bluuebunny/crossref_metadata_2025 huggingface dataset:
Adversarial Attacks in Underwater Acoustic Target Recognition with Deep Learning Models
Authors: Feng Sheng , Zhu Xiaoqian , Ma Shuqing , Lan Qiang
Published: November 2023

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations.
Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area
Authors: Wu Yuewan , Pan Jiayi
Published: November 2023

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.
A Method for Estimating Ship Surface Wind Parameters by Combining Anemometer and X-Band Marine Radar Data
Authors: Zhang Yuying , Lu Zhizhong , Tian Congying , Wei Yanbo , Liu Fanming
Published: November 2023

The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind measurements affected by structure is proposed, and a method for combining anemometer and X-band marine radar (RCRF) data is designed to further obtain wind parameters. The first step is to use the multivariate bias strategy to achieve the optimal layout of multiple anemometers based on computational fluid dynamics (CFD) numerical simulation data. Then, random forest (RF) is employed to train the wind parameter estimation model. Finally, the wind parameters are optimally estimated by combining the anemometer with the X-band radar. Under the ideal simulation, noise, and temporal uncertainty combined with anemometer noise conditions, the RCRF algorithm performance is evaluated. Compared with the bias correction combination four-anemometer weighted fusion algorithm (FAF-BC) and the BP neural network algorithm for radar wind measurement combination (RCBP), the mean errors in wind direction and speed are reduced by 1.99° and 6.99% at most. The maximum errors are reduced by 14.46° and 15.81% at most, respectively.
Investigating a Persistent Stratospheric Aerosol Layer Observed over Southern Europe during 2019
Authors: Voudouri Kalliopi Artemis , Michailidis Konstantinos , Koukouli Maria-Elissavet , Rémy Samuel , Inness Antje , Taha Ghassan , Peletidou Georgia , Siomos Nikolaos , Balis Dimitrios , Parrington Mark
Published: November 2023

A persistent stratospheric aerosol layer first appeared during July 2019 above Thessaloniki, Greece (40.5°N, 22.9°E). It was initially at 12 km and, during August 2019, was even up to 20 km, with increased thickness and reduced attenuated backscatter levels till the end of the year. In this study, we analyze the geometrical and optical properties of this stratospheric layer, using ground-based Lidar measurements, CALIOP/CALIPSO & OMPS-LP space-borne observations, as well as CAMS/ECMWF assimilation experiments. The main aim of the paper is to present an overview of this atmospheric feature and to identify any temporal changes in the aerosol properties that would signify substantial changes in the composition of this long-lasting stratospheric plume over Thessaloniki. This aim is further enhanced by emphasizing the importance of the combined information based on active ground- and space-borne lidars, passive remote sensing, and models during the complex stratospheric aerosol conditions as those encountered during 2019. The layer’s origin is linked to the Raikoke volcanic eruption in the Kuril Islands in June 2019, yielding a particle linear depolarization ratio less than 0.05, while some indications exist that the intense forest fires at mid and high northern latitudes throughout the summer of 2019 also contributed to the persistent layer. We report that in July, mainly volcanic sulphate aerosol layers with a 1–3 km vertical extent were identified in the stratosphere at ~15 km over Thessaloniki, while after August and until the end of 2019, the plume heights showed a significant month-to-month variability and a broadening (with thickness greater than 3 km) towards lower altitudes. The aerosol optical thickness was found to be in the range between 0.004 and 0.125 (visible) and 0.001 and 0.095 (infrared) and the particle depolarization of the detected stratospheric plume was found to be 0.03 ± 0.04, indicative of spherical particles, such as sulphate aerosols.
Performance of Common Scene Stacking Atmospheric Correction on Nonlinear InSAR Deformation Retrieval
Authors: Zhang Zhichao , Feng Wanpeng , Xu Xiaohua , Samsonov Sergey
Published: November 2023

