bluuebunny/crossref_metadata_2025 huggingface dataset:
Protective Effect of Sweetpotato (Ipomoea batatas L.) Leaf Phenolic Acids Extract on IL-1β-Induced Barrier Injury of Caco-2 Monolayers
Authors: Zhang Yi , Sun Jian , Zhao Lu , Niu Fuxiang , Yue Ruixue , Zhu Hong , Zhang Wenting , Ma Chen
Published: October 2022

The status quo of a high-fat diet can impair the integrity of the intestinal barrier and promote inflammatory bowel diseases. The exploration of natural products as sources of new lead compounds that control such diseases becomes essential. Sweetpotato leaves (SPLs) have strong biological activity, and their phenolic acids were recently reported to be able to impart antioxidative, anti-inflammatory, and neuroprotection. The aim of this study was to investigate the effects and mechanisms of sweetpotato leaf phenolic acids (SPLPAs) extract on interleukin 1 beta (IL-1β)-induced barrier injury of Caco-2 monolayers. The safety from 0.02 to 0.2 mg/mL SPLPA extracts were demonstrated using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) method and Trypan blue staining. The 0.2 mg/mL SPLPA extract significantly decreased the release of Nitric oxide (NO) generation and its contribution to the expression of inflammation-related nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and nuclear factor kappa B (NF-κB) were evaluated. Further investigation suggested that the SPLPA extract inhibited the IL-1β-induced decrease in the transepithelial electrical resistance (TEER) value and could upregulate the IL-1β induced decline in tight junction protein levels. The results suggested that the SPLPA extract could enhance the integrity of Caco-2 monolayers. These results clarified the beneficial effects of SPLPA extract on inflammatory Caco-2 monolayers, indicating that the application of SPLPA extract in medicine and functional food has great potential for exploration.
Auto-Ignition Delay Characteristics of Ammonia Substitution on Methane
Authors: Xiao Hua , Chen Aiguo , Guo Yanze , Zhang Lifu , Zhang Minghui , Deng Xi , Li Jun , Ying Wenxuan
Published: October 2022

Ammonia is a promising alternative fuel, which is considered to have the potential to substitute conventional fossil fuels. In the present work, auto-ignition characteristics of ammonia substitution on methane are investigated both experimentally and numerically. The auto-ignition procedure of ammonia-substituted methane/air mixtures are measured behind the reflected shock wave in a shock tube experiment system over temperatures from 1355 to 1877 K, pressure up to 5 atm and an equivalence ratio from 0.5 to 2. Numerical simulation studies using a detailed kinetics mechanism are also performed to gain a deep insight into the auto-ignition procedure of ammonia-substituted methane fuel mixtures. The established numerical model is verified with the measured auto-ignition delay time data by experiments. Then, the auto-ignition delay times are predicted under a wider range of conditions such as equivalence ratio, pressure, temperature, etc. In this way, combustion characteristics of such mixtures are investigated. It is found that adding ammonia fuel to methane will not change the autoignition delay time of methane a lot, while it can effectively benefit the reduction of carbon emissions. Finally, sensitivity analyses are performed to provide essential information for the elementary reaction sensitive to the ignition characteristics. The results present in this work can provide fundamental information for combustion application of ammonia-based fuels.
Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data
Authors: Foody Giles M.
Published: October 2022

