bluuebunny/crossref_metadata_2025 huggingface dataset:
A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
Authors: Bi Lin , Miao Minghui , Di Xiaoqiang
Published: July 2023

Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.
Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
Authors: Yan Xueyuan , Liu Jian , Lin Weiyu , Lan Guoguan , Mao Huimin
Published: July 2023

With the use of seismic isolation techniques on mega-sub structures, several scholars have carried out research on them. However, little research has been carried out on mega-sub isolated structures under multiaxial earthquakes. There have been instances in actual engineering where the peak vertical acceleration exceeded the horizontal direction, and the vertical seismic component will also amplify the dynamic response of the structure in the horizontal direction, so in practice, it is necessary to consider the influence of the vertical component of the earthquake on the structure. To investigate the dynamic response of mega-sub isolated structures under unidirectional earthquake and coupled earthquake, this paper used numerical methods to analyze the influences of the horizontal and vertical damping ratio of the isolation layer, the horizontal and vertical frequency ratio of the main substructure, and the mass ratio of the main substructure on the acceleration and displacement of the main and substructure. The optimum parameter values are finally achieved by analytical calculations, which provide a basis for further optimization and practical design of the structure. Finally, the collision between the main and sub-structures in the mega-sub isolated structure was simulated, and the influences of the peak ground motion, main and subframe spacing, vertical seismic component, and sub-structure height on the collision force and acceleration were analyzed. It was shown that the conflict of the main and sub-structures generates instantaneous, large collision forces, which also instantaneously amplify the acceleration and base shear force response, which should be avoided in practical engineering.
MDAR: A Knowledge-Graph-Enhanced Multi-Task Recommendation System Based on a DeepAFM and a Relation-Fused Multi-Gead Graph Attention Network
Authors: Li Songjiang , Xue Qingxia , Wang Peng
Published: July 2023

In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there are still three challenging issues: historical behavior preferences, missing data, and knowledge graph completion. To tackle these challenging problems, we propose MDAR, a multi-task learning approach that combines DeepFM with an attention mechanism (DeepAFM) and a relation-fused multi-head graph attention network (RMGAT). Firstly, we propose to leverage the attention mechanism in the DeepAFM to distinguish the importance of different features for target prediction by assigning different weights to different interaction features of the user and the item, which solves the first problem. Secondly, we introduce deep neural networks (DNNs) to extract the deep semantic information in the cross-compressed units by obtaining the high-dimensional features of the interactions between the RS task and the KG task to solve the second problem. Lastly, we design a multi-head graph attention network for relationship fusion (RMGAT) in the KGE task, which learns entity representations through the different contributions of the neighbors by aggregating the relationships into the attention network of the knowledge graph and by obtaining information about the neighbors with different importance for different relationships, effectively solving the third problem. Through experimenting on real-world public datasets, we demonstrate that MDAR obtained substantial results over state-of-the-art baselines for recommendations for movie, book, and music datasets. Our results underscore the effectiveness of MDAR and its potential to advance recommendation systems in various domains.
A New Model for Emotion-Driven Behavior Extraction from Text
Authors: Sun Yawei , He Saike , Han Xu , Zhang Ruihua
Published: July 2023

Emotion analysis is currently a popular research direction in the field of natural language processing. However, existing research focuses primarily on tasks such as emotion classification, emotion extraction, and emotion cause analysis, while there are few investigations into the relationship between emotions and their impacts. To address these limitations, this paper introduces the emotion-driven behavior extraction (EDBE) task, which addresses these limitations by separately extracting emotions and behaviors to filter emotion-driven behaviors described in text. EDBE comprises three sub-tasks: emotion extraction, behavior extraction, and emotion–behavior pair filtering. To facilitate research in this domain, we have created a new dataset, which is accessible to the research community. To address the EDBE task, we propose a pipeline approach that incorporates the causal relationship between emotions and driven behaviors. Additionally, we adopt the prompt paradigm to improve the model’s representation of cause-and-effect relationships. In comparison to state-of-the-art methods, our approach demonstrates notable improvements, achieving a 1.32% improvement at the clause level and a 1.55% improvement at the span level on our newly curated dataset in terms of the F1 score, which is a commonly used metric to measure the performance of models. These results underscore the effectiveness and superiority of our approach in relation to existing methods.
Meta-Analysis of Influence of Diversity of Parental Forms on Heterosis and Specific Combining Ability of Their Hybrids
Authors: Bocianowski Jan , Nowosad Kamila , Bujak Henryk
Published: July 2023

