bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Profile, Plant Precursors and Some Properties of Georgian Propolis
Authors: Okińczyc Piotr , Widelski Jarosław , Ciochoń Monika , Paluch Emil , Bozhadze Anna , Jokhadze Malkhaz , Mtvarelishvili Gocha , Korona-Głowniak Izabela , Krzyżanowska Barbara , Kuś Piotr Marek
Published: November 2022

Propolis (bee glue) is a resinous substance produced by different species of bees i.a. from available plant resins, balsams, and exudates. It is characterized by significant biological activity (e.g., antimicrobial and antioxidant) and phytochemical diversity related to the available plant sources in specific geographical regions. The available scientific literature on propolis is quite extensive; however, there are only a few reports about propolis originating from Georgia. Therefore, our research was focused on the characterization of Georgian propolis in terms of phytochemical composition and antimicrobial/antioxidant activity. Performed research included UHPLC-DAD-MS/MS phytochemical profiling, determination of total phenolic and flavonoid content, antiradical and antioxidant activity (DPPH and FRAP assays) as well as antibacterial activity of propolis extracts obtained using 70% ethanol (70EE). Georgian propolis extracts exhibited strong activity against Gram-positive bacteria (22 mm—disc assay/64 µg/mL—MIC for S. aureus, sample from Imereti) and weaker against Gram-negative strains as well as strong antioxidant properties (up to 117.71 ± 1.04 mgGAE/g in DPPH assay, up to 16.83 ± 1.02 mmol Fe2+/g in FRAP assay for samples from Orgora and Qvakhreli, respectively). The phytochemical profile of Georgian propolis was characterized by the presence of flavonoids, free phenolic acids, and their esters. In most of the samples, flavonoids were the main chemical group (52 compounds), represented mainly by 3-O-pinobanksin acetate, pinocembrin, chrysin, galangin, and pinobanksin. The primary plant precursor of the Georgian bee glue is black poplar (Populus nigra L.) while the secondary is aspen poplar (P. tremula L.).
Electrochemically Induced Synthesis of Imidazoles from Vinyl Azides and Benzyl Amines
Authors: Vil’ Vera A. , Grishin Sergei S. , Terent’ev Alexander O.
Published: November 2022

An electrochemically induced synthesis of imidazoles from vinyl azides and benzyl amines was developed. A wide range of imidazoles were obtained, with yields of 30 to 64%. The discovered transformation is a multistep process whose main steps include the generation of electrophilic iodine species, 2H-azirine formation from the vinyl azide, followed by its reactions with benzyl amine and with imine generated from benzyl amine. The cyclization and aromatization of the obtained intermediate lead to the target imidazole. The synthesis proceeds under constant current conditions in an undivided cell. Despite possible cathodic reduction of various unsaturated intermediates with C=N bonds, the efficient electrochemically induced synthesis of imidazoles was carried out.
Coordination Polymers of Polyphenyl-Substituted Potassium Cyclopentadienides
Authors: Komarov Pavel D. , Birin Kirill P. , Vinogradov Alexander A. , Varaksina Evgenia A. , Puntus Lada N. , Lyssenko Konstantin A. , Churakov Andrei V. , Nifant’ev Ilya E. , Minyaev Mikhail E. , Roitershtein Dmitrii M.
Published: November 2022

A series of potassium salts of di- and tri-arylsubstituted cyclopentadienes has been obtained by the metalation of the corresponding cyclopentadienes with benzylpotassium in THF media. Crystals of all compounds, afforded by recrystallization from THF/hexane, diglyme-THF/hexane and toluene/hexane mixtures, have been studied by X-ray diffraction. All studied potassium cyclopentadienides exhibit the luminescence at room temperature and overall quantum yield of photoluminescence for potassium salt of diarylsubstituted cyclopentadiene is 18%.
Effects of Green Kiwifruit Peel Extract on Sleep-Wake Profiles in Mice: A Polysomnographic Study Based on Electroencephalogram and Electromyogram Recordings
Authors: Kim Duhyeon , Yoon Minseok , Kim Seonghui , Um Min Young , Cho Suengmok
Published: November 2022

