bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct Numerical Modeling as a Tool for Optical Coherence Tomography Development: SNR (Sensitivity) and Lateral Resolution Test Target Interpretation
Authors: Lawman Samuel , Shen Yao-Chun
Published: May 2024

Optical Coherence Tomography (OCT) is a growing family of biophotonic imaging techniques, but in the literature there is a lack of easy-to-use tools to universally directly evaluate a device’s theoretical performance for a given metric. Modern computing tools mean that direct numerical modeling can, from first principles, simulate the performance metrics of a specific device directly without relying on analytical approximations and/or complexities. Here, we present two different direct numerical models, along with the example MATLAB code for the reader to adapt to their own systems. The first model is of photo-electron shot noise at the detector, the primary noise source for OCT. We use this firstly to evaluate the amount of additional noise present (1.5 dB) for an experimental setup. Secondly, we demonstrate how to use it to precisely quantify the expected shot noise SNR limit difference between time-domain and Fourier-domain OCT systems in a given hypothetical experiment. The second model is used to demonstrate how USAF 1951 test chart images should be interpreted for a given lateral PSF shape. Direct numerical modeling is an easy and powerful basic tool for researchers and developers, the wider use of which may improve the rigor of the OCT literature.
Rosarugosides A and D from Rosa rugosa Flower Buds: Their Potential Anti-Skin-Aging Effects in TNF-α-Induced Human Dermal Fibroblasts
Authors: Kim Kang Sub , Son So-Ri , Choi Yea Jung , Kim Yejin , Ahn Si-Young , Jang Dae Sik , Lee Sullim
Published: May 2024

This present study investigated the anti-skin-aging properties of Rosa rugosa. Initially, phenolic compounds were isolated from a hot water extract of Rosa rugosa’s flower buds. Through repeated chromatography (column chromatography, MPLC, and prep HPLC), we identified nine phenolic compounds (1–9), including a previously undescribed depside, rosarugoside D (1). The chemical structure of 1 was elucidated via NMR, HR-MS, UV, and hydrolysis. Next, in order to identify bioactive compounds that are effective against TNF-α-induced NHDF cells, we measured intracellular ROS production in samples treated with each of the isolated compounds (1–9). All isolates reduced the level of ROS at a concentration of 10 μM. Particularly, two depsides—rosarugosides A and D (2 and 1)—significantly inhibited ROS expression in TNF-α-induced NHDFs compared to the other phenolic compounds. Subsequently, the production of MMP-1 and procollagen type Ι α1 by these two depsides was examined. Remarkably, rosarugoside A (2) significantly decreased MMP-1 secretion at all concentrations. In contrast, rosarugoside D (1) regulated the expression of procollagen type Ι α1. These findings collectively suggest that Rosa rugosa extracts and their isolated compounds, rosarugosides A (2) and D (1), hold significant potential for protecting against aging and skin damage. Overall, these findings suggest that Rosa rugosa extracts and their isolated compounds, rosarugosides A (2) and D (1), have the potential to prevent and protect against aging and skin damage, although more specific quantitative analysis is needed.
Natural Disasters’ Impact on Water Quality and Public Health: A Case Study of the Cyclonic Season (2019–2023)
Authors: Chilaule Sérgio Mateus , Macuacua Xadreque Vitorino , Mabica Alfredo Pedro , Miranda Nelson Alexandre , Pereira Henrique dos Santos , Gudo Eduardo Samo , Marrufo Tatiana , García-López Santiago , Lopes Myriam
Published: May 2024

