bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Compositions and Antioxidant Activities of Essential Oils Extracted from the Flowers of Paeonia delavayi Using Supercritical Carbon Dioxide Fluid
Authors: Yu Xiao , Zhang Huaibi , Wang Juan , Wang Junming , Wang Zhenxing , Li Jinbo
Published: May 2022

Essential oils were extracted from dark-purple, red and yellow petals of Paeonia delavayi using Supercritical Carbon Dioxide method. The compositions of essential oils were analyzed using gas chromatography-mass spectrometry (GC-MS). Antioxidant activity assays were carried out using DPPH, ABTS- and FRAP methods. Total polyphenols and total flavonoids were measured to evaluate the in vitro antioxidant activity in addition to the volatile compounds contained in the essential oils extracted from the flower petals of P. delavayi with the three flower colors. A total of 194 compounds were detected from essential oils of P. delavayi flowers, including 83 in dark-purple petals, 90 in red petals and 80 in yellow petals. These compounds mainly include alcohols, aldehydes, ketones, alkenes, alkanes, esters and polyphenols. The results showed that the volatile compounds accumulated differentially among the essential oils from the different colors of flower petals. Principal component analysis (PCA) indicated that essential oils derived from dark-purple and red petals were more closely clustered while the yellow petal essential oil was very different with both the purple-red and red. Antioxidant assays suggested that the radical scavenging activity and the iron reduction antioxidant activity in the essential oils were highly correlated with the flower petal colors. These results suggest P. delavayi flower petals are potentially good resources for high quality essential oils and natural antioxidants.
Evolution of Artificial Arginine Analogues—Fluorescent Guanidiniocarbonyl-Indoles as Efficient Oxo-Anion Binders
Authors: Sebena Daniel , Rudolph Kevin , Roy Bibhisan , Wölper Christoph , Nitschke Till , Lampe Sarah , Giese Michael , Voskuhl Jens
Published: May 2022

In this article, we present fluorescent guanidiniocarbonyl-indoles as versatile oxo-anion binders. Herein, the guanidiniocarbonyl-indole (GCI) and methoxy-guanidiniocarbonyl-indole (MGCI) were investigated as ethylamides and compared with the well-known guanidiniocarbonyl-pyrrole (GCP) concerning their photophysical properties as well as their binding behavior towards oxo-anions. Hence, a variety of anionic species, such as carboxylates, phosphonates and sulfonates, have been studied regarding their binding properties with GCP, GCI and MGCI using UV-Vis titrations, in combination with the determination of the complex stoichiometry using the Job method. The emission properties were studied in relation to the pH value using fluorescence spectroscopy as well as the determination of the photoluminescence quantum yields (PLQY). Density functional theory (DFT) calculations were undertaken to obtain a better understanding of the ground-lying electronic properties of the investigated oxo-anion binders. Additionally, X-ray diffraction of GCP and GCI was conducted. We found that GCI and MGCI efficiently bind carboxylates, phosphonates and sulfonates in buffered aqueous solution and in a similar range as GCP (Kass ≈ 1000–18,000 M−1, in bis-tris buffer, pH = 6); thus, they could be regarded as promising emissive oxo-anion binders. They also exhibit a visible fluorescence with a sufficient PLQY. Additionally, the excitation and emission wavelength of MGCI was successfully shifted closer to the visible region of the electromagnetic spectrum by introducing a methoxy-group into the core structure, which makes them interesting for biological applications.
Simple and Expedient Access to Novel Fluorinated Thiazolo- and Oxazolo[3,2-a]pyrimidin-7-one Derivatives and Their Functionalization via Palladium-Catalyzed Reactions
Authors: Blancou Wafa , Jismy Badr , Touil Soufiane , Allouchi Hassan , Abarbri Mohamed
Published: May 2022

