bluuebunny/crossref_metadata_2025 huggingface dataset:
Supervised Learning-Based Prediction of Lightning Probability in the Warm Season
Authors: Shin Kyuhee , Kim Kwonil , Lee GyuWon
Published: September 2024

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model.
Link Aggregation for Skip Connection–Mamba: Remote Sensing Image Segmentation Network Based on Link Aggregation Mamba
Authors: Zhang Qi , Geng Guohua , Zhou Pengbo , Liu Qinglin , Wang Yong , Li Kang
Published: September 2024

The semantic segmentation of satellite and UAV remote sensing imagery is pivotal for address exploration, change detection, quantitative analysis and urban planning. Recent advancements have seen an influx of segmentation networks utilizing convolutional neural networks and transformers. However, the intricate geographical features and varied land cover boundary interferences in remote sensing imagery still challenge conventional segmentation networks’ spatial representation and long-range dependency capabilities. This paper introduces a novel U-Net-like network for UAV image segmentation. We developed a link aggregation Mamba at the critical skip connection stage of UNetFormer. This approach maps and aggregates multi-scale features from different stages into a unified linear dimension through four Mamba branches containing state-space models (SSMs), ultimately decoupling and fusing these features to restore the contextual relationships in the mask. Moreover, the Mix-Mamba module is incorporated, leveraging a parallel self-attention mechanism with SSMs to merge the advantages of a global receptive field and reduce modeling complexity. This module facilitates nonlinear modeling across different channels and spaces through multipath activation, catering to international and local long-range dependencies. Evaluations on public remote sensing datasets like LovaDA, UAVid and Vaihingen underscore the state-of-the-art performance of our approach.
Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms
Authors: Jiang Yunyang , Zhang Zixuan , He Huaijiang , Zhang Xinna , Feng Fei , Xu Chengyang , Zhang Mingjie , Lafortezza Raffaele
Published: September 2024

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.
Rotational Motion Compensation for ISAR Imaging Based on Minimizing the Residual Norm
Authors: Yang Xiaoyu , Sheng Weixing , Xie Annan , Zhang Renli
Published: September 2024

In inverse synthetic aperture radar (ISAR) systems, image quality often suffers from the non-uniform rotation of non-cooperative targets. Rotational motion compensation (RMC) is necessary to perform refocused ISAR imaging via estimated rotational motion parameters. However, estimation errors tend to accumulate with the estimated processes, deteriorating the image quality. A novel RMC algorithm is proposed in this study to mitigate the impact of cumulative errors. The proposed method uses an iterative approach based on a novel criterion, i.e., the minimum residual norm of the signal phases, to estimate different rotational parameters independently to avoid the issue caused by cumulative errors. First, a refined inverse function combined with interpolation is proposed to perform the RMC procedure. Then, the rotation parameters are estimated using an iterative procedure designed to minimize the residual norm of the compensated signal phases. Finally, with the estimated parameters, RMC is performed on signals in all range bins, and focused images are obtained using the Fourier transform. Furthermore, this study utilizes simulated and real data to validate and evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm shows dominance in the aspects of estimation accuracy, entropy values, and focusing characteristics.
TF-REF-RNN: Time-Frequency and Reference Signal Feature Fusion Recurrent Neural Network for Underwater Backscatter Signal Separation
Authors: Liu Jun , Gong Shenghua , Zhang Tong , Zhao Zhenxiang , Dong Hao , Tan Jie
Published: September 2024