An important stage in any breeding activity is selection of suitable individuals for further breeding. Thus, the main goal of breeders becomes such a selection of parental forms that leads to the consolidation and maximization of the value of traits of significant utility and economic importance. Heterosis and specific combining ability are very important parameters in plant and animal breeding. The ability to predict their value and relevance could significantly shorten the breeding process. One way to predict the effects of heterosis and specific combining ability is to select parental forms for crosses. This selection can be made on the basis of variation in parental forms. An analysis was made of publicly available data that contain information about the effects of heterosis, the effects of specific combining ability, and phenotypic and genetic diversity of parental forms. Preliminary studies show that the best approach for obtaining favorable hybrids would be selection of parental forms that are very genetically diverse while being phenotypically equal.
Special Issue on Explosion Effects in the Built Environment
Authors: Langdon Genevieve S.
Published: July 2023

Over the last two decades, highly publicized disasters caused by explosions have become an all too frequent occurrence [...]
A Systematic Review and Meta-Analysis on the Associated Effects of Static Magnetic Fields on Orthodontic Tooth Movement
Authors: Alam Mohammad Khursheed , Awawdeh Mohammed , Alsaeed Suliman , Khanagar Sanjeev B. , Abutayyem Huda , Alswairki Haytham Jamil , Hajeer Mohammad Younis
Published: July 2023

Background: The literature on the use of static magnetic field (SMF), particularly in orthodontics, has certain gaps. Furthermore, the mechanism by which SMF affects orthodontic tooth movement (OTM) is still unclear and quite contradictory. Thus, the goal of this systematic review and meta-analysis was to assess SMF’s effect on OTM. This study also sought to analyse the variability of the studies included in the analysis and the size of the impact of SMF on OTM. Methods: Using the PRISMA guidelines, reviewers implemented a search strategy across several online databases, filtering out the initial articles that were obtained by applying relevant inclusion and exclusion criteria. Results: The overall effect size for the odds ratio was found to be 0.58, with a 95% confidence interval ranging from 0.40 to 0.86. This indicates that exposure to static magnetic fields is associated with a significantly reduced likelihood of orthodontic tooth movement in the animal studies analysed. Similarly, the overall effect size for the risk ratio was calculated to be 0.71, with a 95% confidence interval ranging from 0.55 to 0.91. The risk ratio also suggests a significant impact of SMF on OTM, with animals exposed to magnetic fields being at a lower risk of experiencing substantial tooth movement compared to those not exposed. However, it is important to note that moderate heterogeneity was observed among the included studies. Conclusion: The findings of this systematic review and meta-analysis indicate that there may be a causal relationship between OTM and SMF. However, the small number of studies included in this review and their poor methodological quality limit the available data, highlighting the need for further well-designed research to support these conclusions.
How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects
Authors: Adeli Khaoula , Nachtane Mourad , Faik Abdessamad , Saifaoui Dennoun , Boulezhar Abdelkader
Published: July 2023

As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them widely. The goal of this review paper is to give a complete overview of the latest technology for the manufacture and storage of hydrogen and ammonia. This paper deals with hydrogen and ammonia synthesis and storage. It examines the most recent technological breakthroughs in areas such as electrolysis, reforming, C-ZEROS, HYSATA, DAE, sulfide, and SRBW, as well as novel storage techniques, such as solid-state storage, plasma kinetics, and POWERPASTE. This article examines the history of ammonia production and discusses some of the newer and more sustainable techniques for producing ammonia, such as electrochemical and biological approaches. This study also looks at how artificial intelligence (AI) and additive manufacturing (AM) could be used to revolutionize the way green hydrogen and ammonia are produced, with an emphasis on recent breakthroughs in AI-assisted catalyst design and 3D-printed reactors, as well as considering major investments in the shift to green energy, such as Moroccan government programs, and how they may affect future hydrogen and ammonia production.
Improving Monocular Camera Localization for Video-Based Three-Dimensional Outer Ear Reconstruction Tasks
Authors: Tamulionis Mantas , Serackis Artūras , Bartnykas Kęstutis , Miniotas Darius , Mikučionis Šarūnas , Laptik Raimond , Ušinskas Andrius , Matuzevičius Dalius
Published: July 2023

