bluuebunny/crossref_metadata_2025 huggingface dataset:
Flexural Fatigue in a Polymer Matrix Composite Material Reinforced with Continuous Kevlar Fibers Fabricated by Additive Manufacturing
Authors: Pertuz-Comas Alberto David , Díaz Jorge G. , Meneses-Duran Oscar Javier , Niño-Álvarez Nixon Yesid , León-Becerra Juan
Published: August 2022

Fatigue bending tests, under controlled displacement, were performed on a polymer matrix composite material reinforced with continuous Kevlar fibers. The samples were fabricated using the Fused Filament Fabrication (FFF) technique in a Markforged Two® 3D printer. The static characterization delivered a flexural modulus of elasticity of 4.73 GPa and flexural strength of 110 MPa. The applied loading corresponded to 92.3, 88.5, 86.2, and 84.7% of the static flexural displacement, giving 15, 248, 460, and 711 cycles for failure. Additionally, two numerical models were created: one using orthotropic properties for static loading conditions; and a second one using isotropic in-bulk properties for fatigue modeling. The second model was able to reproduce the experimental fatigue results. Finally, morphological analysis of the fractured surface revealed fiber breakage, fiber tearing, fiber buckling, matrix cracking, and matrix porosity.
The Radiation Shielding Performance of Polyester with TeO2 and B2O3
Authors: Sayyed M. I. , Yasmin Sabina , Almousa Nouf , Elsafi Mohamed
Published: August 2022

In this research, polymers were fabricated through combining polyester, boron oxide (B2O3), and Tellurium oxide (TeO2). B2O3 has good neutron absorption and TeO2 is not only highly dense (5.670 g/cm3) but also environment-friendly, compared to PbO, as well as being a good photon absorber. The radiation protection features for five investigated samples were examined utilizing an HPGe detector and point sources Am-241, Cs-137, and Co-60. The accuracy of the experimental setup of this experiment was proven through the linear attenuation coefficient (LAC) values obtained from the theoretical (XCOM) and experimental (HPGe) values of the newly developed polymers. The attained results showed that the values of LAC decrease with increasing energy. Moreover, at low energy, a noteworthy increase was found for the LAC values with the addition of TeO2. Due to the increase in TeO2 content of the polymers, the value of the half value layer (HVL) decreases from 6.073–4.193 cm at energy 0.662 MeV, from 7.973–5.668 cm at energy 1.173 MeV, and 8.514–6.061 cm at energy 1.333 MeV. The values of the effective atomic number (Zeff) showed an improvement with the increase in TeO2 content in the polymers. For example, at energy 0.150 MeV, the Zeff values of the prepared sample followed this decreasing trend - PBT-40 > PBT-30 > PBT-20 > PBT-10 > PBT-0.
Contribution of GRACE Satellite Mission to the Determination of Orthometric/Normal Heights Corrected for Their Dynamics—A Case Study of Poland
Authors: Szelachowska Malgorzata , Godah Walyeldeen , Krynski Jan
Published: August 2022

Physical heights were traditionally determined without considering the dynamic processes of the Earth induced from temporal mass variations. The Gravity Recovery and Climate Experiment (GRACE) mission provided valuable data that allow the estimation of geoid/quasigeoid height changes and vertical deformations of the Earth’s surface induced from temporal mass loading, and thereby temporal variations of physical heights. The objective of this investigation is to discuss the determination of orthometric/normal heights considering mass transports within the Earth’s system. An approach to determine such heights was proposed. First, temporal variations of orthometric/normal heights (ΔH/ΔH*) were determined using the release 6 GRACE-based Global Geopotential Models together with load Love numbers obtained from the preliminary reference Earth model. Then, those variations were modelled and predicted using the seasonal decomposition (SD) method. The proposed approach was tested on the territory of Poland. The main results obtained reveal that ΔH/ΔH* over the area investigated are at the level of a couple of centimetres and that they can be modelled and predicted with a millimetre accuracy using the SD method. Orthometric/normal heights corrected for their dynamics can be determined by combining modelled ΔH/ΔH* with orthometric/normal heights referred to a specific reference epoch.
A Deep Learning-Based Method for the Semi-Automatic Identification of Built-Up Areas within Risk Zones Using Aerial Imagery and Multi-Source GIS Data: An Application for Landslide Risk
Authors: Francini Mauro , Salvo Carolina , Viscomi Antonio , Vitale Alessandro
Published: August 2022

