bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Topographic Seismic Site Periods in Sloping Terrains
Authors: Diaz-Segura Edgar Giovanny , Oviedo-Veas Jorge Eduardo
Published: August 2024

The fundamental period of a terrain is a key parameter for characterizing the maximum soil amplification. Since the 1960s, research has been conducted for sloping terrains with a focus on evaluating topographic effects. However, few studies have focused on identifying whether the site topography induces an amplification peak that is associated with a characteristic period of sloping terrain. This study conducts a parametric analysis to identify a potential amplification pattern attributable to terrain geometry, using two-dimensional finite element models subjected to the action of a dynamic signal. The periods in which amplification peaks are generated are evaluated and compared with the amplification response recorded in the free field on horizontal terrain. The results reveal that the dynamic response of sloping terrain is a combination of the response from the surrounding terrain to the sloping zone and vice versa, and a distinctive amplification peak linked to the topography is identified. A new expression is proposed to define a topographic seismic site period in terms of shear wave velocity and the total soil thickness from the bedrock to the crest of sloping terrain. This study advances the processes of characterizing the seismic response of sloping terrains by demonstrating that the topographic seismic site period is consistent regardless of the slope angle. This provides engineers with a new dimension of analysis for the practical definition of criteria to determine topographic effects in design spectra.
Degradation and Migration in Olive Oil Packaged in Polyethylene Terephthalate under Thermal Treatment and Storage Conditions
Authors: González-Torres Pablo , García-Ruiz Ángeles , La Rubia M. Dolores
Published: August 2024

The research addresses challenges in food safety related to the migration of contaminants from plastics to food. It focused on the physicochemical and sensory degradation of olive oils packaged in polyethylene terephthalate (PET) and subjected to thermal exposure at 40 °C and 60 °C for several weeks and a subsequent 12 months of storage, as well as the stability and migration of compounds from the PET packaging itself. Olive oils (OO) from Spanish supermarkets (a mixture of refined and virgin, with commercial identifications of mild and intense) were selected and subjected to thermal treatments at 40 °C and 60 °C for 1, 2, and 3 weeks, followed by 12 months of storage. The treatments were conducted through the following two independent experiments: Experiment A focused on immediate analysis post-thermal treatment, while Experiment B included a 12-month storage period post-thermal treatment. The presence of antimony (Sb) was analyzed using acid digestion with nitric acid (HNO3) and high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS), while the metals cadmium (Cd), copper (Cu), lead (Pb), and iron (Fe) were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The PET characterization was assessed using Fourier transform infrared spectroscopy in the mid-infrared range (FT-IR/MIR), carbonyl index, and differential scanning calorimetry (DSC). The results showed increases in the acidity index by 0.29%, the peroxide value by 25.92%, and the K268 coefficient by 51.22% between the control sample and the most severe treatments, with more pronounced effects observed after 12 months. Sensory quality declined, with reduced intensity of the “fruity” attribute and increased presence of the “rancid” defect. PET degradation was reflected in an increase in the carbonyl index and greater structural amorphization. Fe was the predominant metal, and Sb concentration increased after thermal treatments. The lack of studies on the raw consumption of oils packaged in PET and the concerns about the migration of compounds from the packaging to the food highlight the relevance of this research. This study provides new insights into the effects of thermal exposure and storage on the migration of PET contaminants into oils, contributing to the development of strategies to ensure food safety and product quality.
The Lattice Boltzmann Method and Image Processing Techniques for Effective Parameter Estimation of Digital Rock
Authors: Nurcahya Ardian , Alexandra Aldenia , Akmal Fadhillah , Dharmawan Irwan Ary
Published: August 2024

Several numerical simulations of fluid flow were performed using the Lattice Boltzmann method and image processing techniques to estimate the effective properties of 2-D porous rocks. The effective properties evaluated were the physical characteristics that allow fluid flow including the effective porosity, permeability, tortuosity, and average throat size to determine the storage and transport of fluids in porous rocks. The permeability was compared using the Darcy model simulation and the empirical Kozeny–Carman Equation. The results showed that the Lattice Boltzmann method and image processing techniques can estimate the effective parameters of porous rocks. Furthermore, there was a good correlation between permeability and parameters such as effective porosity, tortuosity, and average throat size. The Darcy model simulation revealed a gamma distribution in the permeability, while the empirical Kozeny–Carman Equation showed a log-normal distribution.
On Beamforming with the Single-Sideband Transform
Authors: Curtarelli Vitor Probst , Cohen Israel
Published: August 2024

