bluuebunny/crossref_metadata_2025 huggingface dataset:
Alfalfa, Cabbage, Beet and Fennel Microgreens in Floating Hydroponics—Perspective Nutritious Food?
Authors: Fabek Uher Sanja , Radman Sanja , Opačić Nevena , Dujmović Mia , Benko Božidar , Lagundžija Denis , Mijić Valent , Prša Lucija , Babac Srđan , Šic Žlabur Jana
Published: May 2023

Microgreens are young plants of various vegetables, medicinal and aromatic plants, cereals and edible wild plants that were first associated with nouvelle cuisine as decoration in dishes due to their attractive appearance and strong flavor. Recently, they have become more sought after in the market due to their high nutritional value. This is due to the growing interest of consumers in a healthy lifestyle that includes a varied diet with emphasis on fresh, functional foods. Nowadays, commercial production of microgreens is shifting to modern hydroponic systems due to their numerous advantages, such as accelerated plant growth and biomass production, earlier harvesting, and more production cycles that positively affect yield and chemical composition. Therefore, the aim of this study was to determine the content of specialized metabolites and antioxidant capacity of hydroponically grown alfalfa (Medicago sativa) cv. ‘Kangaroo’, yellow beet (Beta vulgaris var. conditiva) cv. ‘Yellow Lady’, red cabbage (Brassica oleracea L. var. rubra) cv. ‘Red Carpet’, and fennel (Foeniculum vulgare) cv. ‘Aganarpo’ microgreens. The highest content of total phenols (408.03 mg GAE/100 g fw), flavonoids (214.47 mg GAE/100 g fw), non-flavonoids (193.56 mg GAE/100 g fw) and ascorbic acid (74.94 mg/100 g fw) was found in fennel microgreens. The highest content of all analyzed chlorophyll pigments (Chl_a 0.536 mg/g fw, Chl_b 0.248 mg/g fw, and TCh 0.785 mg/g fw) was found in alfalfa microgreens. However, in addition to alfalfa, high levels of chlorophyll a (0.528 mg/g fw), total chlorophyll (0.713 mg/g fw) and the highest level of total carotenoids (0.196 mg/g fw) were also detected in fennel microgreens. The results suggest that microgreens grown on perlite in floating hydroponics have high nutritional potential as a functional food important for human health and therefore could be recommended for daily diet.
Molybdenum Carbide/Ni Nanoparticles Embedded into Carbon Nanofibers as an Effective Non-Precious Catalyst for Green Hydrogen Production from Methanol Electrooxidation
Authors: Abdel-Aty Marwa M. , Gomaa Hassan E. , Abdu Hany Mohamed , Almasri Radwan A. , Irfan Osama M. , Barakat Nasser A. M.
Published: May 2023

Molybdenum carbide co-catalyst and carbon nanofiber matrix are suggested to improve the nickel activity toward methanol electrooxidation process. The proposed electrocatalyst has been synthesized by calcination electrospun nanofiber mats composed of molybdenum chloride, nickel acetate, and poly (vinyl alcohol) under vacuum at elevated temperatures. The fabricated catalyst has been characterized using XRD, SEM, and TEM analysis. The electrochemical measurements demonstrated that the fabricated composite acquired specific activity for methanol electrooxidation when molybdenum content and calcination temperature were tuned. In terms of the current density, the highest performance is attributed to the nanofibers obtained from electrospun solution having 5% molybdenum precursor compared to nickel acetate as a current density of 107 mA/cm2 was generated. The process operating parameters have been optimized and expressed mathematically using the Taguchi robust design method. Experimental design has been employed in investigating the key operating parameters of methanol electrooxidation reaction to obtain the highest oxidation current density peak. The main effective operating parameters of the methanol oxidation reaction are Mo content in the electrocatalyst, methanol concentration, and reaction temperature. Employing Taguchi’s robust design helped to capture the optimum conditions yielding the maximum current density. The calculations revealed that the optimum parameters are as follows: Mo content, 5 wt.%; methanol concentration, 2.65 M; and reaction temperature, 50 °C. A mathematical model has been statistically derived to describe the experimental data adequately with an R2 value of 0. 979. The optimization process indicated that the maximum current density can be identified statistically at 5% Mo, 2.0 M methanol concentration, and 45 °C operating temperature.
Comparison between the Astaxanthin Release Profile of Mesoporous Bioactive Glass Nanoparticles (MBGNs) and Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/MBGN Composite Microspheres
Authors: Aguilar-Rabiela Arturo E. , Homaeigohar Shahin , González-Castillo Eduin I. , Sánchez Mirna L. , Boccaccini Aldo R.
Published: May 2023