The impact of climate change has made weather events more extreme, unpredictable and frequent. In the last 4 years, Mozambique has been devastated by 8 major cyclones, resulting in material and human damage and affecting the functioning of basic local services, such as in the water and health sectors. In this study, we explored the environmental components of the climate–water quality–disease relationship that could drive the dynamics of waterborne diseases. Statistical models and geospatial information technologies (GITs) were used to analyse water quality and the relationship with waterborne diseases between 2016 and 2023. Results indicate that water quality is the main element, between precipitation natural disasters and waterborne diseases, on which a relevant public health intervention can act to ameliorate the future negative impacts of climate change and disease incidence. The results of this study also showed that the quality of water affected mainly by heavy rain events varies in different regions and in different seasons.
Butane Tetracarboxylic Acid Grafted on Polymeric Nanofibrous Aerogels for Highly Efficient Protein Absorption and Separation
Authors: Lu Jianwei , Jiang Yangang , Qiao Yufei , Wen Zihao , Luo Zhengjin , Ahmed Mukhtar , Ali Amjad , Guo Li
Published: May 2024

Developing high-performance and low-cost protein purification materials is of great importance to meet the demands for highly purified proteins in biotechnological industries. Herein, a facile strategy was developed to design and construct high-efficiency protein absorption and separation media by combining aerogels’ molding techniques and impregnation processes. Poly (ethylene-co-vinyl alcohol) (EVOH) nanofibrous aerogels (NFAs) were modified by grafting butane tetracarboxylic acid (BTCA) over them in situ. This modification was carried out using polyphosphoric acid as a catalyst. The resulting EVOH/BTCA NFAs exhibited favorable comprehensive properties. Benefiting from the highly interconnected porous structure, good underwater compressive properties, and abundant absorption ligands, the obtained EVOH/BTCA NFAs possessed a high static absorption capacity of 1082.13 mg/g to lysozyme and a short absorption equilibrium time of about 6 h. A high saturated dynamic absorption capacity for lysozyme (716.85 mg/g) was also realized solely by gravity. Furthermore, EVOH/BTCA NFAs displayed excellent reusability, good acid and alkaline resistance, and unique absorption selectivity performance. The successful synthesis of such aerogels can provide a potential candidate for next-generation protein absorbents for bio-separation and purification engineering.
Fully Bio-Based Polymer Composites: Preparation, Characterization, and LCD 3D Printing
Authors: Colucci Giovanna , Sacchi Francesca , Bondioli Federica , Messori Massimo
Published: May 2024

The present work aimed to prepare novel bio-based composites by adding fillers coming from agro-wastes to an acrylate epoxidized soybean oil (AESO) resin, using liquid crystal display (LCD) 3D printing. Different photocurable formulations were prepared by varying the reactive diluents, iso-bornyl methacrylate (IBOMA) and tetrahydrofurfuryl acrylate (THFA). Then, two fillers derived from different industrial wastes, corn (GTF) and wine (WPL-CF) by-products, were added to the AESO-based formulations to develop polymer composites with improved properties. The printability by LCD of the photocurable formulations was widely studied. Bio-based objects with different geometries were realized, showing printing accuracy, layer adhesion, and accurate details. The thermo-mechanical and mechanical properties of the 3D-printed composites were tested by TGA, DMA, and tensile tests. The results revealed that the agro-wastes’ addition led to a remarkable increase in the elastic modulus, tensile strength, and glass transition temperature in the glassy state for the systems containing IBOMA and for flexible structures in the rubbery region for systems containing THFA. AESO-based polymers demonstrated tunable properties, varying from rigid to flexible, in the presence of different diluents and biofillers. This finding paves the way for the use of this kind of composite in applications, such as biomedical for the realization of prostheses.
Characterization of Chitosan Hydrogels Obtained through Phenol and Tripolyphosphate Anionic Crosslinking
Authors: Hidaka Mitsuyuki , Kojima Masaru , Sakai Shinji , Delattre Cédric
Published: May 2024