Natural disasters have a significant impact on urban areas, resulting in loss of lives and urban services. Using satellite and aerial imagery, the rapid and automatic assessment of at-risk located buildings from can improve the overall disaster management system of urban areas. To do this, the definition, and the implementation of models with strong generalization, is very important. Starting from these assumptions, the authors proposed a deep learning approach based on the U-Net model to map buildings that fall into mapped landslide risk areas. The U-Net model is trained and validated using the Dubai’s Satellite Imagery Dataset. The transferability of the model results are tested in three different urban areas within Calabria Region, Southern Italy, using natural color orthoimages and multi-source GIS data. The results show that the proposed methodology can detect and predict buildings that fall into landslide risk zones, with an appreciable transferability capability. During the prevention phase of emergency planning, this tool can support decision-makers and planners with the rapid identification of buildings located within risk areas, and during the post event phase, by assessing urban system conditions after a hazard occurs.
Alert-Driven Community-Based Forest Monitoring: A Case of the Peruvian Amazon
Authors: Cappello Christina , Pratihast Arun Kumar , Pérez Ojeda del Arco Alonso , Reiche Johannes , De Sy Veronique , Herold Martin , Vivanco Vicencio Rolando Eduardo , Castillo Soto Daniel
Published: August 2022

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems.
Adaptive Cloud-to-Cloud (AC2C) Comparison Method for Photogrammetric Point Cloud Error Estimation Considering Theoretical Error Space
Authors: Huang Hong , Ye Zehao , Zhang Cheng , Yue Yong , Cui Chunyi , Hammad Amin
Published: August 2022

The emergence of a photogrammetry-based 3D reconstruction technique enables rapid 3D modeling at a low cost and uncovers many applications in documenting the geometric dimensions of the environment. Although the theoretical accuracy of photogrammetry-based reconstruction has been studied intensively in the literature, the problem remains in evaluating the accuracy of the generated point cloud in practice. Typically, checking the coordinates of ground control points (GCPs) using a total station is considered a promising approach; however, the GCPs have clear and identifiable features and consistent normal vectors or less roughness, which cannot be considered as a typical sample for an accuracy evaluation of the point cloud. Meanwhile, the cloud-to-cloud (C2C) and cloud-to-mesh (C2M) comparison methods usually consider either the closest point or the neighboring points within a fixed searching radius as the “ground truth”, which may not reflect the actual accuracy; therefore, the present paper proposes an adaptive cloud-to-cloud (AC2C) comparison method to search the potential “ground truth” in the theoretical error space. The theoretical error space of each point is estimated according to the position of the corresponding visible cameras and their distances to a target point. A case study is carried out to investigate the feasibility of the proposed AC2C comparison method. The results presented basically the same error distribution range from 0 to 20 mm with the C2C and C2M methods, but with a higher mean value and a much smaller standard deviation. Compared to the existing methods, the proposed method provides new thinking in evaluating the accuracy of SfM-MVS by including the theoretical error constraints.
How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?
Authors: Bernardes André , Couceiro Ricardo , Medeiros Júlio , Henriques Jorge , Teixeira César , Simões Marco , Durães João , Barbosa Raul , Madeira Henrique , Carvalho Paulo
Published: August 2022

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements’ reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.
Real-Time Loosely Coupled 3DMA GNSS/Doppler Measurements Integration Using a Graph Optimization and Its Performance Assessments in Urban Canyons of New York
Authors: Ng Hoi-Fung , Hsu Li-Ta , Lee Max Jwo Lem , Feng Junchi , Naeimi Tahereh , Beheshti Mahya , Rizzo John-Ross
Published: August 2022

Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3DMA GNSS-positioning system based on state-of-the-art 3DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system’s recorded data in New York City. The real-time forward-processed FGO can provide a root-mean-square error (RMSE) of about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV.
Motion-Based Object Location on a Smart Image Sensor Using On-Pixel Memory
Authors: Valenzuela Wladimir , Saavedra Antonio , Zarkesh-Ha Payman , Figueroa Miguel
Published: August 2022

Object location is a crucial computer vision method often used as a previous stage to object classification. Object-location algorithms require high computational and memory resources, which poses a difficult challenge for portable and low-power devices, even when the algorithm is implemented using dedicated digital hardware. Moving part of the computation to the imager may reduce the memory requirements of the digital post-processor and exploit the parallelism available in the algorithm. This paper presents the architecture of a Smart Imaging Sensor (SIS) that performs object location using pixel-level parallelism. The SIS is based on a custom smart pixel, capable of computing frame differences in the analog domain, and a digital coprocessor that performs morphological operations and connected components to determine the bounding boxes of the detected objects. The smart-pixel array implements on-pixel temporal difference computation using analog memories to detect motion between consecutive frames. Our SIS can operate in two modes: (1) as a conventional image sensor and (2) as a smart sensor which delivers a binary image that highlights the pixels in which movement is detected between consecutive frames and the object bounding boxes. In this paper, we present the design of the smart pixel and evaluate its performance using post-parasitic extraction on a 0.35 µm mixed-signal CMOS process. With a pixel-pitch of 32 µm × 32 µm, we achieved a fill factor of 28%. To evaluate the scalability of the design, we ported the layout to a 0.18 µm process, achieving a fill factor of 74%. On an array of 320×240 smart pixels, the circuit operates at a maximum frame rate of 3846 frames per second. The digital coprocessor was implemented and validated on a Xilinx Artix-7 XC7A35T field-programmable gate array that runs at 125 MHz, locates objects in a video frame in 0.614 µs, and has a power consumption of 58 mW.
Resonance Suppression of Servo System Based on State Equalizer Method
Authors: Li Jinzhao , Song Yueming , Li Xiantao
Published: August 2022