An efficient, versatile, and one-pot method for the preparation of novel fluorinated thiazolo- and oxazolo[3,2-a]pyrimidin-7-ones is described from 2-aminothiazoles or 2-amino-oxazoles and fluorinated alkynoates. This transformation, performed under transition-metal-free conditions, offers new fluorinated cyclized products with good to excellent yields. Moreover, the functionalization of these N-fused scaffolds via the Suzuki-Miyaura and Sonogashira cross-coupling reactions led to the synthesis of highly diverse thiazolo- and oxazolo[3,2-a]pyrimidin-7-ones.
Conversion of Polypropylene Waste into Value-Added Products: A Greener Approach
Authors: Nisar Jan , Aziz Maria , Shah Afzal , Shah Iltaf , Iqbal Munawar
Published: May 2022

Plastic has made our lives comfortable as a result of its widespread use in today’s world due to its low cost, longevity, adaptability, light weight and hardness; however, at the same time, it has made our lives miserable due to its non-biodegradable nature, which has resulted in environmental pollution. Therefore, the focus of this research work was on an environmentally friendly process. This research work investigated the decomposition of polypropylene waste using florisil as the catalyst in a salt bath over a temperature range of 350–430 °C. A maximum oil yield of 57.41% was recovered at 410 °C and a 40 min reaction time. The oil collected from the decomposition of polypropylene waste was examined using gas chromatography-mass spectrometry (GC-MS). The kinetic parameters of the reaction process were calculated from thermogravimetric data at temperature program rates of 3, 12, 20 and 30 °C·min−1 using the Ozawa–Flynn–Wall (OFW) and Kissinger–Akahira–Sunnose (KAS) equations. The activation energy (Ea) and pre-exponential factor (A) for the thermo-catalytic degradation of polypropylene waste were observed in the range of 102.74–173.08 kJ·mol−1 and 7.1 × 108–9.3 × 1011 min−1 for the OFW method and 99.77–166.28 kJ·mol−1 and 1.1 × 108–5.3 × 1011 min−1 for the KAS method at a percent conversion (α) of 0.1 to 0.9, respectively. Moreover, the fuel properties of the oil were assessed and matched with the ASTM values of diesel, gasoline and kerosene oil. The oil was found to have a close resemblance to the commercial fuel. Therefore, it was concluded that utilizing florisil as the catalyst for the decomposition of waste polypropylene not only lowered the activation energy of the pyrolysis reaction but also upgraded the quantity and quality of the oil.
Apple Fibers as Carriers of Blackberry Juice Polyphenols: Development of Natural Functional Food Additives
Authors: Buljeta Ivana , Nosić Mario , Pichler Anita , Ivić Ivana , Šimunović Josip , Kopjar Mirela
Published: May 2022

Blackberry polyphenols possess various health-promoting properties. Since they are very sensitive to environmental conditions such as the presence of light, oxygen and high temperatures, the application of such compounds is restricted. Fibers are recognized as efficient carriers of polyphenols and are often used in polyphenols encapsulation. In the present study, the ability of apple fiber to adsorb blackberry juice polyphenols was examined. Freeze-dried apple fiber/blackberry juice complexes were prepared with different amounts of fibers (1%, 2%, 4%, 6%, 8% and 10%) and a constant amount of blackberry juice. Polyphenol profile, antioxidant activity, inhibition of the α-amylase, color parameters, as well as the IR spectra, of the obtained complexes were assessed. The results showed a negative effect of higher amounts of fiber (more than 2%) on the adsorption of polyphenols and the antioxidant activity of complexes. With the proper formulation, apple fibers can serve as polyphenol carriers, and thus the application as novel food additives can be considered.
Bacillus amyloliquefaciens Enriched Camel Milk Attenuated Colitis Symptoms in Mice Model
Authors: Khalifa Ashraf , Sheikh Abdullah , Ibrahim Hairul Islam Mohamed
Published: May 2022