In recent years, composite biomaterials have attracted attention for drug delivery applications due to the possibility of combining desired properties of their components. However, some functional characteristics, such as their drug release efficiency and likely side effects, are still unexplored. In this regard, controlled tuning of the drug release kinetic via the precise design of a composite particle system is still of high importance for many biomedical applications. This objective can be properly fulfilled through the combination of different biomaterials with unequal release rates, such as mesoporous bioactive glass nanoparticles (MBGN) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) microspheres. In this work, MBGNs and PHBV-MBGN microspheres, both loaded with Astaxanthin (ASX), were synthesised and compared in terms of ASX release kinetic, ASX entrapment efficiency, and cell viability. Moreover, the correlation of the release kinetic to phytotherapeutic efficiency and side effects was established. Interestingly, there were significant differences between the ASX release kinetic of the developed systems, and cell viability differed accordingly after 72 h. Both particle carriers effectively delivered ASX, though the composite microspheres exhibited a more prolonged release profile with sustained cytocompatibility. The release behaviour could be fine-tuned by adjusting the MBGN content in the composite particles. Comparatively, the composite particles induced a different release effect, implying their potential for sustained drug delivery applications.
Energy Recovery and Economic Evaluation for Industrial Fuel from Plastic Waste
Authors: Galaly Ahmed Rida , Dawood Nagia
Published: May 2023

Plasma gasification is considered an environmentally friendly process to convert plastic waste into fuel oil; a prototype system is described to test and validate the plasma treatment of plastic waste as a strategic vision. The proposed plasma treatment project will deal with a plasma reactor with a waste capacity of 200 t/day. The annual plastic waste production in tons in all regions of Makkah city during 27 years for all months in the years 1994 to 2022 is evaluated. A statistics survey of plastic waste displays the average rate generation ranging from 224 thousand tons in the year 1994 to 400 thousand tons in the year 2022, with an amount of recovered pyrolysis oil; 3.17 × 105 t with the equivalent energy; 12.55 × 109 MJ, and an amount of recovered diesel oil; 2.7 × 105 t with an amount of electricity for sale 2.96 × 106 MW.h. The economic vision will be estimated, using the results of energy generated from diesel oil as an industrial fuel extracted from plastic waste equivalent to 0.2 million barrels of diesel oil, with sales revenue and cash recovery of USD 5 million, considering the sale of each one barrel of diesel extracted from plastic waste in the range of USD 25. It is important to consider that the equivalent barrels of petroleum cost, according to the organization of the petroleum-exporting countries’ basket prices, up to USD 20 million. The sales profit (2022) is as follows: for diesel with a sales revenue of diesel oil, USD 5 million, with a rate of return of 4.1%, and a payback period of 3.75 years. The generated electricity reached USD 32 million for households and USD 50 million for factories.
Promotion of B(C6F5)3 as Ligand for Titanium (or Vanadium) Catalysts in the Copolymerization of Ethylene and 1-Hexene: A Computational Study
Authors: Yu Shuyuan , Zhang Chenggen , Wang Fei , Liang Xinru , Yang Mengyao , An Mengyu
Published: May 2023

Density functional theory (DFT) is employed to investigate the promotion of B(C6F5)3 as a ligand for titanium (or vanadium) catalysts in ethylene/1-hexene copolymerization reactions. The results reveal that (I) Ethylene insertion into TiB (with B(C6F5)3 as a ligand ) is preferred over TiH, both thermodynamically and kinetically. (II) In TiH and TiB catalysts, the 2,1 insertion reaction (TiH21 and TiB21) is the primary pathway for 1-hexene insertion. Furthermore, the 1-hexene insertion reaction for TiB21 is favored over TiH21 and is easier to perform. Consequently, the entire ethylene and 1-hexene insertion reaction proceeds smoothly using the TiB catalyst to yield the final product. (III) Analogous to the Ti catalyst case, VB (with B(C6F5)3 as a ligand) is preferred over VH for the entire ethylene/1-hexene copolymerization reaction. Moreover, VB exhibits higher reaction activity than TiB, thus agreeing with experimental results. Additionally, the electron localization function and global reactivity index analysis indicate that titanium (or vanadium) catalysts with B(C6F5)3 as a ligand exhibit higher reactivity. Investigating the promotion of B(C6F5)3 as a ligand for titanium (or vanadium) catalysts in ethylene/1-hexene copolymerization reactions will aid in designing novel catalysts and lead to more cost-effective polymerization production methods.
Anti-Aging and Depigmentation Effect of a Hyaluronic Acid Mechanically Stabilized Complex on Human Skin Explants
Authors: Siquier-Dameto Gabriel , Boisnic Sylvie , Boadas-Vaello Pere , Verdú Enrique
Published: May 2023