Underwater wireless sensor networks play an important role in exploring the oceans as part of an integrated space–air–ground–ocean network. Because underwater energy is limited, the equipment’s efficiency is significantly impacted by the battery duration. Underwater backscatter technology does not require batteries and has significant potential in positioning, navigation, communication, and sensing due to its passive characteristics. However, underwater backscatter signals are susceptible to being swamped by the excitation signal. Additionally, the signals from different reflection signals share the same frequency and overlap, and contain fewer useful features, leading to significant challenges in detection. In order to solve the above problems, this paper proposes a recurrent neural network that introduces time-frequency and reference signal features for underwater backscatter signal separation (TF-REF-RNN). In the feature extraction part, we design an encoder that introduces time-frequency domain features to learn more about the frequency details. Additionally, to improve performance, we designed a separator that incorporates the reference signal’s pure global information features. The proposed TF-REF-RNN network model achieves metrics of 28.55 dB SI-SNRi and 19.51 dB SDRi in the dataset that includes shipsEar noise data and underwater simulated backscatter signals, outperforming similar classical methods.
Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning
Authors: Liu Chang , Calders Kim , Origo Niall , Terryn Louise , Adams Jennifer , Gastellu-Etchegorry Jean-Philippe , Wang Yingjie , Meunier Félicien , Armston John , Disney Mathias , Woodgate William , Nightingale Joanne , Verbeeck Hans
Published: September 2024

Radiative transfer models (RTMs) are often used to retrieve biophysical parameters from earth observation data. RTMs with multi-temporal and realistic forest representations enable radiative transfer (RT) modeling for real-world dynamic processes. To achieve more realistic RT modeling for dynamic forest processes, this study presents the 3D-explicit reconstruction of a typical temperate deciduous forest in 2015 and 2022. We demonstrate for the first time the potential use of bitemporal 3D-explicit RT modeling from terrestrial laser scanning on the forward modeling and quantitative interpretation of: (1) remote sensing (RS) observations of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and canopy light extinction, and (2) the impact of canopy gap dynamics on light availability of explicit locations. Results showed that, compared to the 2015 scene, the hemispherical-directional reflectance factor (HDRF) of the 2022 forest scene relatively decreased by 3.8% and the leaf FAPAR relatively increased by 5.4%. At explicit locations where canopy gaps significantly changed between the 2015 scene and the 2022 scene, only under diffuse light did the branch damage and closing gap significantly impact ground light availability. This study provides the first bitemporal RT comparison based on the 3D RT modeling, which uses one of the most realistic bitemporal forest scenes as the structural input. This bitemporal 3D-explicit forest RT modeling allows spatially explicit modeling over time under fully controlled experimental conditions in one of the most realistic virtual environments, thus delivering a powerful tool for studying canopy light regimes as impacted by dynamics in forest structure and developing RS inversion schemes on forest structural changes.
Quantitative Analysis of the Sloping Terrain on Al-Biruni’s Floor and Implications for the Cratering Process
Authors: Liu Feng , Ma Yuanxu , Ha Guanghao
Published: September 2024

Surface unloading due to impact cratering results in lava filling the crater floor. Elevation differences in the crater floor, a common geological phenomenon on the Moon, represent direct evidence of cratering processes. However, few studies have been conducted on mare-filled craters on the Moon. Al-Biruni (81 km) is a farside impact crater with an inclined topographic profile on its floor. We quantitatively measure the morphology of Al-Biruni and model the basaltic lava emplacement to depict the cratering process. Differential subsidence due to melt cooling, wall collapse, impact conditions and structural failure were assessed as potential factors influencing the formation of the elevation differences on the floor. The results suggest that pre-impact topography is a plausible cause of the differences in floor elevation within Al-Biruni. Other factors may also play a role in this process, affecting lava flow by altering the topography of the crater floor after the impact. Thus, regardless of whether the lava inside the crater is impact-generated or comes from outside the crater, altering topography at different stages of the cratering process is an essential factor in creating the sloped terrain on the crater floor.
Assessment of Habitat Quality in Arid Regions Incorporating Remote Sensing Data and Field Experiments
Authors: Zhang Mingke , Zhang Hao , Deng Wei , Yuan Quanzhi
Published: September 2024