Fermented camel’s milk has various health beneficial prebiotics and probiotics. This study aimed to evaluate the preventive efficacy of Bacillus amyloliquefaciens enriched camel milk (BEY) in 2-, 4- and 6-Trinitrobenzenesulfonic acid (TNBS)-induced colitis mice models. To this end, the immune modulatory effects of Bacillus amyloliquefaciens (BA) on TNF-α challenged HT29 colon cells were estimated using the cell proliferation and cytokines ELISA method. BEY was prepared using the incubation method and nutritional value was quantified by comparing it to commercial yogurt. Furthermore, TNBS-induced colitis was established and the level of disease index, pathological scores, and inflammatory markers of BEY-treated mice using macroscopic and microscopic examinations, qPCR and immunoblot were investigated. The results demonstrate that BA is non-toxic to HT29 colon cells and balanced the inflammatory cytokines. BEY reduced the colitis disease index, and improved the body weight and colon length of the TNBS-induced mice. Additionally, Myeloperoxidase (MPO) and pro-inflammatory cytokines (IL1β, IL6, IL8 and TNF-α) were attenuated by BEY treatment. Moreover, the inflammatory progress mRNA and protein markers nuclear factor kappa B (NFκB), phosphatase and tensin homolog (PTEN), proliferating cell nuclear antigen (PCNA), cyclooxygenase-2 (COX-2) and occludin were significantly down-regulated by BEY treatment. Interestingly, significant suppression of PCNA was observed in colonic tissues using the immunohistochemical examination. Treatment with BEY increased the epigenetic (microRNA217) interactions with PCNA. In conclusion, the BEY clearly alleviated the colitis symptoms and in the future could be used to formulate a probiotic-based diet for the host gut health and control the inflammatory bowel syndrome in mammals.
Poultry Meat and Eggs as an Alternative Source of n-3 Long-Chain Polyunsaturated Fatty Acids for Human Nutrition
Authors: Cartoni Mancinelli Alice , Mattioli Simona , Twining Cornelia , Dal Bosco Alessandro , Donoghue Ann M. , Arsi Komala , Angelucci Elisa , Chiattelli Diletta , Castellini Cesare
Published: May 2022

The beneficial effects of n-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) on human health are widely known. Humans are rather inefficient in synthesizing n-3 LC-PUFA; thus, these compounds should be supplemented in the diet. However, most Western human diets have unbalanced n-6/n-3 ratios resulting from eating habits and the fact that fish sources (rich in n-3 LC-PUFA) are not sufficient (worldwide deficit ~347,956 t/y) to meet the world requirements. In this context, it is necessary to find new and sustainable sources of n-3 LC-PUFA. Poultry products can provide humans n-3 LC-PUFA due to physiological characteristics and the wide consumption of meat and eggs. The present work aims to provide a general overview of the main strategies that should be adopted during rearing and postproduction to enrich and preserve n-3 LC-PUFA in poultry products. The strategies include dietary supplementation of α-Linolenic acid (ALA) or n-3 LC-PUFA, or enhancing n-3 LC-PUFA by improving the LA (Linoleic acid)/ALA ratio and antioxidant concentrations. Moreover, factors such as genotype, rearing system, transport, and cooking processes can impact the n-3 LC-PUFA in poultry products. The use of a multifactorial view in the entire production chain allows the relevant enrichment and preservation of n-3 LC-PUFA in poultry products.
Design of a Multi-Functional Integrated Optical Switch Based on Phase Change Materials
Authors: He Jie , Yang Junbo , Ma Hansi , Jiang Xinpeng , Yuan Huan , Yu Yang
Published: May 2022

An optical switch based on silicon-on-insulator (SOI) technology is proposed that works in the C-band and switches by amorphous (Am) to crystalline (Cr) and Cr-to-Am phase transitions. The optical switch integrates the functions of polarization beam splitting and mode conversion, and consists of two asymmetric directional couplers (ADCs). The TM0 mode is converted to the TM1 mode through an asymmetric coupler to achieve the polarization splitting of the TM0 mode and TE0 mode. The output of the TE0 mode is then controlled by Ge2Sb2Se4Te1 (GSST). When the TE0 mode is input and the wavelength is 1550 nm, the insertion loss (IL) is lower than 0.62 dB and the crosstalk (CT) is lower than −9.88 dB for a directional coupler loaded with GSST that realizes the optical switch function in both amorphous and crystalline GSST. The extinction ratio (ER) of the two waveguides of the directional coupler is lower than −11.40 dB, simultaneously. When the TM0 mode is input and the wavelength is 1550 nm, the IL is lower than 0.62 dB for a directional coupler loaded without GSST.
Reduction of Heavy Hydrocarbons from Oilfield Produced Water
Authors: Mintcheva Neli , Gicheva Gospodinka , Panayotova Marinela
Published: May 2022