Solar radiation and environmental pollutants are factors that cause changes in the skin that trigger skin aging. The objective of the study is to evaluate the rejuvenating effects of a complex formed by hyaluronic acid supplemented with vitamins, amino acids and oligopeptides in explants of human skin. For this, surplus skin samples have been obtained from donors that have been resected and cultivated on slides with membrane inserts. The complex was administered to some skin explants and the percentage of cells with low, medium and high levels of melanin was evaluated as an indicator of the degree of pigmentation. Other skin segments were irradiated with UVA/UVB, then the product was administered on several slides and the levels of collagen, elastin, sulfated GAG and MMP1 were evaluated. The results show that the administration of the complex significantly reduces the percentage of skin cells with a high melanin content by 16%, and that in skin irradiated with UVA/UVB, there is a decrease in the content of collagen, elastin and sulfate GAGs, and the complex reverses this reduction without changing MMP1 levels. This suggests that the compound has anti-aging and depigmentation effects on the skin, giving a skin rejuvenation appearance.
Spectrophotometric-Based Assay to Quantify Relative Enzyme-Mediated Degradation of Commercially Available Bioplastics
Authors: Hoekstra Matthew , Smith Myron L.
Published: May 2023

We present a spectrophotometric-based assay to identify enzymes that degrade commercially available bioplastics. Bioplastics comprise aliphatic polyesters with hydrolysis-susceptible ester bonds and are proposed as a replacement for petroleum-based plastics that accumulate in the environment. Unfortunately, many bioplastics can also persist in environments including seawater and waste centers. Our assay involves an overnight incubation of candidate enzyme(s) with plastic, followed by A610 spectrophotometry using 96-well plates to quantify both a reduction in residual plastic and the liberation of degradation by-products. We use the assay to show that Proteinase K and PLA depolymerase, two enzymes that were previously shown to degrade pure polylactic acid plastic, promote a 20–30% breakdown of commercial bioplastic during overnight incubation. We validate our assay and confirm the degradation potential of these enzymes with commercial bioplastic using established mass-loss and scanning electron microscopy methods. We show how the assay can be used to optimize parameters (temperature, co-factors, etc.) to enhance the enzyme-mediated degradation of bioplastics. The assay endpoint products can be coupled with nuclear magnetic resonance (NMR) or other analytical methods to infer the mode of enzymatic activity. Overall, the screening capacity of the spectrophotometric-based assay was demonstrated to be an accurate method to identify bioplastic-degrading enzymes.
Application of Copper Oxide Nanofluid and Phase Change Material on the Performance of Hybrid Photovoltaic–Thermal (PVT) System
Authors: Tiwari Awaneendra Kumar , Chatterjee Kalyan , Deolia Vinay Kumar
Published: May 2023

The objective of the study is to investigate the thermal, electrical, and exergetic performance of a hybrid photovoltaic–thermal (PVT) system under the influence of copper oxide (CuO) nanofluid and phase change material (Vaseline (petroleum jelly)) as a heat storage medium. A mathematical model was developed with the help of various energy-balance equations over the layers of the hybrid system. The performance evaluation of the PVT system was performed using pure water, CuO-water nanofluid (0.2 and 0.4% weight fractions), and CuO-water nanofluid 0.4% weight fraction with Vaseline as a phase change material. The results of the overall analysis show that the performance of the PVT system is better using CuO-water nanofluid (0.4% wt. fraction) with PCM as compared to the water-cooled PVT system and CuO-water nanofluid. The results obtained from the study show indicate that the cell temperature of PVT was reduced by 4.45% using nanofluid cooling with PCM compared to a water-cooled PVT system. Moreover, the thermal, electrical, and overall efficiencies improved by 6.9%, 4.85%, and 7.24%, respectively, using 0.4% wt. fraction of CuO-water nanofluid with PCM as compared to PVT water-cooled systems. The performance of the PVT system was also investigated by changing the mass flow rate (MFR). The increase in mass flow rate (MFR) from 0.05 kg/s to 0.2 kg/s tends to enhance the electrical and overall efficiencies from 12.89% to 16.32% and 67.67% to 76.34%, respectively, using 0.4% wt. fraction of CuO-PCM as fluid.
Abnormal Situation Management in Chemical Processes: Recent Research Progress and Future Prospects
Authors: Liu Shangzhi , Lei Fangyi , Zhao Dongfeng , Liu Qinglong
Published: May 2023

