bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis, Characterization, and Stability Assessment for the Benzoate, Hydrochloride, Malonate, and Nicotinate Salts of Bedaquiline
Authors: Okezue Mercy A. , Byrn Stephen R.
Published: February 2023

Bedaquiline has been approved as a combination therapy to treat multi-drug-resistant tuberculosis in adults ≥ 18 years old. The citrate, fumarate, phosphate, and tartrate salts have obtained patents, but the structures for these moieties have not been extensively described in the literature; only the powder X-ray patterns have been published. To expand the knowledge of the bedaquiline structure, this study provides detailed information for the synthesis, elucidation, characterization, and stability of four additional new potential molecular entities, namely, benzoate, hydrochloride (HCl), nicotinate, and malonate salts. The salts were formed using a 1:1 ratio of the counter ions (acids) to a 30 mg equivalent of the bedaquiline free base. The principles of the International Conference on Harmonization Q6 were used to characterize the new salts and their stability-indicating parameters were evaluated at 0, 3, and 6 months under accelerated conditions of 40 °C and 75% relative humidity. The benzoate salt exhibited the lowest tendency to lose its chemical potency. Aside from the HCl salt, the others retained their chemical structure, displaying long-term stability. All salts were non-hygroscopic and the hydrated benzoate and nicotinate salts were stable to dehydration. Regarding their chemical potencies, thermal analysis, chemical stability, and water sorption potential, the salts were ranked as follows: benzoate > malonate > nicotinate > HCl.
Biodistribution of Intratracheal, Intranasal, and Intravenous Injections of Human Mesenchymal Stromal Cell-Derived Extracellular Vesicles in a Mouse Model for Drug Delivery Studies
Authors: Tolomeo Anna Maria , Zuccolotto Gaia , Malvicini Ricardo , De Lazzari Giada , Penna Alessandro , Franco Chiara , Caicci Federico , Magarotto Fabio , Quarta Santina , Pozzobon Michela , Rosato Antonio , Muraca Maurizio , Collino Federica
Published: February 2023

Mesenchymal stromal cell-derived extracellular vesicles (MSC-EVs) are extensively studied as therapeutic tools. Evaluation of their biodistribution is fundamental to understanding MSC-EVs’ impact on target organs. In our work, MSC-EVs were initially labeled with DiR, a fluorescent lipophilic dye, and administered to BALB/c mice (2.00 × 1010 EV/mice) through the following routes: intravenous (IV), intratracheal (IT) and intranasal (IN). DiR-labeled MSC-EVs were monitored immediately after injection, and after 3 and 24 hours (h). Whole-body analysis, 3 h after IV injection, showed an accumulation of MSC-EVs in the mice abdominal region, compared to IT and IN, where EVs mainly localized at the levels of the chest and brain region, respectively. After 24 h, EV-injected mice retained a stronger positivity in the same regions identified after 3 h from injection. The analyses of isolated organs confirmed the accumulation of EVs in the spleen and liver after IV administration. Twenty-four hours after the IT injection of MSC-EVs, a stronger positivity was detected selectively in the isolated lungs, while for IN, the signal was confined to the brain. In conclusion, these results show that local administration of EVs can increase their concentration in selective organs, limiting their systemic biodistribution and possibly the extra-organ effects. Biodistribution studies can help in the selection of the most appropriate way of administration of MSC-EVs for the treatment of different diseases.
Delivery Systems for Mitochondrial Gene Therapy: A Review
Authors: Faria Rúben , Boisguérin Prisca , Sousa Ângela , Costa Diana
Published: February 2023

Mitochondria are membrane-bound cellular organelles of high relevance responsible for the chemical energy production used in most of the biochemical reactions of cells. Mitochondria have their own genome, the mitochondrial DNA (mtDNA). Inherited solely from the mother, this genome is quite susceptible to mutations, mainly due to the absence of an effective repair system. Mutations in mtDNA are associated with endocrine, metabolic, neurodegenerative diseases, and even cancer. Currently, therapeutic approaches are based on the administration of a set of drugs to alleviate the symptoms of patients suffering from mitochondrial pathologies. Mitochondrial gene therapy emerges as a promising strategy as it deeply focuses on the cause of mitochondrial disorder. The development of suitable mtDNA-based delivery systems to target and transfect mammalian mitochondria represents an exciting field of research, leading to progress in the challenging task of restoring mitochondria’s normal function. This review gathers relevant knowledge on the composition, targeting performance, or release profile of such nanosystems, offering researchers valuable conceptual approaches to follow in their quest for the most suitable vectors to turn mitochondrial gene therapy clinically feasible. Future studies should consider the optimization of mitochondrial genes’ encapsulation, targeting ability, and transfection to mitochondria. Expectedly, this effort will bring bright results, contributing to important hallmarks in mitochondrial gene therapy.
Essential Oils from Apiaceae, Asteraceae, Cupressaceae and Lamiaceae Families Grown in Serbia: Comparative Chemical Profiling with In Vitro Antioxidant Activity
Authors: Gladikostić Nevena , Ikonić Bojana , Teslić Nemanja , Zeković Zoran , Božović Danica , Putnik Predrag , Bursać Kovačević Danijela , Pavlić Branimir
Published: February 2023