Aiming at the problem of mechanical resonance faced by the servo control system of the aero-optical stabilization platform, based on the proportional integral and disturbance observer combination (PI+DOB) control algorithm, a state equalizer speed closed loop is proposed. Compared with the traditional PI+DOB control algorithm, this new control structure can suppress the resonance peak and the anti-resonance peak at the same time. The experimental results show that compared with the PI+DOB control algorithm, after adding the state equalizer speed closed-loop to compensate for the model, the closed-loop bandwidth is increased by 42%. The anti-disturbance capability of the control system has been significantly improved, and it has good robustness under vibration conditions. To sum up, adding the state equalizer speed closed loop on the basis of PI+DOB has an obvious effect on the suppression of mechanical resonance and the performance improvement of the control system.
Exploration of Emotion Dynamics Sensing Using Trapezius EMG and Fingertip Temperature
Authors: Sato Wataru , Kochiyama Takanori
Published: August 2022

Exploration of the physiological signals associated with subjective emotional dynamics has practical significance. Previous studies have reported that the dynamics of subjective emotional valence and arousal can be assessed using facial electromyography (EMG) and electrodermal activity (EDA), respectively. However, it remains unknown whether other methods can assess emotion dynamics. To investigate this, EMG of the trapezius muscle and fingertip temperature were tested. These measures, as well as facial EMG of the corrugator supercilii and zygomatic major muscles, EDA (skin conductance level) of the palm, and continuous ratings of subjective emotional valence and arousal, were recorded while participants (n = 30) viewed emotional film clips. Intra-individual subjective–physiological associations were assessed using correlation analysis and linear and polynomial regression models. Valence ratings were linearly associated with corrugator and zygomatic EMG; however, trapezius EMG was not related, linearly or curvilinearly. Arousal ratings were linearly associated with EDA and fingertip temperature but were not linearly or curvilinearly related with trapezius EMG. These data suggest that fingertip temperature can be used to assess the dynamics of subjective emotional arousal.
Development of HPLC Method for Simultaneous Determination of Ibuprofen and Chlorpheniramine Maleate
Authors: Aldewachi Hasan , Omar Thamer A.
Published: August 2022

One of the most prevalent over-the-counter cold and cough medications is the chlorpheniramine maleate (CPM)–ibuprofen (IBF) combination. A reversed-phase high-performance liquid chromatography (RP-HPLC) method was effectively optimized and developed for the simultaneous detection of chlorpheniramine maleate and ibuprofen in a pharmaceutical formulation. The mobile phase for the RP-HPLC method was an isocratic combination of acetonitrile and 0.01 M acetate buffer at pH 3.8 (55:45; v/v) on an Eclipse Plus C18 reversed phase column. An ultraviolet (UV) detector with a wavelength of 225 nm was used to detect the analytes at a flow rate of 1.0 mL/min. CPM and IBF were satisfactorily eluted, with mean retention times of 2.09 and 6.27 min, respectively. The approach was shown to be linear (R2 > 0.9998 for CPM and 0.9992 for IBF), precise (% RSD 3.02% for CPM and 3.48% for IBF), accurate (% recoveries 97.7–98.9% for CPM and 101–104.5% for IBF), specific, easy to use, sensitive, quick, and robust. Limits of detection (LODs) were found to be 10 and 27 μg/mL for CPM and IBF, respectively. Without interference from excipients, the validated method could be utilized in regular quality control analysis of various dosage combinations of hard gelatin capsules containing CPM and IBF.
Short-Term Interval Prediction of Wind Power Based on KELM and a Universal Tabu Search Algorithm
Authors: Zhou Qiang , Ma Yanhong , Lv Qingquan , Zhang Ruixiao , Wang Wei , Yang Shiyou
Published: August 2022

