bluuebunny/crossref_metadata_2025 huggingface dataset:
A Remote Sensing-Based Inventory of West Africa Tropical Forest Patches: A Basis for Enhancing Their Conservation and Sustainable Use
Authors: Wingate Vladimir R. , Akinyemi Felicia O. , Iheaturu Chima J. , Ifejika Speranza Chinwe
Published: December 2022

The rate of tropical deforestation is increasing globally, and the fragmentation of remaining forests is particularly high in arable landscapes of West Africa. As such, there is an urgent need to map and monitor these remnant forest patches/fragments and so identify their multiple benefits and values. Indeed, recognizing their existence will help ensure their continued provision of ecosystem services while facilitating their conservation and sustainable use. The aim of this study is therefore to inventory and characterise the current extent and change of remnant forest patches of West Africa, using multi-source remote sensing products, time-series analyses, and ancillary datasets. Specifically, we collate and analyse descriptive and change metrics to provide estimates of fragment size, age, biophysical conditions, and relation to social-ecological change drivers, which together provide novel insights into forest fragment change dynamics for over four decades. We map forests patches outside protected areas with a tree cover ≥30%, a tree height of ≥5 m, an area ≥1 km2 and ≤10 km2. Appended to each patch are descriptive and change dynamics attributes. We find that most fragments are small, secondary forest patches and these cumulatively underwent the most forest loss. However, on average, larger patches experience more loss than smaller ones, suggesting that small patches persist in the landscape. Primary forest patches are scarce and underwent fewer losses, as they may be less accessible. In 1975 most patches were mapped as secondary, degraded forests, savanna, woodland, and mangrove, and relatively few comprised cropland, settlements, and agriculture, suggesting that new forest patches rarely emerged from arable land over the past 45 years (1975–2020), but rather are remnants of previously forested landscapes. Greening is widespread in larger secondary fragments possibly due to regrowth from land abandonment and migration to urban areas. Forest loss and gain are greater across fragments lying in more modified landscapes of secondary forests, while forest loss increases with distance to roads. Finally, larger forest patches harbour a denser tree cover and higher trees as they may be less impacted by human pressures. The number and extent of West African forest patches are expected to further decline, with a concurrent heightening of forest fragmentation and accompanying edge effects. Lacking any conservation status, and subject to increasing extractive demands, their protection and sustainable use is imperative.
An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data
Authors: Saboori Mojtaba , Mousivand Yousef , Cristóbal Jordi , Shah-Hosseini Reza , Mokhtari Ali
Published: December 2022

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.
Unboxing the Black Box of Attention Mechanisms in Remote Sensing Big Data Using XAI
Authors: Hasanpour Zaryabi Erfan , Moradi Loghman , Kalantar Bahareh , Ueda Naonori , Halin Alfian Abdul
Published: December 2022

This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness of CNN-based architectures with and without AMs. Secondly, we attempt to interpret the results produced by the CNNs using explainable artificial intelligence (XAI) methods. We compare CNNs with and without (vanilla) AMs for buildings detection. Five metrics are calculated, namely F1-score, precision, recall, intersection over union (IoU) and overall accuracy (OA). For the XAI portion of this work, the methods of Layer Gradient X activation and Layer DeepLIFT are used to explore the internal AMs and their overall effects on the network. Qualitative evaluation is based on color-coded value attribution to assess how the AMs facilitate the CNNs in performing buildings classification. We look at the effects of employing five AM algorithms, namely (i) squeeze and excitation (SE), (ii) convolution attention block module (CBAM), (iii) triplet attention, (iv) shuffle attention (SA), and (v) efficient channel attention (ECA). Experimental results indicate that AMs generally and markedly improve the quantitative metrics, with the attribution visualization results of XAI methods agreeing with the quantitative metrics.
A Comprehensive Analysis of Ultraviolet Remote Sensing for Aerosol Layer Height Retrieval from Multi-Angle Polarization Satellite Measurements
Authors: Gu Haoran , Zhang Yuhuan , Fan Cheng , Li Zhengqiang , Hou Weizhen , Liu Zhenhai , Xie Yisong , Xu Hua , Zhang Luo , Ma Jinji
Published: December 2022

