bluuebunny/crossref_metadata_2025 huggingface dataset:
Promising Uses of the iPad Pro Point Clouds: The Case of the Trunk Flare Diameter Estimation in the Urban Forest
Authors: Bobrowski Rogério , Winczek Monika , Silva Lucas Polo , Cuchi Tarik , Szostak Marta , Wężyk Piotr
Published: September 2022

The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could help the planning process to give tree roots more space to grow over the years. As such, we compared the applicability of point clouds based on iPad Pro 2020 image processing and a precise terrestrial laser scanner (TLS FARO) for the modeling of the TFD using different modeling procedures. For both scanning methods, 100 open-grown and mature trees of 10 different species were scanned in an urban park in Cracow, Poland. To generate models, we used the PBH (perimeter at breast height) and TFD variables and simple linear regression procedures. We also tested machine learning algorithms. In general, the TFD value corresponded to two times the size of a given DBH (diameter at breast height) for both methods of point cloud acquisition. Linearized models showed similar statistics to machine learning techniques. The random forest algorithm showed the best fit for the TFD estimation, R2 = 0.8780 (iPad Pro), 0.8961 (TLS FARO), RMSE (m) = 0.0872 (iPad Pro), 0.0702 (TLS FARO). Point clouds generated from iPad Pro imageries (matching approach) promoted similar results as TLS FARO for the TFD estimations.
A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau
Authors: Wu Wenhuan , Zhang Qiang , Singh Vijay P. , Wang Gang , Zhao Jiaqi , Shen Zexi , Sun Shuai
Published: September 2022

Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.
Large-Scale Monitoring of Glacier Surges by Integrating High-Temporal- and -Spatial-Resolution Satellite Observations: A Case Study in the Karakoram
Authors: Ke Linghong , Zhang Jinshan , Fan Chenyu , Zhou Jingjing , Song Chunqiao
Published: September 2022

Glacier surges have been increasingly reported from the mountain and high-latitude cryosphere. They represent active glaciological processes that affect the evolution of natural landscapes, and they possibly lead to catastrophic consequences, such as ice collapse, which threatens the downstream communities. Identifying and monitoring surge-type glaciers has been challenging due to the irregularity of the behavior and limitations on the spatiotemporal coverage of remote-sensing observations. With a focus on the Karakoram region, with concentrated surge-type glaciers, we present a new method to efficiently detect glacier-surging activities by integrating the high temporal resolution of MODIS imagery and the long-term archived medium spatial resolution of Landsat imagery. This method first detects the location and initial time of glacier surges by trend analysis (trend and breakpoint) from MODIS data, which is implemented by the Breaks for Additive Seasonal and Trend (BFAST) tool. The initial location and time information is then validated with the detailed surging features, such as the terminus-position changes from Landsat, and the thickness-change patterns from surface-elevation-change maps. Our method identified 74 surging events during 2000–2020 in the Karakoram, including three tributary-glacier surges, and seven newly detected surge-type glaciers. The surge-type glaciers tend to have longer lengths and smaller mean slopes compared with nonsurge-type glaciers. A comparison with previous studies demonstrated the method efficiency for detecting the surging of large-scale and mesoscale glaciers, with limitations on small and narrow glaciers due to the spatial-resolution limitation of MODIS images. For the 38 surge-type nondebris-covered glaciers, we provide details of the surging, which depict the high variability (heavy-tailed distribution) in the surging parameters in the region, and the concentration of the surge initiation during 2008–2010 and 2013–2015. The updated glacier-surging information solidifies the basis for a further investigation of the surging processes at polythermal glaciers, and for an improved assessment of the glacier-mass balance and monitoring of glacier hazards.
A Ship Detection and Imagery Scheme for Airborne Single-Channel SAR in Coastal Regions
Authors: Li Zhenyu , Chen Jianlai , Xiong Yi , Yu Hanwen , Zhang Huaigen , Gao Bing
Published: September 2022

Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a number of maneuvering and inexpensive rotor platforms, which are utilized according to the consideration of flexible observation, cost savings, weight, and space constraints. In this paper, a hierarchical scheme of ship detection, ship imaging, and classification is proposed. It mainly includes three parts. First, a mixture statistical model of semi-parametric K-lognormal distribution based on adaptive background windows with a constant false alarm rate (CFAR) is proposed for ship prescreening in SAR imagery. Then, the discrimination stage, combined with ship imaging via the difference between the true ship targets and the false ones in the aspects of micro-Doppler motion properties, is performed. Finally, the simulation and field data processing results are presented to validate the proposed scheme.
Assessment of Galileo FOC + IOV Signals and Geometry-Based Single-Epoch Resolution of Quad-Frequency Carrier Ambiguities
Authors: Liu Chunyang , Liu Chao , Wang Jian , Zhao Xingwang , Chen Jian , Fan Ya
Published: September 2022

Galileo can independently provide navigation and positioning services globally. Galileo satellites transmit quad-frequency E1, E5a, E5b, and E5 signals, which can benefit the integer ambiguity rapid resolution. Firstly, the qualities of Galileo signals from Carrier-to-Noise (C/N0), Multipath Combination (MPC), and pseudo-range and phase noise using the ultra-short baseline were evaluated. The experimental results indicated that the Galileo E5 signal has the highest C/N0, while the C/N0 of other signals is lower and almost equal. In terms of MPC, the Galileo E1 was the most severe followed by E5a and E5b, and the MPC of E5 is less severe. As for the precision of un-differenced observations, the carrier phase and pseudo-range observations of Galileo E5 had higher accuracy than those of Galileo E5a, E5b, and E1. Secondly, the quad-frequency observations allowed for various linear combinations of different frequencies, which provides some feasibility for improving the performance of ambiguity resolution. Assuming that the phase noise σ∇ΔΦ = 0.01 m and the first-order ionosphere σ∇ΔI1 = 1 m, the total noise of the Extra-Wide-Lane (EWL) combination observation ((0, 0, 1, −1) and (0, −1, 1, 0)) and Very-Wide-Lane (VWL) combination observation ((0, −2, 1, 1), (0, −3, 2, 1)) are still less than 0.5 cycles. Finally, a geometry-based quad-frequency carrier ambiguities (GB-QCAR) method was developed, and all different options of linear combinations were investigated systematically from the ambiguity-fixed rate with two baselines. Experimental results demonstrated that, the ambiguity fixed rate of combination observation (0, −1, 1, 0), (0, −3, 5, −2), (1, −1, 0, 0) and (0, 0, 0, 1) is the highest and the positioning accuracy of VWL combination observation (0, −3, 5, −2) is equivalent to that of the EWL combination observation (0, −1, 1, 0). The positioning accuracies of WL combination observation (1, −1, 0, 0) are preferable to 3 cm and 10 cm in the horizontal and vertical, respectively. The positioning accuracy of NL combination observation E5 in the horizontal direction is about 1 cm, and is better than 4 cm in the vertical direction. Therefore, we can use Galileo observations to realize high-precise navigation services utilizing the proposed GB-QCAR method.
Building Floorplan Reconstruction Based on Integer Linear Programming
Authors: Wang Qiting , Zhu Zunjie , Chen Ruolin , Xia Wei , Yan Chenggang
Published: September 2022

The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP.
Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing
Authors: Yang Yue , Cermak Jan , Yang Kangzhuo , Pauli Eva , Chen Yunping
Published: September 2022

Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city.
Analysis of GNSS-Derived Tropospheric Zenith Non-Hydrostatic Delay Anomaly during Sandstorms in Northern China on 15th March 2021
Authors: Zhou Maosheng , Guo Jinyun , Liu Xin , Hou Rui , Jin Xin
Published: September 2022