China’s arid regions are particularly vulnerable to the adverse effects of climate change and human activities, which pose threats to habitat quality. Consequently, evaluations of these effects are vital for devising ecological strategies and initiating regional remediation efforts. However, environmental variations in arid areas can cause habitat quality fluctuations, which complicates precise assessments. This study introduces a refined methodology that integrates remote sensing data and field survey biomass data to modify the habitat quality estimates obtained from the InVEST model in the Altai region over three decades. A comparative analysis of the unmodified, normalized difference vegetation index (NDVI)-modified and biomass-modified habitat quality estimates was conducted. The results revealed an improvement in the correlation between habitat quality and field observations, with a significant increase in the R2 value from 0.129 to 0.603. The unmodified model exhibits subtle variations in habitat quality in mountainous areas, with a slight decline in the plains. However, the modified model shows an increasing trend in mountainous areas. This finding contrasts with the reductions in mountains typically reported by other studies. The refined approach accurately expresses the variations in habitat quality across different habitat types, with declines in forested areas and improvements in shrubland and grassland regions. This model is suitable for arid regions and accommodates urban and agricultural ecosystems affected by human activities, offering empirical data for biodiversity and habitat management.
Selecting Erosion- and Deposition-Dominated Zones in the Jezero Delta Using a Water Flow Model for Targeting Future In Situ Mars Surface Missions
Authors: Steinmann Vilmos , Bahia Rickbir Singh , Kereszturi Ákos
Published: September 2024

Identifying surface sites with significant astrobiological potential on Mars requires a comprehensive understanding of past geological processes and conditions there, including the shallow subsurface region. Numerical modelling could distinguish between regions dominated by erosion and those characterized by sediment accumulation in ancient wet environments. The target area of Jezero Crater is relatively well explored and thus is an ideal site to evaluate model calculations; however, important works are still missing on expectations related to its shallow subsurface . In this work, the best available approaches were followed, and only surface morphology was considered (supposedly formed by the last fluvial episode). The shallow subsurface became an important target recently, and this model could provide new inputs in this area. Erosion–accumulation models are suitable for terrestrial surface features, but few have been applied to Mars. This work addresses this challenge using the SIMWE (SIMulated Water Erosion) model on the Jezero Crater delta, the landing site of the Perseverance rover. For calculations, the average grain size according to the THEMIS TI data was applied to the target area. The flow depth varied between 1.89 and 34.74 m (average of 12.66 m). The water-filled channel width ranged from 35.3 to 341.42 m. A flow velocity of 0.008–11.6 m/s, a maximum erosion rate of 5.98 g/m2/h, and a deposition 4.07 g/m2/h were estimated. These calculated values are close to the range of estimations from other authors assuming precipitation of 1–20 mm/h and discharges of 60–400 m3/s. The model was able to distinguish between erosion- and accumulation-dominated areas about 1 m above Jezero Crater’s delta that are not visible from above. This model helps to identify the accumulation-dominated areas with the finest grain size with good preservation capability for the shallow but invisible subsurface.
Iterative Mamba Diffusion Change-Detection Model for Remote Sensing
Authors: Liu Feixiang , Wen Yihan , Sun Jiayi , Zhu Peipei , Mao Liang , Niu Guanchong , Li Jie
Published: September 2024

In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing the quality of images shot over various geographical areas, particularly for high-resolution images. However, there are some shortcomings of the widely used Convolutional Neural Networks (CNNs) and Transformers-based CD methods. The former is limited by its insufficient long-range modeling capabilities, while the latter is hampered by its computational complexity. Additionally, the commonly used information-fusion methods for pre- and post-change images often lead to information loss or redundancy, resulting in inaccurate edge detection. To address these issues, we propose an Iterative Mamba Diffusion Change Detection (IMDCD) approach to iteratively integrate various pieces of information and efficiently produce fine-grained CD maps. Specifically, the Swin-Mamba-Encoder (SME) within Mamba-CD (MCD) is employed as a semantic feature extractor, capable of modeling long-range relationships with linear computability. Moreover, we introduce the Variable State Space CD (VSS-CD) module, which extracts abundant CD features by training the matrix parameters within the designed State Space Change Detection (SS-CD). The computed high-dimensional CD feature is integrated into the noise predictor using a novel Global Hybrid Attention Transformer (GHAT) while low-dimensional CD features are utilized to calibrate prior CD results at each iterative step, progressively refining the generated outcomes. IMDCD exhibits a high performance across multiple datasets such as the CDD, WHU, LEVIR, and OSCD, marking a significant advancement in the methodologies within the CD field of RS. The code for this work is available on GitHub.
Remotely Sensed Comparative Spatiotemporal Analysis of Drought and Wet Periods in Distinct Mediterranean Agroecosystems
Authors: Sakellariou Stavros , Dalezios Nicolas R. , Spiliotopoulos Marios , Alpanakis Nikolaos , Faraslis Ioannis , Tziatzios Georgios A. , Sidiropoulos Pantelis , Dercas Nicholas , Domínguez Alfonso , López Higinio Martínez , Montoya Francisco , López-Urrea Ramón , Karam Fadi , Amami Hacib , Nsiri Radhouan
Published: September 2024

