bluuebunny/crossref_metadata_2025 huggingface dataset:
Keypoint3D: Keypoint-Based and Anchor-Free 3D Object Detection for Autonomous Driving with Monocular Vision
Authors: Li Zhen , Gao Yuliang , Hong Qingqing , Du Yuren , Serikawa Seiichi , Zhang Lifeng
Published: February 2023

Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoint-based 3D object detector with monocular vision, named Keypoint3D. We creatively leveraged 2D projected points from 3D objects’ geometric centers as keypoints for object modeling. Additionally, for precise keypoints positioning, we utilized a novel self-adapting ellipse Gaussian filter (saEGF) on heatmaps, considering different objects’ shapes. We tried different variations of DLA-34 backbone and proposed a semi-aggregation DLA-34 (SADLA-34) network, which pruned the redundant aggregation branch but achieved better performance. Keypoint3D regressed the yaw angle in a Euclidean space, which resulted in a closed mathematical space avoiding singularities. Numerous experiments on the KITTI dataset for a moderate level have proven that Keypoint3D achieved the best speed-accuracy trade-off with an average precision of 39.1% at 18.9 FPS on 3D cars detection.
A Rapid Water Region Reconstruction Scheme in 3D Watershed Scene Generated by UAV Oblique Photography
Authors: Qiu Yinguo , Jiao Yaqin , Luo Juhua , Tan Zhenyu , Huang Linsheng , Zhao Jinling , Xiao Qitao , Duan Hongtao
Published: February 2023

Oblique photography technology based on UAV (unmanned aerial vehicle) provides an effective means for the rapid, real-scene 3D reconstruction of geographical objects on a watershed scale. However, existing research cannot achieve the automatic and high-precision reconstruction of water regions due to the sensitivity of water surface patterns to wind and waves, reflections of objects on the shore, etc. To solve this problem, a novel rapid reconstruction scheme for water regions in 3D models of oblique photography is proposed in this paper. It extracts the boundaries of water regions firstly using a designed eight-neighborhood traversal algorithm, and then reconstructs the triangulated irregular network (TIN) of water regions. Afterwards, the corresponding texture images of water regions are intelligently selected and processed using a designed method based on coordinate matching, image stitching and clipping. Finally, the processed texture images are mapped to the obtained TIN, and the real information about water regions can be reconstructed, visualized and integrated into the original real-scene 3D environment. Experimental results have shown that the proposed scheme can rapidly and accurately reconstruct water regions in 3D models of oblique photography. The outcome of this work can refine the current technical system of 3D modeling by UAV oblique photography and expand its application in the construction of twin watershed, twin city, etc.
Rapid Detection and Orbital Parameters’ Determination for Fast-Approaching Non-Cooperative Target to the Space Station Based on Fly-around Nano-Satellite
Authors: Sun Chong , Sun Yongqing , Yu Xiaozhou , Fang Qun
Published: February 2023

Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation.
Cotton Growth Modelling Using UAS-Derived DSM and RGB Imagery
Authors: Psiroukis Vasilis , Papadopoulos George , Kasimati Aikaterini , Tsoulias Nikos , Fountas Spyros
Published: February 2023

Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated the use of a data-processing pipeline to estimate cotton plant height using UAS-derived visible-spectrum vegetation indices and photogrammetric products. Experiments were conducted at an experimental cotton field in Aliartos, Greece, using a DJI Phantom 4 UAS in five different stages of the 2022 summer cultivation season. Ground Control Points (GCPs) were marked in the field and used for georeferencing and model optimization. The imagery was used to generate dense point clouds, which were then used to create Digital Surface Models (DSMs), while specific Digital Elevation Models (DEMs) were interpolated from RTK GPS measurements. Three (3) vegetation indices were calculated using visible spectrum reflectance data from the generated orthomosaic maps, and ground coverage from the cotton canopy was also calculated by using binary masks. Finally, the correlations between the indices and crop height were examined. The results showed that vegetation indices, especially Green Chromatic Coordinate (GCC) and Normalized Excessive Green (NExG) indices, had high correlations with cotton height in the earlier growth stages and exceeded 0.70, while vegetation cover showed a more consistent trend throughout the season and exceeded 0.90 at the beginning of the season.
Integrated Approaches for Field Mapping by Traditional Investigation and Satellite PSInSAR Data: Results from the Montemartano Landslide (Central Italy)
Authors: Di Matteo Lucio , Cardinali Riccardo , Cerboni Valentina , Guadagnano Fabio , Piagnani Giorgio , Ribaldi Claudia , Sotera Biagio Marco , Cencetti Corrado
Published: February 2023