This work addresses challenges related to camera 3D localization while reconstructing a 3D model of an ear. This work explores the potential solution of using a cap, specifically designed not to obstruct the ear, and its efficiency in enhancing the camera localization for structure-from-motion (SfM)-based object reconstruction. The proposed solution is described, and an elaboration of the experimental scenarios used to investigate the background textures is provided; data collection and software tools used in the research are reported. The results show that the proposed method is effective, and using the cap with texture leads to a reduction in the camera localization error. Errors in the 3D location reconstruction of the camera were calculated by comparing cameras localized within typical ear reconstruction situations to those of higher-accuracy reconstructions. The findings also show that caps with sparse dot patterns and a regular knitted patterned winter hat are the preferred patterns. The study provides a contribution to the field of 3D modeling, particularly in the context of creating 3D models of the human ear, and offers a step towards more accurate, reliable, and feasible 3D ear modeling and reconstruction.
Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
Authors: Alwakeel Ahmed M.
Published: July 2023

This study presents a novel keyword-based trust management system for fog computing networks aimed at improving network efficiency and ensuring data integrity. The proposed system establishes and maintains trust between fog nodes using trust keywords recorded in a table on each node. Simulation research is conducted using iFogSim to evaluate the efficacy of the proposed scheme in terms of latency and packet delivery ratio. The study focuses on addressing trust and security challenges in fog computing environments. By leveraging trust keywords, the proposed system enables accurate evaluation of trustworthiness and identification of potentially malicious nodes. The system enhances the security of fog computing by mitigating risks associated with unauthorized access and malicious behavior. While the study highlights the significance of trust keywords in improving network performance and trustworthiness, it fails to provide detailed explanations of the trust mechanism itself. Additionally, the role of fog computing in the proposed approach is not adequately emphasized. Future research directions include refining and optimizing the proposed framework to consider resource constraints, dynamic network conditions, and scalability. Integration of advanced security mechanisms such as encryption and authentication protocols will be explored to strengthen the trust foundation in fog computing environments. In conclusion, the proposed keyword-based trust management system offers potential benefits for improving network performance and ensuring data integrity in fog computing. However, further clarification of the trust mechanism and a stronger emphasis on the role of fog computing would enhance understanding of the proposed approach.
Challenges for Children with Cochlear Implants in Everyday Listening Scenarios: The Competitive Effect of Noise and Face Masks on Speech Intelligibility
Authors: Puglisi Giuseppina Emma , Di Iulio Michele , Bottalico Pasquale , Murgia Silvia , Consolino Patrizia , Bisetti Massimo Spadola , Pittà Giuseppe , Shtrepi Louena , Astolfi Arianna
Published: July 2023

Speech intelligibility (SI) tests under realistic acoustic scenarios are complex tasks to perform. Optimal acoustics, in terms of reverberation and noise, are thus needed. This is particularly true in the presence of young hard-of-hearing (HoH) children equipped with cochlear implants who need speech to be highly intelligible to learn. During the COVID-19 pandemic starting in early 2020, wearing face masks became common to avoid the spread of infection, mainly impacting the increasingly challenging task of listening for HoH listeners. This study investigated the influence of different types of face masks on speech intelligibility and listening difficulty under competitive noise scenarios. Fourteen children with cochlear implants were involved, as well as six children with typical hearing. Three types of face masks with different acoustic, filtration, and breathability characteristics were considered; three signal-to-noise ratios (SNR) of +10 dB, +5 dB, and 0 dB were used. As expected, lower SNRs corresponded to lower speech intelligibility, and SI without a mask was similar to that obtained with a mask at the lowest acoustic attenuation, albeit with a low filtration efficiency. These preliminary outcomes help improve speech communication strategies in classrooms to support optimal listening conditions.
Experimental Research into the Efficiency of an Electromagnetic Mill
Authors: Całus Dariusz
Published: July 2023

This paper presents a method for quantitative assessment of the efficiency of an EMM, taking into consideration its design parameters (dimensions, winding data, etc.) and technological indicators of the milling process (mass of the milled substance, mass of the mill, milling time, etc.). The performance of an EMM is characterized in terms of two indicators—the efficiency of the milling itself, and its quality. The EMM efficiency was expressed as the ratio of the ground mass to the time taken for the grinding, and the grinding quality was given as the ratio of the mass of the smallest fraction obtained as a result of grinding to the total ground mass. Those indicators were calculated on the basis of the analysis of empirical results obtained using an EMM comprising a rotating magnetic field. The efficiency and quality of grinding were taken into account to determine these indicators. Moreover, a deterministic relation was established between this efficiency and quality of milling and the calculation values—the average number of millstone impacts and average impulse magnitude of ground material impacts, calculated using mathematical modelling of the grinding process. An algorithm applicable for determining the performance of the EMM and the quality of grinding was proposed on the basis of the results of this research.
Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
Authors: Gençoğlan Duygu Nazan , Çolak Şule , Palandöken Merih
Published: July 2023