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI12) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI12 = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI12 = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI12 = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI12 values, areal extent and duration (SPI12 = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI12 = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI12 = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions.
Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning
Authors: Conciatori Marco , Tran Nhung Thi Cam , Diez Yago , Valletta Alessandro , Segalini Andrea , Lopez Caceres Maximo Larry
Published: September 2024

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use.
Thin Cloud Removal Generative Adversarial Network Based on Sparse Transformer in Remote Sensing Images
Authors: Han Jinqi , Zhou Ying , Gao Xindan , Zhao Yinghui
Published: September 2024

Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free regions, leading to color distortion, detail loss, and visual artifacts. This study proposes a Sparse Transformer-based Generative Adversarial Network (SpT-GAN) to solve these problems. First, a global enhancement feature extraction module is added to the generator’s top layer to enhance the model’s ability to preserve ground feature information in cloud-free areas. Then, the processed feature map is reconstructed using the sparse transformer-based encoder and decoder with an adaptive threshold filtering mechanism to ensure sparsity. This mechanism enables that the model preserves robust long-range modeling capabilities while disregarding irrelevant details. In addition, inverted residual Fourier transformation blocks are added at each level of the structure to filter redundant information and enhance the quality of the generated cloud-free images. Finally, a composite loss function is created to minimize error in the generated images, resulting in improved resolution and color fidelity. SpT-GAN achieves outstanding results in removing clouds both quantitatively and visually, with Structural Similarity Index (SSIM) values of 98.06% and 92.19% and Peak Signal-to-Noise Ratio (PSNR) values of 36.19 dB and 30.53 dB on the RICE1 and T-Cloud datasets, respectively. On the T-Cloud dataset, especially with more complex cloud components, the superior ability of SpT-GAN to restore ground details is more evident.
Efficient Fourth-Order PSTD Algorithm with Moving Window for Long-Distance EMP Propagation
Authors: Wei Yongli , Cao Baofeng , Li Zongxiang , Zhang Tianchi , Duan Changjiao , Li Xiao , Li Xiaoqiang , Li Peng
Published: September 2024

Satellite-borne electromagnetic pulse (EMP) detection technology plays an important role in military reconnaissance, space environment monitoring, and early warning of natural disasters. However, the complex ionosphere greatly distorts the waveform during propagation and poses a challenge to EMP detection. Therefore, it is necessary to conduct theoretical research on EMP propagation in the ionosphere. Conventional second-order pseudo-spectral time-domain (PSTD-2) algorithm has difficulties in keeping the stability and accuracy of waveforms in calculations over hundreds of kilometers of propagation. To overcome the difficulties, a fourth-order PSTD algorithm incorporating the moving window technique (MWPSTD-4) is proposed. In the numerical examples, the performance of MWPSTD-4 is compared with PSTD-4 and PSTD-2 in the long-distance propagation of EMP. The results show that the MWPSTD-4 improves efficiency while guaranteeing accuracy and is suitable for large-scale electromagnetic field simulation. The proposed method provides a basic algorithm to eliminate the numerical dispersion interference for calculating the long-distance propagation of EMP in complex spaces and is helpful for the design and calibration of satellite-borne EMP detectors.
The Paradox of the ‘Care’ of London’s Children: Discourses of ‘Safety’ and ‘Respect’ in England’s Ministry of Justice Inspection Reports
Authors: Holligan Christopher , Mclean Robert
Published: September 2024