Atmospheric Phase Screen (APS) is a major noise that suppresses the accuracy of InSAR deformation time series products. Several correction methods have been developed to perform APS reduction in the InSAR analysis, in which an algorithm called Common Scene Stacking (CSS) method draws wide attention in the community as the method was supposed to effectively separate atmospheric contributions without any external data. CSS was initially proposed for solving linearly interseismic deformation. Whether CSS can be applied in nonlinear deformation cases remains unsolved. In this study, we first conduct a series of data simulations including variable elastic deformation components and also propose an iterative strategy to address the inherent weak edge constraint issues in CSS under different deformation conditions. The results show that signal-to-noise ratio (SNR) is a key parameter affecting the performance of CSS in APS separation. For example, the recovery rate of deformation can generally be greater than 80% from datasets with SNR greater than 10 dB. Our results imply that CSS can favor further improvement of InSAR measurement accuracy. The proposed method in this study was applied to assessing deformation history across the 2020 Mw 5.7 Dingjie earthquake, in which logarithmic postseismic deformation history and coseismic contribution can be successfully retrieved once.
Above Ground Level Estimation of Airborne Synthetic Aperture Radar Altimeter by a Fully Supervised Altimetry Enhancement Network
Authors: Duan Mengmeng , Lu Yanxi , Wang Yao , Liu Gaozheng , Tan Longlong , Gao Yi , Li Fang , Jiang Ge
Published: November 2023

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm.
Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI
Authors: Mukai Sonoyo , Hioki Souichiro , Nakata Makiko
Published: November 2023

The Earth Observation Satellite Global Change Observation Mission—Climate (GCOM)-C (SHIKISAI in Japanese), carrying a second-generation global imager (SGLI), was launched in 2017 by the Japan Aerospace Exploration Agency. The SGLI performs wide-swath multi-spectral measurements in 19 channels, from near-ultraviolet to thermal infrared (IR), including the red (674 nm; PL1 channel) and near-IR (869 nm; PL2 channel) polarization channels. This work aimed to demonstrate the advantages of SGLI, particularly the significance of simultaneous off-nadir polarized and nadir multi-spectral observations. The PL1 and PL2 channels were tilted at 45° for the off-nadir measurements, whereas the other channels took a straight downward view for the nadir measurements. As a result, the SGLI provided two-directional total radiance data at two wavelengths (674 and 869 nm) that were included in both off-nadir and nadir observations. Using these bidirectional data, an algorithm was applied to derive the altitude of the aerosol plume. Furthermore, because of the significance of the simultaneous observation of polarized and non-polarized light, the sensitivity difference between the radiance and polarized radiance was demonstrated. Severe wildfire events in Indonesia and California were considered as examples of specific applications. Herein, we present the results of our analysis of optically thick biomass-burning aerosol events. The results of the satellite-based analysis were compared with those of a chemical transport model. Exploring the SGLI’s unique capability and continuous 5-year global record paves the way for advanced data exploitation from future satellite missions as a number of multi-directional polarization sensors are programmed to fly in the late 2020s.
Review of the Monothematic Series of Publications Concerning Research on Statistical Distributions of Navigation Positioning System Errors
Authors: Specht Mariusz
Published: November 2023

This review presents the main results of the author’s study, obtained as part of the post-doctoral (habilitation) dissertation entitled “Research on Statistical Distributions of Navigation Positioning System Errors”, which constitutes a series of five thematically linked scientific publications. The main scientific aim of this series is to answer the question of what statistical distributions follow the position errors of navigation systems, such as Differential Global Positioning System (DGPS), European Geostationary Navigation Overlay Service (EGNOS), Global Positioning System (GPS), and others. All of the positioning systems under study (Decca Navigator, DGPS, EGNOS, and GPS) are characterised by the Position Random Walk (PRW), which means that latitude and longitude errors do not appear randomly, being a feature of the normal distribution. The research showed that the Gaussian distribution is not an optimal distribution for the modelling of navigation positioning system errors. A higher fit to the 1D and 2D position errors was exhibited by such distributions as beta, gamma, and lognormal. Moreover, it was proven that the Twice the Distance Root Mean Square (2DRMS(2D)) measure, which assumes a priori normal distribution of position errors in relation to latitude and latitude, was smaller by 10–14% than the position error value from which 95% fixes were smaller (it is known as the R95(2D) measure).
A Nested–Nested Sparse Array Specially for Monostatic Colocated MIMO Radar with Increased Degree of Freedom
Authors: Chen Ye , Yang Meng , Li Jianfeng , Zhang Xiaofei
Published: November 2023