This paper presents attempts to reduce the concentration of organic pollutants in oilfield produced wastewater before its discharge into natural water bodies or reinjection into the wells. The contaminant content was significantly decreased by wastewater treatment, based on solid phase adsorption, photocatalytic degradation of organic molecules and chemical oxidation of oily compounds. The study was conducted with real wastewater, which is in practice released in the environment. The produced water samples, taken from four sampling points in the oilfield site, were analyzed for physicochemical (temperature, redox potential (Eh), conductivity, pH, dissolved oxygen) and specific (chemical oxygen demand (COD), total oily hydrocarbons (TOH), phenols) parameters, cations (Ca2+, Mg2+, Na+, K+) and anions (Cl−, HCO3−, SO42−, S2−), in order to determine the initial water status. The organic contaminants in oilfield produced water showed COD of 39–58 mg/L, TOH of 152–363 mg/L and phenols of 0.07–0.21 mg/L. The TOH was chosen as a suitable parameter for the evaluation of the treatment method efficiency. The adsorption on activated charcoal decreased the TOH levels up to 52 mg/L, which corresponds to 85% removal of oily compounds. Chemical oxidation, carried out with Ca(ClO)2 in a concentration of 400 mg/L for 1 h at room temperature, showed TOH removal in the range of 80–94% for different wastewater samples. The use of 300 mg/L TiO2 or ZnO under UV irradiation for 12 h led to TOH removal of 25–78% and 82–92%, respectively. Both photocatalysts were characterized by using X-ray diffraction, reflectance UV-vis spectroscopy and scanning electron microscopy. The crystal forms anatase and wurtzite for TiO2 and ZnO, respectively, were found. The estimated band gap of 3.48 eV for direct transition in TiO2 and 3.25 eV for ZnO agrees well with that reported in the literature. Higher photodegradation of organic compounds was observed for ZnO, indicating that it absorbed more light photons than TiO2 did. A mechanism for photocatalytic degradation over a more efficient photocatalyst, ZnO, was proposed based on the GC-MS analysis of raw water and treated effluents produced for 6 and 12 h.
Numerical Simulations of a Postulated Methanol Pool Fire Scenario in a Ventilated Enclosure Using a Coupled FVM-FEM Approach
Authors: Tiwari Shashank S. , Bale Shivkumar , Das Diptendu , Tripathi Arpit , Tripathi Ankit , Mishra Pawan Kumar , Ekielski Adam , Suresh Sundaramurthy
Published: May 2022

Numerical investigations have been carried out for a postulated enclosure fire scenario instigated due to methanol pool ignition in a chemical cleaning facility. The pool fire under consideration is radiation-dominated and poses a risk to the nearby objects if appropriate safety requirements are not met. The objective of the current study was to numerically evaluate the postulated fire scenario and provide safety recommendations to prevent/minimize the hazard. To do this, the fire scenario was first modeled using the finite volume method (FVM) based solver to predict the fire characteristics and the resulting changes inside the enclosure. The FDS predicted temperatures were then used as input boundary conditions to conduct a three-dimensional heat transfer analysis using the finite element method (FEM). The coupled FVM–FEM simulation approach enabled detailed three-dimensional conjugate heat transfer analysis. The proposed FVM–FEM coupled approach to analyze the fire dynamics and heat transfer will be helpful to safety engineers in carrying out a more robust and reliable fire risk assessment.
A Numerical Study of Bubble Blockage in Microfluidic Fuel Cells
Authors: Herlambang Yusuf Dewantoro , Kurnianingsih  , Roihatin Anis , Prasetyo Totok , Marliyati  , Taufik  , Shyu Jin-Cherng
Published: May 2022