Using English prison inspectorate reports, the article presents an Ervine Goffman-inspired sociological discourse analysis of official political accounts about the living conditions of incarcerated children held in London’s Feltham prison. Through a close reading of inspection reports, we develop a critical window into their lived experiences in an exceptionally harmful UK prison regime. The construction of this prison estate conjures its dilapidation, unhygienic conditions, and endless social danger. The stigmatizing construction of the child prisoner intimates a pervasive culture of violence and bullying, resulting in their aversion to purposive activities. While, at first blush, prison inspectorate reporting is based on the policy of efficiency to ensure a safe and rehabilitative prison experience for youth, it is argued that the nature of the reporting of incarceration obviates a critique of the wider political fabric that custodial interventions will invariably reproduce. The Inspectorate operates within the state’s dominant class-stratified political ideology. The adoption of a generic labeling discourse in the reports minimizes the communication of harms inflicted on children by criminal ‘justice’ that can only worsen their wellbeing and reproduce the harmful intensity of their pre-existing marginality.
Organizational Context and Satisfaction of Basic Needs of Workers with Intellectual Disability as Sources of Well-Being
Authors: Fajardo-Castro Leady V. , Martínez-Tur Vicente , Moliner Carolina , Plavsic Aleksandar , Arbués Jorge
Published: September 2024

The inclusion of persons with intellectual disabilities (ID) through integrated employment is essential for achieving the United Nations Sustainable Development Goals. This research study examines how organizational contextual factors facilitate or hinder the inclusion and positive contributions of workers with ID in their workplaces, as well as organizational practices that satisfy their basic needs (autonomy, competence, and relatedness), thus promoting their well-being. Semi-structured interviews were conducted with experts in integrated employment, workers with ID, and supervisors. Content analysis was then performed using the grounded theory methodology. Data analysis revealed three main categories: (1) organizational contextual factors that favor inclusion and positive contributions of workers with ID; (2) organizational contextual factors that hinder inclusion and positive contributions of workers with ID; and (3) organizational practices that satisfy basic needs of workers with ID. These findings underscore the importance of creating work environments that promote inclusion, positive contributions, and satisfaction of the basic needs of workers with ID, thus fostering their well-being and professional development.
Sustainable Exploitation of Wine Lees as Yeast Extract Supplement for Application in Food Industry and Its Effect on the Growth and Fermentative Ability of Lactiplantibacillus plantarum and Saccharomyces cerevisiae
Authors: Kokkinomagoulos Evangelos , Kandylis Panagiotis
Published: September 2024

Wine lees, the residue left behind after racking or bottling of wine, are predominantly composed of dead yeast cells, ethanol, phenolic compounds, and tartrates. Yeast extract (i.e., commercial yeast extract), a highly nutritious powder derived from commercially cultivated yeast biomass, is commonly used in nutrient media as a nitrogen source. In the context of by-product valorization, wine lees could potentially be used to produce a substitute for commercial yeast extract (CYE). In our study we investigated the growth and fermentative ability of two major winemaking microorganisms, Lactiplantibacillus plantarum and Saccharomyces cerevisiae, in culture media containing a wine lees yeast extract (WLYE) and a CYE. The effects of yeast extract type, concentration, and initial cell concentration (y0) on key kinetic parameters—maximum specific growth rate (μmax), lag phase duration (λ), and maximum cell concentration (ymax)—were evaluated. For L. plantarum, the results showed that using a WLYE led to similar kinetic parameters to those obtained with a CYE, with λ being unaffected by y0 in samples containing a WLYE. For S. cerevisiae, simultaneous addition of both yeast extracts led to increased μmax values (up to 0.136 h−1) compared to individually added yeast extracts, although this negatively affected λ and ymax. Current research on wine lees is mainly focused on using them as a substrate to produce valuable metabolites through fermentation, overlooking the potential industrial applications of the nutrient-rich autolysate. The findings of this study appear promising for the holistic valorization of wine lees, contributing towards the concepts of sustainability and circular economy.
The Power of Culture: Business Nationalist Culture and ESG Performance
Authors: Xiao Xiaohong , Lin Yuhao
Published: September 2024