In the chemical process, abnormal situations are precursor events of incidents and accidents. Abnormal situation management (ASM) can effectively identify abnormalities and prevent them from evolving into incidents or accidents, ensuring the safe and smooth operation of chemical plants. In recent years, ASM has attracted extensive attention from the process industry and from academia, and a lot of research work has been conducted. However, the intelligence level of ASM in actual chemical plants is still relatively low, and industrial applications still face many difficulties and challenges. This review first summarizes the concepts and involved in the contents of ASM. Then, the latest research progress in various aspects of ASM is systematically reviewed. Finally, the challenges and future research directions of ASM are analyzed based on the perspective of industrial application. This review aims to provide the most cutting-edge reference for follow-up research on ASM, and to promote the intelligent development and practical industrial application of ASM in the chemical process.
Embodying a Different Word about Fat: The Need for Critical Feminist Theologies of Fat Liberation
Authors: Bacon Hannah
Published: May 2023

In contemporary Western society, fatness speaks for itself, affirming the fat person as an aesthetic and moral failure even before they say a word. Fat bodies, and fat female bodies in particular, are produced and reproduced as sites of excess and obscenity. Christian theology has protected itself from the contaminating touch of fat by ignoring fatness in theological discourse. Especially concerning is the relative absence of ‘fat talk’ from liberation and feminist theologies. It is time for a different word to be offered on fat that does not speak for itself and that emerges from the lived experiences of diverse women as they interpret their own faith and fatness. This essay explores the need for critical feminist theologies on fat liberation and identifies some features they might display. Here, I discuss Feminist Participatory Action Research and ethnography as methodologies that might help feminist theologians researching fat to prioritise the overlooked bodies and stories of fat women, and to continue liberation theology’s longstanding commitment to constructing historical projects oriented towards social change. Fat liberation, as a historical and theological project, calls for a ‘conversion’ to fatness and for a critical questioning of assumed ‘truths’ about fat. It positions the struggle against fat hatred as a pursuit of life and as faithful participation in the liberating activity of the God of Life.
Religious Community and Communal Religiosity: Foundations at the Beginning of the 21st Century
Authors: Rashi Tsuriel
Published: May 2023

Robert Putnam observed that more people than ever go bowling, but fewer than ever do so as parts of teams, a phenomenon that he named “Bowling Alone [...]
Correction: Jikeli (2023). How Do Muslims and Jews in Christian Countries See Each Other Today? A Survey Review. Religions 14: 412
Authors: Jikeli Gunther
Published: May 2023

In the original publication (Jikeli 2023), there were mistakes in Table 8 as published [...]
Religion, Mental Health, and the Latter-Day Saints: A Review of Literature 2005–2022
Authors: Dyer William Justin , Judd Daniel K. , Gale Megan , Finlinson Hunter Gibson
Published: May 2023

The objective was to review all peer-reviewed, scholarly articles on the mental health of members of The Church of Jesus Christ of Latter-day Saints from 2005 to 2022. Forty-six studies were identified. Research findings were consistent with the general research on R/S and mental health, which typically finds R/S related to better mental health. When comparisons are made, Latter-day Saints are typically found to have better mental health than those of other religions or no religion. It was found that in the last 10 years, research on sexual minorities has dominated the research on Latter-day Saints’ mental health. Although findings are nuanced, sexual minorities tend to have less favorable mental health when they are only somewhat enacting either a Latter-day Saint and/or sexual minority identity. The research literature on Latter-day Saints’ mental health is in its infancy, with few studies utilizing a high degree of methodologic rigor. More longitudinal and representative research is needed to better understand Latter-day Saints’ mental health. Further, more theoretical work is needed to provide a framework for explaining findings and guiding future research.
Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C-Means Clustering
Authors: Xie Shengkun , Gan Chong
Published: May 2023

Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of insurance. In this work, spatially constrained clustering is first applied to insurance loss data to form such regions, using the forward sortation area (FSA) as a basic rating unit. The groupings of FSA by spatially constrained clustering reduce the insurance rate heterogeneity caused by smaller risk exposures. Furthermore, the generalized linear mixed model (GLMM) is proposed to derive the risk relativities of clusters and each FSA. In addition, as an alternative approach, fuzzy C-Means clustering is proposed to derive the risk relativity of FSA, and the obtained results are compared to the ones from GLMM. The spatially constrained clustering and risk relativity estimation help to retrieve a set of territory risk benchmarks used in rate filings within the regulation process. It also provides guidance for auto insurance companies on rate making.
Spatio-Temporal Evolution of Inland Lakes and Their Relationship with Hydro-Meteorological Factors in Horqin Sandy Land, China
Authors: Zhang Yiran , Tong Xin , Liu Tingxi , Duan Limin , Hao Lina , Singh Vijay P. , Jia Tianyu , Lun Shuo
Published: May 2023