Based on the optimal estimation (OE) theory and information content analysis method, we discuss the ability to include the multi-angle satellite ultraviolet polarization channel to retrieve the aerosol layer height (ALH) for ten typical aerosol types in the China region. We also quantitatively evaluate the effects of polarization measurements and the number of viewing angles on ALH retrieval under different conditions (aerosol model, aerosol optical depth, etc.). By comparing the different degree of freedom for signal (DFS) results of ALH caused by the theoretical retrieval error changes in different microphysical parameters in the aerosol and surface model, we identify the key factors affecting ALH retrieval. The results show that the extended ultraviolet band provides important information for ALH retrieval and is closely related to the scattering and absorption characteristics of aerosol models. The polarization measurements in fine mode reduce the posterior error of ALH retrieval by 6–39%; however, this is relatively small for coarse mode. In particular, when it is applied to the transported dust and background dust aerosol types, the posterior error is only reduced by 1–8% after adding polarization measurements. For these two aerosol types with weak absorption at the ultraviolet band, increasing the number of angles observed in addition to increasing the polarization channel will more effectively improve ALH inversion. Compared with other aerosol and surface model parameters, the retrieval errors for the total volume column, effective variance, real part of the complex refractive index, and surface reflectance are the main factors affecting ALH retrieval. Therefore, reducing the theoretical retrieval error of these parameters will be helpful.
Spatio-Temporal Variability of Wind Energy in the Caspian Sea: An Ecosystem Service Modeling Approach
Authors: Rahimi Milad , Gholamalifard Mehdi , Rashidi Akbar , Ahmadi Bonyad , Kostianoy Andrey G. , Semenov Aleksander V.
Published: December 2022

The ecosystem services that can be obtained from the oceans and seas are very diverse; one of the sources of energy is wind power. The Caspian Sea is characterized by a fragile ecosystem that is under serious anthropogenic stress, including oil and gas production and transportation. In particular, rich oil and gas resources in the region make renewables less important for the Caspian Sea Region. Depletion of hydrocarbon resources, a rise of their price on the international markets, geopolitical tensions, a decrease in the Caspian Sea level, regional climate change, and other factors make exploring offshore wind energy production timely. In order to model the offshore wind energy of the Caspian Sea, data from the ERA-Interim atmospheric reanalysis were used from 1980 to 2015 combined with QuikSCAT and RapidSCAT remote sensing data. The modeling results showed a wind power density of 173 W/m2 as an average value for the Caspian Sea. For the 1980–2015 period, 57% of the Caspian Sea area shows a decreasing trend in wind power density, with a total insignificant drop of 16.85 W/m2. The highest negative rate of change is observed in the Northern Caspian, which seems to be more influenced by regional climate change. The Caspian Sea regions with the highest potential for offshore wind energy production are identified and discussed.
Evaluation of the Radar Echo Tops in Catalonia: Relationship with Severe Weather
Authors: Rigo Tomeu , Farnell Barqué Carme
Published: December 2022

Strong updrafts occur in severe thunderstorms, causing the overshooting tops, an increase in the total lightning activity, and generating a frozen drops nucleus that will produce severe weather when it collapses. The Echo Top is a measurement of the vertical development of the cloud, considering a certain reflectivity threshold: the higher the threshold value, the lower the altitude reached. The present research shows the distribution of the Echo Tops of three reflectivity thresholds (12, 35, and 45 dBZ) in Catalonia for the period 2013–2021, comparing the distribution with the maps of hail, lightning jumps, and the topography of the region. The analysis shows how the maxima occurrence of Echo Tops varies depending on the threshold, indicating that thunderstorms have an initial development at 12 dBZ in the mountainous area, while the 35 and 45 dBZ maxima are shifted to the south, in regions with lower mountains. This last maximum is nearly coincident with the region more hit by large hail.
Post-Fire Forest Vegetation State Monitoring through Satellite Remote Sensing and In Situ Data
Authors: Avetisyan Daniela , Velizarova Emiliya , Filchev Lachezar
Published: December 2022

Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results.
An Integrated Solution of UAV Push-Broom Hyperspectral System Based on Geometric Correction with MSI and Radiation Correction Considering Outdoor Illumination Variation
Authors: Song Liyao , Li Haiwei , Chen Tieqiao , Chen Junyu , Liu Song , Fan Jiancun , Wang Quan
Published: December 2022

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel.
Gaussian Mutation–Spider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Authors: Shaik Abdul Lateef Haroon Phulara , Manoharan Monica Komala , Pani Alok Kumar , Avala Raji Reddy , Chen Chien-Ming
Published: December 2022

Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian Mutation–Spider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset.
A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery
Authors: Xing Huaqiao , Chen Bingyao , Lu Miao
Published: December 2022

Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system.
An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter
Authors: Wang Zhengchun , Xing Li , Xiong Zhi , Ding Yiming , Sun Yinshou , Shi Chenfa
Published: December 2022

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on the active sensing information, combined with the characteristics of particles “not going through the wall” in the indoor map building structure, an improved adaptive particle filter (PF) based on the particle “not going through the wall” method is proposed for pedestrian navigation in this paper. This method can restrain the error divergence of the navigation system for a long time. Compared to the traditional pedestrian navigation method, based on the combination of indoor map assistance (MA) and particle filter, a global search method based on indoor MA is used to solve the indoor positioning problem under the condition of the unknown initial position and heading. In order to solve the problem of low operation efficiency caused by the large number of particles in PF, a calculation method of adaptively adjusting the number of particles in the process of particle resampling is proposed. The results of the simulation data and actual test data show that the proposed indoor integrated positioning method can effectively suppress the error divergence problem of the navigation system. Under the condition that the total distance is more than 415.44 m in the indoor environment of about 2600 m2, the average error and the maximum error of the position are less than two meters relative to the reference point.
Remote Sensing of Infrasound Signals of the “Voice of the Sea” during the Evolution of Typhoons
Authors: Chupin Vladimir , Dolgikh Grigory , Gusev Egor , Timoshina Galina
Published: December 2022

The article presents summarized results of the study of registered periods of microseisms of the “voice of the sea” infrasound oscillations, generated due to the influence of wind of tropical cyclones (typhoons) passing in the Sea of Japan. We compared the recorded signals and how they correspond to the microseismic background in the wind wave range. Using satellite monitoring data, we verified and identified the groups of cyclones that excite the “voice of the sea” microseisms with approximately the same characteristics. We found that the bandwidth of the “voice of the sea” range and its central frequency, with the maximum amplitude in this frequency range, are approximately the same for typhoons (cyclones) propagating along the same routes. Wind speed also determines the central frequency of the “voice of the sea” range for a particular typhoon (tropical cyclone) propagation route.
Exploiting the Combined GRACE/GRACE-FO Solutions to Determine Gravimetric Excitations of Polar Motion
Authors: Śliwińska Justyna , Wińska Małgorzata , Nastula Jolanta
Published: December 2022

Observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions can be used to estimate gravimetric excitation of polar motion (PM), which reflects the contribution of mass changes in continental hydrosphere and cryosphere to PM variation. Many solutions for Earth’s gravity field variations have been developed by institutes around the world based on GRACE/GRACE-FO data; however, it remains inconclusive which of them is the most reliable for the determination of PM excitation. In this study, we present a combined series of GRACE/GRACE-FO-based gravimetric excitation of PM computed using the three-cornered-hat (TCH) method, wherein the internal noise level in a combined solution is reduced to a minimum. We compare the combined series with results obtained from the combined GRACE/GRACE-FO solution provided by COST-G (International Combination Service for Time-variable Gravity Fields) and from the single solution elaborated by the Center for Space Research (CSR). All the gravimetric excitation series are evaluated by comparison with the sum of hydrological and cryospheric signals in geodetically observed PM excitation (called GAO). The results show that by minimizing the internal noise level in the combined excitation series using the TCH method, we can receive higher consistency with GAO than in the case of COST-G and CSR solutions, especially for the non-seasonal oscillations. For this spectral band, we obtained correlations between GAO and the best-combined series as high as 0.65 and 0.72 for the χ1 and χ2 equatorial components of PM excitation, respectively. The corresponding values for seasonal oscillation were 0.91 for χ1 and 0.89 for χ2. The combined series developed in this study explain up to 68% and 60% of overall GAO variability for χ1 and χ2, respectively.
Climate Change Characteristics of Typical Grassland in the Mongolian Plateau from 1978 to 2020
Authors: He Bu , Tuya Wulan , Qinchaoketu Si , Nanzad Lkhagvadorj , Yong Mei , Kesi Tang , Sun Changqing
Published: December 2022