The study presents an integrated investigation of a complex landslide based on multitemporal stereo aerial photographic interpretations, existing geotechnical monitoring data, and different PSInSAR datasets. The available information allowed for the quantification of the deformation history, also in periods with no monitoring in the field, making it possible to revise the geometry of the landslide compared to the official landslide mapping. Data from sparse inclinometers are compared with more spatially continuous satellite measurements, showing that the two monitoring techniques are consistent in terms of cumulative deformation trend and in terms of response to prolonged drought and wet periods. Therefore, reliable landslide mass displacements can be monitored using satellite products when ground instrumentations are no longer operating. Understanding the landslide behavior to rainfall conditions offers an important insight into the velocities and cumulative displacements expected during similar stages of enhanced landslide activity. The findings can be helpful to support urban planners in re-evaluating hazard and risk classification and implementing efficient mitigation techniques to reduce landslide damage.
3D Discrete Fracture Network Modelling from UAV Imagery Coupled with Tracer Tests to Assess Fracture Conductivity in an Unstable Rock Slope: Implications for Rockfall Phenomena
Authors: Mammoliti Elisa , Pepi Alessandro , Fronzi Davide , Morelli Stefano , Volatili Tiziano , Tazioli Alberto , Francioni Mirko
Published: February 2023

The stability of a rock slope is strongly influenced by the pattern of groundwater flow through the fracture system, which may lead to an increase in the water pressure in partly open joints and the consequent decrease in the rock wall strength. The comprehension of the fracture pattern is a challenging but vital aspect in engineering geology since the fractures’ spatial distribution, connectivity, and aperture guide both the water movement and flow quantity within the rock volume. In the literature, the most accepted methods to hydraulically characterise fractured rocks in situ are the single borehole packer test, the high-resolution flow meters for fractures, and the artificial tracer tests performed in boreholes. However, due to the high cost a borehole requires and the general absence of wells along coastal cliffs, these methods may not be appropriate in rockfall-prone areas. In this study, an unsaturated rocky cliff, strongly affected by rockfalls, was investigated by combining kinematic analysis, Discrete Fracture Network (DFN) modelling, and artificial tracer tests. The DFN model and potential rock block failure mechanisms were derived from high-resolution 3D virtual outcrop models via the Structure from Motion (SfM) photogrammetry technique. An artificial tracer was injected using a double ring infiltrometer atop the recharge zone of the slope to determine the infiltration rate and validate the DFN results. The DFN and tracer test methods are frequently used at different spatial scales and for different disciplines. However, the integration of digital photogrammetry, DFN, and tracer tests may represent a new step in rockfall and landslide studies. This approach made possible the identification of groundwater flow patterns within the fracture system and revealed about a 10-day tracer transit time from the injection area and the monitored slope, with similar conductivity values gathered from both the DFN and tracer test. Planar and wedge failures with volumes ranging from 0.1 and 1 m3 are the most probable failure mechanisms in the areas. The results were consistent with the delay between the intense rainfall and the slope failures previously documented in the study area and with their mechanisms.
Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia)
Authors: Hughes-Allen Lara , Bouchard Frédéric , Séjourné Antoine , Fougeron Gabriel , Léger Emmanuel
Published: February 2023