Ground reference data are typically required to evaluate the quality of a supervised image classification analysis used to produce a thematic map from remotely sensed data. Acquiring a suitable ground data set for a rigorous assessment of classification quality can be a major challenge. An alternative approach to quality assessment is to use a model-based method such as can be achieved with a latent class analysis. Previous research has shown that the latter can provide estimates of class areal extent for a non-site specific accuracy assessment and yield estimates of producer’s accuracy which are commonly used in site-specific accuracy assessment. Here, the potential for quality assessment via a latent class analysis is extended to show that an estimate of a complete confusion matrix can be predicted which allows a suite of standard accuracy measures to be generated to indicate global quality on an overall and per-class basis. In addition, information on classification uncertainty may be used to illustrate classification quality on a per-pixel basis and hence provide local information to highlight spatial variations in classification quality. Classifications of imagery from airborne and satellite-borne sensors were used to illustrate the potential of the latent class analysis with results compared against those arising from the use of a conventional ground data set.
Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image
Authors: Xu Xingmei , Wang Lu , Shu Meiyan , Liang Xuewen , Ghafoor Abu Zar , Liu Yunling , Ma Yuntao , Zhu Jinyu
Published: October 2022

Leaf age is an important trait in the process of maize (Zea mays L.) growth. It is significant to estimate the seed activity and yield of maize by counting leaves. Detection and counting of the maize leaves in the field are very difficult due to the complexity of the field scenes and the cross-covering of adjacent seedling leaves. A method was proposed in this study for detecting and counting maize leaves based on deep learning with RGB images collected by unmanned aerial vehicles (UAVs). The Mask R-CNN was used to separate the complete maize seedlings from the complex background to reduce the impact of weeds on leaf counting. We proposed a new loss function SmoothLR for Mask R-CNN to improve the segmentation performance of the model. Then, YOLOv5 was used to detect and count the individual leaves of maize seedlings after segmentation. The 1005 field seedlings images were randomly divided into the training, validation, and test set with the ratio of 7:2:1. The results showed that the segmentation performance of Mask R-CNN with Resnet50 and SmoothLR was better than that with LI Loss. The average precision of the bounding box (Bbox) and mask (Mask) was 96.9% and 95.2%, respectively. The inference time of single image detection and segmentation was 0.05 s and 0.07 s, respectively. YOLOv5 performed better in leaf detection compared with Faster R-CNN and SSD. YOLOv5x with the largest parameter had the best detection performance. The detection precision of fully unfolded leaves and newly appeared leaves was 92.0% and 68.8%, and the recall rates were 84.4% and 50.0%, respectively. The average precision (AP) was 89.6% and 54.0%, respectively. The rates of counting accuracy for newly appeared leaves and fully unfolded leaves were 75.3% and 72.9%, respectively. The experimental results showed the possibility of current research on exploring leaf counting for field-grown crops based on UAV images.
Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction
Authors: Dokoohaki Hamze , Rai Teerath , Kivi Marissa , Lewis Philip , Gómez-Dans Jose L. , Yin Feng
Published: October 2022

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Co- and Postseismic Deformation of the 2020 Mw 6.3 Nima (Tibet, China) Earthquake Revealed by InSAR Observations
Authors: Zhang Miaomiao , Li Zhenhong , Yu Chen , Liu Zhenjiang , Zhang Xuesong , Wang Jiatong , Yang Jing , Han Bingquan , Peng Jianbing
Published: October 2022

On 22 July 2020, an Mw 6.3 earthquake occurred in Nima County, central Qinghai-Tibet Plateau, China. We used the synthetic aperture radar interferometry (InSAR) technique with Sentinel-1 images to retrieve the line of sight (LOS) coseismic deformation fields which indicate that the maximum surface displacement reached ~30 cm. We then processed a series of interferograms spanning one year after the Nima earthquake with the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. The maximum cumulative postseismic LOS surface displacement reached ~8 cm and approximately followed a logarithmic function over time. The inversion of the fault geometry and co- and afterslip distribution shows that the epicenter location was (33.18°N, 86.88°E) at a depth of 7.4 km, and the causative fault had an N29.1°E strike and 50.2° dip. The most coseismic slip was concentrated at depths between 3 to 12 km with a peak value of 2.0 m at 7.4 km, whilst most afterslips were concentrated at depths between 0 to 12 km with a peak value of 0.2 m at 5 km. The postseismic moment energy was about 5.04 × 1017 N∙m 308 days after the event, which was approximately 13.8% of the coseismic moment energy. By analyzing the contribution of afterslip and poroelastic rebound to postseismic deformation, it was concluded that afterslip was the main early postseismic deformation mechanism. Future attention should be paid to the northern segment of the West Yibug Caka fault and East Yibug Caka fault.
A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments
Authors: Iacone Brooke , Allington Ginger R. H. , Engstrom Ryan
Published: October 2022

