bluuebunny/crossref_metadata_2025 huggingface dataset:
Genus Amorphophallus: A Comprehensive Overview on Phytochemistry, Ethnomedicinal Uses, and Pharmacological Activities
Authors: Islam Fahadul , Labib Rafiuddin Khan , Zehravi Mehrukh , Lami Mashia Subha , Das Rajib , Singh Laliteshwar Pratap , Mandhadi Jithendar Reddy , Balan P. , Khan Jishan , Khan Sharuk L. , Nainu Firzan , Nafady Mohamed H. , Rab Safia Obaidur , Emran Talha Bin , Wilairatana Polrat
Published: November 2023

The genus Amorphophallus belongs to the family Araceae. Plants belonging to this genus are available worldwide and have been used in traditional medicines since ancient times, mainly in Ayurveda and Unani medical practices. Amorphophallus species are an abundant source of polyphenolic compounds; these are accountable for their pharmacological properties, such as their analgesic, neuroprotective, hepatoprotective, anti-inflammatory, anticonvulsant, antibacterial, antioxidant, anticancer, antiobesity, and immunomodulatory effects, as well as their ability to prevent gastrointestinal disturbance and reduce blood glucose. Moreover, Amorphophallus species contain numerous other classes of chemical compounds, such as alkaloids, steroids, fats and fixed oils, tannins, proteins, and carbohydrates, each of which contributes to the pharmacological effects for the treatment of acute rheumatism, tumors, lung swelling, asthma, vomiting, abdominal pain, and so on. Additionally, Amorphophallus species have been employed in numerous herbal formulations and pharmaceutical applications. There has been no extensive review conducted on the Amorphophallus genus as of yet, despite the fact that several experimental studies are being published regularly discussing these plants’ pharmacological properties. So, this review discusses in detail the pharmacological properties of Amorphophallus species. We also discuss phytochemical constituents in the Amorphophallus species and their ethnomedicinal uses and toxicological profiles.
Alternative Splicing under Cold Stress in Paper Mulberry
Authors: Yu Zhipeng , Huang Xia , Wen Shuhan , Cao Haijuan , Wang Nan , Shen Shihua , Ding Mingquan
Published: November 2023

The paper mulberry is a commonly found tree species with a long history of cultivation. It also serves as a crucial case study for understanding how woody plants adapt to low temperatures. Under cold treatment, we observed a substantial number of alternative splicing (AS) genes, showcasing the intricate landscape of AS events. We have detected all seven types of AS events, with the alternative 3′ splice site (A3) having the most. We observed that many genes that underwent differential AS were significantly enriched in starch and sucrose metabolism and circadian rhythm pathways. Moreover, a considerable proportion of differentially spliced genes (DSGs) also showed differential expression, with 20.38% and 25.65% under 12 h and 24 h cold treatments, respectively. This suggests a coordinated regulation between gene AS and expression, playing a pivotal role in the paper mulberry’s adaptation to cold stress. We further investigated the regulatory mechanisms of AS, identifying 41 serine/arginine-rich (SR) splicing factors, among which 11 showed differential expression under cold treatment, while 29 underwent alternative splicing. Additionally, genes undergoing AS displayed significantly higher DNA methylation levels under cold stress, while normal splicing (non-AS) genes exhibited relatively lower methylation levels. These findings suggest that methylation may play an important role in governing gene AS. Finally, our research will provide useful information on the role of AS in the cold acclimation tolerance of the paper mulberry.
Halogen-Free Waterborne Polymeric Hybrid Coatings for Improved Fire Retardancy of Textiles
Authors: Yilmaz Onur , Kucuk Mehmet , Darie-Nita Raluca Nicoleta , Cheaburu-Yilmaz Catalina Natalia
Published: November 2023