In this paper, we examine the use of the Single-Sideband Transform (SSBT) for convolutive beamformers. We explore its unique properties and implications for beamformer design. Our study sheds light on the tradeoffs involved in using the SSBT in beamforming applications, offering insights into both its strengths and limitations. Despite the advantage of having real-valued coefficients, we show that the convolution handling of the transform presents challenges that impact fundamental beamforming principles. When compared to the Short-Time Fourier Transform (STFT), the SSBT displays lower robustness, especially in scenarios involving mismatch and modeling noise. Notably, we establish a direct equivalence between the SSBT and STFT when using identical transform parameters, enabling their seamless interchangeability and joint use in time–frequency signal enhancements. We validate our theoretical findings through realistic simulations using the Minimum-Power Distortionless Response beamformer. These simulations illustrate that although the STFT performs marginally better than the SSBT under optimal conditions, it outperforms significantly in non-ideal scenarios.
Genesis of Low-Resistivity Shale Reservoirs and Its Influence on Gas-Bearing Property: A Case Study of the Longmaxi Formation in Southern Sichuan Basin
Authors: Hu Xi , Zhou Anfu , Li Yading , Jiang Hongzong , Fu Yonghong , Jiang Yuqiang , Gu Yifan
Published: August 2024

To mitigate the exploration and development risks, it is necessary to have a deeper understanding of the formation mechanism and gas-bearing control factors of low-resistance shale reservoirs. This study focuses on typical shale gas wells (including low-resistivity wells) in Luzhou area, and identification criteria for low-resistance shale reservoirs are redefined as resistivity less than 10 Ω·m and continuous formation thickness greater than 6 m. At the macro scale, low-resistivity shale reservoirs are characterized by high clay mineral content and high water saturation with low gas content. At the micro scale, the main pore size is less than 10 nm, with a small total pore volume but a large specific surface area. Shale reservoirs close to the Class II fault have high water saturation and strong compaction, which hinders the mutual transformation between minerals, resulting in low-resistivity shale with high clay mineral content, small pore volume, and pore size, which promotes the enhancement of reservoir conductivity. The gas content of low-resistivity shale reservoirs is lower, because the distance from the Class II fault is closer, resulting in high water saturation and strong diagenesis, which is not conducive to pore development and shale gas accumulation. When the water saturation exceeds 40%, the pore volume of shale reservoirs rapidly decreases to as low as 0.0074 cm3/g. In order to reduce the risk of exploration and development of the area, the well location deployment needs to be more than 2.8 km away from the Class II fault.
EMG Activity of Lower Limb Muscles during Anti-Gravity Treadmill Running with Different Loads and Speeds
Authors: Pietraszewski Przemysław , Gołaś Artur , Roczniok Robert , Gepfert Mariola , Zając Adam
Published: August 2024