This paper presents a novel frequency reconfigurable antenna design for sub-6 GHz applications, featuring a unique combination of antenna elements and control mechanisms. The antenna is composed of an outer split-ring resonator loaded with an inner spiral resonator, which can be adjusted through the remote control of PIN diode or Single Pole Double Throw (SPDT) switches. The compact antenna, measuring 22 × 16 × 1.6 mm3, operates in broadband, or tri-band mode depending on the ON/OFF states of switches. The frequency reconfigurability is achieved using two BAR64−02V PIN diodes or two CG2415M6 SPDT switches acting as RF switches. SPDT switches are controlled remotely via Arduino unit. Additionally, the antenna demonstrates an omni-directional radiation pattern, making it suitable for wireless communication systems. Experimental results on an FR-4 substrate validate the numerical calculations, confirming the antenna’s performance and superiority over existing alternatives in terms of compactness, wide operating frequency range, and cost-effectiveness. The proposed design holds significant potential for applications in Wi-Fi (IEEE 802.11 a/n/ac), Bluetooth (5 GHz), ISM (5 GHz), 3G (UMTS), 4G (LTE), wireless backhaul (4G and 5G networks), WLAN (IEEE 802.11 a/n/ac/ax), 5G NR n1 band, and Wi-Fi access points due to its small size and easy control mechanism. The antenna can be integrated into various devices, including access points, gateways, smartphones, and IoT kits. This novel frequency reconfigurable antenna design presents a valuable contribution to the field, paving the way for further advancements in wireless communication systems.
Seismic Fragility Analysis of a High-Pier Bridge under Pulse-like Ground Motion, Based on a PCA and K-Means Approach
Authors: Zhao Jingang , Jia Hongyu , Yang Can , Du Bin
Published: July 2023

The objective of this study is to present a novel fragility analysis method that combines principal component analysis (PCA) and the K-means clustering algorithm for a probability assessment of seismic damage in high-pier bridges undergoing pulse-like ground motions. Firstly, the method uses the correlation coefficient and the condition number as judgment indices to eliminate those seismic intensity measures (IMs) with weak correlation and multicollinearity from all 29 of the initial candidate seismic IMs, the optimal combination of IMs that satisfies the requirements for the PCA method is determined. Secondly, the method utilizes PCA to reduce the dimensionality of the optimal combination of IMs to obtain the principal components, after which the K-means algorithm is applied to classify the original group of selected pulse-like ground motions into four classes. Thirdly, a 3D finite element model of the exemplary high-pier bridge is developed via OpenSees, while incremental nonlinear dynamic time-history analyses are conducted to record the maximum cross-section curvatures of high piers under the influence of various categories of ground motions. Finally, based on the analytical procedures used in the increment dynamic analysis (IDA) method, this study develops and compares the fragility curves for the various classes of pulse-like ground motions. The results indicate the necessity of utilizing the PCA and K-means approach for classifying pulse-like ground motions in the seismic fragility analysis of high-pier bridges. This approach also significantly improves the precision and accuracy of damage probability analysis.
A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
Authors: Lai Ruilin , Zhao Gansen , He Yale , Hou Zhihao
Published: July 2023

Blockchain establishes security and trust in mobile ad hoc networks (MANETs). Due to the decentralized and opportunistic communication characteristics of MANETs, hashgraph consensus is more applicable to the MANET-based blockchain. Sharding scales the consensus further through disjoint nodes in multiple shards simultaneously updating ledgers. However, the dynamic addition and deletion of nodes in a shard pose challenges regarding robustness and efficiency. Particularly, the shard is vulnerable to Sybil attacks and targeted attacks, and dishonest gossip reduces the efficiency of hashgraph consensus. Therefore, we proposed a behavior-based sharding hashgraph scheme. First, dishonest behaviors of nodes are recorded in a decentralized blacklist. Gossip information is sent to a reliable neighbor, and gossip information from another reliable neighbor is received. Second, a tree-assisted inter-sharding consensus is proposed to prevent Sybil attacks. The combination of shard recovery and reconfiguration based on node state is devised to prevent targeted attacks. Finally, we conducted the performance evaluation including security analysis and experimental evaluation to reveal the security and efficiency of the proposed scheme.
Application of the [WO2(C5H7O2)2] Complex in Hydrothermal Synthesis of WO3 Film and Study of Its Electrochromic Properties
Authors: Gorobtsov Philipp Yu. , Grigoryeva Maria K. , Simonenko Tatiana L. , Simonenko Nikolay P. , Simonenko Elizaveta P. , Kuznetsov Nikolay T.
Published: July 2023