The aim of the present study was to investigate the chemical profile and antioxidant activity of essential oils obtained from the most commonly grown plant species in Serbia. Aromatic and medicinal plants from Lamiaceae (Mentha x Piperita, Ocimum basilicum, Origanum majorana, Origanum vulgare, Salvia officinalis, Satureja hortensis, Satureja montana and Thymus vulgaris), Asteraceae (Ehinacea purpurea and Matricaria chamomilla), Apiaceae (Anethum graveolens, Carum carvi, Foeniculum vulgare, Petroselinum crispum and Pimpinella anisum) and Cupressaceae (Juniperus comunis) were selected as raw material for essential oils (EOs)’ isolation. Hydrodistillation (HD) was used for the isolation of EOs while they were evaluated in terms of yield and terpenoid profiles by GC-MS. In vitro radical scavenging DPPH and ABTS+ radical activities were carried out for all EOs. Finally, a principal component analysis (PCA) was performed with the experimental results of the composition and antioxidant activity of the EOs, which showed a clear distinction between the selected plant species for the aforementioned responses. This work represents a screening tool for the selection of other EO candidates for further processing by emerging extraction techniques and the use of EOs as natural additives for meat products.
The ROP2 GTPase Participates in Nitric Oxide (NO)-Induced Root Shortening in Arabidopsis
Authors: Kenesi Erzsébet , Kolbert Zsuzsanna , Kaszler Nikolett , Klement Éva , Ménesi Dalma , Molnár Árpád , Valkai Ildikó , Feigl Gábor , Rigó Gábor , Cséplő Ágnes , Lindermayr Christian , Fehér Attila
Published: February 2023

Nitric oxide (NO) is a versatile signal molecule that mediates environmental and hormonal signals orchestrating plant development. NO may act via reversible S-nitrosation of proteins during which an NO moiety is added to a cysteine thiol to form an S-nitrosothiol. In plants, several proteins implicated in hormonal signaling have been reported to undergo S-nitrosation. Here, we report that the Arabidopsis ROP2 GTPase is a further potential target of NO-mediated regulation. The ROP2 GTPase was found to be required for the root shortening effect of NO. NO inhibits primary root growth by altering the abundance and distribution of the PIN1 auxin efflux carrier protein and lowering the accumulation of auxin in the root meristem. In rop2-1 insertion mutants, however, wild-type-like root size of the NO-treated roots were maintained in agreement with wild-type-like PIN1 abundance in the meristem. The ROP2 GTPase was shown to be S-nitrosated in vitro, suggesting that NO might directly regulate the GTPase. The potential mechanisms of NO-mediated ROP2 GTPase regulation and ROP2-mediated NO signaling in the primary root meristem are discussed.
The Combination of Both Heat and Water Stresses May Worsen Botryosphaeria Dieback Symptoms in Grapevine
Authors: Fernandez Olivier , Lemaître-Guillier Christelle , Songy Aurélie , Robert-Siegwald Guillaume , Lebrun Marc-Henri , Schmitt-Kopplin Philippe , Larignon Philippe , Adrian Marielle , Fontaine Florence
Published: February 2023