The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes in areas of ice-rich permafrost. These lakes are hotspots of greenhouse gas emissions (CO2 and CH4), but with substantial spatial and temporal heterogeneity across Arctic and sub-Arctic regions. In Central Yakutia (Eastern Siberia, Russia), nearly half of the landscape has been affected by thermokarst processes since the early Holocene, resulting in the formation of more than 10,000 partly drained lake depressions (alas lakes). It is not yet clear how recent changes in temperature and precipitation will affect existing lakes and the formation of new thermokarst lakes. A multi-decadal remote sensing analysis of lake formation and development was conducted for two large study areas (~1200 km2 each) in Central Yakutia. Mask Region-Based Convolutional Neural Networks (R-CNN) instance segmentation was used to semi-automate lake detection in Satellite pour l’Observation de la Terre (SPOT) and declassified US military (CORONA) images (1967–2019). Using these techniques, we quantified changes in lake surface area for three different lake types (unconnected alas lake, connected alas lake, and recent thermokarst lake) since the 1960s. Our results indicate that unconnected alas lakes are the dominant lake type, both in the number of lakes and total surface area coverage. Unconnected alas lakes appear to be more susceptible to changes in precipitation compared to the other two lake types. The majority of recent thermokarst lakes form within 1 km of observable human disturbance and their surface area is directly related to air temperature increases. These results suggest that climate change and human disturbances are having a strong impact on the landscape and hydrology of Central Yakutia. This will likely affect regional and global carbon cycles, with implications for positive feedback scenarios in a continued climate warming situation.
Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows
Authors: Chen Guokun , Liu Zicheng , Wen Qingke , Tan Rui , Wang Yiwen , Zhao Jingjing , Feng Junxin
Published: February 2023

The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regional scale identification. In this paper, we proposed a classification method combines multi-source phenology characteristics and random forest algorithm. On the basis of optimization of input samples and features, phenological spectrum, brightness, greenness, wetness, fractional vegetation cover, topography and other features of rubber were extracted. Five classification schemes were constructed for comparison, and the one with the highest classification accuracy was used to identify the spatial pattern of rubber plantations in 2014, 2016, 2018 and 2020 in Xishuangbanna. The results show that: (1) the identification results are in consistent with field survey and rubber plantations area generally shows a first increasing and then decreasing trend; (2) the Overall Accuracy (OA) and Kappa coefficient of the proposed method are 90.0% and 0.86, respectively, with a Producer’s Accuracy (PA) and User’s Accuracy (UA) of 95.2% and 88.8%, respectively; (3) cross-validation was employed to analyze the accuracy evaluation indexes of the identification results: both PA and UA of the rubber plantations stay stable over 85%, with the minimum fluctuation and best stability of UA value. The OA value and Kappa coefficient were stable in the range of 0.88–0.90 and 0.84–0.86, respectively. The method proposed provides reliable results on spatial distribution of rubber, and is potentially transferable to other mountainous areas as a robust approach for rapid monitoring of rubber plantations.
Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF
Authors: Cheng Xiang , Lei Hong
Published: February 2023

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.
Shuffling Softly, Sighing Deeply: A Digital Inquiry into Representations of Older Men and Women in Literature for Different Ages
Authors: Geybels Lindsey
Published: February 2023

When gender is brought into concerns about older people, the emphasis often lies on stereotypes connected to older women, and few comparative studies have been conducted pertaining to the representation of the intersection between older age and gender in fiction. This article argues that not only children’s literature, traditionally considered to be a carrier of ideology, plays a large part in the target readership’s age socialization, but so do young adult and adult fiction. In a large corpus of 41 Dutch books written for different ages, the representation of older men and women is studied through the verbs, grammatical possessions and adjectives associated with the relevant fictional characters, which were extracted from the texts through the computational method of dependency parsing. Older adult characters featured most frequently in fiction for adults, where, more so than in the books for younger readers, they are depicted as being prone to illness, experiencing the effects of a deteriorating body and having a limited social network. In the books for children, little to no association between older adulthood and mortality was found in the data. Ageist stereotypes pertaining to both genders were found throughout the corpus. In terms of characterization, male older adults are associated more with physicality, including matters of illness and mobility, while character traits and emotions show up in a more varied manner in connection to female older characters.
Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability
Authors: Almulla Mohammed Abdullatif , Al-Rahmi Waleed Mugahed
Published: February 2023

