bluuebunny/crossref_metadata_2025 huggingface dataset:
Valorization of Spent Mushroom Substrate: Establishing the Foundation for Waste-Free Production
Authors: Ravlikovsky Andrew , Pinheiro Maria Nazaré Coelho , Dinca Lucian , Crisan Vlad , Symochko Lyudmyla
Published: May 2024

Spent mushroom substrate (SMS), often disregarded as waste despite its abundance in organic matter and mineral micronutrients, has emerged as a valuable resource for diverse applications. While Europe and Asia have witnessed extensive research in this field over the past decade, Ukraine’s exploration remains limited. This study conducts a thorough investigation into SMS recycling and reutilization over a 4-year period. Employing experimental and comparative methods, this research unveils compelling insights into the potential of SMS for reintroduction into the primary production cycle and secondary activities. The main conclusions reveal the success of SMS valorization in the production of additives, fertilizers, and alternative fuels. Furthermore, the application of SMS in agroecosystems significantly enhances the soil biological activity. The integration of these methods into production chains not only yields economic benefits for companies but also fosters environmental stewardship, aligning with waste-free practices and the principles of bioeconomy and sustainability.
Introduction to Syncretism and Pentecostalism in the Global South
Authors: Nel Marius , Kgatle Mookgo Solomon
Published: May 2024

Critiquing current neo-Pentecostal practices is complex and requires a nuanced approach because this movement’s wide range of beliefs and practices [...]
Use of Prediction Bias in Active Learning and Its Application to Large Variable Annuity Portfolios
Authors: Gweon Hyukjun , Li Shu , Xu Yangxuan
Published: May 2024

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model.
Unveiling Deviations from IPCC Temperature Projections through Bayesian Downscaling and Assessment of CMIP6 General Circulation Models in a Climate-Vulnerable Region
Authors: Ferreiro-Lera Giovanni-Breogán , Penas Ángel , del Río Sara
Published: May 2024

The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide.
Advanced Feature Learning on Point Clouds Using Multi-Resolution Features and Learnable Pooling
Authors: Wijaya Kevin Tirta , Paek Dong-Hee , Kong Seung-Hyun
Published: May 2024

Existing point cloud feature learning networks often learn high-semantic point features representing the global context by incorporating sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation. However, this process may result in a substantial loss of granular information due to the sampling operation and the widely-used max pooling feature aggregation, which neglects information from non-maximum point features. Consequently, the resulting high-semantic point features could be insufficient to represent the local context, hindering the network’s ability to distinguish fine shapes. To address this problem, we propose PointStack, a novel point cloud feature learning network that utilizes multi-resolution feature learning and learnable pooling (LP). PointStack aggregates point features of various resolutions across multiple layers to capture both high-semantic and high-resolution information. The LP function calculates the weighted sum of multi-resolution point features through an attention mechanism with learnable queries, enabling the extraction of all available information. As a result, PointStack can effectively represent both global and local contexts, allowing the network to comprehend both the global structure and local shape details. PointStack outperforms various existing feature learning networks for shape classification and part segmentation on the ScanObjectNN and ShapeNetPart datasets, achieving 87.2% overall accuracy and instance mIoU.
Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands
Authors: Miao Lijuan , Zhang Yuyang , Agathokleous Evgenios , Bao Gang , Zhu Ziyu , Liu Qiang
Published: May 2024

Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model’s performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.
A Comparative Study on Multi-Parameter Ionospheric Disturbances Associated with the 2015 Mw 7.5 and 2023 Mw 6.3 Earthquakes in Afghanistan
Authors: Rasheed Rabia , Chen Biyan , Wu Dingyi , Wu Lixin
Published: May 2024