Wildfires are becoming more intense and more frequent, ravaging the habitations and ecosystems in their path. One solution to reducing the risk of damage to buildings and other structures during a fire event is the use of fire-retardant coatings that can stop or slow down the spread of flames, especially for textile materials. The present study focuses on the preparation and application of halogen-free boron/bentonite-based polymeric fire-retardant (FR) hybrid coating formulations for fabrics such as cotton (CO) and polyester (PE) fibers. For the preparation of FR composites, two types of boron derivatives, disodium octaborate and zinc borate, were used in combination with sodium bentonite. A styrene-acrylic copolymer was specifically synthesized and used as a coating binder for FR components to apply on fabrics. The properties of the synthesized copolymer and FR composites were characterized with a particle size analysis, FTIR spectroscopy, a dynamic mechanical thermal analysis (DMTA), and rheological measurements. The obtained hybrid composites based on styrene-acrylic copolymers and two different inorganic fillers were applied on cotton (CO) and polyester (PE) fabrics with a screen-printing technique, and the flame retardancy performance of the finished textile samples was investigated by means of flame spread and limit oxygen index (LOI) tests. The findings showed that the FR-composite-coated fabrics had higher LOI values and much decreased flame spread rates in comparison with uncoated ones. Among the boron derivatives, the composites prepared with disodium octaborate (FR-A) had much more pronounced LOI values and decreased flame spread behavior in comparison with the composite with zinc borate (FR-B). When compared to a commercial product, the FR-A composite, in conjunction with the specially synthesized polymer, demonstrated commendable fire retardancy performance and emerged as a promising candidate for a halogen-free waterborne fire-retardant coating for fabrics.
Micromechanical Models for FDM 3D-Printed Polymers: A Review
Authors: Bol Rowin J. M. , Šavija Branko
Published: November 2023

Due to its large number of advantages compared to traditional subtractive manufacturing techniques, additive manufacturing (AM) has gained increasing attention and popularity. Among the most common AM techniques is fused filament fabrication (FFF), usually referred to by its trademarked name: fused deposition modeling (FDM). This is the most efficient technique for manufacturing physical three-dimensional thermoplastics, such that FDM machines are nowadays the most common. Regardless of the 3D-printing methodology, AM techniques involve layer-by-layer deposition. Generally, this layer-wise process introduces anisotropy into the produced parts. The manufacturing procedure creates parts possessing heterogeneities at the micro (usually up to 1 mm) and meso (mm to cm) length scales, such as voids and pores, whose size, shape, and spatial distribution are mainly influenced by the so-called printing process parameters. Therefore, it is crucial to investigate their influence on the mechanical properties of FDM 3D-printed parts. This review starts with the identification of the printing process parameters that are considered to affect the micromechanical composition of FDM 3D-printed polymers. In what follows, their (negative) influence is attributed to characteristic mechanical properties. The remainder of this work reviews the state of the art in geometrical, numerical, and experimental analyses of FDM-printed parts. Finally, conclusions are drawn for each of the aforementioned analyses in view of microstructural modeling.
Degradation Characteristics of Cellulose Acetate in Different Aqueous Conditions
Authors: Tan Jiao , Liang Yinchun , Sun Lihui , Yang Zhanping , Xu Jingjing , Dong Dejun , Liu Huan
Published: November 2023

Cellulose acetate (CA) is widely used in cigarette filters and packaging films, but due to its acetylation, it is difficult to degrade in the natural environment, and the problem of pollution has become a serious challenge. Understanding the degradation behavior and performance of CA in different environments is the basis and prerequisite for achieving its comprehensive utilization and developing efficient degradation methods. In this study, we investigated the degradation performance of CA in different aqueous environments to evaluate the effects of pH, salinity and microorganisms on CA degradation. The CA tows and films were immersed in HCl, NaOH solution, river water, seawater or homemade seawater for 16 weeks and the degradation mechanism was investigated by the changes in weight loss rate, degree of substitution, hydrophilicity, molecular structure and surface morphology. The results showed that the degradation of CA tow and film were the fastest in NaOH solution; the weight loss rates after 16 weeks were 40.29% and 39.63%, respectively, followed by HCl solution, and the degradation performance of CA tow was better than that of film. After 16 weeks of degradation in river water, seawater and homemade seawater, all the weight loss rates were less than 3%. In summary, this study illustrated that the environmental acidity, basicity and high concentration of inorganic salts had a critical promotion effect on the non-enzymatic hydrolysis of CA, whereas the number and type of microorganisms were the key factors affecting the biodegradation of CA.
Valorization of Agricultural Waste Lignocellulosic Fibers for Poly(3-Hydroxybutyrate-Co-Valerate)-Based Composites in Short Shelf-Life Applications
Authors: Samaniego-Aguilar Kerly , Sánchez-Safont Estefanía , Rodríguez Andreina , Marín Anna , Candal María V. , Cabedo Luis , Gamez-Perez Jose
Published: November 2023