High-resolution Corona imagery acquired by the United States through spy missions in the 1960s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments.
Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data
Authors: Alzahrani Ahmad
Published: October 2022

Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.
Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks
Authors: Tian Yiming , Zhang Jie , Chen Qi , Hou Shuping , Xiao Li
Published: October 2022

Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature.
Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor
Authors: Arshad Muhammad Zeeshan , Jamsrandorj Ankhzaya , Kim Jinwook , Mun Kyung-Ryoul
Published: October 2022

Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. A fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of ±6 TS (±6 ms) and ±1 TS (±1 ms), respectively. Advancing from the previous studies exploring gait event detection, the model demonstrated a great improvement in terms of its prediction error having an MAE of 6.239 ms and 5.24 ms for HS and TO events, respectively, at the tolerance window of ±1 TS. The results demonstrated that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.
Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data
Authors: Kirfel Alexander , Scheer Tobias , Jung Norbert , Busch Christoph
Published: October 2022

Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
Energy-Efficient Cooperative Spectrum Sensing Using Machine Learning Algorithm
Authors: Wu Qingying , Ng Benjamin K. , Lam Chan-Tong
Published: October 2022

Cognitive Radio (CR) is a practical technique for overcoming spectrum inefficiencies by sensing and utilizing spectrum holes over a wide spectrum. In particular, cooperative spectrum sensing (CSS) determines the state of primary users (PUs) by cooperating with multiple secondary users (SUs) distributed around a Cognitive Radio Network (CRN), further overcoming various noise and fading issues in the radio environment. But it’s still challenging to balance energy efficiency and good sensing performances in the existing CSS system, especially when the CRN consists of battery-limited sensors. This article investigates the application of machine learning technologies for cooperative spectrum sensing, especially through solving a multi-dimensional optimization that cannot be readily addressed by traditional approaches. Specifically, we develop a neural network, which involves parameters that are integral to the CSS performance, including a device sleeping rate for each sensor and thresholds used in the energy detection method, and a customized loss function based on the energy consumption of the CSS system and multiple penalty terms reflecting the system requirements. Using this formulation, energy consumption is to be minimized with the guarantee of reaching a certain probability of false alarm and detection in the CSS system. With the proposed method, comparison studies under different hard fusion rules (‘OR’ and ‘AND’) demonstrate its effectiveness in improving the CSS system performances, as well as its robustness in the face of changing global requirements. This paper also suggests the combination of the traditional and the proposed scheme to circumvent the respective inherent pitfalls of neural networks and the traditional semi-analytic methods.
Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
Authors: Schifano Luca , Vervaeke Michael , Rosseel Dries , Verbaenen Jef , Thienpont Hugo , Dewitte Steven , Berghmans Francis , Smeesters Lien
Published: October 2022

Wide field-of-view imaging optics offer a huge potential for space-based Earth observation enabling the capture of global data. Reflective imaging telescopes are often favored, as they do not show chromatic aberrations and are less susceptible to radiation darkening than their refractive counterparts. However, the main drawback of reflective telescopes is that they are limited with respect to field-of-view while featuring large dimensions. We propose the use of freeform optics to maximize the field of view while maintaining diffraction-limited image quality and minimizing system dimensions. In this paper, we present a novel freeform wide field-of-view reflective telescope, starting from the optical design, and continuing to tolerancing analysis and manufacture, towards a proof-of-concept demonstrator. The novel telescope features a full field-of-view of 120° while showing an exceptional spatial resolution of 2.6 km and fitting within 1 CubeSat unit. To the best of our knowledge, this is the widest field-of-view that has ever been realized for a space-based telescope, nearly reaching Earth observation from limb to limb from an altitude of about 700 km. We hope this design paves the way for future space missions enabling improved Earth observation and leading to enhanced monitoring of climate and climate change.
A Single Stage and Single View 3D Point Cloud Reconstruction Network Based on DetNet
Authors: Li Bin , Zhu Shiao , Lu Yi
Published: October 2022