This paper presents a multi-parameter ionospheric disturbance analysis of the total electron content (TEC), density (Ne), temperature (Te), and critical frequency foF2 variations preceding two significant earthquake events (2015 Mw 7.5 and 2023 Mw 6.3) that occurred in Afghanistan. The analysis from various ground stations and low-Earth-orbit satellite measurements involved employing the sliding interquartile method to process TEC data of Global Ionospheric Maps (GIMs), comparing revisit trajectories to identify anomalies in Ne and Te from Swarm satellites, applying machine learning-based envelope estimation for GPS-derived TEC measurements, utilizing the least square method for foF2 data and ionograms obtained from available base stations in the Global Ionosphere Radio Observatory (GIRO). After excluding potential influences caused by solar and geomagnetic activities, the following phenomena were revealed: (1) The GIM-TEC variations displayed positive anomalies one day before the 2015 Mw 7.5 earthquake, while significant positive anomalies occurred on the shock days (7, 11, and 15) of the 2023 Mw 6.3 earthquake; (2) the Swarm satellite observations (Ne and Te) for the two earthquakes followed almost the same appearance rates as GIM-TEC, and a negative correlation between the Ne and Te values was found, with clearer appearance at night; (3) there were prominent positive TEC anomalies 8 days and almost 3 h before the earthquakes at selected GPS stations, which were nearest to the earthquake preparation area. The anomalous variations in TEC height and plasma density were verified by analyzing the foF2, which confirmed the ionospheric perturbations. Unusual ionospheric disturbances indicate imminent pre-seismic events, which provides the potential opportunity to provide aid for earthquake prediction and natural hazard risk management in Afghanistan and nearby regions.
Advancing Physically Informed Autoencoders for DTM Generation
Authors: Alizadeh Naeini Amin , Sheikholeslami Mohammad Moein , Sohn Gunho
Published: May 2024

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to digital terrain models (DTMs). DTMs are used in various fields, including environmental management, where they provide crucial topographical data to accurately model water flow and identify flood-prone areas. However, current DL-based methods require intensive data processing, limiting their efficiency and real-time use. To address these challenges, we have developed an innovative method that incorporates a physically informed autoencoder, embedding physical constraints to refine the extraction process. Our approach utilizes a normalized DSM (nDSM), which is updated by the autoencoder to enable DTM generation by defining the DTM as the difference between the DSM input and the updated nDSM. This approach reduces sensitivity to topographical variations, improving the model’s generalizability. Furthermore, our framework innovates by using subtractive skip connections instead of traditional concatenative ones, improving the network’s flexibility to adapt to terrain variations and significantly enhancing performance across diverse environments. Our novel approach demonstrates superior performance and adaptability compared to other versions of autoencoders across ten diverse datasets, including urban areas, mountainous regions, predominantly vegetation-covered landscapes, and a combination of these environments.
Mapping Water Quality in Nearshore Reef Environments Using Airborne Imaging Spectroscopy
Authors: Hondula Kelly L. , König Marcel , Grunert Brice K. , Vaughn Nicholas R. , Martin Roberta E. , Dai Jie , Jamalinia Elahe , Asner Gregory P.
Published: May 2024

Coral reefs are threatened globally by compounding stressors of accelerating climate change and deteriorating water quality. Water quality plays a central role in coral reef health. Yet, accurately quantifying water quality at large scales meaningful for monitoring impacts on coral health remains a challenge due to the complex optical conditions typical of shallow water coastal systems. Here, we report the performance of 32 remote sensing water quality models for suspended particulate matter and chlorophyll concentrations as well as colored dissolved organic matter absorption, over concentration ranges relevant for reef ecology using airborne imaging spectroscopy and field measurements across 62 stations in nearshore Hawaiian waters. Models were applied to reflectance spectra processed with a suite of approaches to compensate for glint and other above-water impacts on reflectance spectra. Results showed reliable estimation of particulate matter concentrations (RMSE = 2.74 mg L−1) and accurate but imprecise estimation of chlorophyll (RMSE = 0.46 μg L−1) and colored dissolved organic matter (RMSE = 0.03 m−1). Accurately correcting reflectance spectra to minimize sun and sky glint effects significantly improved model performance. Results here suggest a role for both hyperspectral and multispectral platforms and rapid application of simple algorithms can be useful for nearshore water quality monitoring over coral reefs.
Effects of the Architectural Layout of the Sanctuary of Pachacamac (2nd–16th Century CE, Peru) on the Exposure to Rain, Wind, and Solar Radiation from the Morphometric Analysis of Digital Surface Models
Authors: Magnini Luigi , Pozzi-Escot Denise , Oshiro Janet , Angeles Rommel , Apa Maria Ilaria Panaccione , Ventura Guido
Published: May 2024