In the inland closed area of Horqin Sandy Land, China, lakes are the most important source of water, and they maintain the regional hydrological balance and ecosystem health. Clarifying long-term continuous changes of inland lake surface area and water storage in the sandy land is thus of great significance to the management of water resources in arid and semi-arid areas. This study estimated changes in the surface area and water storage of small lakes in the sandy land during 1984–2021 using a multiple index threshold method and an empirical equation based on Shuttle Radar Topography Mission (SRTM) DEM (digital elevation model) data and Landsat 5/7/8 images. Hydro-meteorological variables were also incorporated to explore their potential relationship with changes in the lake elements. The lakes in the sandy land resemble stars or dots, with distinct inhomogeneity. Permanent lakes account for the majority of the total lake area, mostly distributed in the center and east of the study area, whereas most seasonal lakes are small water bodies, with surface areas of 0.1–0.5 km2 and irregularly distributed. Lake surface area and water storage underwent frequent changes during the 38 years, and could be divided into three characteristic fluctuation phases. From 1984 to 1999, the lake surface area fluctuated greatly, and the water storage increased by approximately 0.3732 Gt, with an average expansion rate of 0.0572 Gt/y. Specifically, lakes with a surface area >2 km2 primarily accounted for the expansion. From 1999 to 2004, the lake area shrank sharply to the lowest point, and the water storage capacity decreased by approximately 0.4003 Gt. From 2004 to 2021, the lake surface area and water storage tended to be stable. Annual fluctuations of lake surface area and water storage were mostly affected by precipitation and evapotranspiration, followed by vapor pressure deficit, wet day frequency, and temperature, which have significant periodicity and hysteresis.
Hyperspectral Image Classification Based on Multiscale Hybrid Networks and Attention Mechanisms
Authors: Pan Haizhu , Zhao Xiaoyu , Ge Haimiao , Liu Moqi , Shi Cuiping
Published: May 2023

Hyperspectral image (HSI) classification is one of the most crucial tasks in remote sensing processing. The attention mechanism is preferable to a convolutional neural network (CNN), due to its superior ability to express information during HSI processing. Recently, numerous methods combining CNNs and attention mechanisms have been applied in HSI classification. However, it remains a challenge to achieve high-accuracy classification by fully extracting effective features from HSIs under the conditions of limited labeled samples. In this paper, we design a novel HSI classification network based on multiscale hybrid networks and attention mechanisms. The network consists of three subnetworks: a spectral-spatial feature extraction network, a spatial inverted pyramid network, and a classification network, which are employed to extract spectral-spatial features, to extract spatial features, and to obtain classification results, respectively. The multiscale fusion network and attention mechanisms complement each other by capturing local and global features separately. In the spatial pyramid network, multiscale spaces are formed through down-sampling, which can reduce redundant information while retaining important information. The structure helps the network better capture spatial features at different scales, and to improve classification accuracy. Experimental results on various public HSI datasets demonstrate that the designed network is extremely competitive compared to current advanced approaches, under the condition of insufficient samples.
Deep Learning of High-Resolution Unmanned Aerial Vehicle Imagery for Classifying Halophyte Species: A Comparative Study for Small Patches and Mixed Vegetation
Authors: Kim Keunyong , Lee Donguk , Jang Yeongjae , Lee Jingyo , Kim Chung-Ho , Jou Hyeong-Tae , Ryu Joo-Hyung
Published: May 2023

Recent advances in deep learning (DL) and unmanned aerial vehicle (UAV) technologies have made it possible to monitor salt marshes more efficiently and precisely. However, studies have rarely compared the classification performance of DL with the pixel-based method for coastal wetland monitoring using UAV data. In particular, many studies have been conducted at the landscape level; however, little is known about the performance of species discrimination in very small patches and in mixed vegetation. We constructed a dataset based on UAV-RGB data and compared the performance of pixel-based and DL methods for five scenarios (combinations of annotation type and patch size) in the classification of salt marsh vegetation. Maximum likelihood, a pixel-based classification method, showed the lowest overall accuracy of 73%, whereas the U-Net classification method achieved over 90% accuracy in all classification scenarios. As expected, in a comparison of pixel-based and DL methods, the DL approach achieved the most accurate classification results. Unexpectedly, there was no significant difference in overall accuracy between the two annotation types and labeling data sizes in this study. However, when comparing the classification results in detail, we confirmed that polygon-type annotation was more effective for mixed-vegetation classification than the bounding-box type. Moreover, the smaller size of labeling data was more effective for detecting small vegetation patches. Our results suggest that a combination of UAV-RGB data and DL can facilitate the accurate mapping of coastal salt marsh vegetation at the local scale.
Synergy of Sentinel-1 and Sentinel-2 Imagery for Crop Classification Based on DC-CNN
Authors: Zhang Kaixin , Yuan Da , Yang Huijin , Zhao Jianhui , Li Ning
Published: May 2023