In the previous study, it was reported that green kiwifruit peel ethanol extract (GKPEE) increases sleep duration and decreases sleep latency in pentobarbital-treated mice. The pentobarbital-induced sleep test can be used to verify sleep quantity, which includes factors such as sleep duration and latency, but not sleep quality. In the present study, the sleep-promoting effects of GKPEE were investigated by the analysis of electroencephalogram (EEG) and electromyogram in mice and were compared with the results of diazepam (DZP), a representative sedative-hypnotic agent. The acute administration of GKPEE (250, 500 and 1000 mg/kg) increased the amount of non-rapid eye movement sleep (NREMS) and decreased sleep latency in a dose-dependent manner. The effect of GKPEE at 1000 mg/kg produced persistently significantly different results until the second hour of time-course changes. In particular, GKPEE did not produce any change in delta activity compared to DZP. Furthermore, sub-chronic administration (15 days) of GKPEE (500 mg/kg) continued sleep-promoting effects, whilst the EEG power density of NREMS did not show significant differences, indicating that there were no tolerance phenomena. Our findings suggest that GKPEE may be a promising natural sleep aid for treating sleep disorders. In addition, considering the number of by-products discarded each year by the food industry, the application of GKPEE here contributes to the utilization of processed kiwifruit by-products and can help to solve environmental problems.
PPARγ Gene as a Possible Link between Acquired and Congenital Lipodystrophy and its Modulation by Dietary Fatty Acids
Authors: Rodríguez-García Carmen , Sánchez-Quesada Cristina , Martínez-Ramírez María José , Gaforio José J.
Published: November 2022

Lipodystrophy syndromes are rare diseases that could be of genetic or acquired origin. The main complication of lipodystrophy is the dysfunction of adipose tissue, which leads to an ectopic accumulation of triglycerides in tissues such as the liver, pancreas and skeletal muscle. This abnormal fat distribution is associated with hypertriglyceridemia, insulin resistance, liver steatosis, cardiomyopathies and chronic inflammation. Although the origin of acquired lipodystrophies remains unclear, patients show alterations in genes related to genetic lipodystrophy, suggesting that this disease could be improved or aggravated by orchestrating gene activity, for example by diet. Nowadays, the main reason for adipose tissue dysfunction is an imbalance in metabolism, caused in other pathologies associated with adipose tissue dysfunction by high-fat diets. However, not all dietary fats have the same health implications. Therefore, this article aims to summarize the main genes involved in the pathophysiology of lipodystrophy, identify connections between them and provide a systematic review of studies published between January 2017 and January 2022 of the dietary fats that can modulate the development of lipodystrophy through transcriptional regulation or the regulation of protein expression in adipocytes.
Degradation of Losartan Potassium Highlighted by Correlated Studies of Photoluminescence, Infrared Absorption Spectroscopy and Dielectric Spectroscopy
Authors: Paraschiv Mirela , Smaranda Ion , Zgura Irina , Ganea Paul , Chivu Madalina , Chiricuta Bogdan , Baibarac Mihaela
Published: November 2022

In this paper, new results on the degradation of losartan potassium (LP, (1)), in the absence and presence of excipients, which was induced by UV light, the acid character of phosphate buffer solution (PBS) and alkaline medium, respectively, are reported through correlated studies of FTIR spectroscopy, photoluminescence and dielectric spectroscopy. The photoluminescence (PL) spectra of LP and the drug marked under the name Lorista (LO) are characterized by intense emission bands, peaking at 378 nm and 380 nm, respectively, accompanied by low intensity bands with a maximum at ~450–460 nm. Photodegradation of LO in a solid state is evidenced by a decrease in the intensity of the PL band at 380 nm, a variation that originates both in the adsorption of water vapors from the air and in the interaction of LP with excipients such as cornstarch, silicon dioxide and cellulose. The LP-water interaction is described, taking into account the main electrical parameters, i.e., complex dielectric permittivity and electrical conductivity. Photodegradation of LP and LO also induces an increase in the intensity of the emission band, at ~450–460 nm. The influence of acid and alkaline medium on the LO degradation is analyzed using phosphate buffer (PBS) and NaOH solutions, respectively. In both cases, a decrease in the intensity of the PL band, at 380 nm, is reported. The intensity diminution of the PL spectra of NaOH-reacted LP and LO is the result of the formation of the photodegradation product N-methanolamide-{[2′-(1H-tetrazol-5-yl)(1,1′-biphenyl)-4-yl]methyl} (2). This compound was proven by the studies of FTIR spectroscopy achieved on LP and NaOH-reacted LP. The appearance of the IR band at 1740 cm−1 and the increase in the absorbance in the IR band at 1423 cm−1 indicate that the photodegradation product (2) contains the C=O and C-OH functional groups.
A β-Cyclodextrin-Based Nanoparticle with Very High Transfection Efficiency Unveils siRNA-Activated TLR3 Responses in Human Prostate Cancer Cells
Authors: de la Torre Cristina , Játiva Pablo , Posadas Inmaculada , Manzanares Darío , Blanco José L. Jiménez , Mellet Carmen Ortiz , Fernández José Manuel García , Ceña Valentín
Published: November 2022