The study’s objective was to identify the features of internal movement structure that depend on speed and the importance of unloading when jogging on an anti-gravity treadmill. The aim was to investigate whether the internal structure of running changes under unloaded conditions, using an anti-gravity treadmill. Twenty male competitive middle- and long-distance runners with the following characteristics participated in the study: age, 25 ± 5 years; body weight, 67.2 ± 8.9 kg; body height, 177 ± 11 cm; and training experience, 9 ± 5 years. The gastrocnemius (GC), tibialis anterior (T), quadriceps femoris (Q), biceps femoris (B), and gluteus (G) were the five lower limb muscles whose muscle activity was evaluated. Surface electromyography (sEMG) was used to measure muscle activation while jogging and running on the AlterG Anti-Gravity Treadmill. The study method involved capturing the examined muscular activity at four different speeds: 6, 10, 14, and 18 km/h. At each of these speeds, four two-minute measurements were taken with varying body weight relief: 100%, 75%, 50%, and 25% of body weight. Repeated measures multivariate analysis of variance (RM-MANOVA) [F = 3.4663 p = 0.0001] showed that as running speed increases, the muscular activity of each muscle, expressed as a percentage of maximum muscle tension (%MVIC), decreases significantly. Results indicate that running pace affects the dynamics of the reduction in muscle activity in every examined muscle. As one runs faster, the decline in dynamics becomes more intense. At the slowest jogging pace (6 km/h), the variations were almost negligible (±4 percentage points between 25% and 100% body weight relief) as unloading increased. However, the discrepancies reached up to 14 percentage points at the fastest running speed (18 km/h). In every muscle studied, distinctive patterns and significant dynamics at high speeds were observed. The study’s findings suggest that using an anti-gravity treadmill for training can be beneficial, yet it is important to consider the significant relationships between speed and relief, as these variables could impact maintaining a proper movement pattern and running style. This knowledge may be useful when choosing the right training regimens and loads for runners recovering from injuries.
Effectiveness of Three Turbulence Modeling Approaches in a Crosswind–Sedan–Dune Computational Fluid Dynamics Framework
Authors: Yang Weichao , Wang Jian , Dong Yue
Published: August 2024

The aerodynamic loads of a sedan experience significant fluctuations when passing by a sand dune at the roadside under crosswinds, which can easily cause yawing and overturning. Computational fluid dynamics (CFD) methods, based on different turbulence modeling approaches, yield different aerodynamic results for sedans. This study aims to investigate the effects of three prevailing turbulence modeling approaches (renormalization group (RNG) k-ε, large eddy simulation (LES), and improved delayed detached eddy simulation (IDDES)) on the aerodynamic characteristics of a sedan passing by a sand dune under crosswinds. The CFD dynamic mesh models are constructed using the “mosaic” mesh technique to account for the dune–air–sedan interaction. The reliability of the CFD prediction method is verified by comparing it with field test results. The predictive capabilities of the three turbulence modeling approaches are compared in terms of aerodynamic loads and flow field characteristics. The simulation of sand particle movement is conducted through the discrete phase model, aiming to assess the impact of wind–sand flow on the aerodynamic properties of sedans. Corresponding results show that the aerodynamic loads predicted by the LES model closely match (within 4.4–7.5%) the corresponding data obtained from field tests. While the IDDES and LES models demonstrate similar abilities in characterizing the wind field details, and their results exhibit maximum differences of 8.3–15.7%. Meanwhile, the maximum difference between the results obtained by the RNG k-ε and LES models ranges from 14.8% to 18.4%, attributed to its inability to capture subtle changes in the vortex structure within the flow field. This work will provide a numerical modeling reference for studies on the wind–sand flow and the aerodynamic characteristics of sedans running through the desert, and it has implications for the safe driving of sedans under extreme conditions.
Optimal Agent-Based Pickup and Delivery with Time Windows and Electric Vehicles
Authors: Murarețu Ionuț , Bădică Costin
Published: August 2024

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: “Closest Allocation” and “Negotiation” consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the “Closest Allocation” in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability.
A Machine Learning Predictive Model for Ship Fuel Consumption
Authors: Melo Rhuan Fracalossi , Figueiredo Nelio Moura de , Tobias Maisa Sales Gama , Afonso Paulo
Published: August 2024

Water navigation is crucial for the movement of people and goods in many locations, including the Amazon region. It is essential for the flow of inputs and outputs, and for certain Amazon cities, boat access is the only option. Fuel consumption accounts for over 25% of a vessel’s total operational costs. Shipping companies are therefore seeking procedures and technologies to reduce energy consumption. This research aimed to develop a fuel consumption prediction model for vessels operating in the Amazon region. Machine learning techniques such as Decision Tree, Random Forest, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and CatBoost can be used for this purpose. The input variables were based on the main design characteristics of the vessels, such as length and draft. Through metrics like mean, median, and coefficient of determination (R2), six different algorithms were assessed. CatBoost was identified as the model with the best performance and suitability for the data. Indeed, it achieved an R2 value higher than 91% in predicting and optimizing fuel consumption for vessels operating in the Amazon and similar regions.
Recovery of Saponins, Phenolic Compounds and Antioxidant Capacity from Curculigo orchioides Gaertn Rhizomes by Different Extraction Methods
Authors: Bui Thuc Duyen , Do Linh Thuy Khanh , Dang Thi Ngoc Dung , Pham Van Doanh , Hoang Van Chuyen
Published: August 2024