Over the years, remote sensing technology has become an important means to obtain accurate agricultural production information, such as crop type distribution, due to its advantages of large coverage and a short observation period. Nowadays, the cooperative use of multi-source remote sensing imagery has become a new development trend in the field of crop classification. In this paper, the polarimetric components of Sentinel-1 (S-1) decomposed by a new model-based decomposition method adapted to dual-polarized SAR data were introduced into crop classification for the first time. Furthermore, a Dual-Channel Convolutional Neural Network (DC-CNN) with feature extraction, feature fusion, and encoder-decoder modules for crop classification based on S-1 and Sentinel-2 (S-2) was constructed. The two branches can learn from each other by sharing parameters so as to effectively integrate the features extracted from multi-source data and obtain a high-precision crop classification map. In the proposed method, firstly, the backscattering components (VV, VH) and polarimetric components (volume scattering, remaining scattering) were obtained from S-1, and the multispectral feature was extracted from S-2. Four candidate combinations of multi-source features were formed with the above features. Following that, the optimal one was found on a trial. Next, the characteristics of optimal combinations were input into the corresponding network branches. In the feature extraction module, the features with strong collaboration ability in multi-source data were learned by parameter sharing, and they were deeply fused in the feature fusion module and encoder-decoder module to obtain more accurate classification results. The experimental results showed that the polarimetric components, which increased the difference between crop categories and reduced the misclassification rate, played an important role in crop classification. Among the four candidate feature combinations, the combination of S-1 and S-2 features had a higher classification accuracy than using a single data source, and the classification accuracy was the highest when two polarimetric components were utilized simultaneously. On the basis of the optimal combination of features, the effectiveness of the proposed method was verified. The classification accuracy of DC-CNN reached 98.40%, with Kappa scoring 0.98 and Macro-F1 scoring 0.98, compared to 2D-CNN (OA reached 94.87%, Kappa scored 0.92, and Macro-F1 scored 0.95), FCN (OA reached 96.27%, Kappa scored 0.94, and Macro-F1 scored 0.96), and SegNet (OA reached 96.90%, Kappa scored 0.95, and Macro-F1 scored 0.97). The results of this study demonstrated that the proposed method had significant potential for crop classification.
Remote Sensing Small Object Detection Network Based on Attention Mechanism and Multi-Scale Feature Fusion
Authors: Qu Junsuo , Tang Zongbing , Zhang Le , Zhang Yanghai , Zhang Zhenguo
Published: May 2023

In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the APs values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects.
Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net
Authors: Mohan Divya , Aravinth J. , Rajendran Sankaran
Published: May 2023

This study addresses image denoising alongside the compression and reconstruction of hyperspectral images (HSIs) using deep learning techniques, since the research community is striving to produce effective results to utilize hyperspectral data. Here, the SqueezeNet architecture is trained with a Gaussian noise model to predict and discriminate noisy pixels of HSI to obtain a clean image as output. The denoised image is further processed by the tunable spectral filter (TSF), which is a dual-level prediction filter to produce a compressed image. Subsequently, the compressed image is analyzed through a dense attentional net (DAN) model for reconstruction by reverse dual-level prediction operation. All the proposed mechanisms are employed in Python and evaluated using a Ben-Gurion University-Interdisciplinary Computational Vision Laboratory (BGU-ICVL) dataset. The results of SqueezeNet architecture applied to the dataset produced the denoised output with a Peak Signal to Noise Ratio (PSNR) value of 45.43 dB. The TSF implemented to the denoised images provided compression with a Mean Square Error (MSE) value of 8.334. Subsequently, the DAN model executed and produced reconstructed images with a Structural Similarity Index Measure (SSIM) value of 0.9964 dB. The study proved that each stage of the proposed approach resulted in a quality output, and the developed model is more effective to further utilize the HSI. This model can be well utilized using HSI data for mineral exploration.
Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy
Authors: Joshi Rahul , GG Lakshmi Priya , Faqeerzada Mohammad Akbar , Bhattacharya Tanima , Kim Moon Sung , Baek Insuck , Cho Byoung-Kwan
Published: May 2023

Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.
Effect of Perceptual Training with Sound-Guided and Kinesthetic Feedback on Human 3D Sound Localization Capabilities
Authors: Kumari Ranjita , Lee Sukhan , Shin Jonghwan , Lee Soojin
Published: May 2023