Chitosan is a deacetylated polymer of chitin that is extracted mainly from the exoskeleton of crustaceans and is the second-most abundant polymer in nature. Chitosan hydrogels are preferred for a variety of applications in bio-related fields due to their functional properties, such as antimicrobial activity and wound healing effects; however, the existing hydrogelation methods require toxic reagents and exhibit slow gelation times, which limit their application in biological fields. Therefore, a mild and rapid gelation method is necessary. We previously demonstrated that the visible light-induced gelation of chitosan obtained through phenol crosslinking (ChPh) is a rapid gelation method. To further advance this method (<10 s), we propose a dual-crosslinked chitosan hydrogel obtained by crosslinking phenol groups and crosslinking sodium tripolyphosphate (TPP) and the amino groups of chitosan. The chitosan hydrogel was prepared by immersing the ChPh hydrogel in a TPP solution after phenol crosslinking via exposure to visible light. The physicochemical properties of the dual-crosslinked hydrogels, including Young’s moduli and water retentions, were subsequently investigated. Young’s moduli of the dual-crosslinked hydrogels were 20 times higher than those of the hydrogels without TPP ion crosslinking. The stiffness could be manipulated by varying the immersion time, and the water retention properties of the ChPh hydrogel were improved by TPP crosslinking. Ion crosslinking could be reversed using an iron chloride solution. This method facilitates chitosan hydrogel use for various applications, particularly tissue engineering and drug delivery.
In Situ Changes in Mechanical Properties Based on Gas Saturation Inside Pressure Vessels
Authors: Kim Kwan Hoon , Kim Jae Hoo , Lim Dong Hwan , Kwon Byung Chul , Hong Jin , Yoon Ho Sub , Cha Sung Woon
Published: May 2024

In previous studies, difficulties were encountered in measuring changes within high-pressure vessels owing to limitations such as sensor connectors and sensor failures under high-pressure conditions. In addition, polymer–gas mixtures experience instantaneous gas desorption upon exiting high-pressure vessels owing to pressure differentials, leading to measurement errors. In this study, a device using magnetic sensors was developed to measure the real-time changes in gas-saturated polymers inside pressure vessels. Experiments on polymethyl methacrylate gas adsorption were conducted with parameters including pressure at 5 MPa and temperatures ranging from −20 to 40 °C for 60 and 180 min. It was observed that at −20 °C, the maximum magnetic field force density and deflection were 391.53 μT and 5.83 mm, respectively, whereas at 40 °C, deflection did not occur, with a value of 321.79 μT. Based on gas saturation experiments, a new model for deflection in high-pressure atmospheres is proposed. Additionally, an ANSYS analysis was conducted to predict the changes in Young’s modulus based on gas saturation. In previous studies, mechanical properties were measured outside the pressure vessel, resulting in an error due to a pressure difference, while the proposed method is characterized by the ability to directly measure polymer behavior according to gas saturation in high-pressure vessels using a magnetic sensor in real time. Therefore, it is possible to predict polymer behavior, making it easy to control variables in high-pressure polymer processes.
Dielectric Elastomer Actuators with Enhanced Durability by Introducing a Reservoir Layer
Authors: Jung Sumin , Kang Minchae , Han Min-Woo
Published: May 2024

A Dielectric Elastomer Actuator (DEA) consists of electrodes with a dielectric layer between them. By controlling the design of the electrodes, voltage, and frequency, the operating range and speed of the DEA can be adjusted. These DEAs find applications in biomimetic robots, artificial muscles, and similar fields. When voltage is applied to the DEA, the dielectric layer undergoes compression and expansion due to electrostatic forces, which can lead to electrical breakdown. This phenomenon is closely related to the performance and lifespan of the DEA. To enhance stability and improve dielectric properties, a DEA Reservoir layer is introduced. Here, stability refers to the ability of the DEA to perform its functions even as the applied voltage increases. The Reservoir layer delays electrical breakdown and enhances stability due to its enhanced thickness. The proposed DEA in this paper is composed of a Reservoir layer and electrode layer. The Reservoir layer is placed between the electrode layers and is independently configured, not subjected to applied voltage like the electrode layers. The performance of the DEA was evaluated by varying the number of polymer layers in the Reservoir and electrode designs. Introducing the Reservoir layer improved the dielectric properties of the DEA and delayed electrical breakdown. Increasing the dielectric constant through the DEA Reservoir can enhance output characteristics in response to electrical signals. This approach can be utilized in various applications in wearable devices, artificial muscles, and other fields.
Synthesis and Properties of Photocurable Polymers Derived from the Polyesters of Glycerol and Aliphatic Dicarboxylic Acids
Authors: Hu Rui , Yao Weipeng , Fu Yingjuan , Lu Fuyuan , Chen Xiaoqian
Published: May 2024