Biocircularity could play a key role in the circular economy, particularly in applications where organic recycling (composting) has the potential to become a preferred waste management option, such as food packaging. The development of fully biobased and biodegradable composites could help reduce plastic waste and valorize agro-based residues. In this study, extruded films made of composites of polyhydroxybutyrate-co-valerate (PHBV) and lignocellulosic fibers, namely almond shell (AS) and Oryzite® (OR), a polymer hybrid composite precursor, have been investigated. Scanning electron microscopy (SEM) analysis revealed a weak fiber–matrix interfacial interaction, although OR composites present a better distribution of the fiber and a virtually lower presence of “pull-out”. Thermogravimetric analysis showed that the presence of fibers reduced the onset and maximum degradation temperatures of PHBV, with a greater reduction observed with higher fiber content. The addition of fibers also affected the melting behavior and crystallinity of PHBV, particularly with OR addition, showing a decrease in crystallinity, melting, and crystallization temperatures as fiber content increased. The mechanical behavior of composites varied with fiber type and concentration. While the incorporation of AS results in a reduction in all mechanical parameters, the addition of OR leads to a slight improvement in elongation at break. The addition of fibers improved the thermoformability of PHBV. In the case of AS, the improvement in the processing window was achieved at lower fiber contents, while in the case of OR, the improvement was observed at a fiber content of 20%. Biodisintegration tests showed that the presence of fibers promoted the degradation of the composites, with higher fiber concentrations leading to faster degradation. Indeed, the time of complete biodisintegration was reduced by approximately 30% in the composites with 20% and 30% AS.
Optimization the Stab Resistance and Flexibility of Ultra-High Molecular Weight Polyethylene Knitted Structure Fabric with Response Surface Method
Authors: Yu Xuliang , Su Ting , Liang Xinhua , Cong Honglian
Published: November 2023

At present, the challenging issue of the compatibility between stab resistance and flexibility of materials frequently appears. Thus, this study proposes a novel method to enhance the comprehensive performance of the material matrix with stab resistance. Based on the stab-resistant mechanism analysis of the textile matrix, the influence of four factors on the performance of ultra-high molecular weight polyethylene (UHMWPE) knitted fabric was discussed. And, the optimal process conditions of material for achieving high stab resistance and high flexibility were obtained by the response surface method. A series of experiments proved that among all factors, the fabric structure had the greatest influence on the flexible stab-resistant knitted material. Following that, the thickness of the yarn also plays a significant role. Under the optimal process conditions, the stab peak force of the knitted material was promoted to 52.450 N, and the flexibility was enhanced to 93.6%. Meanwhile, through comparison with products that have undergone the same treatment, there was little difference in stab resistance but significantly improved flexibility. It achieves the initial stab resistance and comfortable wearing softness of the fabric through process optimization. This improvement in overall performance of the textile matrix enables further enhancement treatments.
Desacralization of Religious Concepts: The Prophecy from the Perspective of the Iranian Reformist Scholar Seddigha Wasmaghi
Authors: Poya Abbas
Published: November 2023

This article examines, how the reformist attempts of some Iranian religious intellectuals—consciously or unconsciously—lead to the desacralization of Islamic concepts, using the Iranian jurist and activist Seddigha Wasmaghi as an example. The reformists are, as will be shown with reference to Wasmaghi, concerned with establishing that the normative as well as the theological assumptions in Islam are results of human cognition. Any idea that is qualified as a human assumption, i.e., not sacred and thus open to challenge, can be critically examined, re-read, and perhaps even changed or overruled. Such approaches include, for example, Mohammad Mojtahed Shabestari’s understanding of the Qurʾan as a ‘prophetic reading of the world’ and ʿAbdolkarim Sorush’s interpretation of revelation as ‘prophet’s dreams’. Among the most recent attempts of this kind is Seddigha Wasmaghi’s perception of ‘prophecy as a human construction’. This argument is presented and critically analyzed in this paper.
Sentinel-1 Imagery Used for Estimation of Soil Organic Carbon by Dual-Polarization SAR Vegetation Indices
Authors: Santos Erli Pinto dos , Moreira Michel Castro , Fernandes-Filho Elpídio Inácio , Demattê José Alexandre M. , Dionizio Emily Ane , Silva Demetrius David da , Cruz Renata Ranielly Pedroza , Moura-Bueno Jean Michel , Santos Uemeson José dos , Costa Marcos Heil
Published: November 2023