Installed wind power has significantly grown in recent years to synchronize with the ever-increasing demand for environment-friendly and renewable energy. However, wind energy has significant uncertainty or random futures, and will give rise to destructive effects on the safety operations of the power system. In this respect, an accurate and reliable wind power prediction is of great significance for improving the power system stability and optimizing the dispatch plan. Compared with traditionally deterministic point forecast techniques, probabilistic forecasting approaches can provide more stochastic information to quantify the random characteristics of wind power and to estimate its impacts on the power system. Moreover, the interval of the output power is a key stochastic information on wind power. In general, an interval prediction needs to compromise the calibration and the average width of the predicted interval. To find the best combination of these two metrics, a methodology based on a kernel extreme learning machine (KELM) and an improved universal tabu search algorithm is proposed. In the proposed methodology, to eliminate the inherent randomness on the weights between the input and hidden lays in the commonly used extreme learning machine, a radial-basis-function-based kernel extreme learning machine is proposed, and an improved tabu search method is introduced to optically compromise the calibration and the average width of the predicted interval to overcome the deficiency of existing algorithms, such as the insufficient global search ability of a particle swarm optimization. A prototype wind farm is utilized as a case study to verify the efficiency and advantage of the proposed methodology.
Experimental Investigation of Recycled Fine Aggregate from Demolition Waste in Concrete
Authors: Tabsh Sami W. , Alhoubi Yazan
Published: August 2022

In this study, locally produced recycled fine aggregate from concrete demolition waste was investigated for potential replacement of sand in new concrete mixes. Tests for the waste material included visual examination, chemical composition, grain size distribution, specific gravity, and fineness modulus. Tests on the incorporated recycled fine aggregate in new concrete mixes involved tests of the hardened plain concrete product. In total, eight concrete mixes were considered, of which four had low cement content with 30 MPa target strength, and the other four had high cement content with 55 MPa target strength. For each cement content, the four concrete mixes incorporated fine aggregate replacement ratios of 0% (control), 25%, 50%, and 100%. The hardened concrete tests involved cubes, cylinders, and prisms. The tests addressed compressive strength, tensile strength, and modulus of rupture in accordance with the relevant ASTM standards. In all cases, the average of two tested samples at the age of 28 days was considered. Results of the study showed that the recycled fine aggregate has some cementitious properties, which is capable of hardening when mixed with water and left to dry, even without adding cement from exterior sources. All tested concrete specimens made with recycled fine aggregate exhibited compressive and tensile strengths at least equal to 75% that of the control specimens that contained natural fine aggregate. However, for concrete mixes utilizing low cement content that can yield a compressive strength around 30 MPa with natural aggregate, replacement of 25% or 100% of the natural fine aggregate by weight with locally produced recycled fine aggregate from crushed old concrete can match and often exceeds the compressive and tensile strength of concrete made with virgin aggregate.
Colorimetric Detection of 1-Naphthol and Glyphosate Using Modified Gold Nanoparticles
Authors: Hong Gui-Bing , Hsu Jia-Pei , Chuang Kai-Jen , Ma Chih-Ming
Published: August 2022

Given the high toxicity and carcinogenic properties of pesticides, reducing pesticide residues is crucial for mitigating water pollution and promoting sustainable development. In the present study, a novel colorimetric method using gold nanoparticles (AuNPs) was designed for the detection of target analytes. The Turkevich-Frens method was used to synthesize AuNPs, which were then modified with sodium nitrite and L-cysteine for the detection of 1-naphthol and glyphosate, respectively. Different assay conditions strongly influenced the detection performance of the modified AuNPs, so the assay conditions were optimized for further investigation. In the presence of the target analytes (1-naphthol and glyphosate) under the optimum assay conditions, the absorption peak at 520 nm shifted and a corresponding color change was observed. The limits of detection of 1-naphthol and glyphosate were determined to be 0.15 and 0.27 ppm, respectively. In addition, the modified AuNPs had high selectivity for the target analytes and did not exhibit interference in the presence of other substances. This novel colorimetric method was then applied to detect the target analytes in mineral water and tap water with acceptable results.
Assessment of the Usability of Some Bio-Based Insulation Materials in Double-Skin Steel Envelopes
Authors: Hoxha Dashnor , Ismail Brahim , Rotaru Ancuța , Izabel David , Renaux Thibaut
Published: August 2022