This study aimed to investigate the effects of the solvent type, extraction time, extraction temperature and solvent-to-material ratio on the recovery of the total saponin content (TSC), the total phenolic content (TPC) and the antioxidant activity from the rhizomes of Curculigo orchioides Gaertn. The extraction efficiency of the bioactive compounds by ultrasound-assisted extraction (UAE) was also evaluated. Extraction with 80% ethanol achieved the highest extraction yields, and the optimal conditions for the extraction of TSC were 60 min, 57 °C and an 80 mL/g solvent-to-material ratio. The optimal conditions for the recovery of TPC were 178 min, 45 °C and a solvent-to-material ratio of 68 mL/g. The highest antioxidant activity of the extracts from Curculigo orchioides Gaertn rhizomes was obtained with the optimal conditions of 180 min, 40 °C and an 80 mL/g solvent-to-material ratio. The actual extraction yields obtained from the optimal conditions were 11.33 mg aescin equivalents (AE)/g dry weight (DW) for TSC, 23.58 mg gallic acid equivalents (GAE)/g DW for TPC and DPPH antioxidant activity of 133.45 µM Trolox equivalents (TE)/g DW. UAE using the same type and amount of solvent for only 10 min could result in comparable extraction yields of TSC, TPC and DPPH antioxidant activity to the 180 min conventional extraction process. These promising results suggest the potential for the development of effective extraction processes to recover bioactive compounds from Curculigo orchioides Gaertn rhizomes in practical production.
Improving Performance of Key–Value Stores for High-Performance Storage Devices
Authors: Kim Sunggon , Kim Hwajung
Published: August 2024

Key–value stores (KV stores) are becoming popular in both academia and industry due to their high performance and simplicity in data management. Unlike traditional database systems such as relational databases, KV stores manage data in key–value pairs and do not support relationships between the data. This simplicity enables KV stores to offer higher performance. To further improve the performance of KV stores, high-performance storage devices such as solid-state drives (SSDs) and non-volatile memory express (NVMe) SSDs have been widely adopted. These devices are intended to expedite data processing and storage. However, our studies indicate that, due to a lack of multi-thread-oriented programming, the performance of KV stores is far below the raw performance of high-performance storage devices. In this paper, we analyze the performance of existing KV stores utilizing high-performance storage devices. Our analysis reveals that the actual performance of KV stores is below the potential performance that these storage devices could offer. According to the profiling results, we argue that this performance gap is due to the coarse-grained locking mechanisms of existing KV stores. To alleviate this issue, we propose a multi-threaded compaction operation that leverages idle threads to participate in I/O operations. Our experimental results demonstrate that our scheme can improve the performance of KV stores by up to 16% by increasing the number of threads involved in I/O operations.
Conflict or Coordination? A Coupling Study of China’s Population–Urbanization–Ecological Environment
Authors: Yang Changxin , Su Qingmu , Liang Jiajun
Published: August 2024

Whether the new type of urbanization implemented in China in the past decade has been effective in regulating urbanization and balancing human development and environmental protection remains to be verified. Therefore, this study develops a framework for assessing population-urbanization–ecological environment interactions by combining the coupling coordination degree model and the decoupling index. Firstly, the proposed framework establishes an indicator system of population, economy, society, space, environmental pressure, ecological governance, ecological status, and ecological services based on two sets of national census data; secondly, this study combines the coupling coordination degree model and decoupling index to comprehensively understand the coupling coordination relationship and the decoupling relationship of the population–urbanization–ecological environment across time and space. Overall, this study contributes to a deepened understanding of coupled population–urbanization–ecological environment interactions and provides a scientific basis for effective guidance on urban–rural management and the balance between human development and environmental protection.
Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber
Authors: Mahamat Assia Aboubakar , Boukar Moussa Mahamat , Leklou Nordine , Celino Amandine , Obianyo Ifeyinwa Ijeoma , Bih Numfor Linda , Stanislas Tido Tiwa , Savastanos Holmer
Published: August 2024