(1) Background: Grapevine trunk diseases (GTDs) have become a global threat to vineyards worldwide. These diseases share three main common features. First, they are caused by multiple pathogenic micro-organisms. Second, these pathogens often maintain a long latent phase, which makes any research in pathology and symptomatology challenging. Third, a consensus is raising to pinpoint combined abiotic stresses as a key factor contributing to disease symptom expression. (2) Methods: We analyzed the impact of combined abiotic stresses in grapevine cuttings artificially infected by two fungi involved in Botryosphaeria dieback (one of the major GTDs), Neofusicoccum parvum and Diplodia seriata. Fungal-infected and control plants were subjected to single or combined abiotic stresses (heat stress, drought stress or both). Disease intensity was monitored thanks to the measurement of necrosis area size. (3) Results and conclusions: Overall, our results suggest that combined stresses might have a stronger impact on disease intensity upon infection by the less virulent pathogen Diplodia seriata. This conclusion is discussed through the impact on plant physiology using metabolomic and transcriptomic analyses of leaves sampled for the different conditions.
Marker-Assisted Pyramiding of Blast-Resistance Genes in a japonica Elite Rice Cultivar through Forward and Background Selection
Authors: Zampieri Elisa , Volante Andrea , Marè Caterina , Orasen Gabriele , Desiderio Francesca , Biselli Chiara , Canella Marco , Carmagnola Lorena , Milazzo Joëlle , Adreit Henri , Tharreau Didier , Poncelet Nicolas , Vaccino Patrizia , Valè Giampiero
Published: February 2023

Rice blast, caused by Pyricularia oryzae, is one of the main rice diseases worldwide. The pyramiding of blast-resistance (Pi) genes, coupled to Marker-Assisted BackCrossing (MABC), provides broad-spectrum and potentially durable resistance while limiting the donor genome in the background of an elite cultivar. In this work, MABC coupled to foreground and background selections based on KASP marker assays has been applied to introgress four Pi genes (Piz, Pib, Pita, and Pik) in a renowned japonica Italian rice variety, highly susceptible to blast. Molecular analyses on the backcross (BC) lines highlighted the presence of an additional blast-resistance gene, the Pita-linked Pita2/Ptr gene, therefore increasing the number of blast-resistance introgressed genes to five. The recurrent genome was recovered up to 95.65%. Several lines carrying four (including Pita2) Pi genes with high recovery percentage levels were also obtained. Phenotypic evaluations confirmed the effectiveness of the pyramided lines against multivirulent strains, which also had broad patterns of resistance in comparison to those expected based on the pyramided Pi genes. The developed blast-resistant japonica lines represent useful donors of multiple blast-resistance genes for future rice-breeding programs related to the japonica group.
Experimental and Numerical Investigation of Axial Compression Behaviour of FRP-Confined Concrete-Core-Encased Rebar
Authors: Lu Jingzhou , Huang Han , Li Yunkai , Mou Tong
Published: February 2023

The axial compression behaviour of fibre-reinforced polymer (FRP)-confined concrete-core-encased rebar (FCCC-R) was investigated by performing monotonic axial compression tests on seven groups of FCCC-R specimens and three groups of pure rebar specimens. The research parameters considered were the FRP winding angle (0°, ±45°, and 90°), number of layers (2, 4, and 6 layers), and slenderness ratio of specimens (15.45, 20, and 22.73). The test results showed that FCCC-R’s axial compression behaviour improved significantly compared with pure rebar. The axial load–displacement curves of the FCCC-R specimens had a second ascending branch, and their carrying capacity and ductility were enhanced substantially. The best buckling behaviour was observed for the FRP winding angle of 90°. The capacity and ductility of the specimens were positively related to the number of FRP-wrapped layers and inversely related to the slenderness ratio of the specimens. A finite element model of FCCC-R was constructed and agreed well with the test results. The finite element model was used for parametric analysis to reveal the effect of the area ratio, FRP confinement length, internal bar eccentricity, and mortar strength on the axial compression behaviour of FCCC-R. The numerical results showed that the area ratio had the most significant impact on the axial compression behaviour of FCCC-R. The confinement length of the FRP pipe and internal bar eccentricity had similar effects on the axial compression behaviour of FCCC-R. Both of them had a significant impact on the second ascending branch, with the post-peak behaviour exhibiting minimal differences. The influence of mortar strength on the axial compression behaviour of FCCC-R was observed to be minimal.
Hydrothermal Ageing of Glass Fibre Reinforced Vinyl Ester Composites: A Review
Authors: Thomason James , Xypolias Georgios
Published: February 2023

The use of glass fibre-reinforced polymer (GFRP) composites in load-carrying constructions has significantly increased over the last few decades. Such GFRP composite structures may undergo significant changes in performance as a consequence of long-term environmental exposure. Vinyl ester (VE) resins are a class of thermosetting polymers increasingly being used in such structural composites. This increasing use of VE-based GFRPs in such applications has led to an increasing need to better understand the consequences of long-term environmental exposure on their performance. The reliable validation of the environmental durability of new VE-based GFRPs can be a time- and resource-consuming process involving costly testing programs. Accelerated hydrothermal ageing is often used in these investigations. This paper reviews the relevant literature on the hydrothermal ageing of vinyl ester-based GFRP with special attention to the fundamental background of moisture-induced ageing of GFRP, the important role of voids, and the fibre-matrix interface, on composite mechanical performance.
Multi-Stage Thermal Modelling of Extrusion-Based Polymer Additive Manufacturing
Authors: Yang Jiong , Yue Hexin , Mirihanage Wajira , Bartolo Paulo
Published: February 2023