The rapid development of 3D printing technology and the emerging applications of shape memory elastomer have greatly stimulated the research of photocurable polymers. In this work, glycerol (Gly) was polycondensed with sebacic, dodecanedioic, or tetradecanedioic acids to provide precursor polyesters with hydroxyl or carboxyl terminal groups, which were further chemically functionalized by acryloyl chloride to introduce sufficient, photocurable, and unsaturated double bonds. The chemical structures of the acrylated polyesters were characterized by FT IR and NMR spectroscopies. The photoinitiated crosslinking behavior of the acrylated polyesters under ultraviolet irradiation without the addition of any photoinitiator was investigated. The results showed that the precursor polyesters that had a greater number of terminated hydroxyls and a less branched structure obtained a relatively high acetylation degree. A longer chain of aliphatic dicarboxylic acids (ADCAs) and higher ADCA proportion lead to a relatively lower photopolymerization rate of acrylated polyesters. However, the photocured elastomers with a higher ADCA proportion or longer-chain ADCAs resulted in better mechanical properties and a lower degradation rate. The glass transition temperature (Tg) of the elastomer increased with the alkyl chain length of the ADCAs, and a higher Gly proportion resulted in a lower Tg of the elastomer due to its higher crosslinking density. Thermal gravimetric analysis (TGA) showed that the chain length of the ADCAs and the molar ratio of Gly to ADCAs had less of an effect on the thermal stability of the elastomer. As the physicochemical properties can be adjusted by choosing the alkyl chain length of the ADCAs, as well as changing the ratio of Gly:ADCA, the photocurable polyesters are expected to be applied in multiple fields.
Electrochemical Processes Used to Degrade Thiamethoxam in Water and Toxicity Analyses in Non-Target Organisms
Authors: Forti Juliane C. , Robles Pedro E. M. , Tadayozzi Yasmin S. , Demori Maiara A. F. , Santos Felipe A. , Putti Fernando F. , Vicente Eduardo F.
Published: May 2024

Pesticides ensure greater productivity in less time; however, they spread beyond the perimeters to which they are applied to reach non-target organisms, thereby affecting plant, animal, and human health. Thiamethoxam (TMX) is considered to be one of the main agents responsible for poisoning bees and potentially contaminating surface and groundwater. Conventional water-treatment protocols are unable to degrade thiamethoxam; therefore, electrochemically advanced oxidative processes (EAOPs) have become promising alternatives owing to their ease of operation and cost-effectiveness. Herein, we examined the use of EAOPs to oxidize thiamethoxam in commercial Actara® and analyzed treatment efficiencies through phytotoxicity studies using cucumber and maize seeds as bioindicators. In addition, the cost of each process was analyzed based on the resulting current efficiency. The treated solutions were used to germinate seeds that were analyzed for total protein, hydrogen peroxide, lipid peroxidation (MDA), superoxide dismutase (SOD), and catalase (CAT) activities. EAOPs were found to effectively oxidize TMX, with more than 50% degraded and 80% COD removed under all treatment conditions, even when the commercial product was used. The photoelectro-Fenton process using 10 mg L−1 FeSO4 and 100 mg L−1 H2O2 exhibited the best results, with 79% of the TMX degraded and 83% of the COD removed, additionally exhibiting the lowest estimated operating cost (USD 1.01 dm−3). Higher enzymatic SOD and CAT activities, total protein content, and H2O2 concentration were observed; however, no significant changes in MDA were recorded. This treatment protocol effectively oxidizes TMX and reduces its phytotoxicity in maize and cucumber seedlings.
Geothermal Heat Pump for Space Cooling and Heating in Kuwaiti Climate
Authors: Gharbia Yousef , Derakhshandeh Javad Farrokhi , Amer A. M. , Dinc Ali
Published: May 2024