In this paper, a double-skin steel building-demonstrator, set up using panels of five bio-based insulators and a classical mineral insulating material, is studied. The panels used in the demonstrator are made from industrially manufactured and commercialized bio-materials. To assess the suitability of these panels for use in cold formed steel envelope buildings, their advantages and/or the drawbacks (if any) of the synchronized records of temperatures, relative humidity and thermal flux of each panel are obtained using a system of continuous measurements. Data from 6 months of records in the roof of the demonstrator are used to assess the infield properties of the panels and the seasonal evolution of these properties in relation to the presence of the vapor barrier. The thermal resistance of each panel is determined from these data using two methods: the ISO 9869-1:2014 based on the Heat Flow Meter (HFM) method and an inverse problem identification method. All bio-sourced panels manifest higher thermal resistance than the classical insulation system, whatever conditions of use with or without barrier vapor. The seasonal variations of thermal properties are attenuated when a vapor barrier is used. No risk for water condensation inside the bio-insulations is revealed so far.
Investigation of the Effect of Crumb Rubber on the Static and Dynamic Response of Reinforced Concrete Panels
Authors: El-Sisi Alaa A. , Elkilani Ahmed M. , Salim Hani A.
Published: August 2022

Increasing the mass of a wall system as well as the ability to absorb energy can improve the blast resistance. The role of ductile materials attached externally to the wall tension side has been studied extensively to improve ductility and resistance. In the present study, the use of hyperelastic materials, added internally to wall systems, was analyzed to determine the static resistance of those systems. In this paper, adding shredded rubber to the concrete mix as a replacement for coarse aggregates traditionally used in designing concrete mixes was investigated. The use of shredded rubber to replace coarse aggregates is hypothesized to enhance the concrete wall panels’ blast-resistant by increasing the ductility. In the evaluation of rubber contents, the normal concrete design without rubber was compared to concrete mixes with two rubber contents. Static resistance functions were developed by evaluating the performance of concrete cylinders and concrete wall full-scale specimens with coarse aggregate partially replaced by rubber under simulated uniform loading by a loading tree. According to the results of the test, there was a reduction in compressive strength of specimens due to rubber, which caused the specimens to crack more easily during testing. Increased rubber content decreased the walls’ maximum load and overall resistance. Furthermore, the mode of failure of rubberized concrete specimens was significantly different from those without any rubber.
A Review of Robotic Applications in Hospitality and Tourism Research
Authors: Ye Huiyue , Sun Sunny , Law Rob
Published: August 2022

Recently, robots have been widely adopted in the hospitality and tourism industry. Efficient robots can help hoteliers and tourism suppliers with their repetitive or manual labor. Due to the coronavirus disease (COVID-19) pandemic, there is an increasing number of publications on robotic applications in hospitality and tourism. However, a comprehensive literature review of this realm remains lacking. Therefore, to provide a holistic view of the existing literature on robotic applications in hospitality and tourism, this study reviewed 86 extant robotic application-related articles by conducting descriptive analysis and content analysis. The findings of this study showed that most of the existing relevant studies were conducted from the perspective of consumers in the hospitality context. Potential future research directions for academics are identified herein. Practical implications on robotic adoption are also provided for industry practitioners.
R-LIO: Rotating Lidar Inertial Odometry and Mapping
Authors: Chen Kai , Zhan Kai , Pang Fan , Yang Xiaocong , Zhang Da
Published: August 2022

In this paper, we propose a novel simultaneous localization and mapping algorithm, R-LIO, which combines rotating multi-line lidar and inertial measurement unit. R-LIO can achieve real-time and high-precision pose estimation and map-building. R-LIO is mainly composed of four sequential modules, namely nonlinear motion distortion compensation module, frame-to-frame point cloud matching module based on normal distribution transformation by self-adaptive grid, frame-to-submap point cloud matching module based on line and surface feature, and loop closure detection module based on submap-to-submap point cloud matching. R-LIO is tested on public datasets and private datasets, and it is compared quantitatively and qualitatively to the four well-known methods. The test results show that R-LIO has a comparable localization accuracy to well-known algorithms as LIO-SAM, FAST-LIO2, and Faster-LIO in non-rotating lidar data. The standard algorithms cannot function normally with rotating lidar data. Compared with non-rotating lidar data, R-LIO can improve localization and mapping accuracy in rotating lidar data.
Reversible Data Hiding Scheme Based on Coefficient Pair Mapping for Videos H.264/AVC without Distortion Drift
Authors: Nguyen Thai-Son
Published: August 2022

Reversible data hiding is a technique for embedding secret data into a cover media. Such technique has the ability to recover marked cover media to its original version after extracting the secret data. In this paper, a new reversible data hiding algorithm for videos H.264/AVC is proposed to improve the embedding capacity while the distortion drift is prevented. To embed the secret data into videos H.264/AVC without any intra-frame distortion drift, in our proposed scheme, the relationship of QDCT coefficients is explored. Then, a coefficient pair mapping mechanism in 2D histogram is introduced for embedding data. The experimental results demonstrated that the proposed scheme obtains reversibility. The proposed scheme prevents intra-frame distortion drift on the marked videos. In addition, the embedding capacity of the proposed scheme is superior to that of existing schemes while guaranteeing the high visual quality of marked videos.
Hermite–Hadamard-Type Inequalities Involving Harmonically Convex Function via the Atangana–Baleanu Fractional Integral Operator
Authors: Latif Muhammad Amer , Kalsoom Humaira , Abidin Muhammad Zainul
Published: August 2022