Typical grassland is the core of the Mongolian Plateau grassland belt, and is also an important ecological barrier in the north of China. It is of great significance to explore the real-time changes in grassland climate for the prevention and control of climate disasters, and for ecological protection. In this study, the spatial and temporal variation of temperature, precipitation and maximum wind speed in typical Mongolian Plateau grassland were studied using observation data from 16 meteorological stations from 1978 to 2020, using the linear trend method, cumulative anomaly method, Mann-Kendall test, sliding t-test and Morlet wavelet analysis. The results show that: (1) The typical grassland temperature has been increasing at a rate of 0.4 °C/10a (p < 0.001) over the past 40 years, with the most significant warming in spring and summer; a sudden change from low to high temperature occurred in 1992; the annual average temperature gradually increased from northeast to southwest, with significant warming in the southwest. (2) Annual precipitation decreased slightly at a rate of −2.39 mm/10a, with the most significant decrease in summer precipitation; a sudden change from more to less precipitation occurred in 1998; spatially, precipitation decreased gradually from east to west, with significant moisture reduction in its northern part. (3) The maximum wind speed decreased significantly at a rate of −0.33m/s/10a (p < 0.001), with the most pronounced decrease in spring; the maximum wind speed changed abruptly from strong to weak around 1991; spatially, the annual average maximum wind speed decreased gradually from northwest to southeast and northeast, with the most pronounced decrease in the south and northeast. (4) The wavelet analysis shows that the typical grassland area will still be in a warm, low-rainfall and weak-wind stage in the coming years. Using the above analysis, the typical grassland climate of the Mongolian Plateau has shown a clear trend of warm and dry, weak wind in the past 40 years.
Population Structure and Morphological Pattern of the Black-Spotted Pond Frog (Pelophylax nigromaculatus) Inhabiting Watershed Areas of the Geum River in South Korea
Authors: Park Jun-Kyu , Chung Ki Wha , Kim Ji Yoon , Do Yuno
Published: December 2022

Black-spotted pond frogs (Pelophylax nigromaculatus), widely distributed in East Asia, can be suitably used for the study of population genetic patterns and ecosystem monitoring. To systematically manage, conserve, and study this species, it is necessary to understand its habitat range. We analyzed the genetic and morphological range of black-spotted pond frog populations within a watershed of the Geum River, one of the main rivers in South Korea. We genotyped the frogs based on seven microsatellite loci and defined the skull shape based on landmark-based geometric morphometrics. One watershed area was divided into 14 sub-watershed areas, the smallest unit of the Geum River basin. The genetic structure of frogs among the 14 sub-watershed areas did not differ significantly, nor was correlated with geographic distance. Therefore, frogs within these watershed areas constitute a single population. Morphologically, they differed between some sub-watershed areas, but morphological distance did not correlate with genetic distance but rather with geographic distance. This morphological change differs depending on the environmental gradient rather than the genetic structure. As a single population, frogs in this watershed area need to be managed in an integrated way. We suggest that the identification of response and adaptation by population genetics must be compared across and beyond the watershed range.
The Rise of MCS and EMA in the Sustainable Field: A Systematic Literature Analysis
Authors: Wang Tiantian , Ismail Kamisah , Abas Azmi Khairul Saidah
Published: December 2022