It is a challenging problem to infer objects with reasonable shapes and appearance from a single picture. Existing research often pays more attention to the structure of the point cloud generation network, while ignoring the feature extraction of 2D images and reducing the loss in the process of feature propagation in the network. In this paper, a single-stage and single-view 3D point cloud reconstruction network, 3D-SSRecNet, is proposed. The proposed 3D-SSRecNet is a simple single-stage network composed of a 2D image feature extraction network and a point cloud prediction network. The single-stage network structure can reduce the loss of the extracted 2D image features. The 2D image feature extraction network takes DetNet as the backbone. DetNet can extract more details from 2D images. In order to generate point clouds with better shape and appearance, in the point cloud prediction network, the exponential linear unit (ELU) is used as the activation function, and the joint function of chamfer distance (CD) and Earth mover’s distance (EMD) is used as the loss function of 3DSSRecNet. In order to verify the effectiveness of 3D-SSRecNet, we conducted a series of experiments on ShapeNet and Pix3D datasets. The experimental results measured by CD and EMD have shown that 3D-SSRecNet outperforms the state-of-the-art reconstruction methods.
New Smart Sensor for Voltage Unbalance Measurements in Electrical Power Systems
Authors: Bogarra Santiago , Saura Jaime , Rolán Alejandro
Published: October 2022

This paper deals with voltage unbalances and how they can be quantified according to the standards. Firstly, a comparison between the different unbalance voltage factors is conducted in order to remark on their divergences. Secondly, according to the standard that better represents the phenomenon, i.e., EN 50160, a new methodology is proposed to quantify the voltage unbalance factor (VUF). In order to do so, it is recommended to measure the voltage unbalance in three-phase installations by means of a new smart sensor based on a single voltage sensor, which measures the direct-current (DC) voltage at the output of a three-phase diode bridge rectifier, while current methods make use of three voltage sensors (which can measure either phase-to-neutral voltages or phase-to-phase voltages). Furthermore, both simulation and experimental results have been carried out to validate the proposed methodology. Finally, a new voltage unbalance factor (and the corresponding methodology to obtain it from the measured DC voltage) is proposed.
Muscle Oxygenation Measured with Near-Infrared Spectroscopy Following Different Intermittent Training Protocols in a World-Class Kayaker—A Case Study
Authors: Paulauskas Rūtenis , Nekriošius Ričardas , Dadelienė Rūta , Sousa Ana , Figueira Bruno
Published: October 2022