Kuwait stands as one of the hottest locations globally, experiencing scorching temperatures that can soar to 50 °C during the summer months. Conversely, in the winter months of December and January, temperatures may plummet to less than 10 °C. Maintaining a comfortable temperature indoors necessitates a substantial amount of energy, particularly during the scorching summer seasons. In Kuwait, most of the electrical energy required for functions such as air conditioning and lighting is derived from fossil fuel resources, contributing to escalating air pollution and global warming. To reduce dependence on conventional energy sources for heating and cooling, this article presents a case study to explore the potential of using geothermal energy for space heating and cooling in Kuwait. The case study involves utilizing a geothermal heat pump (water-sourced heat pump) in conjunction with a vertical-borehole ground heat exchanger (VBGHE). The mentioned system is deployed to regulate the climate in a six-floor apartment block comprising a small two-bedroom apartment on each level, each with a total floor area of 57 m2. Two geothermal heat pumps, each with a cooling capacity of 2.58 kW and a heating capacity of 2.90 kW, connected to two vertical-borehole heat exchangers, were deployed for each apartment to maintain temperatures at 22 °C in winter and 26 °C in summer. The findings indicate that the estimated annual energy loads for cooling and heating for the apartment block are 42,758 kWh and 113 kWh, respectively. The corresponding electrical energy consumption amounted to 9294 kWh for space cooling and 113 kWh for space heating. The observed peak cooling load was approximately 9300 kJ/h (2.58 kW) per apartment, resulting in a power density of 45 W/m2. Moreover, the HP system achieved a 22% reduction in annual electric energy consumption compared to conventional air conditioning systems. This reduction in electric energy usage led to an annual CO2 reduction of 6.6 kg/m2.
Impact of Drying Method and Solvent Extraction on Ethiopian Verbascum sinaiticum (Qetetina) Leaves: Metabolite Profiling and Evaluation of Antioxidant Capacity
Authors: Legesse Alemu Belay , Emire Shimelis Admassu , Dadi Debebe Worku , Tadesse Minbale Gashu , Oyinloye Timilehin Martins , Yoon Won Byong
Published: May 2024

The aim of this study was to evaluate the effects of different drying methods on bioactive compounds and to analyze their composition in Verbascum sinaiticum (V. sinaiticum) leaf extracts using UHPLC-ESI-QTOF-MS/MS. V. sinaiticum is traditionally used as an herbal medicine, yet it has undergone limited scientific investigations regarding its secondary metabolites. V. sinaiticum leaves were dried using oven dryers at 50 °C, 60 °C, and 70 °C, as well as a freeze dryer. The leaves were then extracted using 50% and 70% aqueous ethanol and 100% aqueous solutions. The results showed that the highest contents of TPC and TFC were observed when 70% aqueous ethanol was used during freeze drying, reaching 181.73 mg GAE/g dw and 78.57 mg CE/g dw, respectively. The strongest correlations were observed between the TFC and DPPH radical scavenging activity (0.9082), followed by TPC and ABTS assays (0.8933) and TPC and DPPH (0.8272). In the FTIR analysis, freeze drying exhibited a lower intensity of the phenolic -OH functional groups, contrasting with significant denaturation observed during oven drying at 70 °C. Metabolite analysis identified 29 compounds in V. sinaiticum leaves, further confirming the presence of 14 phenolic and flavonoid compounds, including kaempferol, catechin, gallic acid, and myricetin derivatives, consistent with the experimentally observed antioxidant capacity. This study highlights the impact of drying methods on the bioactive composition of V. sinaiticum and underscores its potential as a source of antioxidants for food, nutraceutical, and pharmaceutical applications.
Statistical Reliability Assessment with Generalized Intuitionistic Fuzzy Burr XII Distribution
Authors: Kalam Abdul , Cheng Weihu , Stefanatos Dionisis , Shah Sayed Kifayat
Published: May 2024