Despite optical remote sensing (and the spectral vegetation indices) contributions to digital soil-mapping studies of soil organic carbon (SOC), few studies have used active radar remote sensing mission data like that from synthetic aperture radar (SAR) sensors to predict SOC. Bearing in mind the importance of SOC mapping for agricultural, ecological, and climate interests and also the recently developed methods for vegetation monitoring using Sentinel-1 SAR data, in this work, we aimed to take advantage of the high operationality of Sentinel-1 imaging to test the accuracy of SOC prediction at different soil depths using machine learning systems. Using linear, nonlinear, and tree regression-based methods, it was possible to predict the SOC content of soils from western Bahia, Brazil, a region with predominantly sandy soils, using as explanatory variables the SAR vegetation indices. The models fed with SAR sensor polarizations and vegetation indices produced more accurate results for the topsoil layers (0–5 cm and 5–10 cm in depth). In these superficial layers, the models achieved an RMSE in the order of 5.0 g kg−1 and an R2 ranging from 0.16 to 0.24, therefore explaining about 20% of SOC variability using only Sentinel-1 predictors.
Retrieval of Land Surface Temperature over Mountainous Areas Using Fengyun-3D MERSI-II Data
Authors: Xue Yixuan , Zhu Xiaolin , Wu Zihao , Duan Si-Bo
Published: November 2023

Land surface temperature (LST) is an important physical quantity in the energy exchange of hydrothermal cycles between the land and near-surface atmosphere at regional and global scales. However, the traditional thermal infrared transfer equation (RTE) and LST retrieval algorithms are always based on the underlying assumptions of homogeneity and isotropy, which ignore the terrain effect influence of a heterogeneous topography. It can cause significant errors when traditional RTE and other algorithms are used to retrieve LST in such mountainous research. In this study, the mountainous thermal infrared transfer model considering terrain effect correction is used to retrieve the mountainous LST using FY-3D MERSI-II data, and the in situ site data are simultaneously utilized to evaluate the performance of the iterative single-channel algorithm. The elevation of this study region ranges from 500 m to 2200 m, whereas the minimum SVF can reach 0.75. Results show that the spatial distribution of the retrieved LST is similar to topographic features, and the LST has larger values in the lower valley and smaller values in the higher ridge. In addition, the overall bias and RMSE between the retrieved LSTs and five in situ stations are respectively −0.70 K and 2.64 K, which demonstrates this iterative single-channel algorithm performs well in taking into account the terrain effect influence. Accuracy of the LST estimation is meaningful for mountainous ecological environmental monitoring and global climate research. Such an adjacent terrain effect correction should be considered in future research on complex terrains, especially with high spatial resolution TIR data.
The Detection and Control Factor Analysis of Active Landslides in Guizhou Province, China, Using Sentinel-1 SAR Imagery
Authors: Chen Liquan , Zhao Chaoying , Chen Hengyi , Kang Ya , Li Bin , Liu Xiaojie
Published: November 2023

Catastrophic landslides occur frequently in Guizhou Province, China, and the landslides in this area have special geomorphological, geological, and anthropogenic features. In order to detect and explore the distribution pattern and control factors of active landslides in Guizhou, firstly, a total of 693 active landslides throughout Guizhou Province were mapped based on the deformation rate, which was obtained by spatiotemporal filtering and Intermittent Small Baseline Subset (ISBAS) Interferometric Synthetic Aperture Radar (InSAR) techniques. Then, the relationships between the detected landslides and elevation, aspect, slope gradient, and stratigraphic lithology were analysed. Moreover, it was found that the landslides were mainly concentrated in three stratigraphic combinations, that is T1f~P2l−d, T1f~T1yn, and T2g~T1yn. Thereafter, the correlation coefficients between the landslide density and elevation and distance to the stratigraphic boundary were 0.54 and −0.19, indicating that the distribution of landslides was significantly controlled by the elevation and the boundary of specific stratigraphic combinations. Finally, we chose a typical landslide to explore how landslide development was controlled by the combined effects of elevation and stratigraphy by using ascending and descending InSAR results. We revealed that landslides occurred primarily in areas with a steep slope and a stratigraphy characterized by mudstone and sandstone.
Evaluation of Original and Water Stress-Incorporated Modified Weather Research and Forecasting Vegetation Photosynthesis and Respiration Model in Simulating CO2 Flux and Concentration Variability over the Tibetan Plateau
Authors: Niu Hanlin , Hu Xiao-Ming , Shang Lunyu , Meng Xianhong , Wang Shaoying , Li Zhaoguo , Zhao Lin , Chen Hao , Deng Mingshan , Sheng Danrui
Published: November 2023