Sustainable development is becoming increasingly popular in all fields and the importance of sustainability and business issues will grow. Despite the increase in scholarly attention paid to improving organizational sustainability performance, such as Management Control Systems (MCS) and Environmental Management Accounting (EMA), few studies have been conducted on the relationship between MCS and EMA, with no aggregation of findings and knowledge. To fill this void, this study conducted a systematic literature review of the MCS and EMA in the field of sustainability. A comprehensive search was conducted for journal articles that addressed MCS and EMA issues in the context of sustainability. Considering that EMA and MCS are more widely used in manufacturing, this study focuses on the manufacturing industry. Based on rigorous inclusion and exclusion criteria, 43 journal articles were selected for the final analysis. The bibliometric data from the identified studies, their theoretical and methodological approaches, research themes, and research backgrounds were analyzed in this review. As a result, this study identifies existing gaps in the current literature, provides directions for the organization to research internal systems’ interactions and suggests future research directions with specific research agendas.
Incorporation of Glass and Plastic Waste into Alkali-Activated Mill Residue Bricks
Authors: Zhang Zipeng , Wong Yat Choy , Sofi Massoud , Mendis Priyan
Published: December 2022

Recycling of glass and plastic waste has been increasingly attracting the attention of researchers worldwide. Relevant studies have been conducted to prove the feasibility of incorporating glass and plastic wastes into cement-based concrete and fired bricks. However, the high embedded energy and large carbon footprint of these materials have hindered the achievement of sustainable goals. Hence, this study attempts to diversify the recycling pathways for glass and plastic waste via a low carbon route. The brick clay mill residue has been used as a precursor to prepare alkali-activated bricks containing plastic and glass fines with a specific curing regime. The compressive strength, water absorption, linear shrinkage, and microstructure were investigated with varied content of glass and plastic content. The results showed that the maximum acceptable ratio of glass fines was around 55 wt.% for samples with the glass waste solely, achieving the compressive strength of 22 MPa. While foror samples incorporating plastic (PET) waste only, the maximum allowable ratio was only 2 wt.%, because excessive plastic resulted in the spalling of the sample surface. When both the glass and plastic waste were added to the samples, the maximum substitution ratio was 25 wt.% of glass and 2 wt.% of plastics. Scanning Electron Microscope images indicates that the plastic particles had more adverse effects on the microstructure of the alkali-activated samples than the glass particles. There was little or no bonding between plastic waste and alkali-activated mill residues. In contrast, the bonding between glass particles and alkali-activated mill residues was captured. The effect of the addition of glass and plastic samples on the durability of alkali-activated mill residue material needs to be further investigated, such as dimension stability, resistance to salt attack, freeze and thaw, and so on.
Assessing Provisions and Requirements for the Sustainable Production of Plastics: Towards Achieving SDG 12 from the Consumers’ Perspective
Authors: Leal Filho Walter , Barbir Jelena , Özuyar Pınar Gökçin , Nunez Enrique , Diaz-Sarachaga Jose Manuel , Guillaume Bertrand , Anholon Rosley , Rampasso Izabela Simon , Swart Julia , Velazquez Luis , Ng Theam Foo
Published: December 2022

Plastics are used widely, and modern civilization would have to behave differently without them. However, plastics pose a threat to sustainable life. This paper focuses on some of the provisions being made for sustainable production to date and focuses on one key sector-plastic manufacturing-where sustainable production patterns are urgently needed. The paper describes the latest trends related to plastic production, its environmental impacts, and how this sector is adjusting its processes in order to meet the current and forthcoming legal requirements and consumer demands. The methodological approach of the study has focused on both a literature review on the one hand and the consumers’ perspective obtained via a survey on the other. These two approaches were then crosschecked in order to assess current trends in plastic manufacturing and to understand how consumers see these trends as being consistent with the aims of the UN Sustainable Development Goal 12. The results obtained suggest that a greater engagement of consumers is needed in supporting the efforts to manage plastic more sustainably. Based on its findings, the paper provides useful insights linked to principles and tools for sustainable plastic production and design, and it demonstrates the usefulness and urgency of a sound materials management in order to tackle plastic pollution, one of today’s major environmental problems.
Towards Achieving 100% Renewable Energy Supply for Sustainable Climate Change in Pakistan
Authors: Raza Muhammad Amir , Aman Muhammad Mohsin , Rajpar Altaf Hussain , Bashir Mohamed Bashir Ali , Jumani Touqeer Ahmed
Published: December 2022