Natural events (floods, earthquakes, landslides, etc.) may significantly damage archaeological sites, and therefore reducing their exposure to such events represents a priority for protective and conservation activities. The archaeological Sanctuary of Pachacamac (SP; 2nd–16th century CE; Peru) covers an area of 465 hectares and includes roads, enclosures, huacas with ramps, temples, and palaces located along the central coast of Peru. This area is affected by heavy rain and winds related to the El Niño–Southern Oscillation and to intense solar radiation. We use a 30 cm resolution Digital Surface Model obtained from orthophotogrammetric data and perform a morphometric analysis using geomorphological, hydrological, and climatic quantitative parameters. Our aim is to identify the zones exposed to water flow or stagnation during rainfall, as well as the exposure to winds and solar radiation. The calculated parameters are subsequently processed with an object-based image analysis approach to identify areas with higher climate exposure. We show that the SP architectural layout controls the exposure to water stagnation or flow in the form of rainfall, whereas exposure to wind and solar radiation mainly depends on the topography of an area (e.g., the presence of hills and plains). The methodological approach proposed here may be applied and extended to other archaeological sites.
Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery
Authors: Miao Shengjie , Zhang Kongwen (Frank) , Zeng Hongda , Liu Jane
Published: May 2024

Urban tree classification enables informed decision-making processes in urban planning and management. This paper introduces a novel data reformation method, pseudo tree crown (PTC), which enhances the feature difference in the input layer and results in the improvement of the accuracy and efficiency of urban tree classification by utilizing artificial intelligence (AI) techniques. The study involved a comparative analysis of the performance of various machine learning (ML) classifiers. The results revealed a significant enhancement in classification accuracy, with an improvement exceeding 10% observed when high spatial resolution imagery captured by an unmanned aerial vehicle (UAV) was utilized. Furthermore, the study found an impressive average classification accuracy of 93% achieved by a classifier built on the PyTorch framework, with ResNet50 leveraged as its convolutional neural network layer. These findings underscore the potential of AI-driven approaches in advancing urban tree classification methodologies for enhanced urban planning and management practices.
Pre-Launch Assessment of PACE OCI’s Polarization Sensitivity
Authors: McIntire Jeff , Waluschka Eugene , Meister Gerhard , Knuble Joseph , Cook William B.
Published: May 2024

To provide ongoing continuity for the ocean, cloud, and aerosol science data records, NASA will launch the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission on 8 February 2024. The Ocean Color Instrument (OCI) is the primary sensor onboard PACE and will provide ocean color science data to continue the data sets collected by heritage sensors MODIS, SeaWiFS, and VIIRS, but with increased spectral coverage and improved accuracy. The OCI is a grating spectrometer with hyperspectral coverage from the ultraviolet (about 315 nm) to near-infrared (about 900 nm), with additional filtered channels in the short-wave infrared (940 nm–2260 nm). A rigorous ground test program was conducted to calibrate the instrument and ensure that the calibration can be transferred to on-orbit operations in order to achieve the high levels of accuracy demanded by the science community. Some calibration parameters, such as polarization sensitivity, can only be measured during pre-launch testing. Tests were performed to measure the Mueller matrix components necessary to correct polarized scenes encountered on orbit. Measurements covered all spectral bands and a series of telescope scan angles encompassing the expected on-orbit scan range. The sensitivity (linear diattenuation) was measured above 340 nm to be below 0.6%, except at wavelengths, and was characterized as better than 0.1%. Below 340 nm, the sensitivity can be much higher, but this is not expected to affect the science data significantly. These results indicate that any polarized scenes encountered on orbit can be corrected with a high degree of confidence.
An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering
Authors: Zhou Yu , Cao Ronggang , Zhang Anqi , Li Ping
Published: May 2024