Based on fuel crossover behavior and bubble nucleation in the microfluidic fuel cell’s channel, this research numerically presents the performance of air-breathing direct formic acid microfluidic fuel cells. In the simulation, a three-dimensional microfluidic fuel cell model was used. The continuity, momentum, species transport, and charge equations were used to develop the model transport behavior, whereas the Brinkman equation represented the porous medium flow in the gas diffusion layer. The I–V and power density curves are generated using the Butler–Volmer equation. The simulation and current experimental data were compared under identical operating conditions to validate the I–V curve of the microfluidic fuel cell model. The model was used to investigate the current density distribution in the microchannel due to bubble obstruction and the reactant concentration on both electrodes. Fuel crossover resulted in a large decrease in open-circuit voltage and a reduction in fuel concentration above the anode electrode. The findings also showed that a low-flow rate air-breathing direct formic acid microfluidic fuel cell is more prone to CO2 bubble formation.
Helical Foldamers and Stapled Peptides as New Modalities in Drug Discovery: Modulators of Protein-Protein Interactions
Authors: Tsuchiya Keisuke , Kurohara Takashi , Fukuhara Kiyoshi , Misawa Takashi , Demizu Yosuke
Published: May 2022

A “foldamer” is an artificial oligomeric molecule with a regular secondary or tertiary structure consisting of various building blocks. A “stapled peptide” is a peptide with stabilized secondary structures, in particular, helical structures by intramolecular covalent side-chain cross-linking. Helical foldamers and stapled peptides are potential drug candidates that can target protein-protein interactions because they enable multipoint molecular recognition, which is difficult to achieve with low-molecular-weight compounds. This mini-review describes a variety of peptide-based foldamers and stapled peptides with a view to their applications in drug discovery, including our recent progress.
Data-Wise Spatial Regional Consistency Re-Enhancement for Hyperspectral Image Classification
Authors: Zhou Lijian , Xu Erya , Hao Siyuan , Ye Yuanxin , Zhao Kun
Published: May 2022

Effectively using rich spatial and spectral information is the core issue of hyperspectral image (HSI) classification. The recently proposed Diverse Region-based Convolutional Neural Network (DRCNN) achieves good results by weighted averaging the features extracted from several predefined regions, thus exploring the use of spatial consistency to some extent. However, such feature-wise spatial regional consistency enhancement does not effectively address the issue of wrong classifications at the edge of regions, especially when the edge is winding and rough. To improve the feature-wise approach, Data-wise spAtial regioNal Consistency re-Enhancement (“DANCE”) is proposed. Firstly, the HSIs are decomposed once using the Spectral Graph Wavelet (SGW) to enhance the intra-class correlation. Then, the image components in different frequency domains obtained from the weight map are filtered using a Gaussian filter to “debur” the non-smooth region edge. Next, the reconstructed image is obtained based on all filtered frequency domain components using inverse SGW transform. Finally, a DRCNN is used for further feature extraction and classification. Experimental results show that the proposed method achieves the goal of pixel level re-enhancement with image spatial consistency, and can effectively improve not only the performance of the DRCNN, but also that of other feature-wise approaches.
A Network Combining a Transformer and a Convolutional Neural Network for Remote Sensing Image Change Detection
Authors: Wang Guanghui , Li Bin , Zhang Tao , Zhang Shubi
Published: May 2022

With the development of deep learning techniques in the field of remote sensing change detection, many change detection algorithms based on convolutional neural networks (CNNs) and nonlocal self-attention (NLSA) mechanisms have been widely used and have obtained good detection accuracy. However, these methods mainly extract semantic features on images from different periods without taking into account the temporal dependence between these features. This will lead to more “pseudo-change” in complex scenes. In this paper, we propose a network architecture named UVACD for bitemporal image change detection. The network combines a CNNs extraction backbone for extracting high-level semantic information with a visual transformer. Here, visual transformer constructs change intensity tokens to complete the temporal information interaction and suppress irrelevant information weights to help extract more distinguishable change features. Our network is validated and tested on both the LEVIR-CD and WHU datasets. For the LEVIR-CD dataset, we achieve an intersection over union (IoU) of 0.8398 and an F1 score of 0.9130. For the WHU dataset, we achieve an IoU of 0.8664 and an F1 score of 0.9284. The experimental results show that the proposed method outperforms some previous state of the art change detection methods.
Diurnal Variations of Water Ice in the Martian Atmosphere Observed by Mars Climate Sounder
Authors: Wu Zhaopeng , Li Tao , Li Jing , Yang Chengyun , Cui Jun
Published: May 2022