On the 15th of March 2021, the strongest sandstorm in a decade occurred in northern China, and had a great adverse impact on the natural environment and human health in northern China. Real-time monitoring of dust storms is becoming increasingly important. In order to effectively analyze the non-hydrostatic delay (ZNHD) anomaly during a sandstorm, the method based on GNSS-derived tropospheric ZNHD residual to monitor the sandstorm is proposed at the same time. We studied the relationship between ZNHD/PWV and PM10/PM2.5 in Beijing, Changchun, Pingliang and Zhongwei before and after sandstorms. The ZNHD time series was then decomposed by singular spectrum analysis (SSA) and the residuals were obtained. The relationship between the GNSS-derived ZNHD residual and PM10 was analyzed. The results show that the impact of the sandstorm on PM10 is greater than that on PM2.5. Before the sandstorm, the correlation between PM10 and ZNHD was low, less than 0.25. When the sandstorm occurred, the correlation between PM10 and ZNHD increased significantly, and the maximum was greater than 0.7. When the sandstorm ended, the correlation between PM10 and ZNHD decreased significantly. Through the relationship between the ZNHD residual and PM10, it can be found that when the peak-to-peak values of the ZNHD residual are all above 80 mm, sandstorms may occur. But Rainfall, snowfall, haze and other abnormal weather can also lead to ZNHD anomalies.
Inter-Calibration and Statistical Validation of Topside Ionosphere Electron Density Observations Made by CSES-01 Mission
Authors: Pignalberi Alessio , Pezzopane Michael , Coco Igino , Piersanti Mirko , Giannattasio Fabio , De Michelis Paola , Tozzi Roberta , Consolini Giuseppe
Published: September 2022

The China Seismo-Electromagnetic Satellite (CSES-01) provides in situ electron density (Ne) observations through Langmuir probes (LPs) in the topside ionosphere since February 2018. CSES-01 is a sun-synchronous satellite probing the ionosphere around two fixed local times (LTs), 14 LT in the daytime sector and 02 LT in the night-time sector, at an altitude of about 500 km. Previous studies evidenced that CSES-01 seems to underestimate Ne measurements with respect to those acquired by similar satellites or obtained from different instruments. To overcome this issue, we calibrated CSES-01 LP Ne observations through Swarm B satellite data, which flies approximately at CSES-01 altitude. As a first step, Swarm B LP Ne observations were calibrated through Faceplate (FP) Ne observations from the same satellite. Such calibration allowed solving the Ne overestimation made by Swarm LP during nighttime for low solar activity. Then, the calibrated Swarm B LP Ne observations were used to calibrate CSES-01 Ne observations on a statistical basis. Finally, the goodness of the proposed calibration procedure was statistically assessed through a comparison with Ne observations by incoherent scatter radars (ISRs) located at Jicamarca, Arecibo, and Millstone Hill. The proposed calibration procedure allowed solving the CSES-01 Ne underestimation issue for both daytime and nighttime sectors and brought CSES-01 Ne observations in agreement with corresponding ones measured by Swarm B, ISRs, and with those modelled by the International Reference Ionosphere (IRI). This is a first fundamental step towards a possible future inclusion of CSES-01 Ne observations in the dataset underlying IRI for the purpose of improving the description of the topside ionosphere made by IRI.
Multiscale Ground Validation of Satellite and Reanalysis Precipitation Products over Diverse Climatic and Topographic Conditions
Authors: Nadeem Muhammad Umer , Ghanim Abdulnoor A. J. , Anjum Muhammad Naveed , Shangguan Donghui , Rasool Ghulam , Irfan Muhammad , Niazi Usama Muhammad , Hassan Sharjeel
Published: September 2022

The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From 2010 to 2018, all precipitation products were evaluated on daily, monthly, seasonal, and annual bases at a point-to-pixel scale and over the entire spatial domain. The accuracy of the products was evaluated using commonly used evaluation and categorical indices, including Root Mean Square Error (RMSE), Correlation Coefficient (CC), Bias, Relative Bias (rBias), Critical Success Index (CSI), Success Ratio (SR) Probability of Detection (POD), and False Alarm Ratio (FAR). The results show that: (1) Over the entire country, the spatio-temporal distribution of observed precipitation could be represented by IMERG and TRMM products. (2) All products (reanalysis and SPPs) demonstrated good agreement with the reference data at the monthly scale compared to the daily data (CC > 0.7 at monthly scale). (3) All other products were outperformed by IMERG and TRMM in terms of their capacity to detect precipitation events throughout the year, regardless of the season (i.e., winter, spring, summer, and autumn). Furthermore, both products (IMERG and TRMM) consistently depicted the incidence of precipitation events across Pakistan’s various topography and climatic regimes. (4) Generally, CHIRPS and ERA5 products showed moderate performances in the plan areas. PERSIANN, PERSIANN-CCS, PDIR, PERSIANN-CDR, and MEERA2 products were uncertain to detect the occurrence and precipitation over the higher intensities and altitudes. Considering the finding of this assessment, we recommend the use of daily and monthly estimates of the IMERG product for hydro climatic studies in Pakistan.
SSML: Spectral-Spatial Mutual-Learning-Based Framework for Hyperspectral Pansharpening
Authors: Peng Xianlin , Fu Yihao , Peng Shenglin , Ma Kai , Liu Lu , Wang Jun
Published: September 2022