Additive manufacturing is one the most promising fabrication strategies for the fabrication of bone tissue scaffolds using biodegradable semi-crystalline polymers. During the fabrication process, polymeric material in a molten state is deposited in a platform and starts to solidify while cooling down. The build-up of consecutive layers reheats the previously deposited material, introducing a complex thermal cycle with impacts on the overall properties of printed scaffolds. Therefore, the accurate prediction of these thermal cycles is significantly important to properly design the additively manufactured polymer scaffolds and the bonding between the layers. This paper presents a novel multi-stage numerical model, integrating a 2D representation of the dynamic deposition process and a 3D thermal evolution model to simulate the fabrication process. Numerical simulations show how the deposition velocity controls the spatial dimensions of the individual deposition layers and the cooling process when consecutive layers are deposited during polymer printing. Moreover, numerical results show a good agreement with experimental results.
Hydrogel/β-FeOOH-Coated Poly(vinylidene fluoride) Membranes with Superhydrophilicity/Underwater Superoleophobicity Facilely Fabricated via an Aqueous Approach for Multifunctional Applications
Authors: Tang Yin , Zhu Tang , Liu Huichao , Tang Zheng , Kuang Xingwen , Qiao Yongna , Zhang Hao , Zhu Caizhen
Published: February 2023

Hydrogel coatings that can endow various substrates with superior properties (e.g., biocompatibility, hydrophilicity, and lubricity) have wide applications in the fields of oil/water separation, antifouling, anti-bioadhesion, etc. Currently, the engineering of multifunctional hydrogel-coated materials with superwettability and water purification property using a simple and sustainable strategy is still largely uninvestigated but has a beneficial effect on the world. Herein, we successfully prepared poly(2-acrylamido-2-methyl-1-propanesulfonic acid) hydrogel/β-FeOOH-coated poly(vinylidene fluoride) (PVDF/PAMPS/β-FeOOH) membrane through free-radical polymerization and the in situ mineralization process. In this work, owing to the combination of hydrophilic PAMPS hydrogel coating and β-FeOOH nanorods anchored onto PVDF membrane, the resultant PVDF/PAMPS/β-FeOOH membrane achieved outstanding superhydrophilicity/underwater superoleophobicity. Moreover, the membrane not only effectively separated surfactant-stabilized oil/water emulsions, but also possessed a long-term use capacity. In addition, excellent photocatalytic activity against organic pollutants was demonstrated so that the PVDF/PAMPS/β-FeOOH membrane could be utilized to deal with wastewater. It is envisioned that these hydrogel/β-FeOOH-coated PVDF membranes have versatile applications in the fields of oil/water separation and wastewater purification.
Aryl Hydrocarbon Receptor (AhR) Signaling in Colonic Cells and Tumors
Authors: Safe Stephen , Han Huajun , Jayaraman Arul , Davidson Laurie A. , Allred Clinton D. , Ivanov Ivan , Yang Yongjian , Cai James J. , Chapkin Robert S.
Published: February 2023