This paper mainly investigates the problem of direction of arrival (DOA) estimation for a monostatic MIMO radar. Specifically, the proposed array, which is called a nested–nested sparse array (NNSA), is structurally composed of two nested subarrays, a NA with N1+N2 elements and a sparse NA, respectively, with N3+N4 elements. The design process of NNSA is optimized into two steps and presented in detail. Setting NNSA as transmitter/receiver arrays, we derive the closed-form expression of consecutive DOFs and calculate the mutual coupling coefficient. Eventually, extensive simulations are carried out and the results verify the superiority of the proposed array over the previous arrays in terms of consecutive DOFs, array aperture and mutual coupling effect.
Institutional Solidarity in The Netherlands: Examining the Role of Dutch Policies in Women with Migration Backgrounds’ Decisions to Leave a Violent Relationship
Authors: Roegiers (Mayeux) Chloé , Saharso Sawitri , Tonkens Evelien , Darling Jonathan
Published: November 2023

In The Netherlands, women who experience domestic violence can rely on public policies that aim to support them, such as shelters. Drawing on the lived experiences, through 10 interviews and observations, of women with different cultural backgrounds and nationalities staying in a shelter, and on 37 interviews with social workers working with these women, we observed that this support falls short for them. We argue that immigration rules, together with policies on domestic violence and housing, (unintentionally) often work in tandem with violent partners to prevent women with migration backgrounds from leaving violent relationships. The paper draws on a perspective of institutional considerations of solidarity to unpack the relations between domestic violence, cultural constraints, and public policies but looks also at the positive experiences of women of migrant backgrounds with these Dutch policies. This research indicates that there is a lack of institutional solidarity towards women, especially those arriving as marriage migrants, who have experienced domestic violence. In exploring the intersections of domestic violence and often exclusionary state policies, the paper reflects on how The Netherlands can provide more support to those women and how intersectional justice and solidarity might be expressed.
Insulin Resistance Develops Due to an Imbalance in the Synthesis of Cyclic AMP and the Natural Cyclic AMP Antagonist Prostaglandylinositol Cyclic Phosphate (Cyclic PIP)
Authors: Wasner Heinrich K.
Published: November 2023

The reasons initiating insulin resistance are not identified. Various metabolic derailments have been characterized. These are the outcome and not the initiation of insulin resistance. In animal models of type 2 diabetes and hypertension, a decreased hormonal stimulation of the synthesis of the cyclic AMP antagonist prostaglandylinositol cyclic phosphate (cyclic PIP) was determined. The resultant imbalance of the action of cyclic AMP and cyclic PIP shifts metabolic regulation to the dominance of catabolism and a decrease in imperative anabolism. This dominance develops gradually since the more cyclic AMP dominates, the more the synthesis of cyclic PIP will be inhibited. Vanishing actions of cyclic PIP are its 10-fold activation of glucose uptake in adipocytes, its inhibition of insulin release from pancreatic β-cells, its inhibition of PKA and its 7-fold activation of protein ser/thr phosphatase. Reduced synthesis of cyclic PIP results from (a) decreased substrate availability, (b) long-time elevated cyclic AMP levels resulting from stress overloads and (c) aging and the gradual decrease in the synthesis of hormones which likely maintain mechanisms that stimulate cyclic PIP synthesis. The need is to discover which hormones, such as growth hormone, insulin-like growth factor-1, dehydroepiandrosterone, and testosterone, are involved in maintaining the stimulation of cyclic PIP synthesis.
Inland Waterway Infrastructure Maintenance Prediction Model Based on Network-Level Assessment
Authors: Zhang Fan , Wang Pingyi , Liu Huaihan , Zhang Bin , Sun Jianle , Li Jian
Published: November 2023