This paper considers problems associated with the large size of the hyperspectral pansharpening network and difficulties associated with learning its spatial-spectral features. We propose a deep mutual-learning-based framework (SSML) for spectral-spatial information mining and hyperspectral pansharpening. In this framework, a deep mutual-learning mechanism is introduced to learn spatial and spectral features from each other through information transmission, which achieves better fusion results without entering too many parameters. The proposed SSML framework consists of two separate networks for learning spectral and spatial features of HSIs and panchromatic images (PANs). A hybrid loss function containing constrained spectral and spatial information is designed to enforce mutual learning between the two networks. In addition, a mutual-learning strategy is used to balance the spectral and spatial feature learning to improve the performance of the SSML path compared to the original. Extensive experimental results demonstrated the effectiveness of the mutual-learning mechanism and the proposed hybrid loss function for hyperspectral pan-sharpening. Furthermore, a typical deep-learning method was used to confirm the proposed framework’s capacity for generalization. Ideal performance was observed in all cases. Moreover, multiple experiments analysing the parameters used showed that the proposed method achieved better fusion results without adding too many parameters. Thus, the proposed SSML represents a promising framework for hyperspectral pansharpening.
Denoising Single Images by Feature Ensemble Revisited
Authors: Fahim Masud An Nur Islam , Saqib Nazmus , Siam Shafkat Khan , Jung Ho Yub
Published: September 2022

Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and a concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture’s number of parameters remains smaller than in most of the previous networks and still achieves significant improvements over the current state-of-the-art networks.
A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection
Authors: Pang Lei , Li Baoxuan , Zhang Fengli , Meng Xichen , Zhang Lu
Published: September 2022

Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of difficulty. To solve this problem, we propose a lightweight YOLOV5-MNE, which significantly improves the training speed and reduces the running memory and number of model parameters and maintains a certain accuracy on a lager dataset. By redesigning the MNEBlock module and using CBR standard convolution to reduce computation, we integrated the CA (coordinate attention) mechanism to ensure better detection performance. We achieved 94.7% precision, a 2.2 M model size, and a 0.91 M parameter quantity on the SSDD dataset.
Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease
Authors: Brand Yonatan E. , Schwartz Dafna , Gazit Eran , Buchman Aron S. , Gilad-Bachrach Ran , Hausdorff Jeffrey M.
Published: September 2022

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision–recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
Smartphone-Based Ecological Momentary Assessment for Collecting Pain and Function Data for Those with Low Back Pain
Authors: Kaur Ekjyot , Delir Haghighi Pari , Cicuttini Flavia M. , Urquhart Donna M.
Published: September 2022

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to (i) determine the feasibility of collecting pain and function data using smartphone-based EMA, (ii) examine pain data collected using EMA compared to traditional methods, (iii) characterize individuals’ progress in relation to pain and function, and (iv) investigate the appropriation of the method. Our results showed that an individual’s ‘pain intensity index’ provided a measure of the burden of their low back pain, which differed from but complemented traditional ‘change in pain intensity’ measures. We found significant variations in the pain and function over the course of an individual’s back pain that was not captured by the cohort’s mean scores, the approach currently used as the gold standard in clinical trials. The EMA method was highly acceptable to the participants, and the Model of Technology Appropriation provided information on technology adoption. This study highlights the potential of the smartphone-based EMA method for enhancing the collection of outcome data and providing a personalized approach to the management of low back pain.
Multiprotein Inhibitory Effect of Dietary Polyphenol Rutin from Whole Green Jackfruit Flour Targeting Different Stages of Diabetes Mellitus: Defining a Bio-Computational Stratagem
Authors: Maradesha Tejaswini , Patil Shashank M. , Phanindra Bhaskar , Achar Raghu Ram , Silina Ekaterina , Stupin Victor , Ramu Ramith
Published: September 2022