The goal of this work was the synthesis study of the [WO2(C5H7O2)2] complex and its application as a precursor for the growth of WO3 films in hydrothermal conditions, as well as evaluating the microstructural features and electrochromic properties of the formed materials. Dioxotungsten acetylacetonate was synthesized in an aqueous medium and purified. It was found that during hydrothermal treatment of the alcohol solution of the complex, acetylacetonate ligands undergo partial destructive substitution by alkoxyl groups, intensifying at temperatures above 140 °C. Considering these data and using a [WO2(C5H7O2)2] solution, WO3 films were grown on glass and glass/ITO substrates. The resulting films had different microstructures according to scanning electron microscopy (SEM) and atomic force microscopy (AFM): the former consisted of submicron spheres (~500 nm), distinct nanoparticles (60–160 nm), and submicron- and micron-sized ridges, while the latter consisted of 1D structures (length 350 ± 25 nm, width 110 ± 25 nm). Using Kelvin probe force microscopy (KPFM), the electron work function was determined for the film on glass/ITO substrate (4.77 eV). It was found that the electrochemical coloration process of the obtained WO3 film can proceed in two stages, and the optical contrast is about 17.5% (at the wavelengths of 600–1100 nm). The results obtained show the prospects of applying the proposed approach to obtaining WO3 electrochromic films with a hierarchical microstructure with the hydrothermal method using the [WO2(C5H7O2)2] complex as a precursor.
Comparative Analysis of Cartilage Conduction Hearing Aid Users and Non-Users: An Investigative Study
Authors: Sugimoto Satofumi , Yoshida Tadao , Fukunaga Yukari , Motegi Aya , Saito Ken , Kobayashi Masumi , Sone Michihiko
Published: July 2023

Clinical findings on cartilage conduction hearing aids (CCHAs) have gradually become clear; however, few reports include a large number of cases. This study included 91 ears from 69 patients who underwent CCHA fitting in our hospital. Their ears were divided into six groups (i.e., bilateral aural atresia or severe canal stenosis, unilateral aural atresia or severe canal stenosis, chronic otitis media or chronic otitis externa with otorrhea, sensorineural hearing loss, mixed hearing loss, and conductive hearing loss) according to their clinical diagnosis and type of hearing loss. Most clinical diagnoses were aural atresia or meatal stenosis (bilateral, 21.8%; unilateral, 39.6%). The purchase rate of CCHAs was higher in the closed-ear group (bilateral, 77.3%; unilateral, 62.5%). In the bilateral closed-ear group, air conduction thresholds at 1000, 2000, and 4000 Hz and aided thresholds with CCHAs at 4000 Hz were significantly lower in the purchase group than the non-purchase group. No significant difference was observed between the purchase and non-purchase groups in the unilateral closed-ear group. In the bilateral closed-ear group, air conduction thresholds and aided thresholds were associated with the purchase rate of CCHAs. In the unilateral closed-ear group, factors other than hearing might have affected the purchase rate of CCHAs.
An Equilibrium Strategy for Target Benefit Pension Plans with a Longevity Trend and Partial Information
Authors: Liu Wei , Li Na , Muhammadhaji Ahmadjan
Published: July 2023

This paper considers the problem of portfolio selection and adjustment for target benefit plans (TBP) with longevity trends and partial information. The longevity trends are modeled by a time-varying force function. The financial market consists of risk-free assets and stocks, in which the return rate of stocks is a stochastic process and cannot be completely observed. This paper adopts the mean-variance utility model as an optimization criterion. The aim is to maximize the terminal value of the pension fund and the excess pension benefit after the participant’s retirement. The optimization equations are developed in game theory to obtain explicit solutions for the equilibrium strategies. Finally, the influence of the longevity trend on the internal structure of the pension system and the sensitivity of the equilibrium strategies to the related parameters are explored by numerical analysis. The conclusion shows that this model’s results can provide stable and adequate retirement benefits for participants.
An Integrated Multicriteria Sorting Methodology with q-Rung Orthopair Fuzzy Sets for Evaluating the Impacts of Delays on Residential Construction Projects
Authors: Oraya Aure Flo , Canseco-Tuñacao Hana Astrid , Luciano Ryan , Patadlas Aiza , Baguio Ike , Aro Joerabell Lourdes , Maturan Fatima , Ocampo Lanndon
Published: July 2023