Fractional integrals and inequalities have recently become quite popular and have been the prime consideration for many studies. The results of many different types of inequalities have been studied by launching innovative analytical techniques and applications. These Hermite–Hadamard inequalities are discovered in this study using Atangana–Baleanu integral operators, which provide both practical and powerful results. In this paper, a symmetric study of integral inequalities of Hermite–Hadamard type is provided based on an identity proved for Atangana–Baleanu integral operators and using functions whose absolute value of the second derivative is harmonic convex. The proven Hermite–Hadamard-type inequalities have been observed to be valid for a choice of any harmonic convex function with the help of examples. Moreover, fractional inequalities and their solutions are applied in many symmetrical domains.
An Efficient Identification Scheme Based on Bivariate Function Hard Problem
Authors: Tea Boon Chian , Ariffin Muhammad Rezal Kamel , Ghafar Amir Hamzah Abd , Sapar Siti Hasana , Johari Mohamat Aidil Mohamat
Published: August 2022

Symmetric cryptography allows faster and more secure communication between two entities using the identical pre-established secret key. However, identifying the honest entity with the same secret key before initiating symmetric encryption is vital since the communication may be impersonated. Tea and Ariffin, in 2014, proposed a new identification (ID) scheme based on the Bivariate Function Hard Problem (BFHP) that proved secure against impersonation under passive, active and concurrent attacks via the BFHP-hardness assumption. In this paper, we upgrade the ID scheme and improve some of its settings. Next, we provide the security proof against impersonation under active and concurrent attacks in the random oracle model via the hardness assumption of the One-More BFHP. Finally, we include an additional discussion about the computational efficiency of the upgraded ID scheme based on BFHP and present its comparison with other selected ID schemes.
Two-Stage Fuzzy Interactive Multi-Objective Approach under Interval Type-2 Fuzzy Environment with Application to the Remanufacture of Old Clothes
Authors: Zhou Jian , Wu Sisi , Gao Junjie
Published: August 2022

In this study, a two-stage approach is introduced to obtain a more interactive and flexible solution to deal with the multi-objective programming under interval type-2 fuzzy environment. In the first stage, the fuzzy multi-objective chance-constrained programming with regular symmetric triangular interval type-2 fuzzy set parameters is proposed and transferred into its crisp equivalent form. In the second stage, we use the fuzzy interactive approach to address the crisp multi-objective programming obtained in the first stage by introducing the trade-off rate, which helps the decision maker react via updating their reference member values to obtain a satisfactory solution. Finally, taking a remanufacture of old clothes problem as an example, the comparison of experimental results obtained using a non-interactive method and interactive method shows that the proposed approach is conducive to obtaining satisfactory solutions effectively and efficiently, which broadens the application scope of the multi-objective programming with regular symmetric triangular interval type-2 fuzzy set parameters for sustainable manufacturing.
Low Thrust Propelled Close Approach Maneuvers
Authors: Ferreira Alessandra , Elipe Antonio , De Moraes Rodolpho , Prado Antônio , Winter Othon , Gomes Vivian
Published: August 2022

The study of orbital maneuvers in space missions is a very important problem in astrodynamics. One of the options is the use of a “gravity assisted” maneuver, which is a technique where a spacecraft passes close to a celestial body and uses the gravity of this body to change its trajectory. This approach trajectory has symmetry with respect to the periapsis line when observed from a reference frame fixed in the approached body. There is also a more complex maneuver, when the passage by the celestial body is combined with the application of propulsion, either to give extra energy to the spacecraft or to help to satisfy other constraints required by the mission, like passing by a giving point or region in space. The main object of this study is to measure the efficiency of the application of a continuous thrust combined with the “gravity assisted” maneuver. The effect of this combination is analyzed using maps that give the energy variation of the spacecraft as function of the parameters related to the maneuver. This analysis is made from the point of view of the variation of energy of the spacecraft with respect to the main body of the system. The continuous thrust is applied in the different regions of the trajectory to evaluate the effects of the locations of the thrusting arcs in the variations of energy. The effects of the variations of the direction of the thrust are also studied. This type of combined maneuver may be used to give extra energy to the spacecraft and keeping control of the trajectory during the close approach to better position the spacecraft to complete the mission.
NetDAO: Toward Trustful and Secure IoT Networks without Central Gateways
Authors: Liu Gang , Chen Chi-Yuan , Han Jing-Yuan , Zhou Yi , He Guo-Biao
Published: August 2022