This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties.
Enhanced Upward Translations for Systems with Clusters
Authors: Rejwer-Kosińska Ewa , Linkov Aleksandr , Rybarska-Rusinek Liliana
Published: August 2024

The paper is concerned with using boundary element methods (BEM) for the accurate evaluation of fields in structures with clusters. For large-scale problems, the BEM system is solved iteratively by speeding up matrix-to-vector multiplications by applying a kernel-independent fast multipole method. Multiplication starts with source-to-multipole (S2M) translations, whose accuracy predefines the overall accuracy. We aim to increase the accuracy of these translations. The intensities of sources are assembled into clusters by an algorithm suggested. Each of them is characterized by its representative source, whose intensity equals the sum of the intensities of cluster sources. Thus, with growing distance, its field tends toward the field of the cluster. The accuracy of S2M translations is increased by subtracting from and adding to the far field of the cluster the far field of its representative source, and by using the proposed modified kernel to evaluate the difference of the fields, which decreases faster than the field of the cluster itself. Numerical results for typical kernels show a notable increase in the accuracy provided by the modified S2M translations. Keeping in them merely the added field is acceptable for many practical applications. This simplifies the modified S2M translations by avoiding calculation and storing matrices specific to each of the clusters. The improved translations may be also used for multipole-to-multipole translations, performed on next, after leaves, levels in upward running a hierarchical tree.
Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
Authors: Rosilius Maximilian , Hügel Lukas , Wirsing Benedikt , Geuen Manuel , von Eitzen Ingo , Bräutigam Volker , Ludwig Bernd
Published: August 2024

Emerging assistance systems are designed to enable operators to perform tasks better, faster, and with a lower workload. However, in line with the productivity paradox, the full potential of automation and digitalisation is not being realised. One reason for this is insufficient training. In this study, the statistically significant differences among three different training scenarios on performance, acceptance, workload, and technostress during the execution of immersive measurement tasks are demonstrated. A between-subjects design was applied and analysed using ANOVAs involving 52 participants (with a statistical overall power of 0.92). The ANOVAs were related to three levels of the independent variable: quality training, manipulated as minimal, personal, and optimised training. The results show that the quality of training significantly influences immersive assistance systems. Hence, this article deduces tangible design guidelines for training, with consideration of the system-level hardware, operational system, and immersive application. Surprisingly, an appropriate mix of training approaches, rather than detailed, personalised training, appears to be more effective than e-learning or ‘getting started’ tools for immersive systems. In contrast to most studies in the related work, our article is not about learning with AR applications but about training scenarios for the use of immersive systems.
An Expert Usability Evaluation of a Specialized Platform for Designing and Producing Online Educational Talking Books
Authors: Ahmed Mohamed Elsayed , Hasegawa Shinobu
Published: August 2024

Educational institutions are increasingly using audio-based learning resources and technologies nowadays, especially for students who are auditory learners and visually impaired. The ability to design and create online educational talking books with a pedagogical foundation has become essential for students studying instructional and information technology in the age of digital learning. With the need to enhance such skills to target students in higher educational institutions, instructional and information technology students have no specialized platform for designing and producing an online educational talking book without web programming challenges. This study suggests a new specialized, web-based platform that can assist students in developing online educational talking books. In this study, fourteen instructional technology experts evaluated the proposed platform’s usability. An online questionnaire was utilized to gather data, applying qualitative and quantitative methodologies. The results show that the proposed platform is appropriate for creating and developing an online educational talking book for the intended audience of students. Furthermore, the suggested platform’s current version had a workable design that was appropriate for helping students acquire the necessary abilities.
Long-Term Validation of Aeolus Level-2B Winds in the Brazilian Amazon
Authors: Yoshida Alexandre Calzavara , Venturini Patricia Cristina , Lopes Fábio Juliano da Silva , Landulfo Eduardo
Published: August 2024

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite’s measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson’s coefficient (r) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements.
GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
Authors: Xu Zongqiu , Liu Shuhao , Xu Yantian , Tang Longjiang , Yang Nannan , Zhang Gen
Published: August 2024

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential.
On the Approximation of the Hardy Z-Function via High-Order Sections
Authors: Jerby Yochay
Published: August 2024