Synthetic double-stranded small interfering RNAs (siRNAs) mimic interference RNAs (RNAi) and can bind target mRNAs with a high degree of specificity, leading to selective knockdown of the proteins they encode. However, siRNAs are very labile and must be both protected and transported by nanoparticles to be efficiently delivered into cells. In this work, we used a Janus-type polycationic amphiphilic β-cyclodextrin derivative to efficiently transfect siRNAs targeting mRNAs encoding mitogen-activated protein kinase (p42-MAPK) or Ras homolog enriched in brain (Rheb) into different cancer cell lines as well as astrocytes. We took advantage of this high transfection efficiency to simultaneously knock down p42-MAPK and Rheb to boost docetaxel (DTX)-mediated toxicity in two human prostate cancer cell lines (LNCaP and PC3). We found that double knockdown of p42-MAPK and Rheb increased DTX-toxicity in LNCaP but not in PC3 cells. However, we also observed the same effect when scramble siRNA was used, therefore pointing to an off-target effect. Indeed, we found that the siRNA we used in this work induced toll-like receptor 3 activation, leading to β-interferon production and caspase activation. We believe that this mechanism could be very useful as a general strategy to elicit an immune response against prostate cancer cells.
A Comparative Study of PMETAC-Modified Mesoporous Silica and Titania Thin Films for Molecular Transport Manipulation
Authors: Alberti Sebastian , Giussi Juan , Azzaroni Omar , Soler-Illia Galo J. A. A.
Published: November 2022

The manipulation and understanding of molecular transport across functionalized nanopores will take us closer to mimicking biological membranes and thus to design high-performance permselective separation systems. In this work, Surface-initiated atom transfer radical polymerization (SI-ATRP) of (2-methacryloyloxy)-ethyltrimethylammonium chloride (METAC) was performed on both mesoporous silica and mesoporous titania thin films. Pores were proven to be filled using ellipsometry and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Furthermore, the employed method leads to a polymer overlayer, whose thickness could be discriminated using a double-layer ellipsometry model. Cyclic voltammetry experiments reveal that the transport of electrochemically active probes is affected by the PMETAC presence, both due to the polymer overlayer and the confined charge of the pore-tethered PMETAC. A more detailed study demonstrates that ion permeability depends on the combined role of the inorganic scaffolds’ (titania and silica) surface chemistry and the steric and charge exclusion properties of the polyelectrolyte. Interestingly, highly charged negative walls with positively charged polymers may resemble zwitterionic polymer behavior in confined environments.
Nanocrystallization Improves the Solubilization and Cytotoxic Effect of a Poly (ADP-Ribose)-Polymerase-I Inhibitor
Authors: Alali Amer S. , Kalam Mohd Abul , Ahmed Mohammed Muqtader , Aboudzadeh M. Ali , Alhudaithi Sulaiman S. , Anwer Md. Khalid , Fatima Farhat , Iqbal Muzaffar
Published: November 2022

Olaparib (OLA) is an anticancer agent that acts by inhibiting the poly (ADP-ribose)-polymerase-I (PARP-I). Due to its low solubility and low permeability, it has been placed as a BCS Class-IV drug and hence its clinical use is limited. In this study, we develop the nanocrystals of OLA as a way to improve its solubility and other performances. The OLA-NCs were prepared by antisolvent precipitation method through homogenization and probe sonication technique using a novel amphiphilic polymeric stabilizer (Soluplus®). Particle characterization resulted approximately 103.13 nm, polydispersity-index was 0.104 with positive zeta-potential of +8.67 mV. The crystal morphology by SEM of OLA-NCs (with and without mannitol) exhibited nano-crystalline prism-like structures as compared to the elongated OLA-pure. The DSC, XRD and FTIR were performed to check the interaction of Soluplus, mannitol and OLA did not exhibit any physical interaction among the OLA, Soluplus® and mannitol that is indicated by the presence of parent wave number peak. Two-fold increased solubility of OLA was found in PBS with Soluplus® from the NCs (69.3 ± 6.2 µgmL−1) as compared to pure drug (35.6 ± 7.2 µgmL−1). In vitro release of drug from OLA-NCs was higher (78.2%) at 12 h at pH 6.8 and relatively lower (53.1%) at pH 1.2. In vitro cellular cytotoxicity and anticancer effects were examined on MCF-7 cells. OLA-NCs were found effectively potent to MCF-7 cells compared with OLA-pure with approximately less than half IC50 value during MTT assay. Estimation of p53, Caspase-3 and Caspase-9 in MCF-7 cells indicated that OLA-NCs have significantly (p < 0.05) increased their expressions. After single oral dose in rats, 12 h plasma drug concentration-time profile indicated approximately 2.06-, 2.29-, 2–25- and 2.62-folds increased Cmax, AUC0-12 h, AUC0-∞ and AUMC0-∞, respectively, from the NCs as compared to OLA-pure. Storage stability indicated that the OLA-NCs was physically and chemically stable at 4 °C, 25 °C and 40 °C up to 6-months. Overall, OLA-NCs were deliberated; its potential feasibility to overwhelm the formulation challenges related to poorly soluble drugs and its future clinical applications.
Impact of Micro Silica Filler Particle Size on Mechanical Properties of Polymeric Based Composite Material
Authors: Siraj Sidra , Al-Marzouqi Ali H. , Iqbal Muhammad Z. , Ahmed Waleed
Published: November 2022