The anti-diabetic potential of whole unripe jackfruit (peel with pulp, flake, and seed) was investigated using inhibitory assays for α-glucosidase, α-amylase, aldose reductase, and glycation at multiple stages. Using activity-guided repeated fractionation on a silica gel column chromatography, dietary flavonoid rutin with potent antihyperglycemic activity was extracted from the methanol extract of whole jackfruit flour (MJ). Rutin was found to inhibit both α-glucosidase (IC50: 7.86 µg/mL) and α-amylase (IC50: 22.00 µg/mL) in a competitive manner of inhibition with low Ki values. In addition, in vitro glycation experiments revealed that rutin prevented each stage of protein glycation as well as the production of intermediate molecules. Furthermore, rutin significantly inhibited aldose reductase (IC50: 2.75 µg/mL) in a non-competitive manner. During in silico studies, molecular docking and molecular dynamics simulation studies have suggested that rutin has a high binding affinity for the enzymes studied, which could explain its inhibitory effects. Rutin interacted with the key residues of the target enzymes’ inhibitor binding sites. Compared to the controls used, rutin had a higher binding efficiency as well as stability in the inhibitor binding pocket of the target enzymes. According to our findings, the presence of rutin is more likely to be associated with the potential of MJ in antihyperglycemic activity via inhibition of α-glucosidase and in anti-diabetic action via inhibition of the polyol pathway and protein glycation. The bio-computational study indicates rutin as a potential lead inhibitor of all the target enzymes used and could be used as an effective anti-diabetic drug in the near future.
Examining Differences, Relationships, and Predictors for Loneliness in an Adult Population: The Roles of Personal Characteristics, Place of Residence, Leisure Activities, Mental Health, and Social Outcomes
Authors: Tzouvara Vasiliki , Kupdere Pinar
Published: September 2022

Loneliness is associated with poor mental and social outcomes globally. The literature suggests an association between loneliness and personal characteristics, place of residence, and leisure activities. However, the current literature has produced inconsistent findings and has focused largely on older adults. This study is one of the first to examine the differences, relationships, and predictors of loneliness in an adult population, and the roles of personal characteristics, place of residence, leisure activities, mental health, and social outcomes. A cross-sectional online survey was undertaken. A sample of 155 adults responded, with a mean age of 34.5 years (SD = 13.2), and with 54.1% identifying as female. An analysis revealed experiences of loneliness across the sample. In addition, age was moderately associated with loneliness, while psychological distress, depressive symptoms, and social networks were significantly associated with loneliness. Depressive symptoms were a positive predictor for loneliness, and social networks were a negative predictor for loneliness. This study has confirmed findings from previous research and provided new information on loneliness, which can guide future research and interventions to prevent or support people who suffer from loneliness.
Is K-Struvite Precipitation a Plausible Nutrient Recovery Method from Potassium-Containing Wastes?—A Review
Authors: Kabdaşlı Işık , Siciliano Alessio , Limonti Carlo , Tünay Olcay
Published: September 2022

The definition of technologies capable of removing and recovering nutrients from polluting effluents is a key environmental challenge. Through these technologies, it would be possible to protect aquatic systems and prevent the consumption of natural resources for the production of commercial fertilizers. In this regard, the application of the precipitation processes of struvite-type compounds is an attractive approach. Indeed, these processes are potentially able to remove nutrients from many effluents and produce a precipitate reusable as a slow-release fertilizer. The scientific community has largely focused on the precipitation of magnesium ammonium phosphate (MgNH4PO4·6H2O, MAP), while the recovery of the analogous magnesium potassium phosphate (MgKPO4·6H2O, MPP) has received extensive attention in the last decade. Research on this topic is continuously progressing to improve the precipitation process in different aspects (working conditions, reaction units, interference elimination, etc.). Until now, there has been no paper that comprehensively reviewed the applicability of MPP precipitation for the removal and recovery of nutrients from aqueous waste. To fill this gap, the present paper aimed to provide an exhaustive analysis of the literature reports on MPP processes to help researchers understand the theoretical and applicative aspects, the main problems, and the need for further research. In this regard, the applications in the treatment of various aqueous wastes were considered. The theoretical concepts, the main process parameters, and the effects of inhibiting substances and impurities are presented. Moreover, the development of reactor configurations and their working conditions are evaluated. Finally, the potential use of MPP as a fertilizer and some economic evaluations are reported. On the basis of the conducted analysis, it emerged that the recovery of MPP was mainly affected by the pH, dose, and nature of reagents, as well as the presence of competitive ions. The optimal pH values were reported to be between 9 and 11. Reagent overdoses with respect to the theoretical values improved the process and the use of pure reagents guaranteed superior performance. The stirred-tank reactors and fluidized bed reactors were the most used units with high process yields. The applicability of MPP in agronomic practices appears to be a suitable option.
Will Wind–Sand Activity Bury the Opencut Tunnel along the Linhe–Ceke Railway, China?
Authors: Yan Min , Zuo Hejun
Published: September 2022