The Internet of Things (IoT) suffers from a profound lack of trust between central gateways and sensors, e.g., gateways suspect sensors of flooding malicious packets, and vice versa, sensors suspect gateways of manipulating traffic data. One important reason for the mistrust is the asymmetry of a centralized network organization. A Decentralized Autonomous Organization (DAO) can establish a trustful and symmetric network with the blockchain. However, it is a vacant area for IoT networks to build trust between gateways and sensors within the DAO. In this paper, we firstly propose a trustful and secure IoT Network DAO solution (NetDAO) to mitigate the data manipulation and the malicious flooding packets. In particular, the NetDAO has a security rating algorithm to assign a reputation value for each entity in the network. Based on this, each entity can mitigate the malicious flooding packets using a proof-of-reputation packet-forwarding mechanism. In addition, the NetDAO stores traffic data using the blockchain to mitigate the data manipulation. The experimental results show that the NetDAO effectively mitigates malicious flooding packets and costs 1 s for ∼480 entities to complete the rating algorithm.
Optimal Test Plan of Step-Stress Model of Alpha Power Weibull Lifetimes under Progressively Type-II Censored Samples
Authors: Alotaibi Refah , Almetwally Ehab M. , Kumar Devendra , Rezk Hoda
Published: August 2022

In this study, the estimation of the unknown parameters of an alpha power Weibull (APW) distribution using the concept of an optimal strategy for the step-stress accelerated life testing (SSALT) is investigated from both classical and Bayesian viewpoints. We used progressive type-II censoring and accelerated life testing to reduce testing time and costs, and we used a cumulative exposure model to examine the impact of various stress levels. A log-linear relation between the scale parameter of the APW distribution and the stress model has been proposed. Maximum likelihood estimators for model parameters, as well as approximation and bootstrap confidence intervals (CIs), were calculated. Bayesian estimation of the parameter model was obtained under symmetric and asymmetric loss functions. An optimal test plan was created under typical operating conditions by minimizing the asymptotic variance (AV) of the percentile life. The simulation study is discussed to demonstrate the model’s optimality. In addition, real-world data are evaluated to demonstrate the model’s versatility.
Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Authors: Bakumenko Alexander , Elragal Ahmed
Published: August 2022

Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.
An Integrated Entropy-COPRAS Framework for Ningbo-Zhoushan Port Logistics Development from the Perspective of Dual Circulation
Authors: Zeng Shouzhen , Fang Zitong , He Yuhang , Huang Lina
Published: August 2022

To promote the construction of new development patterns of dual circulation and to accelerate the smooth flow of logistics channels, port logistics has become a new growth point for the logistics industry that accelerates the connection between domestic and foreign dual circulation. Ningbo-Zhoushan Port, as one of the main hub ports in China, is facing the key issue of how to clarify its current development status and future development direction. To scientifically measure and evaluate the status quo of the logistics development of Ningbo-Zhoushan Port, clarify the advantages and disadvantages of the development and construction of the Ningbo-Zhoushan Port logistics industry, based on the situation of new standards and new requirements for the logistics industry in the dual circulation pattern, this study firstly constructs a scientific and reasonable evaluation index system of port logistics from seven aspects, including port infrastructure, international logistics capacity, and smart logistics capacity. An integrated comprehensive evaluation method based on entropy and Complex Proportional Assessment (COPRAS) is then proposed, and a comprehensive evaluation and longitudinal comparative analysis of the logistics level of Ningbo-Zhoushan Port are carried out. The results show that the development of Ningbo-Zhoushan Port in recent years is in line with that of many other ports due to the benefit of green logistics capacity, but it is seriously limited by smart logistics capabilities, and in the future, it should choose to continue to exert efforts in international logistics capabilities, green logistics capabilities, and total logistics capabilities.
The Impact of Green Entrepreneurship on Social Change and Factors Influencing AMO Theory
Authors: Mia Mohammed Mamun , Rizwan Shahid , Zayed Nurul Mohammad , Nitsenko Vitalii , Miroshnyk Oleksandr , Kryshtal Halyna , Ostapenko Roman
Published: August 2022