Intuitionistic fuzzy sets provide a viable framework for modelling lifetime distribution characteristics, particularly in scenarios with measurement imprecision. This is accomplished by utilizing membership and non-membership degrees to accurately express the complexities of data uncertainty. Nonetheless, the complexities of some cases necessitate a more advanced approach of imprecise data, motivating the use of generalized intuitionistic fuzzy sets (GenIFSs). The use of GenIFSs represents a flexible modeling strategy that is characterized by the careful incorporation of an extra level of hesitancy, which effectively clarifies the underlying ambiguity and uncertainty present in reliability evaluations. The study employs a methodology based on generalized intuitionistic fuzzy distributions to thoroughly examine the uncertainty related to the parameters and reliability characteristics present in the Burr XII distribution. The goal is to provide a more accurate evaluation of reliability measurements by addressing the inherent ambiguity in the distribution’s shape parameter. Various reliability measurements, such as reliability, hazard rate, and conditional reliability functions, are derived for the Burr XII distribution. This extensive analysis is carried out within the context of the generalized intuitionistic fuzzy sets paradigm, improving the understanding of the Burr XII distribution’s reliability measurements and providing important insights into its performance for the study of various types of systems. To facilitate understanding and point to practical application, the findings are shown graphically and contrasted across various cut-set values using a valuable numerical example.
SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion
Authors: Li Hongli , Yi Zhiqi , Mei Liye , Duan Jia , Sun Kaimin , Li Mengcheng , Yang Wei , Wang Ying
Published: May 2024

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy.
Integrative Interventions for Improving Outcomes in Depression: A Narrative Review
Authors: Halma Matthew , Plothe Christof , Marik Paul E.
Published: May 2024

Antidepressants are among the most used medications in the US, with significant deleterious effects on people’s well-being. At any given time, depression impacts approximately 1 in 10 Americans, causing wide and broad societal costs. Interest is developing for non-pharmacological treatments and preventative measures. We summarize the literature on non-invasive dietary and lifestyle approaches for treating depression. This review aims to inform future research and treatment programs for depression by providing an evidentiary summary of integrative therapeutic approaches for depression.
Measuring the Density Matrix of Quantum-Modeled Cognitive States
Authors: Chavarría-Garza Wendy Xiomara , Aquines-Gutiérrez Osvaldo , Santos-Guevara Ayax , Martínez-Huerta Humberto , Morones-Ibarra Jose Ruben , Saucedo Jonathan Rincon
Published: May 2024

Inspired by the principles of quantum mechanics, we constructed a model of students’ misconceptions about heat and temperature, conceptualized as a quantum system represented by a density matrix. Within this framework, the presence or absence of misconceptions is delineated as pure states, while the probability of mixed states is also considered, providing valuable insights into students’ cognition based on the mental models they employ when holding misconceptions. Using the analysis model previously employed by Lei Bao and Edward Redish, we represented these results in a density matrix. In our research, we utilized the Zeo and Zadnik Thermal Concept Evaluation among 282 students from a private university in Northeast Mexico. Our objective was to extract information from the analysis of multiple-choice questions designed to explore preconceptions, offering valuable educational insights beyond the typical Correct–Incorrect binary analysis of classical systems. Our findings reveal a probability of 0.72 for the appearance of misconceptions, 0.28 for their absence, and 0.43 for mixed states, while no significant disparities were observed based on gender or scholarship status, a notable difference was observed among programs (p < 0.05). These results are consistent with the previous literature, confirming a prevalence of misconceptions within the student population.
An Agent-Based Market Analysis of Urban Housing Balance in The Netherlands
Authors: Wiegel Erik , Yorke-Smith Neil
Published: May 2024

The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not or cannot leave the social sector. Government policy and market regulations change frequently, not least for political reasons. In view of commonly recognised problems in the housing market, this article considers the ‘internal demand’ of those households that are dissatisfied with their current residence. We examine the effects of regulatory policy by means of an exploratory agent-based simulation. The results provide perspectives on how internal demand is impacted by regulations in a housing market that is suffering from a shortage, and allow decision makers to weigh the pros and cons of policy measures.
Special Issue Kinematics and Robot Design VI, KaRD2023
Authors: Di Gregorio Raffaele
Published: May 2024