The opencut tunnel is a kind of linear arch construction and a new linear sand-prevention structure. In order to clarify the wind-proof mechanism of the opencut tunnel, this paper seeks to answer the question of whether the opencut tunnel of the Lin–Ce railway will be buried. In this paper, a wind tunnel experiment combined with field investigation and testing is used to systematically study the mechanism of wind and sand resistance in an opencut tunnel, and it is verified whether the sand burial of the opencut tunnel is possible. The results show that the airflow greater than 0.7 times of the height is in a state of acceleration and uplift, and no sand-filling phenomenon occurs at the ventilation vent at the top of the opencut tunnel. More than 85% of the sediment transport on the windward side was within the height of 0–10 cm, and 80% on the leeward side was concentrated in the height of 30–70 cm; the greater the angle between the opencut tunnel and the wind direction, the higher the potential of wind and sand resistance. In the past 20 years, no mass accumulation of shifting sand occurred along the opencut tunnel. Furthermore, the shifting sand could not bury the opencut tunnel in the small-scale time range. Wind-field characteristics determine angle between the opencut tunnel and wind direction as 75°−90°, setting the optimal scope of protection. However, different angles can effectively prevent and control sand flow hazards in railways, ensuring smooth railway operation.
Low-Carbon Technologies to Remove Organic Micropollutants from Wastewater: A Focus on Pharmaceuticals
Authors: Krahnstöver Thérèse , Santos Naiara , Georges Karyn , Campos Luiza , Antizar-Ladislao Blanca
Published: September 2022

Pharmaceutical residues are of environmental concern since they are found in several environmental compartments, including surface, ground and waste waters. However, the effect of pharmaceuticals on ecosystems is still under investigation. To date, the removal of these micropollutants by conventional treatment plants is generally ineffective, in addition to producing a considerable carbon footprint. In this sense, to achieve the current zero-pollution ambition, a reduction in the negative impacts of chemical substances such as pharmaceuticals on the environment must be aligned with initiatives such as the European Ecological Compact, Environment Action Programme, and Circular Economy Action Plan, among others. This review provides insight into the key drivers for changing approaches, technologies, and governance of water in Europe (Germany, Switzerland, and the UK), including improving wastewater treatment in sewage treatment plants for the removal of pharmaceuticals and their carbon footprint. In addition, an overview of emerging low-carbon technologies (e.g., constructed wetlands, anaerobic membrane bioreactors, and enzymes) for the removal of pharmaceuticals in sewage treatment works is provided. In general, the removal efficiency of pharmaceuticals could be achieved up to ca. 100% in wastewater, with the exception of highly recalcitrant pharmaceuticals such as carbamazepine (removal <60%). These technologies have the potential to help reduce the carbon footprint of wastewater treatment, which can therefore contribute to the achievement of the Europe Union’s objective of being carbon neutral by 2050.
A Business Process Reference Model for the Development of a Wine Traceability System
Authors: Gayialis Sotiris P. , Kechagias Evripidis P. , Papadopoulos Georgios A. , Panayiotou Nikolaos A.
Published: September 2022