Maintenance decision optimization based on network-level assessment has a long history in road transportation infrastructure and has greatly assisted management departments in saving in expenditure on maintenance costs. However, its application and research in water transportation infrastructure have been lacking. This paper aims to design a predictive model for waterway improvement building maintenance based on network-level assessment and provide a new solution for optimizing the allocation of limited maintenance funds for inland waterway infrastructure. The proposed network-level assessment framework and predictive model comprise data collection, maintenance prediction, and maintenance decision modules. A small time-series dataset was constructed based on the classification proportions of improvement building technical conditions in the jurisdiction of the Yangtze River trunk waterway over the past five years. The two-parameter moving average method was transformed into a single-parameter “jurisdiction moving average method” to suit the characteristics of the dataset. Three models, namely the jurisdiction moving average (JMA), the linear regression (LR), and the quadratic curve regression (QCR) models, were employed to perform calculations on the dataset, which were evaluated using t-tests and error analysis. The research results indicated that both the JMA and LR models showed good overall performance and were recommended for use. Especially, the confidence intervals of the JMA model increased the credibility of the prediction results, making it the ideal choice. This study also found that the inland waterway maintenance prediction technology based on the network-level evaluation has higher overall efficiency than the known existing technologies. The proposed predictive model allows for a simple and rapid assessment of the overall risk status of regional waterway facilities and is easy to promote and apply.
Impacts of Grazing on Vegetation and Soil Physicochemical Properties in Northern Yinshan Mountain Grasslands
Authors: Yang Zhenqi , Miao Ping , Zheng Yufeng , Guo Jianying
Published: November 2023

The grasslands at the northern foot of the Yinshan Mountains are an integral component of the northern grassland ecosystem in China. The ecosystem in this region has low stability and poor resistance to disturbance. In this study, experiments were conducted to evaluate the possible changes in vegetation community structure and soil physicochemical properties due to overgrazing in the grasslands. Completely randomized group experiments were designed with grazing intensity as the single-factor study was conducted using natural grassland located in Xilamuren (in Inner Mongolia, northern China) as the study area. Three blocks were created, each having four plots of different grazing intensities and each block having an area of 100 m × 100 m. The experiments were conducted to evaluate the possible variances both in the structure of the vegetation community and the soil physicochemical properties resulting from overgrazing in the grasslands at the northern foot of Yinshan Mountain. The results were as follows: The importance values of dominant species, such as Heteropappus altaicus and Artimisia gmelinii, exhibited varying degrees of change with an increase in the grazing intensity. The surface vegetation cover decreased significantly with an increase in the grazing intensity. The increasing grazing intensity led to a significant increase in the content of very coarse sand grains in the soil. Severe grazing increased the exposed surface area, intensified the effects of blowing wind and scouring action of water, and led to the coarsening of topsoil particles. At 0–5 cm depth, the bulk density of soil exhibited an increasing tendency with an increase in the grazing intensity. The organic matter content of the soil in the heavily grazed plots decreased by 11.74%, 11.00%, and 14.08%, respectively, when compared to that in the 0–40 cm soil layer with no grazing, light grazing, and moderate grazing. The results emphasized the importance of managing grazing intensity for soil and vegetation restoration. Thus, the effects of short-term grazing (for example, 5 years) on vegetation community composition and species diversity may be less pronounced. This study contributes to our understanding of pasture management and the restoration of grassland species diversity.
Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities
Authors: Alanazi Fayez , Alshammari Talal Obaid , Azam Abdelhalim
Published: November 2023

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.
Selection of Industrial Sites Using a Web-Based Geographical Information System to Minimize Risks: A Case Study in West Java, Indonesia
Authors: Chumaidiyah Endang , Dewantoro Mouli De Rizka , Fauzi Putri Maulani , Kamil Anton Abdulbasah
Published: November 2023

The efficient selection of a perfect site for the development of an industrial site in spatial planning should consider the land condition and environmental factors to minimize the impacts and risks. This paper aimed to create a model to assess if an area that can be used for the development of an industrial site meets multiple criteria. The new method in this research integrates the GIS and AHP approaches into a web-based geographic information system application that is used to easily support widespread assessment and mapping. The assessment of the development potential of industrial areas in West Java Province showed that 77% of the land was appropriate for industrial sites, whereas the other 33% was less suitable for use as an industrial site location. The assessment of 23 industrial site development location points in the Industrial Site Spatial Plan of West Java Province showed that 14 locations were in the highly suitable category (P1) and 9 other locations were in the suitable category (P2).
Effects of Deforestation on Foraging Behavior, Ectoparasites, and Adult Survival in the Vulnerable La Selle Thrush, Turdus swalesi, in Haiti
Authors: Exantus Jean-Marry , Vidal Annabelle , Cézilly Frank
Published: November 2023