Fossil fuel-based energy systems are mostly used for supplying energy that creates negative impacts on the environment, so in this study, the development of a 100% renewable energy system is evaluated for Pakistan for sustainable climate change. This study modeled three scenarios, namely, ongoing (ONG), energy saving policy (ESP), and green energy policy (GEP) scenarios using low emission analysis platform (LEAP) software for the study period 2022 to 2050 for Pakistan. The results revealed that a 100% renewable energy supply could be achieved through the GEP scenario. Model results show that the share of renewable sources in the total energy mix is 1117.08 TWh and non-renewable sources contribute only 18.12 TWh to meet the energy demand of 966.05 TWh until 2050. Non-renewable production leads to the generation of 8.85 million metric tons of carbon emissions, which is too low compared with the 135.47 million metric tons under the ONG scenario. The USD 1482.46 billion investment cost required for adding renewable energy capacity until 2050 is too high as compared with the USD 46.80 billion under the ONG scenario. Energy demand and production requirements are reduced by 34.18% under the ESP scenario until 2050. This approach can also be applicable to the majority of nations worldwide.
Modelling Erosion and Floods in Volcanic Environment: The Case Study of the Island of Vulcano (Aeolian Archipelago, Italy)
Authors: Bonasia Rosanna , Turchi Agnese , Madonia Paolo , Fornaciai Alessandro , Favalli Massimiliano , Gioia Andrea , Di Traglia Federico
Published: December 2022

The re-mobilization of volcaniclastic material poses a hazard factor which, although it decreases with time since the last eruption, remains present in the hydrographic basins of volcanic areas. Herein, we present the results of the numerical modelling of erosive phenomena of volcanic deposits, as well as of flooding in the volcanic area. The proposed approach includes runoff estimation, land use analysis, and the application of hydraulic and erosion modelling. It exploits the Iber software, a widely used and validated model for rainfall-runoff, river flooding, and erosion and sediment transport modelling. The methodology was applied to the Island of Vulcano (Italy), known for the erosion phenomena that affect the slopes of one of its volcanic cones (La Fossa cone). The rainfall excess was calculated using a 19-year dataset of hourly precipitations, and the curve number expressed by the information on soil cover in the area, derived from the land cover and land use analysis. The erosion and flow models were performed considering different rainfall scenarios. Results show a particularly strong erosion, with thicknesses greater than 0.4 m. This is consistent with field observations, in particular with some detailed data collected both after intense events and by long-term observation. Results of the hydraulic simulations show that moderate and torrential rainfall scenarios can lead to flood levels between 0.2 and 0.6 m, which mostly affect the harbours located in the island’s inhabited area.
COVID-19 and Households Waste in Hispanic America: An Assessment of Trends
Authors: Leal Filho Walter , Lange Salvia Amanda , Sierra Javier , Fletcher Carly A. , Banks Craig E. , Velazquez Luis , Anholon Rosley , Rampasso Izabela Simon , Maclean Claudia , Barbir Jelena , Neiva Samara
Published: December 2022

The COVID-19 pandemic has caused many social and economic problems in Hispanic America, a region with fragile health and economic systems and many inequalities. The pandemic has negatively influenced various aspects of life and led to changes in various habits and behaviours, including consumption. However, the extent to which the pandemic has influenced households, and waste production, in particular, is not well known. In this context, this paper reports on a study aimed at identifying changes in waste production across households under the special conditions created by the pandemic in Hispanic America. The majority of the respondents stated that their level of satisfaction with waste management policies in their countries did not change much during the pandemic. Only a few stated that they were more satisfied than before. Overall, the results suggest that, like previous crises, the COVID-19 outbreak may generate changes regarding household consumption and waste management in Hispanic America. At the same time, these findings stress the need to improve waste management practices. Some measures that may be adopted to allow Hispanic American countries to better cope with increases in waste production in times of pandemics are listed.
Hydrolyzed Fish Collagen Serum from By-Product of Food Industry: Cosmetic Product Formulation and Facial Skin Evaluation
Authors: Amnuaikit Thanaporn , Shankar Rajeev , Benjakul Soottawat
Published: December 2022