This study offers an integrated evaluation methodology for construction project delay causes viewed as a multicriteria sorting (MCS) problem. Time, cost, and quality were the three project management factors considered as criteria to evaluate 38 identified delay causes. The priority weights were extracted using the integration of Weighted Influence Non-linear Gauge Systems (WINGS) and Level-Based Weight Assessment (LBWA) to capture the inherent interdependencies of the criteria. The sorting of 38 delay causes was performed using FlowSort. To handle the uncertainty and vagueness of the judgments of the decision makers in the evaluation process, q-rung fuzzy orthopair fuzzy sets (q-ROFS) were integrated within the proposed computational framework. The proposed novel q-ROF–WINGS–LBWA–FlowSort method was applied in an actual case study in residential construction projects. The delay causes were categorized under three categories of construction firm vulnerability into four levels of impact. In highly vulnerable construction firms, thirty-five delay causes have a high impact, two have a moderate impact, and one has the least impact. In moderately vulnerable and least-vulnerable construction firms, 32 and 28 delay causes have a medium impact, respectively. The results may provide insights for decision makers in highly vulnerable construction firms, i.e., small companies with limited resources and networks. Layers of sensitivity and comparative analyses were put forward to test the robustness of the approach.
Representation of Some Ratios of Horn’s Hypergeometric Functions H7 by Continued Fractions
Authors: Antonova Tamara , Dmytryshyn Roman , Kril Pavlo , Sharyn Serhii
Published: July 2023

The paper deals with the problem of representation of Horn’s hypergeometric functions via continued fractions and branched continued fractions. We construct the formal continued fraction expansions for three ratios of Horn’s hypergeometric functions H7. The method employed is a two-dimensional generalization of the classical method of constructing a Gaussian continued fraction. It is proved that the continued fraction, which is an expansion of each ratio, uniformly converges to a holomorphic function of two variables on every compact subset of some domain of C2, and that this function is an analytic continuation of such a ratio in this domain. To illustrate this, we provide some numerical experiments at the end.
Sampling Plan for the Kavya–Manoharan Generalized Inverted Kumaraswamy Distribution with Statistical Inference and Applications
Authors: Alsadat Najwan , Hassan Amal S. , Elgarhy Mohammed , Chesneau Christophe , El-Saeed Ahmed R.
Published: July 2023

In this article, we introduce the Kavya–Manoharan generalized inverse Kumaraswamy (KM-GIKw) distribution, which can be presented as an improved version of the generalized inverse Kumaraswamy distribution with three parameters. It contains numerous referenced lifetime distributions of the literature and a large panel of new ones. Among the essential features and attributes covered in our research are quantiles, moments, and information measures. In particular, various entropy measures (Rényi, Tsallis, etc.) are derived and discussed numerically. The adaptability of the KM-GIKw distribution in terms of the shapes of the probability density and hazard rate functions demonstrates how well it is able to fit different types of data. Based on it, an acceptance sampling plan is created when the life test is truncated at a predefined time. More precisely, the truncation time is intended to represent the median of the KM-GIKw distribution with preset factors. In a separate part, the focus is put on the inference of the KM-GIKw distribution. The related parameters are estimated using the Bayesian, maximum likelihood, and maximum product of spacings methods. For the Bayesian method, both symmetric and asymmetric loss functions are employed. To examine the behaviors of various estimates based on criterion measurements, a Monte Carlo simulation research is carried out. Finally, with the aim of demonstrating the applicability of our findings, three real datasets are used. The results show that the KM-GIKw distribution offers superior fits when compared to other well-known distributions.
Strong Convergence of a Two-Step Modified Newton Method for Weighted Complementarity Problems
Authors: Liu Xiangjing , Zhang Jianke
Published: July 2023