High-quality development is the theme of China’s economic and social development in the new era, and corporate ESG performance is a comprehensive indicator for evaluating the level of corporate environmental responsibility, social responsibility and governance, as well as an important yardstick for identifying the high-quality development of enterprises. This paper takes Chinese non-financial listed companies from 2011 to 2022 as the research sample and empirically examines the impact of corporate nationalism culture on corporate ESG performance and its mechanism by quantifying corporate nationalism culture using the text of corporate annual reports, natural language processing and text analysis methods. The results of the study show that corporate nationalism culture significantly enhances corporate ESG performance. The mechanism analysis suggests that corporate nationalism culture, as an internal informal system, can play a governance role and promote corporate ESG practices by changing attention allocation and mitigating agency problems. The positive effect of corporate nationalism culture on corporate ESG performance is more pronounced in the grouping of firms with lower institutional investor shareholding, fewer analysts’ attention and embedded party organisations. A heterogeneity analysis reveals that the corporate nationalism culture driving effect on corporate ESG performance is more significant in the subsample of firms with weak financing constraints, in the growth period and in the decline period. This study reveals the positive role of soft cultural factors in enhancing corporate ESG performance, providing useful managerial evidence for companies to integrate ESG concepts at the strategic level for high-quality economic development.
Net-Zero Considerations within the Delivery of Major AEC Projects in the UK: A Thematic Analysis of the Key Challenges for Project Managers
Authors: Navarro-Bringas Eduardo , Godawatte Godawatte Arachchige Gimhan Rathnagee
Published: September 2024

The growing emphasis on carbon considerations and the pursuit of net-zero emissions have brought about a paradigm shift in project management. To successfully facilitate the transition towards net-zero emissions, major projects must not only adapt existing systems but also embed carbon targets into their core strategies. While several studies have investigated carbon integration during the procurement phase, limited attention has been given to the construction project manager (PM) perspective. This study aims to bridge this research gap by exploring the challenges and barriers faced by construction PMs when integrating carbon targets and metrics into major Architectural, Engineering and Construction (AEC) projects, as well as evaluating the readiness of project teams to deliver on these. This study deployed a qualitative exploratory research design, where semi-structured interviews were conducted with 17 AEC project professionals actively engaged in the planning and execution of major projects in the UK. Thematic analysis of the data revealed a range of challenges and barriers faced by PM teams delivering these projects. The research findings contribute to the field of construction major projects and project management by enhancing the understanding of the challenges faced by PMs when planning and delivering major AEC projects within the context of the net-zero transition. This study uncovers a series of challenges and prevalent practices that have the potential to impede progress towards net zero. A conceptual model is also proposed, offering a synthesis of the different PM perspectives on carbon integration.
Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov–FLUS Model
Authors: Inalpulat Melis
Published: September 2024

Greenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, Türkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov–FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region.
Measuring Household Thermal Discomfort Time: A Japanese Case Study
Authors: Nadimi Reza , Nazarahari Amin , Tokimatsu Koji
Published: September 2024