The aryl hydrocarbon receptor (AhR) is overexpressed in many tumor types and exhibits tumor-specific tumor promoter and tumor suppressor-like activity. In colon cancer, most but not all studies suggest that the AhR exhibits tumor suppressor activity which is enhanced by AhR ligands acting as agonists. Our studies investigated the role of the AhR in colon tumorigenesis using wild-type and AhR-knockout mice, the inflammation model of colon tumorigenesis using mice treated with azoxymethane (AOM)/dextran sodium sulfate (DSS) and APCS580/+; KrasG12D/+ mice all of which form intestinal tumors. The effects of tissue-specific AhR loss in the intestine of the tumor-forming mice on colonic stem cells, organoid-initiating capacity, colon tumor formation and mechanisms of AhR-mediated effects were investigated. Loss of AhR enhanced stem cell and tumor growth and in the AOM/DSS model AhR-dependent suppression of FOXM1 and downstream genes was important for AhR-dependent anticancer activity. Furthermore, the effectiveness of interleukin-22 (IL22) in colonic epithelial cells was also dependent on AhR expression. IL22 induced phosphorylation of STAT3, inhibited colonic organoid growth, promoted colonic cell proliferation in vivo and enhanced DNA repair in AOM/DSS-induced tumors. In this mouse model, the AhR suppressed SOCS3 expression and enhanced IL22-mediated activation of STAT3, whereas the loss of the AhR increased levels of SOCS3 which in turn inhibited IL22-induced STAT3 activation. In the APCS580/+; KrasG12D/+ mouse model, the loss of the AhR enhanced Wnt signaling and colon carcinogenesis. Results in both mouse models of colon carcinogenesis were complemented by single cell transcriptomics on colonic intestinal crypts which also showed that AhR deletion promoted expression of FOXM1-regulated genes in multiple colonic cell subtypes. These results support the role of the AhR as a tumor suppressor-like gene in the colon.
A Kantian Response to the Problem of Evil: Living in the Moral World
Authors: Insole Christopher J.
Published: February 2023

James Sterba has presented a powerful and existentially sincere form of the problem of evil, arguing that it is logically impossible for God to exist, given that there are powerful moral requirements to prevent evil, where one can, and that these requirements would bind an all-powerful and good God, who would indeed be able to prevent such evil. The ‘Kantian’ argument that I set out, if accepted, would undermine the following stage of Sterba’s argument: Significant and especially horrendous evil consequences of immoral actions do obtain all around us, which, if God exists, would have to be through his permission. The Kantian argument will hold that we are able to believe that, in some sense, such horrendous evil consequences do not really obtain, although they appear to. The claim is not that the Kantian argument is ‘persuasive’, but that if some Kantian assumptions are granted, we do have a response to Sterba, which throws open a different way of looking at things. I conclude with some more informal reflections on what we might take away from the Kantian argument, even if we do not accept the deep assumptions, or the progression of the argument. I will not worry too much about demonstrating that this is a ‘correct reading’ of Kant, although I think it is.
Mystical Experience: Women’s Pathway to Knowledge
Authors: Bingemer Maria Clara
Published: February 2023

This article aims to reflect on two issues within the vast area of mystical studies: 1. The link between mystical experience and knowledge; 2. Mystical experiences lived through by women as a pathway to and from knowledge. Firstly, we will try to circumscribe the concept of mystical experience by retrieving some of the main thoughts of scholars who have studied the mystical phenomenon and the writings of individuals who experienced it. Secondly, we will pursue our reflection with the aid of philosophers and theologians who thought and argued that mystical experience is and contains knowledge and bears not only affective and spiritual, but also intellectual, fruit. Thirdly, we will attempt to demonstrate how, throughout the history of Christianity, a significant number of women were the specific protagonists of this synthesis between experience and knowledge and how this allowed them to bring original contributions to their context and historical time. We conclude with a detailed commentary and reflection on the French 20th-century mystic Simone Weil who, as both an intellectual and a mystic, was a pioneer in bringing a prophetic vision on issues that would inspire society, the Church, and spiritual life many decades after her death. Our conclusions will be based on this and on the countercultural benefits that mystical experience lived through by women can bring to contemporary times.
Formulating MCoVaR to Quantify Joint Transmissions of Systemic Risk across Crypto and Non-Crypto Markets: A Multivariate Copula Approach
Authors: Hakim Arief , Syuhada Khreshna
Published: February 2023

Evidence that cryptocurrencies exhibit speculative bubble behavior is well documented. This evidence could trigger global financial instability leading to systemic risk. It is therefore crucial to quantify systemic risk and investigate its transmission mechanism across crypto markets and other global financial markets. We can accomplish this using the so-called multivariate conditional value-at-risk (MCoVaR), which measures the tail risk of a targeted asset from each market conditional on a set of multiple assets being jointly in distress and on a set of the remaining assets being jointly in their median states. In this paper, we aimed to find its analytic formulas by considering multivariate copulas, which allow for the separation of margins and dependence structures in modeling the returns of the aforementioned assets. Compared to multivariate normal and Student’s t benchmark models and a multivariate Johnson’s SU model, the copula-based models with non-normal margins produced a MCoVaR forecast with superior conditional coverage and backtesting performances. Using a corresponding Delta MCoVaR, we found the crypto assets to be potential sources of systemic risk jointly transmitted within the crypto markets and towards the S&P 500, oil, and gold, which was more apparent during the COVID-19 period encompassing the recent 2021 crypto bubble event.
Estimation of Geopotential Value W0 for the Geoid and Local Vertical Datum Parameters
Authors: Liu Xinyu , Li Shanshan , Yuan Jiajia , Fan Diao , Tan Xuli
Published: February 2023