The Z-function is the real function given by Z(t)=eiθ(t)ζ12+it, where ζ(s) is the Riemann zeta function, and θ(t) is the Riemann–Siegel theta function. The function, central to the study of the Riemann hypothesis (RH), has traditionally posed significant computational challenges. This research addresses these challenges by exploring new methods for approximating Z(t) and its zeros. The sections of Z(t) are given by ZN(t):=∑k=1Ncos(θ(t)−ln(k)t)k for any N∈N. Classically, these sections approximate the Z-function via the Hardy–Littlewood approximate functional equation (AFE) Z(t)≈2ZN˜(t)(t) for N˜(t)=t2π. While historically important, the Hardy–Littlewood AFE does not sufficiently discern the RH and requires further evaluation of the Riemann–Siegel formula. An alternative, less common, is Z(t)≈ZN(t)(t) for N(t)=t2, which is Spira’s approximation using higher-order sections. Spira conjectured, based on experimental observations, that this approximation satisfies the RH in the sense that all of its zeros are real. We present a proof of Spira’s conjecture using a new approximate equation with exponentially decaying error, recently developed by us via new techniques of acceleration of series. This establishes that higher-order approximations do not need further Riemann–Siegel type corrections, as in the classical case, enabling new theoretical methods for studying the zeros of zeta beyond numerics.
mKdV Equation on Time Scales: Darboux Transformation and N-Soliton Solutions
Authors: Jin Baojian , Fang Yong , Sang Xue
Published: August 2024

In this paper, the spectral problem of the mKdV equation satisfying the compatibility condition on time scales is directly constructed. By using the zero-curvature equation on time scales, the mKdV equation on time scales is obtained. When x∈R and t∈R, the equation degenerates to the classical mKdV equation. Then, the single-soliton, two-soliton, and N-soliton solutions of the mKdV equation under the zero boundary condition on time scales are presented via employing the Darboux transformation (DT). Particularly, we obtain the corresponding single-soliton solutions expressed using the Cayley exponential function on four different time scales (R, Z, q-discrete, C).
Estimation of the Reliability Function of the Generalized Rayleigh Distribution under Progressive First-Failure Censoring Model
Authors: Gong Qin , Chen Rui , Ren Haiping , Zhang Fan
Published: August 2024

This study investigates the statistical inference of the parameters, reliability function, and hazard function of the generalized Rayleigh distribution under progressive first-failure censoring samples, considering factors such as long product lifetime and challenging experimental conditions. Firstly, the progressive first-failure model is introduced, and the maximum likelihood estimation for the parameters, reliability function, and hazard function under this model are discussed. For interval estimation, confidence intervals have been constructed for the parameters, reliability function, and hazard function using the bootstrap method. Next, in Bayesian estimation, considering informative priors and non-information priors, the Bayesian estimation of the parameters, reliability function, and hazard function under symmetric and asymmetric loss functions is obtained using the MCMC method. Finally, Monte Carlo simulation is conducted to compare mean square errors, evaluating the superiority of the maximum likelihood estimation and Bayesian estimation under different loss functions. The performance of the estimation methods used in the study is illustrated through illustrative examples. The results indicate that Bayesian estimation outperforms maximum likelihood estimation.
Data Privacy and Security in Autonomous Connected Vehicles in Smart City Environment
Authors: Alam Tanweer
Published: August 2024

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network’s recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed.
An Efficient Algorithm for Sorting and Duplicate Elimination by Using Logarithmic Prime Numbers
Authors: Yeh Wei-Chang , Forghani-elahabad Majid
Published: August 2024