Although range-restricted Neotropical bird species are highly endangered, little is known about their ability to cope with environmental disturbance. We studied the vulnerable, Hispaniola- endemic La Selle Thrush (LST), Turdus swalesi, in a protected forested area in Haiti threatened by agriculture, livestock and logging. We used capture–mark–recapture, visual observations, and camera traps to document foraging ecology and estimate adult survival over 26 months. LST foraged mainly in deciduous woodlots. However, some individuals regularly foraged on or around dung pats in fallow pastures, whereas no other ground-dwelling bird species was observed to do so. Coincidently, 16.5% of 79 mist-netted LST harbored Ixodid ticks, compared to none of the 2131 individuals belonging to 29 other species mist-netted in the area. This suggests that infestation with ticks might come as a cost of opportunistically exploiting a new food resource. Apparent annual adult survival rate was independent of sex, and varied between 0.393 and 0.440, depending on the inclusion of a transience effect in our models. This low value was possibly due to “permanent” emigration from the site during the study. We discuss the potential effects of deforestation on the ecology and demography of LST and make recommendations for future conservation-oriented research in Haiti.
The Ecological Efficiency of Green Materials in Sustainable Urban Planning—A Model for Its Measurement
Authors: Fasolino Isidoro , Cicalese Federica , Bellino Alessandro , Grimaldi Michele , del Caz-Enjuto M. Rosario , Baldantoni Daniela
Published: November 2023

Urban green planning is crucial in promoting sustainable urban ecosystems through the mindful use of vegetation, but few approaches are currently able to account for the ecosystem services provided by urban green planning in ex ante planning applications. The present research proposes a methodological approach to sustainable urban planning that accounts for the ecological role of vegetation in urban ecosystems. Indeed, by estimating the functions exerted by different vegetation elements in urban ecosystems through a purposely developed set of equations, the procedure allows for the optimization of the development of urban plans by maximizing the contribution of vegetation to ecosystem dynamics. Specifically, the proposed methodology is articulated in two phases, i.e., the functional role of vegetation is firstly modeled through simple geometric features and specific ecological traits accounting for plant interactions with the environment, and then the selected vegetation traits are used in guiding the choice of the species. The approach has been exemplified through case studies, thereby highlighting its ability to guide planning decisions based on the type, abundance, and spatial organization of vegetation to promote the sustainability of urban development.
Electrospun Microstructured Biopolymer Fibers Containing the Self-Assembled Boc–Phe–Ile Dipeptide: Dielectric and Energy Harvesting Properties
Authors: Handa Adelino , Baptista Rosa M. F. , Santos Daniela , Silva Bruna , Rodrigues Ana Rita O. , Oliveira João , Almeida Bernardo , de Matos Gomes Etelvina , Belsley Michael
Published: November 2023

Hybrid biomaterials were engineered using the electrospinning technique, incorporating the dipeptide Boc–L-phenylalanyl–L-isoleucine into microfibers composed of biocompatible polymers. The examination by scanning electron microscopy affirmed the morphology of the microfibers, exhibiting diameters ranging between 0.9 and 1.8 µm. The dipeptide self-assembles into spheres with a hydrodynamic size between 0.18 and 1.26 µm. The dielectric properties of these microfibers were characterized through impedance spectroscopy where variations in both temperature and frequency were systematically studied. The investigation revealed a noteworthy rise in the dielectric constant and AC electric conductivity with increasing temperature, attributable to augmented charge mobility within the material. The successful integration of the dipeptide was substantiated through the observation of Maxwell–Wagner interfacial polarization, affirming the uniform dispersion within the microfibers. In-depth insights into electric permittivity and activation energies were garnered using the Havriliak–Negami model and the AC conductivity behavior. Very importantly, these engineered fibers exhibited pronounced pyroelectric and piezoelectric responses, with Boc–Phe–Ile@PLLA microfibers standing out with the highest piezoelectric coefficient, calculated to be 56 pC/N. These discoveries help us understand how dipeptide nanostructures embedded into electrospun nano/microfibers can greatly affect their pyroelectric and piezoelectric properties. They also point out that polymer fibers could be used as highly efficient piezoelectric energy harvesters, with promising applications in portable and wearable devices.
Improvement of the Soil and Water Assessment Tool Model and Its Application in a Typical Glacial Runoff Watershed: A Case Study of the Qarqan River Basin, China
Authors: Ding Junwei , Wang Yi , Cao Chenglin , Sun Wei
Published: November 2023