Unification of the global vertical datum has been a key problem to be solved for geodesy over a long period, and the main challenge for unifying the global vertical datum system is to determine the geopotential value W0 of the geoid and to calculate the vertical offset between the local vertical datum and the global vertical datum W0. The geopotential value W0 can be calculated using the grid mean sea surface (GMSS) data and the global geopotential model (GGM). In this study, this GMSS data was measured with adjustment methods and 24 years of merged multi-satellite altimetry data. The data of HaiYang-2A (HY-2A) and Jason-3 were first used to calculate W0. The geopotential value W0 was determined to be 62,636,856.82 m2s−2 by combining the EIGEN-6C4 (European Improved Gravity Model of the Earth by New Techniques) and the GMSS data. Then, the geopotential difference approach and geodetic boundary value problem (GBVP) approach were used to determine the vertical datum parameters in this study. To compensate for the omission error of the GGM, this study utilized the remove–compute–restore (RCR) technique and the residual terrain model (RTM)-recovered high-frequency gravity signals. Finally, as a result of the GBVP solution, the geopotential value of the Australian Height Datum (AHD) was 62,636,851.935 m2s−2, and the vertical offset of the AHD relative to the global vertical datum W0 was 0.4885 m. As a result of the geopotential difference approach, the geopotential value of the Chinese Height datum was 62636861.412 m2s−2, and the vertical offset of the Chinese Height datum was −0.4592 m.
Voronoi Centerline-Based Seamline Network Generation Method
Authors: Yuan Xiuxiao , Cai Yang , Yuan Wei
Published: February 2023

Seamline network generation is a crucial step in mosaicking multiple orthoimages. It determines the topological and mosaic contribution area for each orthoimage. Previous methods, such as Voronoi-based and AVOD (area Voronoi)-based, may generate mosaic holes in low-overlap and irregular orthoimage cases. This paper proposes a Voronoi centerline-based seamline network generation method to address this problem. The first step is to detect the edge vector of the valid orthoimage region; the second step is to construct a Voronoi triangle network using the edge vector points and extract the centerline of the network; the third step is to segment each orthoimage by the generated centerlines to construct the image effective mosaic polygon (EMP). The final segmented EMP is the mosaic contribution region. All EMPs are interconnected to form a seamline network. The main contribution of the proposed method is that it solves the mosaic holes in the Voronoi-based method when processing with low overlap, and it solves the limitation of the AVOD-based method polygon shape requirement, which can generate a complete mosaic in any overlap and any shape of the orthoimage. Five sets of experiments were conducted, and the results show that the proposed method surpasses the well-known state-of-the-art method and commercial software in terms of adaptability and effectiveness.
Calibration of the ESA CCI-Combined Soil Moisture Products on the Qinghai-Tibet Plateau
Authors: Yu Wenjun , Li Yanzhong , Liu Guimin
Published: February 2023

Soil moisture (SM) retrieved from satellite and spaceborn sensors provides useful parameters for earth system models (ESMs). The Climate Change Initiative (CCI) SM products released by the European Space Agency have been widely used in many humid/semi-humid climatic regions due to their relatively long-term record. However, the performance of these products in cold and arid regions, such as the Qinghai-Tibetan Plateau (QTP), is largely unknown, necessitating urgent evaluation and calibration in these areas. In this work, we evaluated the reliability and improved the accuracy of the active-passive combined CCI products (CCI-C) using in-situ measured SM contents (SMC) under different underlying surface conditions. First, some conventional models were used to investigate the relationship between the CCI-C and the in-situ observed SMC, yielding similar fitting performances. Next, the random forest method and multiple linear regression were used to contrast the conventional models to calibrate and validate the CCI-C SM product based on the in-situ observed SMC, and the random forest method was found to have the highest accuracy. However, calibration of the CCI-C SM data with the best-performed random forest method based on different spatial zonation methods, e.g., by climate, topography, land cover, and vegetation, resulted in distinct spatial patterns of SM. Compared to a widely-used satellite SM product, namely the Soil Moisture Active Passive (SMAP) SM dataset, the calibrated CCI-C SM data based on climatic and vegetation zonation were larger but had similar spatial patterns. This study also points to the value of the calibrated CCI-C SM product to support land surface studies on the QTP.
Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
Authors: Khan M. Jalal , Khan Manzoor Ahmed , Ullah Obaid , Malik Sumbal , Iqbal Farkhund , El-Sayed Hesham , Turaev Sherzod
Published: February 2023