Training elite kayakers at a distance of 1000 m is associated with aerobic and anaerobic metabolism, while intermittent training, in a variety of forms, is one of the effective ways to improve cardiorespiratory and metabolic function. Thus, this study aimed to investigate muscle oxygenation responses during repetition training (RT), interval training (IT), and sprint interval training (SIT). Near-infrared spectroscopy (NIRS) monitors were placed on the latissimus dorsi (LD), pectoralis major (PM), and vastus lateralis (VL) of a world-class kayaker during their preparatory period. The intensity of work, relief, and recovery intervals were the independent variables that were manipulated using three different training protocols. The inferential analysis between intermittent training protocols showed significant differences for all variables except total the hemoglobin (tHb) index in LD during bout 2 (F = 2.83, p = 0.1, ηp2 = 0.205); bout 3 (F = 2.7, p = 0.125, ηp2 = 0.193); bout 4 (F = 1.8, p = 0.202, ηp2 = 0.141); and bout 6 (F = 1.1, p = 0.327, ηp2 = 0.092). During the rest bouts, all training protocols showed significant differences for all variables except muscle oxygen saturation (SmO2) in the VL during bout 5 (F = 4.4, p = 0.053, ηp2 = 0.286) and tHb in VL during bout 1 (F = 2.28, p = 0.132, ηp2 = 0.172); bout 2 (F = 0.564, p = 0.561, ηp2 = 0.049); bout 3 (F = 1.752, p = 0.205, ηp2 = 0.137); bout 4 (F = 1.216, p = 0.301, ηp2 = 0.1); and bout 6 (F = 4.146, p = 0.053, ηp2 = 0.274). The comparison between IT protocols RT and SIT presented similar results. All variables presented higher values during SIT, except HR results. Finally, the comparison between IT and SIT showed significant differences in several variables, and a clear trend was identified. The results of this study suggest that the application of different intermittent exercise protocols promotes distinct and significant changes in the peripheral effect of muscle oxygenation in response to training stimuli and may be internal predictors of hemodynamic and metabolic changes.
FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image
Authors: Lu Haoxiang , Gong Junming , Liu Zhenbing , Lan Rushi , Pan Xipeng
Published: October 2022

Low-illumination images exhibit low brightness, blurry details, and color casts, which present us an unnatural visual experience and further have a negative effect on other visual applications. Data-driven approaches show tremendous potential for lighting up the image brightness while preserving its visual naturalness. However, these methods introduce hand-crafted holes and noise enlargement or over/under enhancement and color deviation. For mitigating these challenging issues, this paper presents a frequency division and multiscale learning network named FDMLNet, including two subnets, DetNet and StruNet. This design first applies the guided filter to separate the high and low frequencies of authentic images, then DetNet and StruNet are, respectively, developed to process them, to fully explore their information at different frequencies. In StruNet, a feasible feature extraction module (FFEM), grouped by multiscale learning block (MSL) and a dual-branch channel attention mechanism (DCAM), is injected to promote its multiscale representation ability. In addition, three FFEMs are connected in a new dense connectivity meant to utilize multilevel features. Extensive quantitative and qualitative experiments on public benchmarks demonstrate that our FDMLNet outperforms state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability.
A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System
Authors: Liu Ang , Lin Shiwei , Wang Jianguo , Kong Xiaoying
Published: October 2022

Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.
User Experience during an Immersive Virtual Reality-Based Cognitive Task: A Comparison between Estonian and Italian Older Adults with MCI
Authors: Mondellini Marta , Arlati Sara , Gapeyeva Helena , Lees Kairi , Märitz Ingrid , Pizzagalli Simone Luca , Otto Tauno , Sacco Marco , Teder-Braschinsky Anneli
Published: October 2022

Mild cognitive impairment (MCI) is an early stage of cognitive abilities loss and puts older adults at higher risk of developing dementia. Virtual reality (VR) could represent a tool for the early assessment of this pathological condition and for administering cognitive training. This work presents a study evaluating the acceptance and the user experience of an immersive VR application representing a supermarket. As the same application had already been assessed in Italy, we aimed to perform the same study in Estonia in order to compare the outcomes in the two populations. Fifteen older adults with MCI were enrolled in one Rehabilitation Center of Estonia and tried the supermarket once. Afterwards, they were administered questionnaires aimed at evaluating their technology acceptance, sense of presence, and cybersickness. Estonian participants reported low side effects and discrete enjoyment, and a sense of presence. Nonetheless, their intention to use the technology decreased after the experience. The comparison between Italian and Estonian older adults showed that cybersickness was comparable, but technology acceptance and sense of presence were significantly lower in the Estonian group. Thus, we argue that: (i) cultural and social backgrounds influence technology acceptance; (ii) technology acceptance was rather mediated by the absence of positive feelings rather than cybersickness.
Climate Change, Food Security, and Health: Harnessing Agroecology to Build Climate-Resilient Communities
Authors: Amoak Daniel , Luginaah Isaac , McBean Gordon
Published: October 2022