Data structures such as sets, lists, and arrays are fundamental in mathematics and computer science, playing a crucial role in numerous real-life applications. These structures represent a variety of entities, including solutions, conditions, and objectives. In scenarios involving large datasets, eliminating duplicate elements is essential to reduce complexity and enhance performance. This paper introduces a novel algorithm that uses logarithmic prime numbers to efficiently sort data structures and remove duplicates. The algorithm is mathematically rigorous, ensuring correctness and providing a thorough analysis of its time complexity. To demonstrate its practicality and effectiveness, we compare our method with existing algorithms, highlighting its superior speed and accuracy. An extensive experimental analysis across one thousand random test problems shows that our approach significantly outperforms two alternative techniques from the literature. By discussing the potential applications of the proposed algorithm in various domains, including computer science, engineering, and data management, we illustrate its adaptability through two practical examples in which our algorithm solves the problem more than 3×104 and 7×104 times faster than the existing algorithms in the literature. The results of these examples demonstrate that the superiority of our algorithm becomes increasingly pronounced with larger problem sizes.
DaSAM: Disease and Spatial Attention Module-Based Explainable Model for Brain Tumor Detection
Authors: Tehsin Sara , Nasir Inzamam Mashood , Damaševičius Robertas , Maskeliūnas Rytis
Published: August 2024

Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. The improved visibility of malignancies in MRI makes therapy easier. The diagnosis and treatment of brain cancers depend on their identification and treatment. Numerous deep learning models are proposed over the last decade including Alexnet, VGG, Inception, ResNet, DenseNet, etc. All these models are trained on a huge dataset, ImageNet. These general models have many parameters, which become irrelevant when implementing these models for a specific problem. This study uses a custom deep-learning model for the classification of brain MRIs. The proposed Disease and Spatial Attention Model (DaSAM) has two modules; (a) the Disease Attention Module (DAM), to distinguish between disease and non-disease regions of an image, and (b) the Spatial Attention Module (SAM), to extract important features. The experiments of the proposed model are conducted on two multi-class datasets that are publicly available, the Figshare and Kaggle datasets, where it achieves precision values of 99% and 96%, respectively. The proposed model is also tested using cross-dataset validation, where it achieved 85% accuracy when trained on the Figshare dataset and validated on the Kaggle dataset. The incorporation of DAM and SAM modules enabled the functionality of feature mapping, which proved to be useful for the highlighting of important features during the decision-making process of the model.
Aroma Potential of German Riesling Winegrapes during Late-Stage Ripening
Authors: Nguyen Thi H. , Zimmermann Daniel , Durner Dominik
Published: August 2024

The “aromatic maturity” of winegrapes is not fully understood, particularly during the later stages of ripening. The contribution of grapes to wine aroma has historically been challenging to determine, given most aroma compounds originate from nonvolatile precursors. In this study, an analytical approach previously developed for red winegrapes was adapted to assess the “aroma potential” of Riesling from two vineyards in Essenheim and Durbach, Germany, during the 2022 vintage, by extracting and hydrolyzing aroma precursors in an anoxic model wine matrix. Following sensory and chemical analyses of the hydrolysates using flash profiling and gas chromatography, a multiple factor analysis revealed vineyard- and ripening-dependent changes to aroma, even after total soluble solids had plateaued. As samples matured, green apple and fresh/vegetal aromas were prominent among the Durbach hydrolysates, likely due to persistent concentrations of hexanol. Hydrolysates from both vineyards nonetheless developed more pronounced citrus fruit, tropical fruit, and floral aromas, reflecting increased concentrations of various norisoprenoids and terpenoids. Findings suggest delaying harvest past technological maturity could confer greater aromatic intensity and complexity. The analytical approach used here appears promising for future studies on other grape varieties and other factors that could influence aroma, such as viticultural practices and environmental conditions.
CycP: A Novel Self-Assembled Vesicle-Forming Cyclic Antimicrobial Peptide to Control Drug-Resistant S. aureus
Authors: Baindara Piyush , Roy Dinata , Mandal Santi M.
Published: August 2024