In this study, silica in the form of raw local natural sand was added to high-density-polyethylene (HDPE) in order to develop a composite material in the form of sheets that could have potential applications in thin film industries, such as packaging, or recycling industries, such as in 3D printing. The silica/HDPE composite sheets were developed using a melt extruder followed by using a hot press for compression molding. The impact of two different particle sizes (25 µm and 5 µm) of the silica particles on selected properties such as toughness, elastic modulus, ductility, and composite density were analyzed. A considerable increase in the toughness and elastic modulus was observed from 0 wt% to 20 wt% with a 25 µm filler size. However, a general decreasing trend was observed in the material’s toughness and elastic modulus with decreasing particle size. A similar trend was observed for the ductility and the tensile strength of the sheets prepared from both filler particle sizes. In terms of the composite density, as the filler was increased from 20 wt% to 50 wt%, an increase in the composite densities was noticed for both particle sizes. Additionally, the sheets developed with 25 µm particle size had a slightly higher density than the 5 µm particle size, which is expected as the size can account for the higher weight. Results from this work aim to analyze the use of local sand as a filler material that can contribute towards maximizing the potential of such composite materials developed in extrusion industries.
A Comparison of Machine Learning and Geostatistical Approaches for Mapping Forest Canopy Height over the Southeastern US Using ICESat-2
Authors: Tiwari Kasip , Narine Lana L.
Published: November 2022

The availability of canopy height information in the Ice, Cloud, and Land Elevation Satellite-2’s (ICESat-2’s) land and vegetation product, or ATL08, presents opportunities for developing full-coverage products over broad spatial scales. The primary goal of this study was to develop a 30-meter canopy height map over the southeastern US, for the Southeastern Plains ecoregion and the Middle Atlantic Coastal Plains ecoregion. More specifically, this work served to compare well-known modeling approaches for upscaling canopy information from ATL08 to develop a wall-to-wall product. Focusing on only strong beams from nighttime acquisitions, the h_canopy parameter was extracted from ATL08 data. Landsat-8 bands and derived vegetation indices (normalized difference vegetation index, enhanced vegetation index, and modified soil-adjusted vegetation index) along with National Land Cover Database’s canopy cover and digital elevation models were used to extrapolate ICESat-2 canopy height from tracks to the regional level. Two different modeling techniques, random forest (RF) and regression kriging (RK), were applied for estimating canopy height. The RF model estimated canopy height with a coefficient of determination (R2) value of 0.48, root-mean-square error (RMSE) of 4.58 m, mean absolute error (MAE) of 3.47 and bias of 0.23 for independent validation, and an R2 value of 0.38, RMSE of 6.39 m, MAE of 5.04 and bias of −1.39 when compared with airborne lidar-derived canopy heights. The RK model estimated canopy heights with an R2 value of 0.69, RMSE of 3.49 m, MAE of 2.61 and bias of 0.03 for independent validation, and an R value of 0.68, R2 value of 0.47, RMSE of 5.96m, MAE of 4.52 and bias of −1.81 when compared with airborne lidar-derived canopy heights. The results suggest feasibility for the implementation of the RK method over a larger spatial extent and potential for combining other remote sensing and satellite data for future monitoring of canopy height dynamics.
Accessible Remote Sensing Data Mining Based Dew Estimation
Authors: Suo Ying , Wang Zhongjing , Zhang Zixiong , Fassnacht Steven R.
Published: November 2022