In this paper, we experimentally investigate how the 3D sound localization capabilities of the blind can improve through perceptual training. To this end, we develop a novel perceptual training method with sound-guided feedback and kinesthetic assistance to evaluate its effectiveness compared to conventional training methods. In perceptual training, we exclude visual perception by blindfolding the subjects to apply the proposed method to the visually impaired. Subjects used a specially designed pointing stick to generate a sound at the tip, indicating localization error and tip position. The proposed perceptual training aims to evaluate the training effect on 3D sound localization, including variations in azimuth, elevation, and distance. The six days of training based on six subjects resulted in the following outcomes: (1) In general, accuracy in full 3D sound localization can be improved based on training. (2) Training based on relative error feedback is more effective than absolute error feedback. (3) Subjects tend to underestimate distance when the sound source is near, less than 1000 mm, or larger than 15° to the left, and overestimate the elevation when the sound source is near or in the center, and within ±15° in azimuth estimations.
PlantInfoCMS: Scalable Plant Disease Information Collection and Management System for Training AI Models
Authors: Jin Dong , Yin Helin , Zheng Ri , Yoo Seong Joon , Gu Yeong Hyeon
Published: May 2023

In recent years, the development of deep learning technology has significantly benefited agriculture in domains such as smart and precision farming. Deep learning models require a large amount of high-quality training data. However, collecting and managing large amounts of guaranteed-quality data is a critical issue. To meet these requirements, this study proposes a scalable plant disease information collection and management system (PlantInfoCMS). The proposed PlantInfoCMS consists of data collection, annotation, data inspection, and dashboard modules to generate accurate and high-quality pest and disease image datasets for learning purposes. Additionally, the system provides various statistical functions allowing users to easily check the progress of each task, making management highly efficient. Currently, PlantInfoCMS handles data on 32 types of crops and 185 types of pests and diseases, and stores and manages 301,667 original and 195,124 labeled images. The PlantInfoCMS proposed in this study is expected to significantly contribute to the diagnosis of crop pests and diseases by providing high-quality AI images for learning about and facilitating the management of crop pests and diseases.
A Workflow for Studying the Stump–Socket Interface in Persons with Transtibial Amputation through 3D Thermographic Mapping
Authors: Cutti Andrea Giovanni , Morosato Federico , Gentile Cosimo , Gariboldi Francesca , Hamoui Giovanni , Santi Maria Grazia , Teti Gregorio , Gruppioni Emanuele
Published: May 2023

The design and fitting of prosthetic sockets can significantly affect the acceptance of an artificial limb by persons with lower limb amputations. Clinical fitting is typically an iterative process, which requires patients’ feedback and professional assessment. When feedback is unreliable due to the patient’s physical or psychological conditions, quantitative measures can support decision-making. Specifically, monitoring the skin temperature of the residual limb can provide valuable information regarding unwanted mechanical stresses and reduced vascularization, which can lead to inflammation, skin sores and ulcerations. Multiple 2D images to examine a real-life 3D limb can be cumbersome and might only offer a partial assessment of critical areas. To overcome these issues, we developed a workflow for integrating thermographic information on the 3D scan of a residual limb, with intrinsic reconstruction quality measures. Specifically, workflow allows us to calculate a 3D thermal map of the skin of the stump at rest and after walking, and summarize this information with a single 3D differential map. The workflow was tested on a person with transtibial amputation, with a reconstruction accuracy lower than 3 mm, which is adequate for socket adaptation. We expect the workflow to improve socket acceptance and patients’ quality of life.
An Image Detection Method for Image Stabilization Deviation of the Tank Gunner’s Primary Sight
Authors: Guo Zhannan , Xie Baoqi , Li Yingshun , Sun Ximing
Published: May 2023

The primary sight control system of a tank gunner has image stabilization as one of its primary functions. The image stabilization deviation in the aiming line is a key indicator for evaluating the operational status of Gunner’s Primary Sight control system. Employing image detection technology to measure image stabilization deviation enhances the effectiveness and accuracy of the detection process and allows for the evaluation of image stabilization functionality. Hence, this paper proposes an image detection method aimed at the Gunner’s Primary Sight control system of a specific tank which utilizes an enhanced You Only Look Once version 5 (YOLOv5) sight-stabilizing deviation algorithm. At first, a dynamic weight factor is integrated into SCYLLA-IoU (SIOU), creating δ-SIOU, which replaces Complete IoU (CIoU) as the loss function of YOLOv5. After that, the Spatial Pyramid Pool module of YOLOv5 was enhanced to improve the multi-scale feature fusion ability of the model, thereby elevating the performance of the detection model. Finally, the C3CA module was created by embedding the Coordinate Attention (CA) attention mechanism into the CSK-MOD-C3 (C3) module. The Bi-directional Feature Pyramid (BiFPN) network structure was also incorporated into the Neck network of YOLOv5 to improve the model’s ability to learn target location information and image detection accuracy. Based on data collected by a mirror control test platform, experimental results indicate an improvement in the detection accuracy of the model by 2.1%. These findings offer valuable insights into measuring the image stabilization deviation in the aiming line and facilitating the development of the parameter measurement system for Gunner’s Primary Sight control system.
Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model
Authors: Khayam Sadia Umer , Ajmal Ammar , Park Junyoung , Kim In-Ho , Park Jong-Woong
Published: May 2023

Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring the tendon force to prevent excessive creep. Estimating tendon stress is challenging due to limited access to prestressing tendons. This study utilizes a strain-based machine learning method to estimate real-time applied tendon stress. A dataset was generated using finite element method (FEM) analysis, varying the tendon stress in a 45 m girder. Network models were trained and tested on various tendon force scenarios, with prediction errors of less than 10%. The model with the lowest RMSE was chosen for stress prediction, accurately estimating the tendon stress, and providing real-time tensioning force adjustment. The research offers insights into optimizing girder locations and strain numbers. The results demonstrate the feasibility of using machine learning with strain data for instant tendon force estimation.
Skywave Detection and Mitigation for the MF R-Mode Continuously Operating Reference Station
Authors: Son Pyo-Woong , Park Jongmin , Yu Jaewon , Jeong Suhui , Han Younghoon , Fang Tae Hyun
Published: May 2023

There is an increasing need for an independent terrestrial navigation system, owing to the increasing reliance on global navigation satellite systems (GNSS). The medium-frequency range (MF R-Mode) system is considered a promising alternative; however, the skywave effect caused by ionospheric changes at night can degrade its positioning accuracy. To address this problem, we developed an algorithm to detect and mitigate the skywave effect on MF R-Mode signals. The proposed algorithm was tested using data collected from Continuously Operating Reference Stations (CORS) monitoring the MF R-Mode signals. The skywave detection algorithm is based on the signal-to-noise ratio (SNR) induced by the groundwave and skywave composition, whereas the skywave mitigation algorithm was derived from the I and Q components of the signals obtained through IQ modulation. The results demonstrate a significant improvement in the precision and standard deviation of the range estimation using CW1 and CW2 signals. The standard deviations decreased from 39.01 and 39.28 m to 7.94 and 9.12 m, respectively, while the precision (2-sigma) increased from 92.12 and 79.82 m to 15.62 and 17.84 m, respectively. These findings confirm that the proposed algorithms can enhance the accuracy and reliability of MF R-Mode systems.
Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review
Authors: Acosta-Vargas Patricia , Flor Omar , Salvador-Acosta Belén , Suárez-Carreño Franyelit , Santórum Marco , Solorzano Santiago , Salvador-Ullauri Luis
Published: May 2023

The objective of this scoping review is to characterize the current panorama of inertia sensors for the rehabilitation of hip arthroplasty. In this context, the most widely used sensors are IMUs, which combine accelerometers and gyroscopes to measure acceleration and angular velocity in three axes. We found that data collected by the IMU sensors are used to analyze and detect any deviation from the normal to measure the position and movement of the hip joint. The main functions of inertial sensors are to measure various aspects of training, such as speed, acceleration, and body orientation. The reviewers extracted the most relevant articles published between 2010 and 2023 in the ACM Digital Library, PubMed, ScienceDirect, Scopus, and Web of Science. In this scoping review, the PRISMA-ScR checklist was used, and a Cohen’s kappa coefficient of 0.4866 was applied, implying moderate agreement between reviewers; 23 primary studies were extracted from a total of 681. In the future, it will be an excellent challenge for experts in inertial sensors with medical applications to provide access codes for other researchers, which will be one of the most critical trends in the advancement of applications of portable inertial sensors for biomechanics.
Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review
Authors: Cheng Zhenpeng , Peng Chaoyong , Chen Meirong
Published: May 2023

With the gradual development of and improvement in earthquake early warning systems (EEWS), more accurate real-time seismic intensity measurements (IMs) methods are needed to assess the impact range of earthquake intensities. Although traditional point source warning systems have made some progress in terms of predicting earthquake source parameters, they are still inadequate at assessing the accuracy of IMs predictions. In this paper, we aim to explore the current state of the field by reviewing real-time seismic IMs methods. First, we analyze different views on the ultimate earthquake magnitude and rupture initiation behavior. Then, we summarize the progress of IMs predictions as they relate to regional and field warnings. The applications of finite faults and simulated seismic wave fields in IMs predictions are analyzed. Finally, the methods used to evaluate IMs are discussed in terms of the accuracy of the IMs measured by different algorithms and the cost of alerts. The trend of IMs prediction methods in real time is diversified, and the integration of various types of warning algorithms and of various configurations of seismic station equipment in an integrated earthquake warning network is an important development trend for future EEWS construction.