E-learning is expected to become a common teaching and learning approach in educational institutions in the near future; thus, the success of e-learning initiatives must be ensured in order to make this a sustainable mode of learning. In order to improve students’ learning performance through the use of e-learning in Saudi Arabia’s higher education, it was the objective of this paper to examine the relationships between social cognitive theory and learning input factors and the reflective thinking and inquiry learning style as well as the indirect effects of student problem-solving and critical thinking skills. As a result, this study thoroughly assessed the social cognitive theory that is currently in use, along with learning input components and situational factors that should be carefully taken into account while introducing an online education system into Saudi Arabia’s top universities as a way of ensuring learning sustainability. As a result, 294 university students completed a questionnaire that served as the initial dataset for the research study, and the proposed conceptual model was comprehensively assessed using SEM. The research results demonstrated that the inquiry style of learning and reflective thinking have always had a significant impact on the social involvement, human engagement, social power, social identity, and social support. Similar findings were obtained regarding the impact of problem-solving and critical thinking skills on the inquiry-based learning approach and reflective thinking. Thus, students’ ability to learn in Saudi Arabia’s higher education is greatly influenced by their ability to solve problems and think critically. Therefore, it is almost certain that this research study will aid university policy makers in their decision on whether to fully deploy an online learning system as a way of ensuring learning sustainability at educational institutions throughout Saudi Arabia.
Water Pollution and Pollution–Control Capacity in Chinese Provinces: Panel Estimations of Provincial Environmental Kuznets Curves
Authors: Taguchi Hiroyuki , Fujino Takeshi , Asada Hidekatsu , Ma Jun-Jun
Published: February 2023

China’s rapid industrialization and urbanization in recent decades have deteriorated its water resource quality. This study focused on water pollution in terms of the chemical oxygen demand (COD) and the levels of ammonia nitrogen in the industrial and household discharges of different Chinese provinces. Although the heterogeneity of environmental Kuznets curves (EKCs) from Chinese provinces has been studied, the positions of provincial EKCs (which reflect the province-specific pollution effects not affected by the provincial income levels) have not been investigated to date. Therefore, through a factor analysis of the heterogeneity of provincial pollution under the EKC framework, we investigated how the capacity shortage for pollution control contributes to the provincial pollution levels. We also evaluated the heterogeneity of the EKCs from the provinces in terms of their positions (not their shapes), using a fixed-effect model to extract the province-specific pollution effects. The capacity shortage for pollution control, as one of the pollution factors, accounted for (a) 30% of industrial COD and industrial ammonia nitrogen; (b) 60% of household COD; and (c) 80% of household ammonia nitrogen. Our results indicate that China still has a large capacity to mitigate water pollution via policies and by building the capacity for pollution control through the development and training of human resources.
A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies
Authors: Hassan Ch. Anwar Ul , Karim Faten Khalid , Abbas Assad , Iqbal Jawaid , Elmannai Hela , Hussain Saddam , Ullah Syed Sajid , Khan Muhammad Sufyan
Published: February 2023

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection.
A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?
Authors: Chen Chun-Wei
Published: February 2023

In the era when product design must meet the needs of consumers, the products preferred by consumers are an important source of design creativity and design reference for product designers to design products. Therefore, how to effectively grasp the products that consumers prefer has become an important issue for product designers. In order to allow designers to have more convenient and accurate consumer preference product prediction tools, this study proposed machine learning (ML) to analyze and predict sustainable patterns in consumer product preferences and conducted a feasibility study on the use of ML for predicting sustainable patterns in consumer product preferences. A total of three experiments were carried out in this study: the KJ method to predict consumer product preference experiment, the AHP method to predict consumer product preference experiment, and ML to predict consumer product preference experiment. This study uses the three experiments to discuss and compare the prediction ability of ML and the current commonly used forecasting tools, namely the KJ method and AHP method. The research results show that no matter what kind of consumer product attribute preference is predicted, the accuracy rate of consumer product preference prediction by ML is much higher than that of the KJ method and AHP method. These research results show that no matter the product attribute dimension, ML has the ability to predict consumer preferences, and ML has a better ability to predict consumer preferences than traditional tools. Therefore, this study believes that ML can be used to analyze and predict sustainable patterns in consumer product preferences. Therefore, this study suggests that product designers can use ML technology to assist in the analysis and prediction of consumer product preferences, so as to improve the grasp of consumer preference products.
System Dynamics Theory Applied to Differentiated Levels of City–Industry Integration in China
Authors: Li Yunchang , Cao Xia , Cui Can
Published: February 2023