What would our concept of life be without motion [...]
Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data
Authors: Yang Yongjun , Dong Jing , Tang Jiajia , Zhao Jiao , Lei Shaogang , Zhang Shaoliang , Chen Fu
Published: May 2024

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities.
Drought Risk Assessment of Winter Wheat at Different Growth Stages in Huang-Huai-Hai Plain Based on Nonstationary Standardized Precipitation Evapotranspiration Index and Crop Coefficient
Authors: Chen Wenhui , Yao Rui , Sun Peng , Zhang Qiang , Singh Vijay P. , Sun Shao , AghaKouchak Amir , Ge Chenhao , Yang Huilin
Published: May 2024

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions.
Domain Feature Decomposition for Efficient Object Detection in Aerial Images
Authors: Jin Ren , Jia Zikai , Yin Xingyu , Niu Yi , Qi Yuhua
Published: May 2024

Object detection in UAV aerial images faces domain-adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on the network’s generalizability. To tackle these challenges, we initially decompose image features into domain-invariant and domain-specific features using practical imaging condition parameters. The composite feature can improve domain generalization and single-domain accuracy compared to the conventional fine-grained domain-detection method. Then, to solve the problem of the overfitting of high-frequency imaging condition parameters, we mixed images from different imaging conditions in a balanced sampling manner as input for the training of the detection network. The data-augmentation method improves the robustness of training and reduces the overfitting of high-frequency imaging parameters. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively. The airborne experiments validate that the algorithm achieves a 20 Hz processing performance for 720P images on an onboard computer with Nvidia Jetson Xavier NX.
SWIFT: Simulated Wildfire Images for Fast Training Dataset
Authors: Fernando Luiz , Ghali Rafik , Akhloufi Moulay A.
Published: May 2024

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires.
Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data
Authors: Sato Yuki , Tsuji Takeshi , Matsuoka Masayuki
Published: May 2024

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.
Spatiotemporal Distribution Characteristics and Influencing Factors of Freeze–Thaw Erosion in the Qinghai–Tibet Plateau
Authors: Yang Zhenzhen , Ni Wankui , Niu Fujun , Li Lan , Ren Siyuan
Published: May 2024

Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for the protection and restoration of the regional ecological environment. FT erosion is the third major type of soil erosion after water and wind erosion. Although FT erosion is one of the major soil erosion types in cold regions, it has been studied relatively little in the past because of the complexity of several influencing factors and the involvement of shallow surface layers at certain depths. The QTP is an important ecological barrier area in China. However, this area is characterized by harsh climatic and fragile environmental conditions, as well as by frequent FT erosion events, making it necessary to conduct research on FT erosion. In this paper, a total of 11 meteorological, vegetation, topographic, geomorphological, and geological factors were selected and assigned analytic hierarchy process (AHP)-based weights to evaluate the FT erosion intensity in the QTP using a comprehensive evaluation index method. In addition, the single effects of the selected influencing factors on the FT erosion intensity were further evaluated in this study. According to the obtained results, the total FT erosion area covered 1.61 × 106 km2, accounting for 61.33% of the total area of the QTP. The moderate and strong FT erosion intensity classes covered 6.19 × 105 km2, accounting for 38.37% of the total FT erosion area in the QTP. The results revealed substantial variations in the spatial distribution of the FT erosion intensity in the QTP. Indeed, the moderate and strong erosion areas were mainly located in the high mountain areas and the hilly part of the Hoh Xil frozen soil region.
From Point Cloud to BIM: A New Method Based on Efficient Point Cloud Simplification by Geometric Feature Analysis and Building Parametric Objects in Rhinoceros/Grasshopper Software
Authors: Pepe Massimiliano , Garofalo Alfredo Restuccia , Costantino Domenica , Tana Federica Francesca , Palumbo Donato , Alfio Vincenzo Saverio , Spacone Enrico
Published: May 2024