Terrestrial carbon fluxes are crucial to the global carbon cycle. Quantification of terrestrial carbon fluxes over the Tibetan Plateau (TP) has considerable uncertainties due to the unique ecosystem and climate and scarce flux observations. This study evaluated our recent improvement of terrestrial flux parameterization in the weather research and forecasting model coupled with the vegetation photosynthesis and respiration model (WRF-VPRM) in terms of reproducing observed net ecosystem exchange (NEE), gross ecosystem exchange (GEE), and ecosystem respiration (ER) over the TP. The improvement of VPRM relative to the officially released version considers the impact of water stress on terrestrial fluxes, making it superior to the officially released model due to its reductions in bias, root mean square error (RMSE), and ratio of standard deviation (RSD) of NEE to 0.850 μmol·m−2·s−1, 0.315 μmol·m−2·s−1, and 0.001, respectively. The improved VPRM also affects GEE simulation, increasing its RSD to 0.467 and decreasing its bias and RMSE by 1.175 and 0.324 μmol·m−2·s−1, respectively. Furthermore, bias and RMSE for ER were lowered to −0.417 and 0.954 μmol·m−2·s−1, with a corresponding increase in RSD by 0.6. The improved WRF-VPRM simulation indicates that eastward winds drive the transfer of lower CO2 concentrations from the eastern to the central and western TP and the influx of low-concentration CO2 inhibits biospheric CO2 uptake. The use of an improved WRF-VPRM in this study helps to reduce errors, improve our understanding of the role of carbon flux cycle over the TP, and ultimately reduce uncertainty in the carbon flux budget.
Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica
Authors: Alemán-Montes Bryan , Zabala Alaitz , Henríquez Carlos , Serra Pere
Published: November 2023

Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as the lack of operational models for predicting yields and joint estimation with sugar content. Our study is a contribution to this topic that aims to apply an empirical, operational, and robust method to estimate sugarcane yield (SCY) and sugar content (SC) through the combination of field variables, climatic data, and RS vegetation indices (VIs) extracted from Sentinel-2 and Landsat-8 imagery in a cooperative in Costa Rica for four sugarcane harvest cycles (2017–2018 to 2020–2021). Based on linear regression models, four approaches using different VIs were evaluated to obtain the best models to improve the RMSE results and to validate them (using the harvest cycle of 2021–2022) at two management scales: farm and plot. Our results show that the historical yield average, the maximum historical yield, and the growing cycle start were essential factors in estimating SCY and the former variable for SC. For SCY, the most explicative VI was the Simple Ratio (SR), whereas, for SC, it was the Ratio Vegetation Index (RVI). Adding VIs from different months was essential to obtain the phenological variability of sugarcane, being the most common results September, December and January. In SC estimation, precipitation (in May and December) was a clear explicatory variable combined mainly with RVI, whereas in SCY, it was less explanatory. In SCY, RMSE showed values around 8.0 t·ha−1, a clear improvement from 12.9 t·ha−1, which is the average obtained in previous works, whereas in SC, it displayed values below 4.0 kg·t−1. Finally, in SCY, the best validation result was obtained at the plot scale (RMSE of 7.7 t·ha−1), but this outcome was not verified in the case of SC validation because the RMSE was above 4.0 kg·t−1. In conclusion, our operational models try to represent a step forward in using RS techniques to improve sugarcane management at the farm and plot scales in Costa Rica.
A Semantics-Guided Visual Simultaneous Localization and Mapping with U-Net for Complex Dynamic Indoor Environments
Authors: Zeng Zhi , Lin Hui , Kang Zhizhong , Xie Xiaokui , Yang Juntao , Li Chuyu , Zhu Longze
Published: November 2023