The development of city–industry integration is crucial for modern cities and is a core element of city competitiveness enhancement and sustainable development. This study considers system dynamics theory to examine city–industry integration and constructs an index system to measure the degree of integration. For this purpose, 31 regions in China (including provinces, autonomous regions, and municipalities directly under the central government) are considered as research samples. Objective evaluation methods such as factor analysis and entropy methods are applied to evaluate the target value. The research results reveal a wide gap in the levels of city–industry integration in various regions of China. Furthermore, the Middle East outperforms the Western and Northeastern regions. Accordingly, the advantages of the Central and Eastern regions should be combined, and a leading and radiation-driven role should be played. Moreover, capital investment in the Western and Northeastern regions should be increased, and emphasis should be placed on local characteristics. Moreover, urban economic development, industrial transformation, and industrial upgrading should be promoted, and the sustainable development capacity of cities should be enhanced.
Toward Deep Decontamination of Intermediate-Level-Activity Spent Ion-Exchange Resins Containing Poorly Soluble Inorganic Deposits
Authors: Palamarchuk Marina , Chervonetskiy Maxim , Polkanova Natalya , Bratskaya Svetlana
Published: February 2023

Spent ion-exchange resins (SIERs) generated yearly in large volumes in nuclear power plants (NPPs) require particular predisposal handling and treatment with the primary objectives of waste volume reduction and lowering the disposal class. Deep decontamination of the SIERs using solution chemistry is a promising approach to reduce the amount of intermediate-level radioactive waste (ILW) and, thus, SIER disposal costs. However, the entrapment of nonexchangeable radionuclides in poorly soluble inorganic deposits on SIERs significantly complicates the implementation of this approach. In this work, the elemental and radiochemical compositions of inorganic deposits in an intermediate-level-activity SIER sample with an activity of 310 kBq/g have been analyzed, and a feasibility study of SIER decontamination using solution chemistry has been conducted. The suggested approach included the magnetic separation of crud, removal of cesium radionuclides using alkaline solutions in the presence of magnetic resorcinol-formaldehyde resin, removal of cobalt radionuclides using acidic EDTA-containing solutions, and hydrothermal oxidation of EDTA-containing liquid wastes with immobilization of radionuclides in poorly soluble oxides. The decontamination factors for 137Cs, 60Co, and 94Nb radionuclides were 3.9 × 103, 7.6 × 102, and 1.3 × 102, respectively, whereas the activity of the decontaminated SIER was 17 Bq/g, which allows us to classify it as very low-level waste.
Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems
Authors: Shaqarin Tamir
Published: February 2023

In partial shading situations, the power–voltage (P–V) characteristics of photovoltaic (PV) systems become more complex due to many local maxima. Hence, traditional maximum power point tracking (MPPT) techniques fail to recognize the global maximum power point (MPP), resulting in a significant drop in the produced power. Global optimization strategies, such as metaheuristic approaches, efficiently address this issue. This work implements the recent “particle swarm optimization through targeted position-mutated elitism” (PSO-TPME) with a reinitialization mechanism on a PV system under partial shading conditions. The fast-converging and global exploration capabilities of PSO-TPME make it appealing for online optimization. PSO-TPME also offers the flexibility of tuning the particle classifier, elitism, mutation level, and mutation probability. This work analyzes several PSO-TPME parameter settings for the MPPT of partially shaded PV systems. Simulations of the PV system under varying shading patterns show that PSO-TPME, with balanced exploitation–exploration settings, outperforms PSO in terms of convergence speed and the amount of captured energy during convergence. Furthermore, simulations of partial shading conditions with fast-varying, smooth, and step-changing irradiance demonstrated that the proposed MPPT technique is capable of dealing with these severe conditions, capturing more than 97.7% and 98.35% of the available energy, respectively.
Elucidation of Microstructural and Mechanical Properties of Coconut Husk Mortar as a Sustainable Building Material for Ferrocement
Authors: Kumarasamy Kalaivani , Kandasamy Gunasekaran , Ramasamy Annadurai
Published: February 2023