This study proposes a metric to measure households’ discomfort related to thermal consumption time (hereafter referred to as t-discomfort). This metric relies on an ideal thermal consumption and calculates the gap between the usage times of thermal devices in vulnerable households compared to the ideal household. The t-discomfort is quantified using thermal data collected from 1298 households in the Tokyo and Oita prefectures in Japan. To create the ideal usage times of thermal devices, households are categorized into three clusters—Vulnerable (Vu), Semi-vulnerable (SVu), and Invulnerable (IVu)—based on their energy poverty ratio, and t-discomfort is subsequently calculated for each group. The IVu households are used as the ideal reference point for measuring thermal device usage in the other two categories. The findings of the study indicate that energy poverty does not necessarily lead to t-discomfort. Interestingly, the consumption time of heating devices among Vu households in both prefectures is longer than that of IVu households, despite the high energy prices. Conversely, SVu households, which do not experience severe energy poverty, tend to sacrifice their comfort by reducing their thermal consumption time. Additionally, the consumption time of cooling devices among Vu households in Oita is longer than that of IVu households, whereas in Tokyo, it is shorter. Two treatment strategies are evaluated to mitigate thermal discomfort in households without compromising resource availability. The first strategy integrates the thermal device consumption time with Japan’s current regulated time-of-use rates plan (daytime and nighttime). The results propose a three-tiered tariff plan (off-peak, mid-peak, and peak) to reduce the energy cost burden for Vu households. The second strategy recommends the installation of 12 rooftop solar panels for households in Tokyo and 11 panels for households in Oita. This strategy aims to maintain thermal comfort via a sustainable natural energy resource while minimizing energy costs.
Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm
Authors: Yuvaraj Thangaraj , Prabaharan Natarajan , John De Britto Chinnappan , Thirumalai Muthusamy , Salem Mohamed , Nazari Mohammad Alhuyi
Published: September 2024

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.
Economic and Environmental Aspects of Applying the Regeneration of Spent Moulding Sand
Authors: Łucarz Mariusz
Published: September 2024

This article presents issues related to the rational management of foundry sand in the context of sustainable development. Attention was drawn to the need to take appropriate measures to protect available natural deposits of good foundry sands in terms of their depletion. The main objective of the analyses undertaken was to find out whether more expensive but more efficient thermal regeneration can compensate for the higher energy consumption in relation to mechanical regeneration of spent moulding sand with an organic binder. This aspect was considered from the point of view of the multiple operations performed to clean the grain matrix from the spent binder, taking into account the direct and indirect costs of the process. This paper presents a comparative analysis of the mechanical and thermal regeneration of spent moulding sand on equipment offered by an exemplary manufacturer. Attention was drawn to the successively increasing price of the regeneration process. When analysing the grain matrix recovery process for sustainability reasons, attention was drawn to an important factor in grain matrix management related to its yield in different regeneration methods. Based on an analysis of the costs of regenerating 1 tonne of spent moulding sand, it was concluded that, in the long term, thermal regeneration, which is more expensive due to the cost of equipment and energy consumption, can offset the outlay incurred. Sand consumption was found to be 4.6 times higher by mechanical regeneration in the case studied. At the same time, the grain matrix after thermal regeneration was found to have significantly better and more stable technological parameters in subsequent sand mould preparation cycles. The reproducibility and stability of the technological process can also be an important component of economic growth as part of sustainable development.
The Effect of Job Satisfaction on Life Satisfaction: The Mediating Effect of Positive Affect Moderated by COVID-19
Authors: Na Tae-Kyun , Kim Beom-Soo , Han Saem
Published: September 2024

This study examines the effect of job satisfaction on life satisfaction, focusing on the mediating effect of positive affect moderated by COVID-19. The participants were 287 wage-earning graduates under 35 years of age who had graduated from culinary arts programmes and participated in the 2017–2019 Graduates Occupational Mobility Survey conducted by the Korea Employment Information Service. Hypotheses were tested using Hayes’ MACRO process models 4 and 8. The results are summarised as follows. First, higher extrinsic and intrinsic job satisfaction among culinary graduates was associated with increased positive affect and life satisfaction. Second, positive affect partially mediated the relationship between job satisfaction and life satisfaction. Finally, the indirect effect of positive affect on the relationship between extrinsic job satisfaction and life satisfaction decreased after the COVID-19 pandemic compared to before the pandemic. Therefore, the industry should develop systems and programmes to enhance both extrinsic job satisfaction (such as wages and working hours) and intrinsic job satisfaction (such as personal growth, development potential, and a sense of accomplishment) among young chefs. Furthermore, industries and government agencies should prepare sustainable measures to maintain job satisfaction, positive affect, and life satisfaction among employees during crises such as the COVID-19 pandemic.
Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling
Authors: Lv Zhuoyu , Wang Shanshan , Yan Shuhao , Han Jianyun , Zhang Gaoqiang
Published: September 2024

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.