Climate change threatens human health, food security, and ecological sustainability. In marginalized and vulnerable communities around the globe, there is a crucial need to initiate actions to reduce adverse climatic impacts and support sustainable development goals (SDGs), particularly on food and health. Climate change’s multidimensional and complex impact on food and health has prompted calls for an integrated, science-based approach that could simultaneously improve the environment and nourish development-constrained communities. This paper examines a transdisciplinary practice of agroecology that bridges the gap between science, practice, and policy for climate action. We also analyze the significance of agroecology in building climate-resilient communities through sustainable food systems. We assert that the marriage of science and local knowledge that addresses access inequities through agroecology can lessen the impact of climate change on rural communities to achieve healthier, more sustainable, and equitable food systems. Furthermore, a transformative agroecological paradigm can provide farmers with a host of adaptive possibilities leading to healthier communities, improved food security, and restored lands and forests that can sequester greenhouse gases. Based on our findings, we call on the science and policy communities to integrate agroecology as part of the broader strategic approach to climate change adaptation and mitigation.
The Consequence of Combining Indigenous Techniques with a Flexible Design to Reduce Energy Consumption in Residential Buildings for Future Architecture
Authors: Ramezani Hoda , Reza Ehsan
Published: October 2022

A significant amount of research has addressed the issue of energy consumption reduction and the use of natural ventilation. Nevertheless, fewer studies have discussed the use of vernacular solutions and their integration with modern architecture on a global scale. Therefore, the primary motivation of this article is to answer the question of how combining indigenous techniques for natural ventilation with characteristics of flexible design can be reflected in reducing energy consumption in residential buildings Since natural ventilation is one of the most effective factors in creating thermal comfort and this factor creates comfortable conditions in hot and humid climates by taking advantage of airflow, reducing relative humidity, and increasing surface evaporation, this article examines the Shavadoon, which is an indigenous technique to escape from the excruciating heat of summer in Iran, and, by integrating it with modern architecture in Northern Cyprus, it seeks its effects on cooling the building and reducing energy consumption. The details of local solutions, natural ventilation, flexible design, and modern architecture will be extracted from the literature. Subsequently, via case study and, eventually, using the knowledge of BIM, the effect of their integration in reducing energy consumption will be investigated. Finally, new solutions for future architects in line with the construction of energy-efficient residential buildings will be provided.
Fuzzy Comprehensive Evaluation of Human Work Efficiency in a High-Temperature Thermal-Radiation Environment
Authors: Zhai Yingni , Wang Xinta , Niu Haobo , Wang Xianglin , Nie Yangwen , Huang Yanqiu
Published: October 2022

Work efficiency is not only related to objective cognitive performance, but it is also closely related to subjective psychological feelings. The existing research mostly focuses on the human safety tolerance limit from the physiological perspective, and there are few studies on work efficiency. In order to evaluate the work efficiency of humans in a high-temperature thermal-radiation environment, this paper proposes a method of personnel work efficiencies evaluation based on the fuzzy comprehensive evaluation method. In this method, the objective cognitive ability and subjective questionnaire data of human subjects in a thermal-radiation environment were obtained by building a high-temperature thermal-radiation platform, and the ergonomic status score (ESS) was calculated to determine the work efficiency evaluation grade. The results of this study indicate that subjective job satisfaction has a strong influence on work efficiencies. The safe WBGT temperature threshold that can be borne by personnel in a high-temperature thermal-radiation environment is lower than that in a single high-temperature environment. The ergonomic status score decreased significantly with the increase of radiation temperature. When the radiation temperature is 300 °C (WBGT temperature ≥ 33.2 °C), the work should be stopped immediately to ensure the safety and health of personnel. The research results can provide a reference for labor protection in high-temperature working environments.
Enhancing Emotional Resilience in the Face of Climate Change Adversity: A Systematic Literature Review
Authors: Easton-Gomez Shona C. , Mouritz Mike , Breadsell Jessica K.
Published: October 2022