The main objective of this study is to use coconut husk to produce mortar for ferrocement. Mortar mix proportions are selected per ACI codes’ recommendation and the WRD Handbook. Four types of mortars: Cement and River Sand mortar (CSM), Cement, River Sand and Steel fibre mortar (CSSFM), Cement and Coconut Husk mortar (CCHM), and Cement, River Sand (60%), Coconut Husk (40%), and Steel fibre mortar (CSCHSFM) are used for this study Microstructural studies like SEM, EDX, XRD, and FTIR analysis on cement mortar constituents and mortar mixes are studied and reported. At 3, 7, and 28 days tests of hardened mortar, such as compressive, split tensile, flexural strength, and impact strength resistance, were studied. Test results revealed that the coconut husk is innovative and sustainable and could be an alternative fine aggregate that can be utilized in place of river sand, which in turn can be used for mortar production. Since it has a lesser density which proves to be an advantage for developing lightweight mortar, it can be used for ferrocement applications.
Influence of Eco-Innovation and Recycling on Raw Material Consumption; Econometric Approach in the Case of the European Union
Authors: Platon Victor , Pavelescu Florin Marius , Surugiu Marius , Frone Simona , Mazilescu Raluca , Constantinescu Andreea , Popa Florina
Published: February 2023

The main objective of this paper is to identify the intensity and the sense of the impact that eco-innovation and recycling exert on raw material consumption in the European Union member-states. The basic hypothesis is that eco-innovation and recycling have a significant influence on raw material consumption. The authors consider that, in the long run, by promoting eco-innovation and recycling, countries will minimize their demand and use of raw materials. In the paper, the material footprint per capita has been considered as an adequate indicator to measure the material consumption induced by economic and social activities. The model used has estimated the influence of eco-innovation and recycling on the material footprint per capita using panel data regression by considering the 27 EU countries, for the 2010–2019 time period. The authors found that the most adequate econometric model is the panel model with fixed effects. The respective econometric model validates the hypothesis that eco-innovation and recycling have significant influence on reducing the material footprint per capita and reveals heterogeneity existing among EU countries. At the same time, the model shows the intensity of influence exerted by each regressor. The eco-innovation and recycling have an important role in decreasing the material footprint per capita and should be in the centre of policies aimed at decoupling the economic growth from the raw material consumption.
Risk Assessment of Heat Stroke during the Marathon of the Tokyo 2020 Olympics in Sapporo, Hokkaido
Authors: Hisato Osawa , Mori Taro , Shinagawa Kouichi , Nakayama Satoshi , Hosobuchi Hayato , Mushtaha Emad
Published: February 2023

A marathon is a grueling endurance race, and runners are at risk of heatstroke. Therefore, the thermal environment of the marathon course should be accurately assessed. Solar radiation, a factor in the thermal environment, is particularly affected by the surrounding environment. In this study, we proposed a method to calculate wet-bulb globe temperature (WBGT) in a short period of time using geographic information system (GIS) data, considering changes in solar radiation. WBGT is used as an indicator of heat stroke. This method was used to assess the risk of heat stroke due to weather conditions, span of time, and location on the marathon course of the Tokyo 2020 Olympics. According to the analysis results, in Sapporo, trees and buildings can suppress WBGT by about 2 °C, but on days when the average temperature exceeded 26 °C, WBGT exceeded 21 °C from 5:00 to 13:00. On the contrary, even in August, the hottest month of the year, there were days when WBGT was almost always below 21 °C from 5:00 to 13:00. In other words, it is concluded that the risk of heat stroke can be reduced if the marathon is held in accordance with the weather conditions of the day. In addition, by mapping and identifying locations where there is little change in attenuated WBGT, it is possible to identify locations where the thermal environment should be improved.
Spatio-Temporal Variability of Soil Properties and Nutrient Uptake for Sustainable Intensification of Rainfed Pigeon Pea (Cajanus cajana) in Semi-Arid Tropics of India
Authors: Rajesh Neralikere Lakkappa , Narayana Rao Koluru , Desai Bheemsain Rao K. , Sathishkumar Umapathi , Basavaraj Kasareddy , Rudramurty Halagappala Verappa , Wali Vijay B. , Umesh Mathada Rangappa
Published: February 2023