The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc.
Impact of Urbanization on Cloud Characteristics over Sofia, Bulgaria
Authors: Danchovski Ventsislav
Published: May 2024

Urban artificial surfaces and structures induce modifications in land–atmosphere interactions, affecting the exchange of energy, momentum, and substances. These modifications stimulate urban climate formation by altering the values and dynamics of atmospheric parameters, including cloud-related features. This study evaluates the presence and quantifies the extent of such changes over Sofia, Bulgaria. The findings reveal that estimations of low-level cloud base height (CBH) derived from lifting condensation level (LCL) calculations may produce unexpected outcomes due to microclimate influence. Ceilometer data indicate that the CBH of low-level clouds over urban areas exceeds that of surrounding regions by approximately 200 m during warm months and afternoon hours. Moreover, urban clouds exhibit reduced persistence relative to rural counterparts, particularly pronounced in May, June, and July afternoons. Reanalysis-derived low-level cloud cover (LCC) shows no significant disparities between urban and rural areas, although increased LCC is observed above the western and northern city boundaries. Satellite-derived cloud products reveal that the optically thinnest low-level clouds over urban areas exhibit slightly higher cloud tops, but the optically thickest clouds are more prevalent during warm months. These findings suggest an influence of urbanization on cloudiness, albeit nuanced and potentially influenced by the city size and surrounding physical and geographical features.
Subcranial Encephalic Temnograph-Shaped Helmet for Brain Stroke Monitoring
Authors: Cuccaro Antonio , Dell’Aversano Angela , Basile Bruno , Maisto Maria Antonia , Solimene Raffaele
Published: May 2024

In this contribution, a wearable microwave imaging system for real-time monitoring of brain stroke in the post-acute stage is described and validated. The system exploits multistatic/multifrequency (only 50 frequency samples) data collected via a low-cost and low-complexity architecture. Data are collected by an array of only 16 antennas moved by pneumatic system. Phantoms, built from ABS material and filled with appropriate Triton X-100-based mixtures to mimic the different head human tissues, are employed for the experiments. The microwave system exploits the differential scattering measures and the Incoherent MUSIC algorithm to provide a 3D image of the region under investigation. The shown results, although preliminary, confirm the potential of the proposed microwave system in providing reliable results, including for targets whose evolution is as small as 16 mL in volume.
Fourier Transform Infrared (FT-IR) Spectroscopy and Simple Algorithm Analysis for Rapid and Non-Destructive Assessment of Cotton Fiber Maturity and Crystallinity for Plant Mapping
Authors: Kim Hee-Jin , Liu Yongliang , Zeng Linghe
Published: May 2024

Information on boll distribution within a cotton plant is critical to evaluate the adaptation and response of cotton plants to environmental and biotic stress in cotton production. Cotton researchers have applied available conventional fiber measurements, such as the high volume instrument (HVI) and advanced fiber information system (AFIS), to map the location and the timing of boll development and distribution within plants and further to determine within-plant variability of cotton fiber properties. Both HVI and AFIS require numerous cotton bolls combined for the measurement. As an alternative approach, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was proposed to measure fiber maturity (MIR) and crystallinity (CIIR) of a sample as little as 0.5 mg lint. Extending fiber maturity and crystallinity measurement into a single boll for node-by-node mapping, FT-IR method might be advantageous due to less sampling amount compared with HVI and AFIS methods. Results showed that FT-IR technique enabled the evaluation of fiber MIR and CIIR at a boll level, which resulted in average MIR and CIIR values highly correlated with HVI micronaire (MIC) and AFIS maturity ratio (M). Hence, FT-IR technique possesses a good potential for a rapid and non-destructive node-by-node mapping of cotton boll maturity and crystallinity distribution.
Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art
Authors: Pahnabi Narges , Schumacher Thomas , Sinha Arijit
Published: May 2024

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR’s capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material’s complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.