This study analyses the importance of the entrepreneurial intention of university students to promote social change by green entrepreneurship in regard to the three most vibrant components of AMO (Ability, Motivation, and Opportunity) theory, developed by the partial least square structural equation model (PLS-SEM). The entrepreneurial intention among students is identified via a deductive approach and this approach is developed using a PLS-SEM. The literature exploited and the methodology used comprise a full exploratory analysis technique to collect empirical data to find the predictor variables that influence the promotion of social changes connected to the mediating variable of green entrepreneurship. The survey data were collected from a total of 302 respondents through survey questionnaires from the students. The data were examined statistically to demonstrate the hypotheses predicted from the literature review. The outcomes of the hypothesis association showed that AMO theory influences the predictor variables of skills, incentives, and entrepreneurship education, and that these skills are statistically significant and accepted towards green entrepreneurship. However, the importance of a green entrepreneurship strategy is influenced by the entrepreneurial intention that encourages the promotion of social change. Therefore, the present study helps researchers to find the structural relationship between different wings connecting AMO theory with the entrepreneurial intention that incurs and develops sustainable business performance to create jobs, instead of searching for jobs. Secondly, this study also indicates a mixed approach where participants can openly discuss their opinion and understanding. Ultimately, this study encourages the use of the covariance-based structural equation model (CB-SEM) by confirming its theory, and testing the confirmatory factor analysis in particular.
Toxic-Induced Nonthyroidal Illness Syndrome Induced by Acute Low-Dose Pesticides Exposure—Preliminary In Vivo Study
Authors: Cobilinschi Cristian , Țincu Radu , Ungureanu Raluca , Dumitru Ioana , Băetu Alexandru , Isac Sebastian , Cobilinschi Claudia Oana , Grințescu Ioana Marina , Mirea Liliana
Published: August 2022

Background and Objectives: Conditions such as trauma, burns, sepsis, or acute intoxications have considerable consequences on the endocrine status, causing “sick euthyroid syndrome”. Organophosphate exposure may induce an increase in acetylcholine levels, thus altering the thyroid’s hormonal status. The present study aims to identify the effects of acetylcholinesterase inhibition on thyroid hormones. Material and methods: A prospective experimental study was conducted on twenty Wistar rats. Blood samples were drawn to set baseline values for thyroid-stimulating hormone (TSH), triiodothyronine (T3), and thyroxine (T4). Chlorpyrifos 0.1 mg/kg was administered by oral gavage to induce acetyl-cholinesterase inhibition. After exhibiting cholinergic symptoms, blood samples were collected to assess levels of cholinesterase and thyroid hormones using ELISA. Results: Butyrylcholinesterase levels confirmed major inhibition immediately after intoxication compared to the baseline, certifying the intoxication. A significant increase in T4 levels was noted (p = 0.01) both at 2 h and 48 h after administration of organophosphate in sample rats. Similarly, T3 almost doubled its value 2 h after poisoning (4.2 ng/mL versus 2.5 ng/mL at baseline). Surprisingly, TSH displayed acute elevation with an afterward slow descending trend at 48 h (p = 0.1), reaching baseline value. Conclusions: This study demonstrated that cholinesterase inhibition caused major alterations in thyroid hormone levels, which may be characterized by a transient hypothyroidism status with an impact on survival prognosis.
Safety of the Oral Triphala Recipe from Acute and Chronic Toxicity Tests in Sprague-Dawley Rats
Authors: Arpornchayanon Warangkana , Subhawa Subhawat , Jaijoy Kanjana , Lertprasertsuk Nirush , Soonthornchareonnon Noppamas , Sireeratawong Seewaboon
Published: August 2022

Background: The Indian Ayurvedic herbal formula Triphala (TPL) is known for its pharmacological properties for immunomodulation, anti-inflammation, antioxidant, and anti-cancer. This study aimed to investigate the acute and chronic toxicities of the Triphala recipe in a rat model. Methods: To assess the acute toxicities, 5000 mg/kg of TPL was orally administered to Sprague-Dawley rats. For chronic toxicities, different dose levels of TPL at 600, 1200, and 2400 mg/kg/day were given daily for 270 days. General health and behaviors and the body and organ weights of the rats were monitored. At the end of the experiment, blood samples were evaluated for hematology and biochemistry profiles. The evaluation of the internal organs’ appurtenance and necropsy was performed to confirm the tissue histopathology. Results: The results showed that there was no sign of acute toxicity in the TPL group with a decrease in sex organ weights. No significant differences in the rats’ behaviors, physical health, body, or organ weights were found between the controls and the rats receiving the 270/day of oral Triphala at 600, 1200, and 2400 mg/kg/day. However, some alterations in blood chemistries and hematology, including glucose, BUN, red blood cells, Hb, HCT, and MCV, were observed without abnormalities in histopathology. Conclusions: It has been demonstrated that the long-term use of TPL in rat models is safe. No toxic effects were found, suggesting possible safety for long-term use in humans.