Traceability is among the most significant challenges in supply chains, where multiple stakeholders and activities are involved in the production and distribution of products. No supply chain can become sustainable without effectively addressing the problem of traceability by recognizing, monitoring, and implementing all necessary activities of the processes. This research provides a reference model for effective wine supply chain traceability and is part of a research project for the development of a blockchain-enabled traceability system. The reference model not only depicts processes but also covers all views that are necessary for achieving the whole picture of an effective traceability system. These views include the value chain, organizational resources, functions, processes, systems, data, and risks that are related to wine production and distribution. The reference model has a strong contribution to practice and research as it pertains to bridging the barrier between developers and users while also offering significant research outcomes. The research output is the reference model that includes standard wine traceability processes and all necessary data for effective wine supply chain traceability. The results of this research will be used for creating the traceability system’s specifications and ensuring that it will be effectively designed and implemented. The reference model can also be used for the implementation and adaptation of the traceability system to the stakeholders of the wine supply chain.
Making Sustainability a Core Competency: Consumer Response to Sustainable Innovative Products
Authors: Hull Clyde Eiríkur , Russell Jennifer D. , Kukar-Kinney Monika
Published: September 2022

Research suggests that sustainability may not be sufficient to yield a competitive advantage. Building on the resource-based view, this research evaluates three questions: (1) Can using sustainability as a differentiator lead to consumers choosing sustainable products? (2) Does product sustainability appeal more to environmentally concerned consumers? (3) Does product sustainability appeal more when paired with innovation? To test the hypotheses, an online survey of 344 US respondents was conducted. Consumers were given a hypothetical budget for an office chair and asked to choose between two products at a time. Hypotheses were tested with frequency and Chi-square tests and logistic regression. Findings indicate that the innovative product was preferred over the undifferentiated one, but the sustainable product was preferred over both innovative and undifferentiated products. The sustainability–innovativeness bundle was not preferred over the sustainable product. Environmental concern increased preference for the sustainable product over the innovative product, but not over the undifferentiated one. These findings suggest that sustainability is a stronger differentiator than innovation, but that bundling both features does not further enhance product choice. Attitude toward the environment may not predict behavior. Instead, preference for the sustainable product may originate in variety-seeking behavior, with sustainability seen as an innovation.
Market Regeneration in Line with Sustainable Urban Development
Authors: Borucka Justyna , Czyż Piotr , Gasco Giorgio , Mazurkiewicz Weronika , Nałęcz Dorota , Szczepański Marcin
Published: September 2022

This article presents the study of the optimal design solutions for regeneration of marketplaces. It examines the design variants for the revitalisation of the marketplace, in particular, investment in their modernisation in order to find the most optimal model for transforming these public spaces to have a significant impact on the city’s development. The research is a comparative analysis of the implementation of regeneration design models on the marketplace within the Oliwa district of Gdansk (Poland).The data for the case study design models includes analysis based on various optimisation criteria, taking into account the urban and economic aspects of the city landscape when selecting a specific space revitalisation design model. The implementation of regeneration investment includes a number of complex processes that must be sustainable and so require rational social and spatial planning, as well as proper organisation in terms of cost and time.
Tripartite Evolutionary Game Analysis of Stakeholder Decision-Making Behavior in Energy-Efficient Retrofitting of Office Buildings
Authors: Xie Ying , Liu Yisheng
Published: September 2022

Environmental degradation is significantly influenced by the construction industry. Energy-efficient retrofitting of existing office buildings has become an effective means of reducing building energy consumption. Implementation of retrofits requires the support and cooperation of stakeholders. However, existing studies on the dynamics of decision-making behavior among stakeholders are still relatively limited. This study constructed a tripartite evolutionary game model including the government, property owners, and end users, to better understand the behavioral evolution and evolutionary stabilization strategies of stakeholders. The results show that: stakeholders’ decision-making behavior has obvious mutual influence; benefits and costs are the dominant factors in stakeholders’ decision making; the effects of government supervision policies depend on the profitability of the project; and government behavior appears to be influenced by public willingness. In addition, targeted countermeasures were proposed for the development of the energy-efficiency retrofit market. This study provides a generic model that fits various contexts and can be used to inform a reference for scientific decision making by stakeholders.
The Cooling Effect of an Urban River and Its Interaction with the Littoral Built Environment in Mitigating Heat Stress: A Mobile Measurement Study
Authors: Wang Yasha , Ouyang Wanlu , Zhan Qingming , Zhang Li
Published: September 2022