Dew has been considered a supplementary water resource as it constitutes an important water supply in many ecosystems, especially in arid and semiarid areas. Remote sensing allows large-scale surface observations, offering the possibility to estimate dew in such arid and semiarid regions. In this study, by screening and combining different remote sensing variables, we obtained a well-performing monthly scale dew yield estimation model based on the support vector machine (SVM) learning method. Using daytime and nighttime land surface temperatures (LST), the normalized difference vegetation index (NDVI), and three emissivity bands (3.929–3.989 µm, 10.780–11.280 µm, and 11.770–12.270 µm) as the model inputs, the simulated site-scale monthly dew yield achieved a correlation coefficient (CC) of 0.89 and a root mean square error (RMSE) of 0.30 (mm) for the training set, and CC = 0.59 and RMSE = 0.55 (mm) for the test set. Applying the model to the Heihe River Basin (HRB), the results showed that the annual dew yield ranged from 8.83 to 20.28 mm/year, accounting for 2.12 to 66.88% of the total precipitation, with 74.81% of the area having an annual dew amount of 16 to 19 mm/year. We expanded the model application to Northwest China and obtained a dew yield of 5~30 mm/year from 2011 to 2020, indicating that dew is a non-negligible part of the water balance in this arid area. As a non-negligible part of the water cycle, the use of remote sensing to estimate dew can provide better support for future water resource assessment and analysis.
SealNet 2.0: Human-Level Fully-Automated Pack-Ice Seal Detection in Very-High-Resolution Satellite Imagery with CNN Model Ensembles
Authors: Gonçalves Bento C. , Wethington Michael , Lynch Heather J.
Published: November 2022

Pack-ice seals are key indicator species in the Southern Ocean. Their large size (2–4 m) and continent-wide distribution make them ideal candidates for monitoring programs via very-high-resolution satellite imagery. The sheer volume of imagery required, however, hampers our ability to rely on manual annotation alone. Here, we present SealNet 2.0, a fully automated approach to seal detection that couples a sea ice segmentation model to find potential seal habitats with an ensemble of semantic segmentation convolutional neural network models for seal detection. Our best ensemble attains 0.806 precision and 0.640 recall on an out-of-sample test dataset, surpassing two trained human observers. Built upon the original SealNet, it outperforms its predecessor by using annotation datasets focused on sea ice only, a comprehensive hyperparameter study leveraging substantial high-performance computing resources, and post-processing through regression head outputs and segmentation head logits at predicted seal locations. Even with a simplified version of our ensemble model, using AI predictions as a guide dramatically boosted the precision and recall of two human experts, showing potential as a training device for novice seal annotators. Like human observers, the performance of our automated approach deteriorates with terrain ruggedness, highlighting the need for statistical treatment to draw global population estimates from AI output.
Development of a New Vertical Water Vapor Model for GNSS Water Vapor Tomography
Authors: Wan Moufeng , Zhang Kefei , Wu Suqin , Sun Peng , Li Longjiang
Published: November 2022

One of the main challenges of Global Navigation Satellite System (GNSS) tomography is in solving ill-conditioned system equations. Vertical constraint models are typically used in the solution procedure and play an important role in the quality of the GNSS tomography, in addition to helping resolve ill-posed problems in system equations. In this study, based on a water vapor (WV) parameter, namely IRPWV, a new vertical constraint model with six sets of coefficients for six different WV states was developed and tested throughout 2019 in the Hong Kong region with four tomographic schemes, which were carried out with the model and the traditional vertical constraint model using three different types of water vapor scale height parameters. Experimental results were numerically compared against their corresponding radiosonde-derived WV values. Compared with the tests that used the traditional model, our results showed that, first, for the daily relative error of WV density (WVD) less than 30%, the new model can lead to at least 10% and 49% improvement on average at the lower layers (below 3 km, except for the ground surface) and the upper layers (about 5–10 km), respectively. Second, the skill score of the monthly root-mean-square error (RMSE) of layered WVD above 10 accounted for about 83%, 87%, and 64%. Third, for the annual biases of layered WVD, the new model significantly decreased by 1.1–1.5 g/m3 at layers 2–3 (about 1 km), where all schemes showed the maximal bias value. Finally, for the annual RMSE of layered WVD, the new model at the lower (about 0.6–3 km) and upper layers improved by 13–42% and 5–47%, respectively. Overall, the new model performed better on GNSS tomography and significantly improved the accuracy of GNSS tomographic results, compared to the traditional model.
Open-Source Data Alternatives and Models for Flood Risk Management in Nepal
Authors: Thakuri Sudeep , Parajuli Binod Prasad , Shakya Puja , Baskota Preshika , Pradhan Deepa , Chauhan Raju
Published: November 2022