Traditional simultaneous localization and mapping (SLAM) system tends to operate in small-area static environments, and its performance might degrade when moving objects appear in a highly dynamic environment. To address this issue, this paper proposes a dynamic object-aware visual SLAM algorithm specifically designed for dynamic indoor environments. The proposed method leverages a semantic segmentation architecture called U-Net, which is utilized in the tracking thread to detect potentially moving targets. The resulting output of semantic segmentation is tightly coupled with the geometric information extracted from the corresponding SLAM system, thus associating the feature points captured by images with the potentially moving targets. Finally, filtering out the moving feature points can greatly enhance localization accuracy in dynamic indoor environments. Quantitative and qualitative experiments were carried out on both the Technical University of Munich (TUM) public dataset and the real scenario dataset to verify the effectiveness and robustness of the proposed method. Results demonstrate that the semantics-guided approach significantly outperforms the ORB SLAM2 framework in dynamic indoor environments, which is crucial for improving the robustness and reliability of the SLAM system.
SatelliteSkill5—An Augmented Reality Educational Experience Teaching Remote Sensing through the UN Sustainable Development Goals
Authors: McNerney Eimear , Faull Jonathan , Brown Sasha , McNerney Lorraine , Foley Ronan , Lonergan James , Rickard Angela , Doganca Kucuk Zerrin , Behan Avril , Essel Bernard , Mensah Isaac Obour , Castillo Campo Yeray , Cullen Helen , Ffrench Jack , Abernethy Rachel , Cleary Patricia , Byrne Aengus , Cahalane Conor
Published: November 2023

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations’ Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory.
Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning
Authors: Zhu Ping , Yu Xuexiang , Han Yuchen , Xiao Xingxing , Liu Yu
Published: November 2023

As micro-electro-mechanical systems (MEMS) technology continues its rapid ascent, a growing array of smart devices are integrating lightweight, compact, and cost-efficient magnetometers and inertial sensors, paving the way for advanced human motion analysis. However, sensors housed within smartphones frequently grapple with the detrimental effects of magnetic interference on heading estimation, resulting in diminished accuracy. To counteract this challenge, this study introduces a method that synergistically employs convolutional neural networks (CNNs) and support vector machines (SVMs) for adept interference detection. Utilizing a CNN, we automatically extract profound features from single-step pedestrian motion data that are then channeled into an SVM for interference detection. Based on these insights, we formulate heading estimation strategies aptly suited for scenarios both devoid of and subjected to magnetic interference. Empirical assessments underscore our method’s prowess, boasting an impressive interference detection accuracy of 99.38%. In indoor environments influenced by such magnetic disturbances, evaluations conducted along square and equilateral triangle trajectories revealed single-step heading absolute error averages of 2.1891° and 1.5805°, with positioning errors averaging 0.7565 m and 0.3856 m, respectively. These results lucidly attest to the robustness of our proposed approach in enhancing indoor pedestrian positioning accuracy in the face of magnetic interferences.
Running Economy in the Vertical Kilometer
Authors: Bascuas Pablo Jesus , Gutiérrez Héctor , Piedrafita Eduardo , Rabal-Pelay Juan , Berzosa César , Bataller-Cervero Ana Vanessa
Published: November 2023

New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efficiency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer field test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Significant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed significant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically significantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running.
Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm
Authors: Guo Ying-Qing , Zhang Yan , Xu Zhao-Dong , Fang Yu , Zhang Zhi-Wei
Published: November 2023

Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines.
Measurement Method of Physical Parameters of Two-Phase Flow Based on Dual-Frequency Demodulation
Authors: Song Chunhui , Yao Chengzhi , Liu Qinghong , Sun Wenyu , Zhang Hui
Published: November 2023

Oil-water two-phase flow commonly occurs in the process of crude oil electric dehydration. Here, through dynamic changes in the water content and conductivity of oil-water two-phase flow in the process of electric dehydration, the influence of water content and conductivity on the efficiency and stability of electric dehydration is analyzed. Using real-time in-line measurements of water content and conductivity, the electric dehydration system is kept in an optimal state, which provides a basis for realizing efficient oil-water separation. Measurements of the physical parameters of oil-water two-phase flow is affected by many factors, such as the temperature of the two-phase flow, composition of the two-phase flow medium, structure of the measurement sensor, coupling of the conventional resistance–capacitance excitation signal, and processing of the measurement data. This complexity causes, some shortcomings to the control system, such as a large measurement error, limited measurement range, inability to measure the medium water phase as a conductive water phase, etc., and not meeting the requirements of the electric dehydration process. To solve that the conductivity and water content of high-conductivity crude oil emulsions cannot be measured synchronously, the RC relationship of oil-water emulsions is measured synchronously using dual-frequency digital demodulation technology, which verifies the feasibility of our test method for the synchronous measurement of physical parameters of homogeneous oil-water two-phase flow. Experimental results show that the novel measuring method (which is within the target measuring range) can be used to measure water content 0~40% and conductivity 1 ms/m~100 ms/m. The measuring error of the water content is less than 2%, and the measuring error of the conductivity is less than 5%.
A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures
Authors: Rehman Mustafa Ur , Shah Kamran , Haq Izhar Ul , Iqbal Sajid , Ismail Mohamed A.
Published: November 2023