Simulation studies have proposed a significant thermal effect of water ice clouds on the Martian atmosphere and climate. However, previous studies focused more on seasonal variations but less on short-term changes. In this work, we used the MCS multi-local time data to investigate the water ice diurnal variations on Mars. We quantified its diurnal variations with amplitude and phase by applying the tidal fitting method to the water ice abundance. In addition, we found a close correlation (antiphase relation) between the thermal tide and water ice diurnal variations during the aphelion seasons that was not sensitive to both the background water ice and dust opacity but increased with the tidal amplitude. In the perihelion seasons, the antiphase relation was sensitive to the water ice and dust opacity, both affected by the dust storm activity. Finally, the statistic results suggested an unexpected low threshold of diurnal tide amplitude (2 to 3 K) for generating a relevant water ice diurnal variation, accounting for the ubiquitous water ice diurnal variations in the Martian atmosphere. These new observational results can help further understand the phase transition process between ice and vapor in the Martian atmosphere and better constrain the Martian global climate model in the future.
Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China
Authors: Lu Rui , Wang Nan , Zhang Yanbin , Lin Yeneng , Wu Wenqiang , Shi Zhou
Published: May 2022

Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management.
An Evaluation of Wind Turbine-Induced Topographic Change in the Offshore Intertidal Sandbank Using Remote Sensing-Constructed Digital Elevation Model Data
Authors: Zhang Dong , Zhang Huiming , Zhou Yong , Cutler Mark E. J. , Cui Dandan
Published: May 2022

With the rapid development of wind power generation, many marine wind farms have been developed on the offshore intertidal sandbank (OIS) along the coastal regions of Jiangsu Province, China during the last decade. In order to quantitatively assess the stability of offshore wind turbines and their induced topographic changes on the OIS, a digital elevation model (DEM)-based analysis supported by satellite remote sensing is adopted in the present study. Taking the Liangsha OIS at the middle of Jiangsu coast, China as the research area, we first used an enhanced waterline method (EWM) to construct the 30 m resolution DEMs for the years 2014 and 2018 with the embedment of tidal creeks to effectively express the detailed characteristics of the micro-terrain. Then, a hypothetical sandbank surface discrimination method (HSSDM) was proposed. By comparing the height difference between the hypothetical and the real terrain surface during the operation period, the wind turbine-induced topographic change rate (TCR) was estimated from the DEM of 2018. The results show that 73.47% of the 49 wind turbines in the Liangsha OIS have an erosional/depositional balanced influence on the intertidal sand body, 8.16% show a weak depositional influence, and 18.36% lead to weak erosion. The average erosional depth, 58.6 cm, reached nearly 6% to 10% of the maximum possible erosion estimated by the hydrodynamic model. Furtherly, using two DEMs for the years 2014 and 2018, the topographic change depths at the location of wind turbines were calculated. By comparing the wind turbine-induced terrain change with the naturally erosional/depositional depths of the OIS, the average contribution rate caused by the wind turbines achieved 42.17%, which meant that the impact of wind turbines on terrain changes could not be ignored. This work shows the potential of utilizing satellite-based remote sensing to monitor topographic changes in the OIS and to assess the influence of morphological variations caused by wind turbines, which will be helpful for offshore wind farm planning and intertidal environment protection.
One-Shot Dense Network with Polarized Attention for Hyperspectral Image Classification
Authors: Pan Haizhu , Liu Moqi , Ge Haimiao , Wang Liguo
Published: May 2022

In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples.
Adaptive Subspace Signal Detection in Structured Interference Plus Compound Gaussian Sea Clutter
Authors: Wang Zeyu , Liu Jun , Li Yachao , Chen Hongmeng , Peng Mugen
Published: May 2022

This paper discusses the problem of detecting subspace signals in structured interference plus compound Gaussian sea clutter with persymmetric structure. The sea clutter is represented by a compound Gaussian process wherein the texture obeys the inverse Gaussian distribution. The structured interference lies in a known subspace, which is independent with the target signal subspace. By resorting to the two-step generalized likelihood ratio test, two-step Rao, and two-step Wald design criteria, three adaptive subspace signal detectors are proposed. Moreover, the constant false-alarm rate property of the proposed detectors is proved. The experimental results based on IPIX real sea clutter data and simulated data illustrate that the proposed detectors outperform their counterparts.
The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment
Authors: Bian Chao , Ye Bing , Mihailidis Alex
Published: May 2022