Availability and applications of open-source data for disaster risk reductions are increasing. Flood hazards are a constant threat to local communities and infrastructures (e.g., built-up environment and agricultural areas) in Nepal. Due to its negative consequences on societies and economic aspects, it is critical to monitor and map those risks. This study presents the open access earth observation (EO) data, geospatial products, and different analytical models available for flood risk assessment (FRA) and monitoring in Nepal. The status of flood risk knowledge and open-source data was reviewed through a systematic literature review. Multispectral optical data are widely used, but use of microwave data is extremely low. With the recent developments in this field, especially optical and microwave data, the monitoring, mapping, and modeling of flood hazards and risk have been more rapid and precise and are published in several scientific articles. This study shows that the choice of appropriate measurements and data for a flood risk assessment and management involves an understanding of the flood risk mechanism, flood plain dynamics, and primary parameter that should be addressed in order to minimize the risk. At the catchments, floodplains, and basin level, a variety of open data sources and models may be used under different socioeconomic and environmental limitations. If combined and analyzed further, multi-source data from different models and platforms could produce a new result to better understand the risks and mitigation measures related to various disasters. The finding of this study helps to select and apply appropriate data and models for flood risk assessment and management in the countries like Nepal where the proprietary data and models are not easily accessible.
UAV-Assisted Fair Communication for Mobile Networks: A Multi-Agent Deep Reinforcement Learning Approach
Authors: Zhou Yi , Jin Zhanqi , Shi Huaguang , Wang Zhangyun , Lu Ning , Liu Fuqiang
Published: November 2022

Unmanned Aerial Vehicles (UAVs) can be employed as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground users (GUs). However, most existing works mainly focus on optimizing coverage and maximizing throughput, without considering the fairness of the GUs in communication services. This may result in certain GUs being underserviced by UAV-BSs in pursuit of maximum throughput. In this paper, we study the problem of UAV-assisted communication with the consideration of user fairness. We first design a Ratio Fair (RF) metric by weighting fairness and throughput to evaluate the tradeoff between fairness and communication efficiency when UAV-BSs serve GUs. The problem is formulated as a mixed-integer non-convex optimization problem based on the RF metric and we propose a UAV-Assisted Fair Communication (UAFC) algorithm based on multi-agent deep reinforcement learning to maximize the fair throughput of the system. The UAFC algorithm comprehensively considers fair throughput, UAV-BSs coverage, and flight status to design a reasonable reward function. In addition, the UAFC algorithm establishes an information sharing mechanism based on gated functions by sharing neural networks, which effectively reduces the distributed decision-making uncertainty of UAV-BSs. To reduce the impact of state dimension imbalance on the convergence of the algorithm, we design a new state decomposing and coupling actor network architecture. Simulation results show that the proposed UAFC algorithm increases fair throughput by 5.62%, 26.57% and fair index by 1.99%, 13.82% compared to the MATD3 and MADDPG algorithms, respectively. Meanwhile, UAFC can also meet energy consumption limitation and network connectivity requirement.
Superpixel and Supervoxel Segmentation Assessment of Landslides Using UAV-Derived Models
Authors: Farmakis Ioannis , Karantanellis Efstratios , Hutchinson D. Jean , Vlachopoulos Nicholas , Marinos Vassilis
Published: November 2022

Reality capture technologies such as Structure-from-Motion (SfM) photogrammetry have become a state-of-the-art practice within landslide research workflows in recent years. Such technology has been predominantly utilized to provide detailed digital products in landslide assessment where often, for thorough mapping, significant accessibility restrictions must be overcome. UAV photogrammetry produces a set of multi-dimensional digital models to support landslide management, including orthomosaic, digital surface model (DSM), and 3D point cloud. At the same time, the recognition of objects depicted in images has become increasingly possible with the development of various methodologies. Among those, Geographic Object-Based Image Analysis (GEOBIA) has been established as a new paradigm in the geospatial data domain and has also recently found applications in landslide research. However, most of the landslide-related GEOBIA applications focus on large scales based on satellite imagery. In this work, we examine the potential of different UAV photogrammetry product combinations to be used as inputs to image segmentation techniques for the automated extraction of landslide elements at site-specific scales. Image segmentation is the core process within GEOBIA workflows. The objective of this work is to investigate the incorporation of fully 3D data into GEOBIA workflows for the delineation of landslide elements that are often challenging to be identified within typical rasterized models due to the steepness of the terrain. Here, we apply a common unsupervised image segmentation pipeline to 3D grids based on the superpixel/supervoxel and graph cut algorithms. The products of UAV photogrammetry for two landslide cases in Greece are combined and used as 2D (orthomosaic), 2.5D (orthomosaic + DSM), and 3D (point cloud) terrain representations in this research. We provide a detailed quantitative comparative analysis of the different models based on expert-based annotations of the landscapes and conclude that using fully 3D terrain representations as inputs to segmentation algorithms provides consistently better landslide segments.
Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time “Out of the Lab” Data Processing
Authors: Varga Zsófia , Vörös Fanni , Pál Márton , Kovács Béla , Jung András , Elek István
Published: November 2022