Fish collagen is a good source of protein and essential amino acids. It has a wide range of applications in food manufacturing, pharmaceutics, cosmetics, etc. By-products obtained from fish processing industries are currently used as alternative sources for the production of collagen and gelatin. Hydrolyzed collagen derived from fish skin has properties that are beneficial in cosmetic products. The aim of this study was to: (1) formulate a cosmetic product containing hydrolyzed fish collagen obtained from a by-product of fish processing industries, and other effective ingredients such as vitamin C, vitamin E and vitamin B3, (2) evaluate the physicochemical properties and stability of the product, and (3) conduct a facial skin evaluation test wherein volunteers use the product and have their facial skin analyzed. The formulated serum was translucent with low turbidity and had a pH of 7.7, and a viscosity of 1333 cps. The stability of the formulation was good, without any significant change in appearance, pH and viscosity as tested by freeze–thaw cycle testing, and after storage at room temperature for a period of 6 months. Furthermore, the results from the Visia skin analysis instrument revealed that the product was able to enhance facial skin moisture, reduce skin pores and wrinkles, and brighten the skin significantly when regularly used for at least 2 weeks. In conclusion, it was observed that hydrolyzed fish collagen obtained from a by-product of fish processing industries could be a sustainable and suitable material utilized in the production of safe and useful cosmeceutical products.
Preliminary Analysis of Coseismic Landslides Induced by the 1 June 2022 Ms 6.1 Lushan Earthquake, China
Authors: Shao Xiaoyi , Xu Chong , Ma Siyuan
Published: December 2022

At 17:00 (UTC+8) on 1 June 2022, an Ms 6.1 reverse earthquake struck Lushan County, Ya’an City, Sichuan Province. This earthquake event had a focal depth of 10 km and the epicenter was located at 30.37° N and 102.94° E. The purpose of this study is to document a comprehensive coseismic landslide inventory for this event and analyze the distribution pattern and factors controlling the landslides. After careful visual interpretations, this quake event was determined to have in total triggered about 2352 landslides in an area of 3900 km2, including both shallow disrupted landslides and collapses, for which the spatial distribution was statistically related to regional topography, geology, and seismicity. Notably, a vast majority of the landslides were located on the NW plate of the seismogenic fault, and were distributed in the area with a seismic intensity of VII. In addition, coseismic landslides were more likely to appear in areas with high altitude, relief, and large slope. The landslide area density (LAD) increased with an increase in the above factors and is explained by an exponential relationship, indicating that the occurrence of coseismic landslides in this area was more easily affected by topographic factors than seismic factors. Most small-scale landslides were clustered in the ridge area, which shows the seismic amplification effects of mountain slopes. Due to the impact of seismic wave propagation direction, hillslopes facing northeast-east (NE-E) were more prone to collapse than southwest-facing ones. Based on the distribution pattern of the landslides, we suggest that the seismogenic fault of this event was NW dipping. These findings indicate that it is effective to identify the dipping of seismogenic faults using the spatial distribution pattern of coseismic landslides.
Analyzing Health, Safety, and Environmental Risks of Construction Projects Using the Fuzzy Analytic Hierarchy Process: A Field Study Based on a Project Management Body of Knowledge
Authors: Soltanzadeh Ahmad , Mahdinia Mohsen , Omidi Oskouei Alireza , Jafarinia Ehsan , Zarei Esmaeil , Sadeghi-Yarandi Mohsen
Published: December 2022