Australians have experienced repetitive exposure to climate change adversity (either in-person or through televised exposure from media and internet sources). As these events become more prevalent and severe, developing, and implementing strategies to enhance emotional resilience at individual, community, and government levels is beneficial. This literature review examines one way of addressing this issue: enhancement of societal emotional resilience capabilities that can minimize negative impacts of climate change adversity upon emotional wellbeing. The study also offers an initial exploration of climate change adversity and emotional resilience. A systematic literature review identified key research themes. Subsequent findings identified policy and framework recommendations aimed at supporting emotional resilience in communities. Such strategies should focus on community preparedness through needs-based assessments and community engagement, fostering knowledge-building pathways and ongoing collaboration among system levels to address a diversity of community needs. These systematic approaches would ensure the accessibility and appropriateness of relevant supports by establishing frameworks that are regularly monitored, evaluated, and promoted.
Research on the Practical Path of Resource-Based Enterprises to Improve Environmental Efficiency in Digital Transformation
Authors: Ruan Tianshun , Gu Ying , Li Xinhao , Qu Rong
Published: October 2022

Increasingly serious environmental problems force resource-based enterprises to find effective means to break through the original resource dilemma. With the acceleration of the digital transformation process, digital technology can guarantee the green transformation process and realize the sustainable development of economic growth and environmental protection. Based on the perspective of efficiency, this paper selects 46 resource-based enterprises as research object, adopts a strategic triangle research framework of “operational capability–policy environment–value objective” and, through the fuzzy-set qualitative comparative analysis method, analyzes the formation mechanism and practice pathways of sustainable development in the resource-based enterprises’ digital transformation. Research suggests that: (1) The realization path of high-level enterprise environmental efficiency includes four configurations: technology guarantee type, strategy driven type, pressure lead type and policy pulled type. (2) The characteristics and strategies of resource-based enterprises using digital technology to deal with changes in the business environment and achieve sustainable development can be divided into two models: the “capability-oriented” model and “environment-oriented” model. (3) The heterogeneity study shows that the green development of the power supply industry and the material processing industry is more suitable for the “capability-oriented” model, and the green development of the iron and steel manufacturing industry and energy extraction industry is more suitable for the “environment-oriented” model. The research results can provide a new theoretical perspective and strategy for resource-based enterprises to use digital technology for green development and provide a new optimization pathway for China’s industrial practice.
Trends Shaping Western European Agrifood Systems of the Future
Authors: Preiss Myriam , Vogt Julia H.-M. , Dreher Carsten , Schreiner Monika
Published: October 2022

Western Europe’s agrifood systems are highly developed, extremely complex, and dependably produce food for billions. Securing their functionality is imperative whilst dealing with varieties of major challenges and opportunities in the future. Multiple stakeholders are involved in system transitions; therefore, synthesizing views from different scientific disciplines is essential for a robust trend analysis. Through workshops with a variety of experts, extensive research, followed by close monitoring over 5 years, we identified trends that will influence the shape of the evolving agrifood systems. Based on this, we determined which trends need addressing by agrifood research to secure the system’s future functioning. We detected nine trends with 50 sub-topics that will shape the future of Western European agrifood systems, of which 5 are classified as macro- and 4 as micro-trends. Our second objective was to improve the efforts of the stakeholders in- and outside of the agrifood area to secure functioning and further improvement through giving a comprehensive overview. This contributes to enhanced strategies for sustainable and resilient agrifood systems that produce sufficient affordable nutritious food for a planetary health diet, and hence, supporting successful implementation of selected goals from the 2030 Agenda for Sustainable Development and the European Green Deal.
Enhanced CO Gas Sensing with DFT Optimized PbS Loading on ZnO and CrZnO Nanocomposites
Authors: Brahim Nur , Thotagamuge Roshan , Kooh Muhammad , Lim Chee , Syaahiran Mohammad , Usman Anwar , Shahri Nurulizzatul , Chou Chau Yuan-Fong , Chou Chao Chung-Ting , Chiang Hai-Pang , Mahadi Abdul
Published: October 2022