Antimicrobial peptides (AMPs) are considered a promising alternative to conventional antibiotics to fight against the rapid evolution of antibiotic resistance. Other than their potent antimicrobial properties, AMP-based vesicles can be used as efficient drug-delivery vehicles. In the present study, we synthesized and characterized a new cyclic AMP, consisting of all-hydrophobic cores with antimicrobial activity against S. aureus. Interestingly, CycP undergoes supramolecular self-assembly, and self-assembled CycP (sCycP) vesicles are characterized under an electron microscope; however, these vesicles do not display antimicrobial activity. Next, sCycP vesicles are used in combination with SXT (sulfamethoxazole–trimethoprim) vesicles to check the drug loading and delivery capacity of sCycP vesicles to bacterial cell membranes. Interestingly, sCycP vesicles showed synergistic action with SXT vesicles and resulted in a significant reduction in MIC against S. aureus. Further, electron microscopy confirmed the membrane-specific killing mechanism of SXT-loaded sCycP vesicles. Additionally, CycP showed high binding affinities with the β-lactamase of S. aureus, which was one of its possible antimicrobial mechanisms of action. Overall, the results suggested that CycP is a novel self-assembled dual-action cyclic AMP with non-cytotoxic properties that can be used alone as an AMP or a self-assembled drug delivery vehicle for antibiotics to combat S. aureus infections.
Driving Deployment of Bioengineered Products—An Arduous, Sometimes Tedious, Challenging, Rewarding, Most Exciting Journey That Has to Be Made!
Authors: Wallace Gordon George
Published: August 2024

More than three decades ago, we embarked on a number of bioengineering explorations using the most advanced materials and fabrication methods. In every area we ventured into, it was our intention to ensure fundamental discoveries were deployed into the clinic to benefit patients. When we embarked on this journey, we did so without a road map, not even a compass, and so the path was arduous, sometimes tedious. Now, we can see the doorway to deployment on the near horizon. We now appreciate that overcoming the challenges has made this a rewarding and exciting journey. However, maybe we could have been here a lot sooner, and so maybe the lessons we have learned could benefit others and accelerate progress in clinical translation. Through a number of case studies, including neural regeneration, cartilage regeneration, skin regeneration, the 3D printing of capsules for islet cell transplantation, and the bioengineered cornea, here, we retrace our steps. We will summarise the journey to date, point out the obstacles encountered, and celebrate the translational impact. Then, we will provide a framework for project design with the clinical deployment of bioengineered products as the goal.
Evaluation of Load on Cervical Disc Prosthesis by Imposing Complex Motion: Multiplanar Motion and Combined Rotational–Translational Motion
Authors: Ansaripour Hossein , Ferguson Stephen J. , Flohr Markus
Published: August 2024

(1) Background: The kinematic characteristics of disc prosthesis undergoing complex motion are not well understood. Therefore, examining complex motion may provide an improved understanding of the post-operative behavior of spinal implants. (2) Methods: The aim of this study was to develop kinematic tests that simulate multiplanar motion and combined rotational–translational motion in a disc prosthesis. In this context, five generic zirconia-toughened alumina (BIOLOX®delta, CeramTec, Germany) ball and socket samples were tested in a 6 DOF spine simulator under displacement control with an axial compressive force of 100 N in five motion modes: (1) flexion–extension (FE = ± 7.5°), (2) lateral bending (LB = ± 6°), (3) combined FE-LB (4) combined FE and anteroposterior translation (AP = 3 mm), and (5) combined LB and lateral motion (3 mm). For combined rotational–translational motion, two scenarios were analyzed: excessive translational movement after sample rotation (scenario 1) and excessive translational movement during rotation (scenario 2). (3) Results: For combined FE-LB, the resultant forces and moments were higher compared to the unidirectional motion modes. For combined rotational–translational motion (scenario 1), subluxation occurred at FE = 7.5° with an incremental increase in AP translation = 1.49 ± 0.18 mm, and LB = 6° with an incremental increase of lateral translation = 2.22 ± 0.16 mm. At the subluxation point, the incremental increase in AP force and lateral force were 30.4 ± 3.14 N and 40.8 ± 2.56 N in FE and LB, respectively, compared to the forces at the same angles during unidirectional motion. For scenario 2, subluxation occurred at FE = 4.93° with an incremental increase in AP translation = 1.75 mm, and LB = 4.52° with an incremental increase in lateral translation = 1.99 mm. At the subluxation point, the incremental increase in AP force and lateral force were 39.17 N and 38.94 N in FE and LB, respectively, compared to the forces in the same angles during the unidirectional motion. (4) Conclusions: The new test protocols improved the understanding of in vivo-like behavior from in vitro testing. Simultaneous translation–rotation motion was shown to provoke subluxation at lower motion extents. Following further validation of the proposed complex motion testing, these new methods can be applied future development and characterization of spinal motion-preserving implants.