Due to their unique nature, construction projects are considered one of the world’s most hazardous and incident-prone industrial sectors. The present study aimed to analyze health, safety and environmental (HSE) risks relating to construction projects based on the project management body of knowledge (PMBOK) and sustainability approach. This study was conducted with the participation of 30 experts, using the semi-quantitative risk assessment technique, in nine areas of the project management’s body of knowledge, based on the fuzzy analytic hierarchy process. Risk, in this study, was estimated using a two-dimensional matrix of incident probability and severity, each of which has four sub-parameters. The HSE risks pertaining to each of the nine areas of PMBOK were identified. After that, the two dimensions of risk, including incident probability and severity, were measured. Thirty-seven risk sources associated with nine areas of the PMBOK were identified. Risk analysis revealed that 20 sources were at an unacceptable risk level, and 17 risks were at a tolerable risk level. Identifying HSE-related risk sources in accordance with the nine areas of PMBOK, and using FAHP to assess the risk of these hazards in construction projects, can lead to a more realistic estimate of risk in construction projects. The presented method in the current study can create a novel perspective in terms of the construction industry’s risk management and assessment.
Research on Intelligent Analysis of Healthy Training Progress of Teenage Sports Athletes Using Various Modalities
Authors: Chen Wenzheng , Syed Ali Syed Kamaruzaman Bin , Zulnaidi Hutkemri , Qi Dongkai
Published: December 2022

The study compared different training modalities, such as high-intensity interval training versus continuous endurance training, on selected health-related physical fitness, biochemical and haematological parameters of teenage players yielding similar mechanical work and training duration. A random sampling technique was employed and affirmed by the Ethical Committee of Sichuan Province, China. Forty-five teenage sports players aging 18–19 from Sichuan Province, China, were categorized into two experimental and one control group (n = 15) subjects. The groups were allocated separately to high-intensity interval training, continuous endurance training, and control group for a three-month physical exercise program. Each group’s training intensity was set based on the resting heart rate, to assess the effects on health-related physical fitness using fitness gram variables such as cardiovascular endurance, muscular endurance, and flexibility. Biochemical and haematological fasting blood samples were taken and tested at baseline and post three months of training. Detailed descriptive statistics were carried out for the interpretation of the data. All the statistical analysis was completed using the IBM SPSS 20. Paired t-test and one-way ANCOVA were used to evaluate the effect of two aerobic training methods, and to determine the exact difference between the groups, Scheffe’s post hoc test was employed. The result of the study shows that cardiovascular endurance in HIIT showed a better performance of (25.6%) than in C.E.T. (18.1%). Regarding muscular endurance, C.E.T. achieved 19.6% and HIIT 16.2%. For flexibility, HIIT improved by (6.5%) while C.E.T. (5.8%). Accordingly, the study revealed significant changes in teenage sports players’ health-related fitness, biochemical and haematological parameters.
Prediction of Pork Supply Based on Improved Mayfly Optimization Algorithm and BP Neural Network
Authors: Wang Ji-Quan , Zhang Hong-Yu , Song Hao-Hao , Zhang Pan-Li , Bei Jin-Ling
Published: December 2022

Focusing on the issues of slow convergence speed and the ease of falling into a local optimum when optimizing the weights and thresholds of a back-propagation artificial neural network (BPANN) by the gradient method, a prediction method for pork supply based on an improved mayfly optimization algorithm (MOA) and BPANN is proposed. Firstly, in order to improve the performance of MOA, an improved mayfly optimization algorithm with an adaptive visibility coefficient (AVC-IMOA) is introduced. Secondly, AVC-IMOA is used to optimize the weights and thresholds of a BPANN (AVC-IMOA_BP). Thirdly, the trained BPANN and the statistical data are adopted to predict the pork supply in Heilongjiang Province from 2000 to 2020. Finally, to demonstrate the effectiveness of the proposed method for predicting pork supply, the pork supply in Heilongjiang Province was predicted by using AVC-IMOA_BP, a BPANN based on the gradient descent method and a BPANN based on a mixed-strategy whale optimization algorithm (MSWOA_BP), a BPANN based on an artificial bee colony algorithm (ABC_BP) and a BPANN based on a firefly algorithm and sparrow search algorithm (FASSA_BP) in the literature. The results show that the prediction accuracy of the proposed method based on AVC-IMOA and a BPANN is obviously better than those of MSWOA_BP, ABC_BP and FASSA_BP, thus verifying the superior performance of AVC-IMOA_BP.