponential grid allows the determination of realistic values of parameters at a moderate cost. This allows us to perform many simulations over a wide range of parameters, thereby leading to general scaling laws of transport efficiency that can then be used to parametrize the turbulent transport in general climate models for Earth or other planets. We illustrate this process using the equation describing heat transport in a dry atmosphere to obtain the scaling laws for the onset of convection as a function of rotation. We confirm the theoretical scaling of the critical Rayleigh number, Rac∼E−4/3, over a wide range of parameters. We have also demonstrated the existence of two regimes of convection: one laminar regime extending near the convection onset, and one turbulent regime occurring as soon as the vertical Reynolds number reaches a value of 104. We derive general scaling laws for these two regimes, both for the transport of heat and the dissipation of kinetic energy, and values of anisotropy and temperature fluctuations.
Fixed Point Theorems in Rectangular b-Metric Space Endowed with a Partial Order
Authors: Horvat-Marc Andrei , Cufoian Mariana , Mitre Adriana , Taşcu Ioana
Published: November 2023

The purpose of this paper is to present some fixed-point results for self-generalized contractions in ordered rectangular b-metric spaces. We also provide some examples that illustrate the non-triviality and richness of this area of research.
Effects of Plasma Treatment on the Strength of Bonding to Ceramic Surfaces in Orthodontics—A Comprehensive Review
Authors: Gershater Elizabeth , Griswold Olivia , Talsania Brooke , Zhang Yu , Chung Chun-Hsi , Zheng Zhong , Li Chenshuang
Published: November 2023

Over the past several decades, orthodontic treatment has been increasingly sought out by adults, many of whom have undergone restorative dental procedures that cover enamel. Because the characteristics of restorative materials differ from those of enamel, typical bonding techniques do not yield excellent restoration–bracket bonding strengths. Plasma treatment is an emerging surface treatment that could potentially improve bonding properties. The purpose of this paper is to evaluate currently available studies assessing the effect of plasma treatment on the shear bond strength (SBS) and failure mode of resin cement/composite on the surface of ceramic materials. PubMed and Google Scholar databases were searched for relevant studies, which were categorized by restorative material and plasma treatment types that were evaluated. It was determined that cold atmospheric plasma (CAP) treatment using helium and H2O gas was effective at raising the SBS of feldspathic porcelain to a bonding agent, while CAP treatment using helium gas might also be a potential treatment method for zirconia and other types of ceramics. More importantly, CAP treatment using helium has the potential for being carried out chairside due to its non-toxicity, low temperature, and short treatment time. However, because all the studies were conducted in vitro and not tested in an orthodontic setting, further research must be conducted to ascertain the effectiveness of specific plasma treatments in comparison to current orthodontic bonding treatments in vivo.
Speech Perception Improvement Algorithm Based on a Dual-Path Long Short-Term Memory Network
Authors: Koh Hyeong Il , Na Sungdae , Kim Myoung Nam
Published: November 2023

Current deep learning-based speech enhancement methods focus on enhancing the time–frequency representation of the signal. However, conventional methods can lead to speech damage due to resolution mismatch problems that emphasize only specific information in the time or frequency domain. To address these challenges, this paper introduces a speech enhancement model designed with a dual-path structure that identifies key speech characteristics in both the time and time–frequency domains. Specifically, the time path aims to model semantic features hidden in the waveform, while the time–frequency path attempts to compensate for the spectral details via a spectral extension block. These two paths enhance temporal and spectral features via mask functions modeled as LSTM, respectively, offering a comprehensive approach to speech enhancement. Experimental results show that the proposed dual-path LSTM network consistently outperforms conventional single-domain speech enhancement methods in terms of speech quality and intelligibility.