Carbon monoxide (CO) is a poisonous gas that is harmful at a certain dose, and monitoring of this gas is essential in some industries. ZnO, CrZnO, and their PbS-loaded nanocomposites were synthesized using a sol–gel method and were used for the fabrication of CO gas sensors. The synthesized materials were characterized using DFT, XRD, SEM, UV–Vis, and BET analyses. DFT calculation was carried out to obtain useful insights into the nanocomposites’ properties such as energy band gap, chemical hardness, total adsorption energy, etc., which were then compared with experimental data. PbS-loaded ZnO and CrZnO nanocomposites at 1.5 wt% were tested for CO gas sensitivity at 300 °C for gas concentrations of 100, 200, and 300 ppmv. The gas sensing analyses showed that PbS-CrZnO had better sensitivity at 300 ppmv when compared to the pure nanocomposite. Response-recovery times for the gas sensors were also calculated and showed no significant differences. Both the theoretical and experimental data are in agreement that nanocomposites with lower band gap values exhibit an increase in electrical conductivity, indicating a better CO sensing performance. The mechanism may be due to the heterojunction effect, which improves electron transportation and prevents energy loss by suppressing charge-carrier recombination.
Tourism Eco-Efficiency and Influence Factors of Chinese Forest Parks under Carbon Peaking and Carbon Neutrality Target
Authors: Li Deli , Zhai Yingjie , Tian Gang , Mendako Richard K.
Published: October 2022

(1) This study aims to solve the problems of sustainable development in the forestry tertiary industry, to address the imbalanced state of natural environmental resources and the forestry industry, to improve ecological and environmental management, and to prepare for carbon peak and carbon neutral goals. (2) Using panel data from forest park tourism in 30 provinces and cities in China from 2010 to 2019, the DEA game cross-efficiency model is adapted to evaluate its tourism eco-efficiency, and the primary factors affecting forestry tourism eco-efficiency are selected; additionally, a panel tobit regression model is established for analysis to find policy entry points for enhancing forestry tourism eco-efficiency through empirical analysis. (3) The results show that transportation network construction and tourism input have a negative impact on the eco-efficiency of forestry tourism in China, while ecological construction, economic level, and environmental regulations are positively correlated with the eco-efficiency of forestry tourism. (4) Therefore, suggestions are made to optimize the allocation of tourism resource inputs and adopt appropriate development models, and to promote the interactive development of forestry industries.
Corporate Sustainability Disclosure and Investment Efficiency: The Saudi Arabian Context
Authors: Kouaib Amel
Published: October 2022

Drawing on legitimacy and stakeholders’ perspectives, this research aims to investigate the association between investment efficiency, a value-added corporate activity important to firm viability and profitability, and the environmental, social, and governance (ESG) reporting extent in a Saudi Arabian context. A sample of 25 Saudi firms reporting ESG information is used to test the research hypotheses. The sample is listed on the Saudi Exchange, with the research period spanning from 2014 to 2021. An OLS regression analysis shows that adopting ESG disclosure practices promote and maintain corporate investment efficiency. It displays a significant effect of corporate sustainability disclosure on the under- and over-investment levels of Saudi indexed firms. These findings are important in terms of sustainable reporting and development for the Middle East region in general and for Saudi Arabia in particular. They provide confirmation of the importance of ESG reporting as a key driver of enhanced corporate investment and bring additional evidence for regulators, policymakers, and standard-setters in terms of the effect of ESG on each sector.