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland–Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings.
Single Image Video Prediction with Auto-Regressive GANs
Authors: Huang Jiahui , Chia Yew Ken , Yu Samson , Yee Kevin , Küster Dennis , Krumhuber Eva G. , Herremans Dorien , Roig Gemma
Published: May 2022

In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use “one shot” generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a “complementary mask” module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.
Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey
Authors: Zhong Lisha , He Shuling , Lin Jinzhao , Wu Jia , Li Xi , Pang Yu , Li Zhangyong
Published: May 2022

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.
Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax
Authors: Gharye Mirzaei Mehran , Goodarzian Fariba , Maddah Saeid , Abraham Ajith , Abdelkareim Gabralla Lubna
Published: May 2022

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.
A Holistic Strategy for Classification of Sleep Stages with EEG
Authors: Prabhakar Sunil Kumar , Rajaguru Harikumar , Ryu Semin , Jeong In cheol , Won Dong-Ok
Published: May 2022

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.
Goal Conflicts, Classical Management and Constructivism: How Operators Get Things Done
Authors: Boskeljon-Horst Leonie , De Boer Robert J. , Sillem Simone , Dekker Sidney W. A.
Published: May 2022

In this study we identify the differences in goal realisation when applying two conflicting paradigms regarding rule perception and management. We gathered more than 30 scenarios where goal conflicts were apparent in a military operational unit. We found that operators repetitively utilized certain routines in executing their tasks in an effort to realize several conflicting goals. These routines were not originally intended nor designed into the rules and not explicitly included in documentation. They were not necessarily at odds with the literal wording and/or the intent of rules and regulations, although we did find examples of this. Our data showed that local ingenuity was created innovatively within the frame of existing rules or kept invisible to those outside the unit. The routines were introduced and passed on informally, and we found no evidence of testing for the introduction of new risks, no migration into the knowledge base of the organisation, and no dissemination as new best practices. An explanation for this phenomenon was found in the fact that the military organisation was applying a top-down, classical, rational approach to rules. In contrast, the routines were generated by adopting a constructivist view of rules as dynamic, local, situated constructions with operators as experts. The results of this study suggest that organisations are more effective in solving goal conflicts and creating transparency on local ingenuity if they adopt a constructivist paradigm instead of, or together with, a classical paradigm.
Philopatry as a Tool to Define Tentative Closed Migration Cycles and Conservation Areas for Large Pelagic Fishes in the Pacific
Authors: Relano Veronica , Pauly Daniel
Published: May 2022

Migrations of large pelagic fishes across the Pacific are usually inferred from tagging or genetic studies. Even though these techniques have improved over time, they still fail to demonstrate large transoceanic migrations, usually proposing ‘routes’ that do not cycle seasonally. The current study uses the concept of ‘philopatry’ in 11 large pelagic fish species, i.e., the tendency for animals to return to their natal site to reproduce. Tentative migration routes and maps emerge by applying this concept to the movements extracted through a comprehensive review of the literature on satellite and conventional tagging, and population and subpopulation linkages inferred from genetic and/or genomic studies. Moreover, when comparing these proposed migration routes and the mapped reconstructed catch (1950–2016, Sea Around Us) of each species in the Pacific, similarities emerge, reinforcing the accuracy of these migration cycles informed by philopatry. Finally, by superposing the migration routes of our 11 species, we identified areas of the Pacific that are part of the inferred migration routes of multiple species, leading to a discussion of possible ‘blue corridors’ that would protect the studied species’ key migration routes and stocks, which are important for the fisheries, culture and nutrition of Pacific islanders.
Risk Perception Thresholds and Their Impact on the Behavior of Nearby Residents in Waste to Energy Project Conflict: An Evolutionary Game Analysis
Authors: Quan Xiongwei , Zuo Gaoshan , Sun Helin
Published: May 2022