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.
Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment
Authors: Chen Guangqiu , Hao Youfei , Duan Jin , Liu Ju , Jia Linfeng , Song Jingyuan
Published: May 2024

Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.
Prediction of Pre-Loading Relaxation of Bolt Structure of Complex Equipment under Tangential Cyclic Load
Authors: Lu Xiaohan , Zhu Min , Li Chao , Li Shengnan , Wang Shengao , Li Ziwei
Published: May 2024

Bolts have the advantages of simple installation and easy removal. They are widely applied in aerospace and high-speed railway traffic. However, the loosening of bolts under mixed loads can lead to nonlinear decreases in pre-loading. This affects the safety performance of the structure and may lead to catastrophic consequences. Existing techniques cannot be used to monitor the bolt performance status in time. This has caused significant problems with the safety and reliability of equipment. In order to study the relaxation law of bolt pre-loading, this paper carries out an experimental analysis for 8.8-grade hexagonal bolts and calibrates the torque coefficient. We also studied different loading waveforms, nickel steel plate surface roughnesses, tangential displacement frequencies, four different strengths and bolt head contact areas of the bolt, the initial pre-loading, and the effects of tangential cyclic displacement on pre-loading relaxation. This was done in order to accurately predict the degree of bolt pre-loading loosening under external loads. The laws are described using the allometric model function and the nine-stage polynomial function. The least squares method is used to identify the parameters in the function. The results show that bolts with a smooth surface of the connected structure nickel steel flat plate, high-frequency working conditions, half-sine wave, and a high-strength have better anti-loosening properties. Taking 5–10 cycles of cyclic loading as a boundary, the pre-loading relaxation is divided into two stages. The first stage is a stage of rapid decrease in bolt pre-loading, and the second stage is the slow decrease process. The performance prediction study shows that the allometric model function is the worst fitted, at 71.7% for the small displacement condition. Other than that, the allometric model function and the nine-stage polynomial function can predict more than 85.5% and 90.4%, which require the use of least squares to identify two and ten unknown parameters, respectively. The complexity of the two is different, but both can by better indicators than the pre-loading relaxation law under specific conditions. It helps to improve the monitoring of bolt loosening and the system use cycle, and it can provide theoretical support for complex equipment working for a long time.
Adaptive Multi-Sensor Joint Tracking Algorithm with Unknown Noise Characteristics
Authors: Sun Weihao , Wang Yi , Diao Weifeng , Zhou Lin
Published: May 2024

In this study, to solve the low accuracy of multi-space-based sensor joint tracking in the presence of unknown noise characteristics, an adaptive multi-sensor joint tracking algorithm (AMSJTA) is proposed. First, the coordinate transformation from the target object to the optical sensors is considered, and the observation vector-based measurement model is established. Then, the measurement noise characteristics are assumed to be white Gaussian noise, and the measurement covariance matrix is set as a constant. On this premise, the traditional iterative extended Kalman filter is applied to solve this problem. However, in most actual engineering applications, the measurement noise characteristics are unknown. Thus, a forgetting factor is introduced to adaptively estimate the unknown measurement noise characteristics, and the AMSJTA is designed to improve the tracking accuracy. Furthermore, the lower bound of the proposed algorithm is theoretically proved. Finally, numerical simulations are executed to verify the effectiveness and superiority of the proposed AMSJTA.
Changes in Soil Microbial Communities Associated with Pinus densiflora and Larix kaempferi Seedlings under Extreme Warming and Precipitation Manipulation
Authors: Kwon Minyoung , Li Guanlin , Jo Heejae , Kim Gwang-Jung , Chung Haegeun , Son Yowhan
Published: May 2024