Force myography (FMG) represents a promising alternative to surface electromyography (EMG) in the context of controlling bio-robotic hands. In this study, we built upon our prior research by introducing a novel wearable armband based on FMG technology, which integrates force-sensitive resistor (FSR) sensors housed in newly designed casings. We evaluated the sensors’ characteristics, including their load–voltage relationship and signal stability during the execution of gestures over time. Two sensor arrangements were evaluated: arrangement A, featuring sensors spaced at 4.5 cm intervals, and arrangement B, with sensors distributed evenly along the forearm. The data collection involved six participants, including three individuals with trans-radial amputations, who performed nine upper limb gestures. The prediction performance was assessed using support vector machines (SVMs) and k-nearest neighbor (KNN) algorithms for both sensor arrangments. The results revealed that the developed sensor exhibited non-linear behavior, and its sensitivity varied with the applied force. Notably, arrangement B outperformed arrangement A in classifying the nine gestures, with an average accuracy of 95.4 ± 2.1% compared to arrangement A’s 91.3 ± 2.3%. The utilization of the arrangement B armband led to a substantial increase in the average prediction accuracy, demonstrating an improvement of up to 4.5%.
A Digital Image Correlation Technique for Laboratory Structural Tests and Applications: A Systematic Literature Review
Authors: Mousa Mohammed Abbas , Yussof Mustafasanie M. , Hussein Thulfiqar S. , Assi Lateef N. , Ghahari SeyedAli
Published: November 2023

Digital image correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of the contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for the precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the effectiveness of the DIC methodology has been validated as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.
Development of Low-Cost IoT System for Monitoring Piezometric Level and Temperature of Groundwater
Authors: Espinoza Ortiz Mauro , Apún Molina Juan Pablo , Belmonte Jiménez Salvador Isidro , Herrera Barrientos Jaime , Peinado Guevara Héctor José , Santamaria Miranda Apolinar
Published: November 2023

Rural communities in Mexico and other countries with limited economic resources require a low-cost measurement system for the piezometric level and temperature of groundwater for their sustainable management, since anthropogenic action (pumping extractions), natural recharge and climate change phenomena affect the behavior of piezometric levels in the aquifer and its sustainability is at risk. Decrease in the piezometric level under a balanced level promotes salt intrusion from ocean water to the aquifer, salinizing and deteriorating the water quality for agriculture and other activities; and a decrease in water level under the pumps or well drilling depth could deprive communities of water. Water temperature monitoring is essential to determine electric conductivity and dissolved salt content in groundwater. Using IoT technology, a device was developed that monitors both variables inside the well, and the ambient temperature and atmospheric pressure outside the well. The measurements are made in real time, with sampling every second and sending data to a dedicated server every 15 min so that the visualization can be accessed through a device with Internet access. The time series of the variables measured inside and outside the well were obtained over a period of three months in the rural community of Agua Blanca, Guasave, Sinaloa, Mexico. Through these records, a progressive temporary drawdown of the piezometric level is observed, as well as the frequency of pumping. This low-cost IoT system shows potential use in hydrological processes of interest such as the separation of regional and local flow, drawdown rates and recognition of geohydrological parameters.
Research Hotspots and Trends of Social Robot Interaction Design: A Bibliometric Analysis
Authors: Wang Jianmin , Chen Yongkang , Huo Siguang , Mai Liya , Jia Fusheng
Published: November 2023