Rivers within urban areas could influence the thermal environment by modifying air temperature (AT) and relative humidity (RH). To further explore the cooling effect of a large river on surrounding urban areas, we conducted mobile measurements near the Yangtze River in Wuhan during a hot and humid summer day. This research examined the river cooling effect, determined the thresholds of the cooling and humidifying distance using a third-order polynomial method, assessed littoral heat stress at the pedestrian level and investigated the impact of the littoral built environment using stepwise regression analysis. The results revealed that (1) the cooling and humidifying effect of the river was up to 3.55 °C, 1741 m and 17.25%, 1369 m in the daytime, which was much greater than that at night; (2) the river significantly mitigated pedestrian heat stress within 1200 m from the riverbank in the daytime, but showed a weak negative effect at night; (3) the built environment has a significant impact on the littoral areas, e.g., urban greening during the daytime and building morphology at night. The findings of this study provide empirical evidence for the cooling effect of a large river in urban areas and further support climate-sensitive planning and design for our thermal environment.
Understanding the Economic Responses to China’s Electricity Price-Cutting Policy: Evidence from Zhejiang Province
Authors: Xu Yang , Hu Jiahua , Wang Yizheng , Zhang Weiwei , Wu Wei
Published: September 2022

As a critical pattern to promote industrial growth and recover the economy, China has widely applied an electricity price-cutting policy, especially after the global spread of COVID-19. However, the current insufficient research indicates a noticeable knowledge gap around the actual outcome of this policy measure. This paper, therefore, aimed at filling this gap by focusing on the case of Zhejiang province. Recently, there have been three main kinds of price-cutting policies employed in Zhejiang, decreasing the general industrial electricity price, temporary electricity price-cutting, and targeted price reduction for large industries. Using a computable general equilibrium model that incorporates the Zhejiang electricity system’s operating characteristics, we simulated the economic impact of these electricity price adjustment policies. The results indicate that it was practical to promote the economy by reducing electricity prices, but the specific arrangements of policy have led to differences in economic stimulus consequences. This paper further obtained the multiplier effect in each industry. If electricity price adjustment policies were introduced only for industries with a higher comprehensive multiplier effect, the policy effectiveness would be further optimized.
The Role of Digital Soil Information in Assisting Precision Soil Management
Authors: Iticha Birhanu , Kamran Muhammad , Yan Rui , Siuta Dorota , Al-Hashimi Abdulrahman , Takele Chalsissa , Olana Fayisa , Kukfisz Bożena , Iqbal Shehzad , Elshikh Mohamed S.
Published: September 2022

Soil information is the basis for the site-specific management of soils. The study aimed to digitize soil information and classify it into soil mapping units (SMUs) using geostatistics. The study area was grouped into 12 SMUs, or management zones. The pH of the soils ranged from 7.3 in SMU2 to 8.6 in SMU5. Most SMUs exhibited low total nitrogen (TN) that could be attributed to very low soil organic carbon (SOC) in the soils. Available phosphorus (AvP) was very low in all the mapping units. The exchangeable K varied between 0.12 cmol(+) kg−1 (SMU7) and 0.95 cmol(+) kg−1 (SMU10). SMU12 was identified as marginally sodic and at a high risk of developing severe alkalinity unless possible management measures are implemented. Our findings show that a lack of soil information causes an imbalance between soil requirements and external nutrient inputs, negatively affecting crop production. Therefore, high-resolution digital soil information can assist the site-specific application of soil nutrients and amendments based on spatial variability in line with soil requirements.
Exploring the Relationships among Brand Experience, Perceived Product Quality, Hedonic Value, Utilitarian Value, and Brand Loyalty in Unmanned Coffee Shops during the COVID-19 Pandemic
Authors: Bae Jun-Ho , Jeon Hyeon-Mo
Published: September 2022

This study aims to find the antecedents that enhance consumer value and brand loyalty to unmanned coffee shops (UCS) that provide unmanned services during the COVID-19 pandemic. The analysis developed and tested a series of hypotheses based on data collected from 463 customers who had visited UCS in South Korea. The influence of brand experience and perceived product quality on customers’ hedonic and utilitarian values were examined, and the utilitarian values were found to have a significant effect on brand loyalty. This result signifies the importance of brand experience and perceived product quality in inducing consumers’ perceptions of value and brand loyalty in the context of unmanned services. The study’s design and results differ from those of previous brand experience studies on manned stores in the food service industry. Therefore, this study contributes to hospitality literature by applying brand experience theory, which has been applied to research on human and unmanned services. In addition, it makes an important contribution by presenting practical implications for the sustainable management of the food service industry during the COVID-19 era.