A study was conducted at Kalamandari Tanda-1 micro-watershed, Karnataka, India, to assess the spatio-temporal variability of surface soil properties sampled from 23 varied soil-phase units, delineated using IRS P6 LISS-IV merged Cartosat-I imagery (2.5 m spatial resolution). Three sets of surface soil samples (231 in each set) were collected, analyzed, and interpolated to evaluate the soil spatio-temporal variability. Further soil-phase-wise composite representative samples (23 in each set) were collected before the sowing and after the harvest of pigeon peasin January 2019 and 2020. Results showed that the soil OC with N, Mg with Ca, exchangeable sodium percentage (ESP), and cation exchange capacity (CEC) with Mn and K2O have a significantly positive association (p < 0.01) during both years. Similarly, a significantly positive association (p < 0.05) was observed among soil OC and N with Cu, Mn with CEC, and Zn with Fe, while a negative association was seen between available P2O5 and Na. Skewness from descriptive statistics revealed that pH, EC, Cu, Mg, B, and ESP distribution varied temporally, whereas other parameters remained almost unchanged. Geo-statistically, the spatial distribution of measured soil properties was best fitted to exponential, stable, and K-Bessel models. The available N in 2018 and the ESP in 2019 have shown weak spatial dependency, whereas the rest of the soil parameters showed moderate and strong spatial dependency. These interpolated maps were exported as vector layers to quantify soil-phase-wise spatio-temporal variability of soil properties.
Deriving Land Management Practices for Reduced Nutrient Movement from an Agricultural Watershed Using the AGNPS Model
Authors: Kasthuri Thilagam Venkatachalam , Manivannan Sandrasekaran , Khola Om Pal Singh
Published: February 2023

The effect of nutrient management practices and the land-use system on nutrient enrichment in water resources of a hilly watershed was assessed with an event-based agricultural non-point source (AGNPS) model. The model intended to assess the runoff, sediment and nutrient loads in a typical hilly agricultural watershed. The model was calibrated, evaluated and applied in integration with GIS to predict the soil and nutrient loss. Two nutrient management scenarios were simulated with 25 and 50% reductions in the nutrient application from the present nutrient application. The third scenario was simulated by converting 25% of the agricultural land-use to tea plantations. A total of 15 simulations were run for the different rainfall intensities of the year 2017. The existing land-use scenario simulated the maximum soil loss and Nitrogen and phosphorus load of 8.23 t ha−1, 22.8 and 5.0 kg ha−1. The 50% nutrient reduction scenario reduced 48 and 36% of the Nitrogen and phosphorus load compared to the existing farmers’ practice. The same nutrient dose was compared with the STCR equation, developed for major crops such as potato and carrot in Nilgiris soil, and confirmed the sufficient nutrient supply to produce a sustainable yield. The conversion of 25% of the agricultural land-use to tea plantations reduced the soil loss by 10% from the current land-use. The Nitrogen and phosphorus load was reduced to 56 and 48%, respectively. Hence, the farmers may convert 25% of the land area to tea plantations and reduce 50% of the present fertilizer dose for the major vegetable crops with INM to reduce the nutrient enrichment in the surface water bodies. This study demonstrated the applicability of the AGNPS model in similar watersheds for deriving possible management strategies to reduce soil loss and nutrient movement. Further, the hydrological models can provide valuable insights for promptly prioritizing and making policy decisions in ungauged/data-scarce watersheds.
Exposures to Particles and Volatile Organic Compounds across Multiple Transportation Modes
Authors: Molden Nick , Hemming Carl , Leach Felix , Levine James G. , Ropkins Karl , Bloss William
Published: February 2023