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied “out of the lab”. During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.
Structural Optimisation and Design of a Cable-Driven Hyper-Redundant Manipulator for Confined Semi-Structured Environments
Authors: Al-Khulaidi Rami , Akmeliawati Rini , Grainger Steven , Lu Tien-Fu
Published: November 2022

Structural optimisation of robotic manipulators is critical for any manipulator used in confined semi-structured environments, such as in agriculture. Many robotic manipulators utilised in semi-structured environments retain the same characteristics and dimensions as those used in fully-structured industrial environments, which have been proven to experience low dexterity and singularity issues in challenging environments due to their structural limitations. When implemented in environments other than fully-structured industrial environments, conventional manipulators are liable to singularity, joint limits and workspace obstacles. This makes them inapplicable in confined semi-structured environments, as they lack the flexibility to operate dexterously in such challenging environments. In this paper, structural optimisation of a hyper-redundant cable-driven manipulator is proposed to improve its performance in semi-structured and challenging confined spaces, such as in agricultural settings. The optimisation of the manipulator design is performed in terms of its manipulability and kinematics. The lengths of the links and the joint angles are optimised to minimise any error between the actual and desired position/orientation of the end-effector in a confined semi-structured task space, as well as to provide optimal flexibility for the manipulators to generate different joint configurations for obstacle avoidance in confined environments. The results of the optimisation suggest that the use of a redundant manipulator with rigid short links can result in performance with higher dexterity in confined, semi-structured environments, such as agricultural greenhouses.
Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition
Authors: Babu Eaby Kollonoor , Mistry Kamlesh , Anwar Muhammad Naveed , Zhang Li
Published: November 2022

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations.
Probabilistic Fusion for Pedestrian Detection from Thermal and Colour Images
Authors: Shaikh Zuhaib Ahmed , Van Hamme David , Veelaert Peter , Philips Wilfried
Published: November 2022

Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.
Wireless Temperature, Relative Humidity and Occupancy Monitoring System for Investigating Overheating in Buildings
Authors: Szagri Dóra , Dobszay Bálint , Nagy Balázs , Szalay Zsuzsa
Published: November 2022

With the climate change we are experiencing today, the number and intensity of heatwaves are increasing dramatically, significantly impacting our buildings’ overheating. The majority of the prefabricated concrete panel buildings in Hungary are considered outdated from an energy point of view. These buildings may be at greater risk from extreme weather events. To examine this, long-term monitoring measurements are needed. Therefore, we developed a unique, reliable, and cost-effective wireless monitoring system, which can track in real time the indoor air quality data (temperature, relative humidity, CO2) of the investigated apartment building, as well as users’ habits, such as resident presence, window opening, and blind movement. The data were used to analyse and quantify the summer overheating of the dwelling and user habits. The measurements showed that the average temperature in all rooms was above 26 °C, and there were several occasions when the temperature exceeded 30 °C. Overheating in apartment buildings in summer is a significant problem that needs to be addressed. Further investigation of ventilation habits will help develop favourable ventilation strategies, and using these measurements in dynamic simulations will also help improve the models’ validity for further studies.
Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling
Authors: Mazzei Daniele , Ramjattan Reshawn
Published: November 2022

Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power. Industry 4.0 encapsulates a new stage of industrial processes and value chains driven by smart connection and automation. Large-scale problems within these industrial settings are a prime example of an environment that can benefit from ML. However, a clear view of how ML currently intersects with industry 4.0 is difficult to grasp without reading an infeasible number of papers. This systematic review strives to provide such a view by gathering a collection of 45,783 relevant papers from Scopus and Web of Science and analysing it with BERTopic. We analyse the key topics to understand what industry applications receive the most attention and which ML methods are used the most. Moreover, we manually reviewed 17 white papers of consulting firms to compare the academic landscape to an industry perspective. We found that security and predictive maintenance were the most common topics, CNNs were the most used ML method and industry companies, at the moment, generally focus more on enabling successful adoption rather than building better ML models. The academic topics are meaningful and relevant but technology focused on making ML adoption easier deserves more attention.
Ultrasonic Testing of Mechanical Changes in a Water-Filled Pipe with Multi-Mode and Broadband Signals and Two-Level Compensation
Authors: Ju Taeho , Findikoglu Alp T.
Published: November 2022