Soil microbial communities are essential to the terrestrial ecosystem processes by mediating nutrient cycling, and their function and composition may be altered under climate change. In this study, the effects of extreme climate events (extreme warming and precipitation pattern) on the microbial communities and extracellular enzyme activities in the soils planted with 1-year-old Pinus densiflora and Larix kaempferi seedlings were investigated. Open-field warming (+3 °C and +6 °C) and precipitation manipulation including drought induced by the complete interception of rainfall and heavy rainfall (113 mm per day) were applied from 13 July to 20 August 2020. The activities of soil enzymes, including β-glucosidase, acid phosphatase, N-acetyl-glucosaminidase, and leucine aminopeptidase, microbial biomass carbon and nitrogen, and changes in microbial community composition were determined. The microbial biomass carbon was 15.26% higher in Larix kaempferi-planted soils than in Pinus densiflora-planted soils. Fungal Chao 1 in the heavy rainfall and drought plots were 53.86% and 0.84% lower than the precipitation control, respectively, and 49.32% higher in the Larix kaempferi plots than under the Pinus densiflora. The fungal Shannon index was 46.61% higher in plots planted with Larix kaempferi than in those planted with Pinus densiflora. Regarding the dominant phyla, the relative abundance of Ascomycota in heavy rainfall plots was 14.16% and 13.10% higher than in the control and drought plots, respectively, and the relative abundance of Mortierllomycota was 55.48% higher under Larix kaempferi than under Pinus densiflora. The overall results are considered to reflect the microbial sensibility to environmental conditions and interaction with the planted species. Since the current study observed only short-term responses to extreme climate events, further study is required to determine the continuous effects of environmental changes on the associations between plants and soil microbes.
Effect of the Spading Machine on Various Soil Parameters at Different Tillage Depths
Authors: Singh Neeraj Kumar , Dogra Baldev , Manes Gursahib Singh , Parihar Dilwar Singh , Salem Ali , Elbeltagi Ahmed
Published: May 2024

This study was carried out to investigate the effect of the spading machine as a sustainable tillage tool at different depths of tillage on soil parameters. The spading machine was evaluated for two independent parameters: soil type and depth of cut. The observed dependent parameters were the bulk density of soil, mean clod size, soil strength, torque requirement, fuel consumption, and cost of operation. The bulk density varied from 1.18 to 1.39 g/cm3, the mean clod size between 12.72 and 68.37 mm, the soil strength between 69 and 4714.5 kPa, the torque requirement between 206 and 344.62 N-m, the fuel consumption between 13.60 and 21.83 l/ha, and cost of operation ranged between 2670.37 INR/ha (34.71 USD/ha) and 3635.87 INR/ha (47.27 USD/ha). It was observed that with the increase in tillage depth, the dependent parameters were also increasing, except the cost of operation varied nonsignificantly. In comparison to the spading machine, other primary tillage tools resulted in higher bulk density and fuel consumption, whereas mean clod size and cost of operation were reported higher except for the rotary tillage tool. Thus, the spading machine could be a sustainable tillage tool for long-term agricultural goals.
Multi-Objective Optimization for Food Availability under Economic and Environmental Risk Constraints
Authors: Hassna Bashar , Namany Sarah , Alherbawi Mohammad , Elomri Adel , Al-Ansari Tareq
Published: May 2024

Food security remains a critical global challenge, increasingly threatened by the adverse effects of climate change on agricultural productivity and food supply chains. Ensuring the stability, availability, and accessibility of food resources necessitates innovative strategies to assess and mitigate climate-related risks. This study presents a comprehensive analysis of the impact of climate change on global food systems, focusing on the risk assessment and optimization of food supply chains from the perspective of importers. Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. The findings underscore the importance of a comprehensive approach in managing food supply chains and offer insights to enhance the resilience and sustainability of global food systems amid climate uncertainties. This study contributes to the literature by applying AHP and multi-objective optimization in climate risk management within food systems, providing valuable perspectives for policymakers and stakeholders in the agricultural sector. Furthermore, the multi-objective optimization model analyzed three crop networks, yielding total costs of USD 16 million, USD 6 million, and USD 10 million for tomatoes, onions, and cucumbers, respectively, with associated CO2eq emissions and risk percentages. The findings reveal concentrated global vegetable markets, with major importers accounting for over 60% of imports, though the leading importers differ across crops, highlighting regional demand and production disparities, potentially impacting food security and supply chain resilience.
Financing and Management Strategies for Expanding Green Development Projects: A Case Study of Energy Corporation in China’s Renewable Energy Sector Using Machine Learning (ML) Modeling
Authors: Han Chen , Yang Lu
Published: May 2024