The composition of runoff in the basins located in the arid region of Northwest China is complex. How to better simulate and analyze the hydrological process and runoff situation of the basin through hydrological models is the key to the effective management of the regional water resources. This study focused on the Qarqan River Basin in Xinjiang, which is a typical river originating from glaciers and crucial for maintaining the oasis ecology in the downstream Tarim River. Based on the original SWAT model, a SWAT-Gla model containing a new glacier module was constructed according to the characteristics of the basin. After simulating, parameter calibration, and verification of the models, it was found that during the calibration period (1975–2009) and verification period (2010–2019), the R2, NSE, and PBIAS results of the SWAT-Gla model were much better than the original SWAT model. Moreover, SWAT-Gla could also simulate the runoff much better, especially in the peak and recession parts of the hydrograph compared with the original SWAT model. It was found that SWAT-Gla could better describe the runoff process in the basin where glacier recharge was the main component of runoff. Further, the analysis revealed that over the past 50 years, glacier and snowmelt water contributed to approximately 80% of the total basin recharge, which predominantly occurred from March to September. The volume of glacier meltwater exhibited a strong correlation with temperature, with both demonstrating an upward trend. The simulation found that in terms of groundwater, the groundwater recharge in the Qarqan River was relatively stable, stabilizing at 13% over the past 30 years, and groundwater recharge was mainly distributed in spring and summer, accounting for about 95% in total. Overall, we observed an increasing trend in the replenishment of glacial meltwater in both the surface runoff and groundwater in the basin. Therefore, it is essential to pay more attention to the future changes in water resources in the basin to ensure the sustainable development of water resources.
The Effectiveness of Municipal Comprehensive Planning in Mitigating Forest Fragmentation
Authors: Zhang Yan , Göçmen Z. Aslıgül
Published: November 2023

To mitigate forest fragmentation, it is critical to investigate the factors that contribute to it. Considerable research examines various factors shaping forest fragmentation, but these studies have not typically identified land use planning as an important factor. Our study helps fill this gap in the literature. To examine the influence of land use planning on forest fragmentation, we assessed the quality of 29 municipal comprehensive plans with respect to forest protection in Wisconsin, U.S.A., a sample drawn from a spatial analysis of forest fragmentation dynamics from 2001 to 2011. We found that the comprehensive plans of communities that have experienced lower-than-expected forest fragmentation change were, on average, of significantly higher quality than those of the communities that have experienced higher-than-expected forest fragmentation change during this period. We conclude that the quality of community-based comprehensive plans, especially the strength and breadth of policies addressing forestland, matters in mitigating the fragmentation of forestland.
Sustainable Energy Production in Smart Cities
Authors: Salama Ramiz , Al-Turjman Fadi
Published: November 2023

Finding a method to provide the installed Internet of Things (IoT) nodes with energy that is both ubiquitous and long-lasting is crucial for ensuring continuous smart city optimization. These and other problems have impeded new research into energy harvesting. After the COVID-19 pandemic and the lockdown that all but ended daily activity in many countries, the ability of human remote connections to enforce social distancing became crucial. Since they lay the groundwork for surviving a lockdown, Internet of Things (IoT) devices are once again widely recognised as crucial elements of smart cities. The recommended solution of energy collection would enable IoT hubs to search for self-sustaining energy from ecologically large sources. The bulk of urban energy sources that could be used were examined in this work, according to descriptions made by researchers in the literature. Given the abundance of free resources in the city covered in this research, we have also suggested that energy sources can be application-specific. This implies that energy needs for various IoT devices or wireless sensor networks (WSNs) for smart city automation should be searched for near those needs. One of the important smart, ecological and energy-harvesting subjects that has evolved as a result of the advancement of intelligent urban computing is intelligent cities and societies. Collecting and exchanging Internet of Things (IoT) gadgets and smart applications that improve people’s quality of life is the main goal of a sustainable smart city. Energy harvesting management, a key element of sustainable urban computing, is hampered by the exponential rise of Internet of Things (IoT) sensors, smart apps, and complicated populations. These challenges include the requirement to lower the associated elements of energy consumption, power conservation, and waste management for the environment. However, the idea of energy-harvesting management for sustainable urban computing is currently expanding at an exponential rate and requires attention due to regulatory and economic constraints. This study investigates a variety of green energy-collecting techniques in relation to edge-based intelligent urban computing’s smart applications for sustainable and smart cities. The four categories of energy-harvesting strategies currently in use are smart grids, smart environmental systems, smart transportation systems, and smart cities. In terms of developed algorithms, evaluation criteria, and evaluation environments, this review’s objective is to discuss the technical features of energy-harvesting management systems for environmentally friendly urban computing. For sustainable smart cities, which specifically contribute to increasing the energy consumption of smart applications and human life in complex and metropolitan areas, it is crucial from a technical perspective to examine existing barriers and unexplored research trajectories in energy harvesting and waste management.
Research on Integrated Energy Distributed Sharing in Distribution Network Considering AC Power Flow and Demand Response
Authors: Shi Zhihan , Zhang Guangming , Zhou Xiaoxiong , Han Weisong , Zhu Mingxiang , Bai Zhiqing , Lv Xiaodong
Published: November 2023