Travellers may be exposed to a wide range of different air pollutants during their journeys. In this study, personal exposures within vehicles and during active travel were tested in real-world conditions across nine different transport modes on journeys from London Paddington to Oxford City Centre, in the United Kingdom. The modes tested covered cycling, walking, buses, coaches, trains and private cars. Such exposures are relevant to questions of traveller comfort and safety in the context of airborne diseases such as COVID-19 and a growing awareness of the health, safety and productivity effects of interior air quality. Pollutants measured were particle number (PN), particle mass (PM), carbon dioxide (CO2) and speciated volatile organic compounds (VOCs), using devices carried on or with the traveller, with pumped sampling. Whilst only a relatively small number of journeys were assessed—inviting future work to assess their statistical significance—the current study highlights where a particular focus on exposure reduction should be placed. Real-time results showed that exposures were dominated by short-term spikes in ambient concentrations, such as when standing on a train platform, or at the roadside. The size distribution of particles varied significantly according to the situation. On average, the coach created the highest exposures overall; trains had mixed performance, while private cars and active transport typically had the lowest exposures. Sources of pollutants included both combustion products entering the vehicle and personal care products from other passengers, which were judged from desk research on the most likely source of each individual compound. Although more exposed to exhaust emissions while walking or cycling, the active traveller had the benefit of rapid dilution of these pollutants in the open air. An important variable in determining total exposure was the journey length, where the speed of the private car was advantageous compared to the relative slowness of the coach.
Composting Heat Recovery for Residential Consumption: An Assessment of Viability
Authors: Sessa Vittorio , Bhandari Ramchandra
Published: February 2023

The European heating sector is currently heavily dominated by fossil fuels. Composting is a naturally occurring process in which heat is liberated from the composting substrate at a higher rate than the process needs to support itself. This difference could be harnessed for low-heat applications such as residential consumption, alleviating some of the impacts fossil fuel emissions represent. In this study, the composting heat recovery reported in the literature was compared to the energy demand for space and water heating in four European countries. A review of potential heat production from the waste representative of the residential sector was performed. We found that the theoretically recoverable composting heat does not significantly reduce the need for district heating. However, it can significantly reduce the energy demand for water heating, being able to supply countries such as Greece with between 36% and 100% of the yearly hot water demand, or 12% to 53% of the yearly hot water of countries such as Switzerland, depending on the efficiency of heat recovery.
Construction and Application of a Groundwater Overload Evaluation System Based on the PSR Model
Authors: Gao Jing , Zhou Weibo , Li Shuwu , Li Changhu , Wang Qun , Li Zhengzheng , Chen Haiyun , Zhang Tingyu
Published: February 2023

Groundwater assessment has several technical deficiencies, especially because the decision-making platform for basic data collection, analysis, and evaluation has not been established. Based on the computer architecture theory and business processing logic, we designed a set of groundwater resource overload evaluation systems using the ArcGIS platform and Python language based on the comprehensive analysis of many research schemes. According to the pressure–state–response (PSR) theoretical programming model, the system pre-sets the evaluation index from three aspects: overexploitation, bearing capacity, and overload. The entropy weight method was used to determine the factor weight. The system was used to evaluate and analyze the groundwater overload in Xi’an, and the performance and accuracy of the system were also verified. The results showed that the serious overload area of groundwater in Xi’an was located in the alluvial fan area of Yanliang District, Gaoling District, and Huyi District, and the alluvial fan area of Lintong District, City Six Districts, and Zhouzhi County. The general overload area was located in the alluvial plain area of City Six Districts, Chang’an District, the flood plain and the first terrace of Huyi District, the loess tableland area of Zhouzhi County, and the alluvial fan area of Lantian County. The remaining areas were not overloaded. The evaluation results matched the study, which confirmed that the evaluation system was stable and that the results were reliable.
The Impact of Value Cocreation on CSR Innovation and Economic Performance
Authors: Wu Rong , Zhang Jian , Yu Yubing , Jasimuddin Sajjad M. , Zhang Justin Zuopeng
Published: February 2023

Based on the resource-based view (RBV) and value cocreation theories, this study investigated how supplier value cocreation, intracompany value cocreation, and customer value cocreation influence corporate social responsibility (CSR) innovation and economic performance. We collected data from 200 manufacturers to test the proposed relationships. The results showed that value cocreation had different positive impacts on CSR; intracompany value cocreation had the greatest impact, followed by customer and supplier value cocreation. CSR did not have a significant positive effect on economic performance. Companies’ innovation capacities completely mediated the relationship between CSR and economic performance. The findings of the analysis have theoretical and practical implications. Theoretically, this study broadens the research scope on CSR innovation and value cocreation. Practically, it helps companies realize that by sharing information, establishing cooperative relations, and conducting interactions with stakeholders, which can promote the implementation of CSR innovation practices, economic performance can be improved.