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving.
A Novel Mine-Specific Eco-Environment Index (MSEEI) for Mine Ecological Environment Monitoring Using Landsat Imagery
Authors: Zhang Peipei , Chen Xidong , Ren Yu , Lu Siqi , Song Dongwei , Wang Yingle
Published: February 2023

The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p < 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments.
Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection
Authors: D’hooge Laurens , Verkerken Miel , Wauters Tim , De Turck Filip , Volckaert Bruno
Published: February 2023

Recently proposed methods in intrusion detection are iterating on machine learning methods as a potential solution. These novel methods are validated on one or more datasets from a sparse collection of academic intrusion detection datasets. Their recognition as improvements to the state-of-the-art is largely dependent on whether they can demonstrate a reliable increase in classification metrics compared to similar works validated on the same datasets. Whether these increases are meaningful outside of the training/testing datasets is rarely asked and never investigated. This work aims to demonstrate that strong general performance does not typically follow from strong classification on the current intrusion detection datasets. Binary classification models from a range of algorithmic families are trained on the attack classes of CSE-CIC-IDS2018, a state-of-the-art intrusion detection dataset. After establishing baselines for each class at various points of data access, the same trained models are tasked with classifying samples from the corresponding attack classes in CIC-IDS2017, CIC-DoS2017 and CIC-DDoS2019. Contrary to what the baseline results would suggest, the models have rarely learned a generally applicable representation of their attack class. Stability and predictability of generalized model performance are central issues for all methods on all attack classes. Focusing only on the three best-in-class models in terms of interdataset generalization, reveals that for network-centric attack classes (brute force, denial of service and distributed denial of service), general representations can be learned with flat losses in classification performance (precision and recall) below 5%. Other attack classes vary in generalized performance from stark losses in recall (−35%) with intact precision (98+%) for botnets to total degradation of precision and moderate recall loss for Web attack and infiltration models. The core conclusion of this article is a warning to researchers in the field. Expecting results of proposed methods on the test sets of state-of-the-art intrusion detection datasets to translate to generalized performance is likely a serious overestimation. Four proposals to reduce this overestimation are set out as future work directions.
Surface Monitoring of an MSW Landfill Based on Linear and Angular Measurements, TLS, and LIDAR UAV
Authors: Pasternak Grzegorz , Zaczek-Peplinska Janina , Pasternak Klaudia , Jóźwiak Jacek , Pasik Mariusz , Koda Eugeniusz , Vaverková Magdalena Daria
Published: February 2023

Surface monitoring of landfills is crucial not only during their operation but also for later land restoration and development. Measurements concern environmental factors, such as leachate, migration of pollutants to water, biogas, and atmospheric emissions, and geotechnical factors, such as stability and subsidence. Landfill subsidence can be measured using modern surveying techniques. Modern measurement methods for landfill body displacement monitoring and their control after restoration and adaptation as recreational areas include terrestrial laser scanning (TLS), and scanning and low-altitude photogrammetric measurements from an unmanned aerial vehicle (UAV). The acquired measurement data in the form of 3D point clouds should be referenced to the local control network to enable a comprehensive analysis of data acquired using various techniques, including geotechnical sensors such as benchmarks, piezometers, and inclinometers. This study discusses the need for surface monitoring of municipal solid waste (MSW) landfills. A properly 3-D mapped landfill mass is the basis for ensuring the geotechnical safety of the restored landfill. Based on archival data and current measurements of the Radiowo landfill (Poland), this study compares the advantages and limitations of the following measurement techniques: linear and angular measurements, satellite measurements, TLS, and UAV scanning and photogrammetry, considering specific conditions of the location and vegetation of the landfill. Solutions for long-term monitoring were proposed, considering the cost and time resolution necessary for creating a differential model of landfill geometry changes.
Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics
Authors: Deepaisarn Somrudee , Yiwsiw Paphana , Chaisawat Sirada , Lerttomolsakul Thanakit , Cheewakriengkrai Leeyakorn , Tantiwattanapaibul Chanon , Buaruk Suphachok , Sornlertlamvanich Virach
Published: February 2023

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.
Static and Dynamic Multiparameter Assessment of Structural Elements Using Chirped Fiber Bragg Gratings
Authors: Macedo Leandro , Souza Edson A. , Frizera Anselmo , Pontes Maria José , Marques Carlos , Leal-Junior Arnaldo
Published: February 2023