Ultrasonic testing (UT) has been widely used for the Nondestructive Evaluation (NDE) of pipes due to its many favorable characteristics. However, one of the main challenges in the general use of UT for real-world pipelines is the sensitivity of this method to environmental and operational condition changes. This paper proposes a new UT method with enhanced compensation for environmental effects and operational condition changes. In particular, the effectiveness of the new method is tested in the presence of temperature variations, and changes in water flow rate inside a stainless-steel pipe. The proposed UT method uses multi-mode and broadband guided ultrasonic waves in the pipe walls, excited and received by single-element ultrasonic sensors that are spatially separated, forming a measurement zone between any pair of such transmit and receive sensors. Amplitude changes, time shifts, and frequency content variations in the ultrasonic signal due to temperature changes and water flow are evaluated and compensated for reliable UT of mechanical changes in the pipe. It is observed that spurious effects of water flow on ultrasonic response, if not properly compensated, can dominate over effects due to actual mechanical changes, but such liquid-boundary effects can be compensated effectively by the proposed time- and frequency-filtering method.
Measurement of a Vibration on a Robotic Vehicle
Authors: Klimenda Frantisek , Cizek Roman , Suszynski Marcin
Published: November 2022

This article deals with the design and construction of a robotic vehicle. The first part of the paper focuses on the selection of suitable variants for the robotic vehicle arrangement, i.e., frame, electric motors with gearboxes, wheels, steering and accumulators. Based on the selection of individual components, the robotic vehicle was built. An important part of the robotic vehicle was the design of the suspension of the front wheels. The resulting shape of the springs was experimentally developed from several design variants and subsequently produced by an additive manufacturing process. The last part of article is devoted to the experimental measurement of the acceleration transfer to the upper part of the frame during the passage of the robotic vehicle over differently arranged obstacles. Experimental measurements measured the accelerations that are transferred to the top of the robotic vehicle frame when the front wheels of the vehicle cross over the obstacle (obstacles). The maximum acceleration values are 0.0588 m/s2 in the x-axis, 0.0149 m/s2 in the y-axis and 0.5755 m/s2 in the z-axis. This experimental solution verifies the stiffness of the designed frame and the damping effect of the selected material of the designed springs on the front wheels of the robotic vehicle.
V2ReID: Vision-Outlooker-Based Vehicle Re-Identification
Authors: Qian Yan , Barthelemy Johan , Iqbal Umair , Perez Pascal
Published: November 2022

With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for individuals and organisations, as it is difficult to navigate through the myriad of complex research in this field.
Evaluation of the Olfactory Quality of Roasted Coffee Beans Using a Digital Nose
Authors: Barea-Ramos Juan Diego , Cascos Gema , Mesías Marta , Lozano Jesús , Martín-Vertedor Daniel
Published: November 2022

The roasting process is one of the critical points to obtain a product of the highest quality with certain sensorial properties including the aroma of coffee. Samples of coffee beans were roasted at different thermal treatment intensities with the aim of obtaining aromatic compounds detected with an electronic device. Sensory analysis, volatile compound profiling, and electronic nose analysis were carried out. Through principal component analysis (95.8% of the total variance of the data was explained by PC1 and PC2) and partial least squares discriminant analysis (the sum of the diagonal elements gave a hit rate of 94%), it could be demonstrated that the E-nose is able to discriminate roasted coffee beans subjected to different thermal treatments. Aromatic profiling was carried out by a testing panel and volatile compounds (VOCs) for the discrimination of roasted coffee samples. Alcohols, aromatics, esters, ketones and furanone were found in higher proportions in samples at the lowest thermal treatment. The VOCs with positive attributes were 1-(4-nitrophenyl)-3-phenylamino-propenone, carboxylic acids, 2-methoxy-4-vinylphenol, and 2-phenylethyl alcohol, while the compounds with negative ones were 2-methyl-furan, 2,5-dimethyl-pyridine, 2-methyl-butanal, and 2-furfurylthiol. The PLS model allows for the quantification of the positive and negative aromas (RCV2 = 0.92) of roasted coffee by using the E-nose. Therefore, the E-nose, that is, an inexpensive and nondestructive instrument, could be a chemometric tool able to discriminate between different qualities of coffee during processing.