(1) Background: Social robot interaction design is crucial for determining user acceptance and experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, we conducted this study in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: We conducted a comprehensive review based on 2416 papers related to social robot interaction design obtained from the Web of Science (WOS) database. Our review utilized bibliometric techniques and integrated VOSviewer and CiteSpace to construct a knowledge map. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1 the study of human–robot relationships in social robots, #2 research on the emotional design of social robots, #3 research on social robots for children’s psychotherapy, #4 research on companion robots for elderly rehabilitation, and #5 research on educational social robots. The reference co-citation analysis identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.
Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework
Authors: Javed Mannan , Tariq Noshina , Ashraf Muhammad , Khan Farrukh Aslam , Asim Muhammad , Imran Muhammad
Published: November 2023

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.
Enhancing Urban Sustainability: Unravelling Carbon Footprint Reduction in Smart Cities through Modern Supply-Chain Measures
Authors: Issa Zadeh Seyed Behbood , Garay-Rondero Claudia Lizette
Published: November 2023

The worldwide Sustainable Development Goals (SDGs) for smart cities and communities focus significant attention on air quality and climate change. Technology and management can reduce fossil fuel dependence in smart cities’ energy supply chains (SC). A sustainable smart city and reduced carbon emissions require coordinated technology and management with appropriate infrastructure. A systematic review of smart city SC management literature that reduces the carbon footprint (C.F) inspired this study. The study shows how each attribute reduces greenhouse gas (GHG) emissions. The Introduction highlights the subject matter and principal goal, which is to investigate how SC management strategies could assist smart cities in lowering their C.F. The Methods and Materials section provides a succinct description of the refining process in Systematic Reviews and Meta-Analyses in Scoping Reviews (PRISMA-ScR) relevant to C.F mitigation in smart city (SC) management. Significant works are described in the Results and Findings section, which exposes how smart cities and SC measurements reduce C.F. The Discussion section examines and scientifically debates the research findings. The Conclusion provides a scientific analysis based on the presented insights and features to enhance how policies must be coordinated to achieve the goal of this research study in a comprehensive way. Furthermore, it provides suggestions for practitioners and governments, and proposals for future research. The main contribution of this paper is conducting and proposing a framework for a better understanding of how the novel digital SCs, their components, and their management practices can help smart cities reduce their C.F.
Efficiency in the Last Mile of Autonomous Ground Vehicles with Lockers: From Conventional to Renewable Energy Transport
Authors: Levkovych Olga , Saraceni Adriana
Published: November 2023

This research aims to compare autonomous ground vehicles with conventional and electric vans on the basis of associated vehicle costs and benefits related to their use, taking into account economic feasibility. Cost per vehicle kilometre is derived using the total cost of ownership method adjusted with the inclusion of labour costs and the impact of solar panel application on fuel efficiency while travel time-related and capacity occupations and reliability benefits serve as a basis for the total possible number of parcels delivered. The results show that, under the current structural and infrastructural conditions of urban delivery, the experimental model can be potentially successful in terms of cost per kilometre (0.133/km) but not as effective in terms of the total possible number of parcels delivered. This study defines autonomous ground vehicles with lockers as an innovative last mile solution and contributes to the academic literature by investigating the concept’s efficiency competitiveness.
Musculoskeletal Disorders in Primary School Teachers
Authors: Ramírez-García César O. , Lluguay-Quispillo Danny J. , Inga-Lafebre Jorge D. , Cuenca-Lozano María F. , Ojeda-Zambrano Rosa M. , Cárdenas-Baque Carmen C.
Published: November 2023

Teachers in schools often face working conditions imposed by educational institutions that can pose ergonomic risks and affect their health. This research aims to determine the presence of musculoskeletal disorders and the ergonomic risk factors associated with this population. We collected anthropometric data, assessed musculoskeletal symptoms using the Nordic Kuorinka questionnaire in 134 teachers, and analyzed their workplaces using the ERGO/IBV software. The results include an anthropometric description, highlighting that 94% of women and 87% of men are overweight or obese. It also shows the presence of musculoskeletal discomfort, with 69% and 49% of teachers reporting neck and back/lumbar pain, respectively. Among the main attributions to these discomforts, teachers mention that repetitive tasks, improper postures, and stress are the causes. Finally, the results of the ergonomic assessment are mentioned. Using the rapid entire body assessment method, a high and very high risk level was identified for each subtask, while in respect to repetitive tasks, the neck was identified as the most critical area due to the activities performed. In conclusion, we found an association between back/lumbar pain and body mass index, suggesting that overweight or obesity may increase the risk of musculoskeletal disorders. Additionally, cervical pain is directly related to the repetitive tasks performed by teachers.