This paper presents the development, analysis, and application of chirped fiber Bragg gratings (CFBGs) for dynamic and static measurements of beams of different materials in the single-cantilever configuration. In this case, the beams were numerically analyzed using the finite-element method (FEM) for the assessment of the natural frequencies and vibration modes of the beam for the dynamic analysis of the structural element. Furthermore, the static numerical analysis was performed using a load at the free end of the beam, where the maximum strain and its distribution along the beam were analyzed, especially in the region at which the FBG was positioned. The experimental evaluation of the proposed CFBG sensor was performed in static conditions for forces from 0 to 50 N (in 10 N steps) applied at the free end of the beam, whereas the dynamic evaluation was performed by means of positioning an unbalanced motor at the end of the beam, which was excited at 16 Hz, 65 Hz, 100 Hz, and 131 Hz. The results showed the feasibility of the proposed device for the simultaneous assessment of the force and strain distribution along the CFBG region using the wavelength shift and the full-width at half-maximum (FWHM), respectively. In these cases, the determination coefficients of the spectral features as a function of the force and strain distribution were higher than 0.99 in all analyzed cases, where a potential resolution of 0.25 N was obtained on the force assessment. In the dynamic tests, the frequency spectrum of the sensor responses indicated a frequency peak at the excited frequency in all analyzed cases. Therefore, the proposed sensor device is a suitable option to extend the performance of sensors for structural health assessment, since it is possible to simultaneously measure different parameters in dynamic and static conditions using only one sensor device, which, due to its multiplexing capabilities, can be integrated with additional optical fiber sensors for the complete shape reconstruction with millimeter-range spatial resolution.
Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks
Authors: Sampath Vignesh , Maurtua Iñaki , Aguilar Martín Juan José , Iriondo Ander , Lluvia Iker , Aizpurua Gotzone
Published: February 2023

Surface defect identification based on computer vision algorithms often leads to inadequate generalization ability due to large intraclass variation. Diversity in lighting conditions, noise components, defect size, shape, and position make the problem challenging. To solve the problem, this paper develops a pixel-level image augmentation method that is based on image-to-image translation with generative adversarial neural networks (GANs) conditioned on fine-grained labels. The GAN model proposed in this work, referred to as Magna-Defect-GAN, is capable of taking control of the image generation process and producing image samples that are highly realistic in terms of variations. Firstly, the surface defect dataset based on the magnetic particle inspection (MPI) method is acquired in a controlled environment. Then, the Magna-Defect-GAN model is trained, and new synthetic image samples with large intraclass variations are generated. These synthetic image samples artificially inflate the training dataset size in terms of intraclass diversity. Finally, the enlarged dataset is used to train a defect identification model. Experimental results demonstrate that the Magna-Defect-GAN model can generate realistic and high-resolution surface defect images up to the resolution of 512 × 512 in a controlled manner. We also show that this augmentation method can boost accuracy and be easily adapted to any other surface defect identification models.
Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples
Authors: Urbina Ortega Carlos , Quevedo Gutiérrez Eduardo , Quintana Laura , Ortega Samuel , Fabelo Himar , Santos Falcón Lucana , Marrero Callico Gustavo
Published: February 2023

Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.
YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Detection in Aerial Imagery
Authors: Betti Alessandro , Tucci Mauro
Published: February 2023

Small target detection is still a challenging task, especially when looking at fast and accurate solutions for mobile or edge applications. In this work, we present YOLO-S, a simple, fast, and efficient network. It exploits a small feature extractor, as well as skip connection, via both bypass and concatenation, and a reshape-passthrough layer to promote feature reuse across network and combine low-level positional information with more meaningful high-level information. Performances are evaluated on AIRES, a novel dataset acquired in Europe, and VEDAI, benchmarking the proposed YOLO-S architecture with four baselines. We also demonstrate that a transitional learning task over a combined dataset based on DOTAv2 and VEDAI can enhance the overall accuracy with respect to more general features transferred from COCO data. YOLO-S is from 25% to 50% faster than YOLOv3 and only 15–25% slower than Tiny-YOLOv3, outperforming also YOLOv3 by a 15% in terms of accuracy (mAP) on the VEDAI dataset. Simulations on SARD dataset also prove its suitability for search and rescue operations. In addition, YOLO-S has roughly 90% of Tiny-YOLOv3’s parameters and one half FLOPs of YOLOv3, making possible the deployment for low-power industrial applications.