Under the constraint of the AC power flow architecture considering reactive power regulation, the examination of integrated energy distributed transactions among multiple subsystems can promote the research in the field of energy sharing. It is difficult to fully cover the consideration of AC power flow, demand response, integrated energy, and other factors in traditional related research. In response, a study is therefore conducted in this paper on integrated energy sharing in the distribution network. First, this paper introduces the system operation framework of the proposed distribution network model, and explains the interaction between all the players. Secondly, a distribution network power flow model and an integrated energy subsystem model are respectively. In particular, the subsystem model specifically considers new energy, demand response, integrated energy, and other factors. Then, a cooperative game model is constructed based on the cooperative relationship among subsystems in the distribution network system, followed by the analysis of the benefits brought by cooperation to the distribution network and the subsystems themselves. Finally, a distributed solution flow is established for the model based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The results of the example analysis reveal the effectiveness of the model proposed in increasing the degree of energy utilization and further absorbing new energy in the distribution network system, each subsystem can generate up to 12% more absorption capacity than it would otherwise operate separately to accommodate more renewable energy in the distribution system.
A Learning Ecology Perspective of Energy Literacy among Youth: A Case Study from Alabama High Schools
Authors: Ji Hyunjung , Coronado Alexandria B. , Mueller Mark A. , Esposito Laurel J. , Tait Daniel , Kim Hyun Jin
Published: November 2023

Developing energy literacy and pathways for youth to clean energy careers are vital for sustainable development, aligning with various Sustainable Development Goals (SDGs), from climate action to quality education. Despite the increasing focus on nurturing energy-literate youth, there is a lack of comprehensive insights into how students develop their energy literacy within diverse learning environments. This study addresses the research question of what factors across various learning environments play a significant role in the development of energy literacy among youth. To address the research question, we develop a conceptual framework for understanding the development of energy literacy among high school students based on a learning ecology perspective. Focusing on students’ energy literacy, encompassing information search, attitudes, behaviors, and knowledge of energy, we examined the influence of school-based clean energy program participation, virtual information sources, and interactions with peers and family. Furthermore, we conducted an empirical analysis to identify factors associated with energy literacy among high school students from Alabama high schools. The student sample was intentionally recruited from case study high schools in Alabama that implemented a clean energy education pilot project. This sample includes both program participants and non-participants who completed pre- and post-program surveys, resulting in a total of 189 survey responses. Findings indicate a positive association between the school-based clean energy program and a significantly higher increase in energy-related information searches among program participants compared with non-participants. The virtual learning setting, especially video platforms, was also significantly associated with students’ energy-related information searches and positive attitudes toward energy. Findings also indicate that family relationships and pre-existing STEM interests significantly determine youth energy literacy. These findings provide valuable insights for the development and expansion of future energy education programs. If the energy education program incorporates these crucial learning factors and establishes an interconnected learning environment, the convergence of multiple learning aspects within the program can foster a synergistic learning ecosystem for youth energy literacy.
Using Big Data to Assess Park System Performance during the COVID-19 Pandemic
Authors: Li Shujuan , Yang Bo , Li Haiquan
Published: November 2023