This paper focuses on the weighted complementarity problem (WCP), which is widely used in the fields of economics, sciences and engineering. Not least because of its local superlinear convergence rate, smoothing Newton methods have widespread application in solving various optimization problems. A two-step smoothing Newton method with strong convergence is proposed. With a smoothing complementary function, the WCP is reformulated as a smoothing set of equations and solved by the proposed two-step smoothing Newton method. In each iteration, the new method computes the Newton equation twice, but using the same Jacobian, which can avoid consuming a lot of time in the calculation. To ensure the global convergence, a derivative-free line search rule is inserted. At the same time, we develop a different term in the solution of the smoothing Newton equation, which guarantees the local strong convergence. Under appropriate conditions, the algorithm has at least quadratic or even cubic local convergence. Numerical experiments indicate the stability and effectiveness of the new method. Moreover, compared to the general smoothing Newton method, the two-step smoothing Newton method can significantly improve the computational efficiency without increasing the computational cost.
First-Order Differential Subordinations and Their Applications
Authors: Ebadian Ali , Aghalary Rasoul , Shams S. , Cho Nak Eun , Alavi R.
Published: July 2023

In this paper, we consider some relations related to the representations of starlike and convex functions, and obtain some sufficient conditions for starlike and convex functions by using the theory of differential subordination. Actually, we generalize a result by Suffridge for analytic functions with missing coefficients and then we apply that generalization for obtaining the different methods to the implications of starlike or convex functions. Our results generalize and improve the previous results in the literature.
Geometry of Harmonic Nearly Trans-Sasakian Manifolds
Authors: Rustanov Aligadzhi R.
Published: July 2023

This paper considers a class of nearly trans-Sasakian manifolds. The local structure of nearly trans-Sasakian structures with a closed contact form and a closed Lee form is obtained. It is proved that the class of nearly trans-Sasakian manifolds with a closed contact form and a closed Lee form coincides with the class of almost contact metric manifolds with a closed contact form locally conformal to the closely cosymplectic manifolds. A wide class of harmonic nearly trans-Sasakian manifolds has been identified (i.e., nearly trans-Sasakian manifolds with a harmonic contact form) and an exhaustive description of the manifolds of this class is obtained. Also, examples of harmonic nearly trans-Sasakian manifolds are given.
A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure
Authors: Choi Wansuk , Choi Taeseok , Heo Seoyoon
Published: July 2023

The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM’s lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker’s implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended.
Automated Segmentation of Levator Ani Muscle from 3D Endovaginal Ultrasound Images
Authors: Rabbat Nada , Qureshi Amad , Hsu Ko-Tsung , Asif Zara , Chitnis Parag , Shobeiri Seyed Abbas , Wei Qi
Published: July 2023

Levator ani muscle (LAM) avulsion is a common complication of vaginal childbirth and is linked to several pelvic floor disorders. Diagnosing and treating these conditions require imaging of the pelvic floor and examination of the obtained images, which is a time-consuming process subjected to operator variability. In our study, we proposed using deep learning (DL) to automate the segmentation of the LAM from 3D endovaginal ultrasound images (EVUS) to improve diagnostic accuracy and efficiency. Over one thousand images extracted from the 3D EVUS data of healthy subjects and patients with pelvic floor disorders were utilized for the automated LAM segmentation. A U-Net model was implemented, with Intersection over Union (IoU) and Dice metrics being used for model performance evaluation. The model achieved a mean Dice score of 0.86, demonstrating a better performance than existing works. The mean IoU was 0.76, indicative of a high degree of overlap between the automated and manual segmentation of the LAM. Three other models including Attention UNet, FD-UNet and Dense-UNet were also applied on the same images which showed comparable results. Our study demonstrated the feasibility and accuracy of using DL segmentation with U-Net architecture to automate LAM segmentation to reduce the time and resources required for manual segmentation of 3D EVUS images. The proposed method could become an important component in AI-based diagnostic tools, particularly in low socioeconomic regions where access to healthcare resources is limited. By improving the management of pelvic floor disorders, our approach may contribute to better patient outcomes in these underserved areas.
Initial Development of an Immediate Implantation Model in Rats and Assessing the Prognostic Impact of Periodontitis on Immediate Implantation
Authors: Wang Yingying , Cao Ximeng , Shen Yingyi , Zhong Qi , Huang Yujie , Zhang Yifan , Wang Shaohai , Xu Chun
Published: July 2023