In China, waste to energy (WTE) projects are currently considered the best choice for dealing with municipal solid waste (MSW), but their siting often leads to conflicts. The perceptions of proximate residents to the changes and uncertainty induced by WTE projects are the main reasons for such conflicts. Determining the indicators used to measure these changes is crucial for an evaluation of surrounding residents’ risk perception. One indicator is residents’ risk perception thresholds. Our paper employs evolutionary game theory to deduce the risk perception threshold of surrounding residents related to a WTE project, which provides a novel contribution to the literature. The results of a case study and simulations show that the level of the risk perception threshold has a crucial effect on the behavior choices of surrounding residents. Two important parameters that affect the value of this risk perception threshold are possible economic compensation and possible resistance costs. A change to the values of these two parameters can change the value of the risk perception threshold of nearby residents. If the change in the risk perceived by surrounding residents is lower than the threshold they can tolerate, they will accept construction of the project. However, if surrounding residents are worried about this risk fluctuating as a result of construction of the plant, they will behave more cautiously and conservatively, and if the possible risk exceeds the threshold that they are willing to tolerate, then they will boycott the plan and protest against the construction of the project. In this case, the surrounding residents will still show restraint. This study tries to provide a theoretical and practical basis for effective resolution by government of the public’s risk concerns and existing or imminent conflicts.
A Combined IO-DEMATEL Analysis for Evaluating Sustainable Effects of the Sharing Related Industries Development
Authors: Wang Dan , Yan Liang , Ruan Fangli
Published: May 2022

Emerging economies suffer more severe pressure to coordinate sustainable production and consumption, and new industry input–output (IO) solutions are urgent. An emerging service supply and consumption mode, the sharing economy (SE) penetrates various industries and rewrites the technological–economic links between sectors via integrating scattered and idle resources. The sustainable effects of such industrial linkages changes triggered by its specific activities remain unclear. The study aims to clarify the mechanism of SE in coordinating the relationship between production and consumption among industries and demonstrate its particular role in cleaner production and sustainable consumption to reveal its multistage and systematic impact on industrial development through input–output linkages. Grounded on the IO and complex systems theory, we establish an IO-DEMATEL industrial development evaluation model embedded in the IO table. The comparative analysis of IO cross-sectional data in 2007 and 2018 deduces the dynamic evolution of industrial interaction and structure under the penetration of SE, as well as its contributions to the national economy in China. The result reveals that sharing activities raise the industry prominence moderately and promote the cleanliness and resource-conservation of industrial connections. However, the prominence of the five sectors regarding input, output, or comprehensive influence currently ranks in the lower-middle levels among all industries. Industrial structure optimization has only been observed on the supply side. Our findings complement a deepened empirical evidence of SE and its sustainability, which is also of practical significance for policymakers to identify the critical industries that conduct cleaner production and sustainable consumption.
Numerical Optimization of the Blade Profile of a Savonius Type Rotor Using the Response Surface Methodology
Authors: Gallo Luis A. , Chica Edwin L. , Flórez Elkin G.
Published: May 2022

The present study aims to numerically determine the geometric proportions that maximize the performance of a Savonius rotor with a split Bach blade profile. For this, the response surface methodology was used through a full factorial experimental design, comprised of four factors corresponding to the width (C) and length (L) of the overlap, and the eccentricity (E) and radius (R) of the blade, which define the geometry of the rotor. The models built from the different treatments of the experimental designs were analyzed using computational simulations in order to obtain the power coefficient (CP), considered as the response variable. The same parameters and models of computational fluid dynamics were used to analyze each geometry through the ANSYS Fluent software. The analysis of the obtained results showed that there is a great interaction among the evaluated factors, which demonstrates the importance of analyzing them together. The results obtained with the full factorial experiment design were compared with those obtained from a face-centered central composite design, evidencing a difference of only 0.30% in the estimate of the regression model. A CP of 0.2661 was obtained from the optimized geometry, which represents a 36.50% increase in its performance with respect to the conventional semicircular profile. The optimal dimensions obtained are 4.69, 21.45, 5.52 and 25.15 in percentage values of the rotor diameter, for parameters C, L, E and R parameters, respectively. Experimental data available in the literature were used to contrast the numerical results and a good fit was revealed.