Parks provide essential services to urban dwellers, but the global COVID-19 pandemic significantly disrupted park usage. Despite this, little is known about the adaptation of visiting behaviors by the public and how visitation patterns vary across different types of parks. In this study, we utilized SafeGraph cellular human movement data to compare park visits in Tucson, Arizona (USA) before and during the pandemic (2019 vs. 2020). We reviewed park management measures in response to the pandemic alongside park visit data. Furthermore, we conducted a GIS analysis to compare the changes in park visits across different park types throughout various days and months. Results indicate that (1) fluctuations in park visits are strongly correlated with COVID-19-related measures; (2) different types of parks experience vastly different processes of visit decline and recovery; (3) river and linear parks maintain their appeal, likely due to the perception of reduced virus transmission risk associated with their primary activities, such as walking and bicycling; and (4) the contrast between weekend and weekday visit patterns reflects the extent of the pandemic impact. These findings offer valuable guidance for park management and park usage, attendance prediction, and design adaptations for future pandemics. We conclude that SafeGraph big data are effective for evaluating park system performance on a broader scale.
Coordination Dynamics between Population Change and Built-Up Land Expansion in Mainland China during 2000–2020
Authors: Zhao Tianqing , Wang Wen
Published: November 2023

Coordination between population growth and built-up land expansion is a major challenge for regional sustainable development. This paper proposed a dual indicator approach, which consists of the ratio of the built-up land expansion rate to population growth rate (HLEC) and the change rate of the built-up area per capita (BPR), and explored the dynamics of the human–land coordination relationship in mainland China using gridded population data and remotely sensed land-cover data. Four gridded population datasets (GPWv4, LandScan, WorldPop, and China gridded population datasets) were evaluated using county-level census data from 2000 and 2010, which showed that WorldPop had the highest correlation with the census data, CASpop had the smallest RMSE, and LandScan had the worst performance. The population of mainland China continued to rise from 2000 to 2020, but the average annual growth rate declined significantly. The built-up land expanded across China, with northwestern China experiencing the fastest growth and the eastern coastal regions experiencing a slower rate of expansion. The HLEC was 5.83, and the BPR increased by approximately 50%, indicating that the rate of population growth is lagging behind the rate of built-up land expansion in most regions, with the exception of Beijing, Tianjin, and Shanghai. Topographical and socio-economic factors have nonlinear effects on the coordination state of the human–land relationship. This approach can be used in areas with no change in population and can better characterize the human–land relationship and its coordination dynamics.
Urban Growth in a Mediterranean Island-State: A Data-Driven Study of Malta’s Development Permits in the Last Thirty Years
Authors: Pace Andrea , Attard Maria , Camilleri Michel , Valentino Gianluca
Published: November 2023

Land is a natural resource, and settlement development is central for human activities. The growth of settlements comes at the expense of natural and agricultural land. Understanding urban growth dynamics, defined in this study as new urban development, can lead to an increased awareness of the stresses on land resources and the suitability of current land use policies. It can also reflect how changes in the economic cycles and planning policies can influence these dynamics. This is even more critical in small island states, where land is a scarce resource. This study uses data extracted from development-planning applications between 1993 and 2019 in order to understand urban growth in the island-state of Malta. A methodology was developed to (i) use web-scraping techniques to extract development-planning applications data from a public online database in order to define development descriptors, (ii) establish keywords that imply growth within the urban area, and (iii) analyse growth in view of local planning zones and regulations. The results show that over the years, there has been a decrease in the share of granted development-planning applications that lead to the growth of the urban area when compared to those granted applications permitting other forms of development, such as that linked to minor modifications to existing buildings and restoration works amongst others. Nonetheless, the absolute number of granted development-planning applications resulting in growth has increased, particularly in the last construction boom (post 2013). The share of applications denoting growth granted within the development zone has remained stable throughout the years. The years with the highest numbers of permits granted outside the development zone, where there was a disposition against development, were 2017 and 2018. In conclusion, the trends and patterns resulting from development cycles were analysed and discussed. The outcomes of this work will lead to better measurement of urban growth in Malta and assess the efficacy of current national development-planning policies. This is in line with the UN’s Sustainable Development Goal 11, which aims at making “cities and human settlements inclusive, safe, resilient, and sustainable”. The research specifically addresses Target 11a, which is focused on supporting “positive economic, social, and environmental links between urban, peri-urban, and rural areas by strengthening national and regional development planning”.