Background: To establish an immediate implantation rat model and to evaluate the effects of pre-existing periodontitis and two different socket rinse solutions on immediate implantation prognosis. Methods: Sprague-Dawley (SD) rats were randomly divided into three groups before immediate implantation, including the control group, the group with experimentally induced periodontitis (EP), in which rats have been experimentally induced periodontitis before implantation, and the group with induced periodontitis and with extraction sockets rinsed with three percent H2O2 (EP-H2O2), in which rats have been induced periodontitis before implantation, and extraction sockets were rinsed with three percent H2O2. Periodontitis was induced by ligating the thread around the molars for four weeks. Six weeks after titanium alloy implants were self-tapped and left to heal transmucosally, maxillae were dissected after the clinical examination to perform micro-CT and histological analysis. Results: An immediate implantation model was successfully built in rats. There was no significant difference in implant survival rates between the EP and control groups. However, the clinical examination results, micro-CT analysis, and histological analysis in EP and EP-H2O2 groups showed a significantly worse prognosis than in the control group. Three percent H2O2 showed a similar effect with saline. Conclusion: This study presented a protocol for establishing a rat immediate implantation model and showed that periodontitis history might negatively affect the prognosis of immediate implantation. These findings urge caution and alternative strategies for patients with periodontal disease history, enhancing the long-term success of immediate implantation in dental practice. Additionally, the comparable outcomes between 3% H2O2 and saline suggest the use of saline as a cost-effective and safer alternative for implant site preparation in dental practice.
Trophic Resource Use by Sympatric vs. Allopatric Pelomedusid Turtles in West African Forest Waterbodies
Authors: Petrozzi Fabio , Gonedele Bi Sery , Segniagbeto Gabriel Hoinsoudé , Pacini Nic , Fa Julia E. , Luiselli Luca
Published: July 2023

Organisms that are similar in size, morphological characteristics, and adaptations, including vertebrates, often coexist by partitioning the available resources (food, space, and time). So, studies of the dynamics of these cases of coexistence are scientifically interesting. Here, we study a coexistence case of two species of freshwater turtles inhabiting the forest waterbodies of West Africa, focusing on the dietary habits of the two species. We found that both turtle species are omnivorous generalists, eating both vegetal and animal matter abundantly. However, there were clear interspecific differences, with the larger of the two species (P. cupulatta) eating more vertebrates (mainly fish but occasionally other vertebrates), whereas P. castaneus consumed more invertebrates. These patterns appeared consistently within the species and across sites, highlighting that the same patterns were likely in other conspecific populations from the Upper Guinean forest streams (Côte d’Ivoire and Liberia). Our study also showed that interspecific competition for food does not occur between these two species; instead, previous studies uncovered that a clear partitioning of the habitat niche occurs. We conclude that the food resource is likely unlimited in the study areas, as it is not the case in more arid environments (since food shortages may occur during the dry season). We anticipate that, within the Pelomedusidae communities throughout Africa, intense competition for food probably occurs in the Sahel and Sudanian vegetation zones, particularly during the dry months, but is unlikely within the Guinea and wet savannah region and even less likely in the Guineo-Congolian rainforest region.
High-Throughput Assay of Cytochrome P450-Dependent Drug Demethylation Reactions and Its Use to Re-Evaluate the Pathways of Ketamine Metabolism
Authors: Davydova Nadezhda Y. , Hutner David A. , Gaither Kari A. , Singh Dilip Kumar , Prasad Bhagwat , Davydov Dmitri R.
Published: July 2023

In a search for a reliable, inexpensive, and versatile technique for high-throughput kinetic assays of drug metabolism, we elected to rehire an old-school approach based on the determination of formaldehyde (FA) formed in cytochrome P450-dependent demethylation reactions. After evaluating several fluorometric techniques for FA detection, we chose the method based on the Hantzsch reaction with acetoacetanilide as the most sensitive, robust, and adaptable to high-throughput implementation. Here we provide a detailed protocol for using our new technique for automatized assays of cytochrome P450-dependent drug demethylations and discuss its applicability for high-throughput scanning of drug metabolism pathways in the human liver. To probe our method further, we applied it to re-evaluating the pathways of metabolism of ketamine, a dissociative anesthetic and potent antidepressant increasingly used in the treatment of alcohol withdrawal syndrome. Probing the kinetic parameters of ketamine demethylation by ten major cytochrome P450 (CYP) enzymes, we demonstrate that in addition to CYP2B6 and CYP3A enzymes, which were initially recognized as the primary metabolizers of ketamine, an important role is also played by CYP2C19 and CYP2D6. At the same time, the involvement of CYP2C9 suggested in the previous reports was deemed insignificant.