This study investigates potential financing and management strategies that the Energy Corporation, a Chinese renewable energy company, could adopt in order to expand its green development projects. While China has made significant advancements in renewable energy, its heavy reliance on fossil fuels necessitates a shift towards a more sustainable energy system. To analyze the factors driving and impeding sustainability, this article provides an overview of China’s energy sector and policies. Through case studies of the Energy Corporation and other prominent renewable energy companies, the study showcases a range of demonstration projects, financing models, and management technologies that have the potential to accelerate the growth of sustainable initiatives. Recommendations from expert interviews are also provided, covering areas such as optimizing investment, monitoring distributed assets, and balancing social and environmental impacts. The results show that the Energy Corporation can effectively develop wind, solar, and energy efficiency projects nationwide by leveraging partnerships, utilizing green bonds, employing big data platforms, and engaging stakeholders, while also setting sustainability benchmarks. With a strategic approach, Energy Corporation aims to invest USD 1 billion over the next five years, targeting a renewable energy capacity of 5000 MW and a 20% reduction in CO2 emissions. Achieving these goals would position Chinese companies as global leaders in the transition to renewable energy. The study also utilized an artificial neural network (ANN) to analyze the impact of increasing green jobs and renewable energy capacities on CO2 emission reduction and economic growth. The results indicate that green jobs have a more significant effect on reducing CO2 emissions compared to renewable energy capacities. When green jobs increased while energy capacities remained constant, substantial CO2 reductions were observed, but the economic growth was only 1%. However, when there was a moderate increase in jobs alongside a four-fold increase in renewable energy capacities, economic growth reached 4%. The neural network’s prediction errors were deemed acceptable based on linear regression analysis and experimental results.
Watchdogs or Enablers? Analyzing the Role of Analysts in ESG Greenwashing in China
Authors: Gao Yingxue , Chen Yan
Published: May 2024

In this study, we investigate whether analysts in China can effectively function as watchdogs to monitor managerial ESG greenwashing practices or if they inadvertently play a role in fostering such practices. Analyzing a sample of 8498 annual records from 1282 firms listed on the Chinese A-share market from 2012 to 2022, our linear regression analysis in Stata reveals that firms with significant analyst coverage are more prone to ESG greenwashing, even after controlling for firm-level variables such as leverage (Lev), return on assets (ROA), and cash flow. This finding supports the analyst pressure hypothesis. Notably, the effect is particularly pronounced in poorly performing firms but diminishes when star analysts are involved. Furthermore, we examine the moderating effect of corporate reputation on the relationship between analyst coverage and ESG greenwashing. The results indicate that a robust firm reputation amplifies the impact of analyst scrutiny on ESG greenwashing, especially by raising stakeholder expectations. Our aim is not to undermine the role of analysts but to highlight the need for strengthened ESG regulations to enhance analyst oversight and reduce ESG greenwashing. While our findings, influenced by their Chinese context, may not be universally applicable across all regions, they offer valuable insights for emerging markets seeking to implement effective ESG practices.
Environmental Impacts on Soil and Groundwater of Informal E-Waste Recycling Processes in Ghana
Authors: Owusu-Sekyere Karoline , Aladago David Alatule , Leverenz Dominik , Oteng-Ababio Martin , Kranert Martin
Published: May 2024

This study examines the environmental impacts of informal e-waste recycling processes in Agbogbloshie, Ghana, which is one of the most notorious e-waste recycling sites in sub-Saharan Africa. Despite being unsafe and unorganized, the informal sector is still actively involved in dismantling, extracting, and disposing of e-waste in unauthorized locations on a considerably large scale. However, the academic research on the environmental consequences of informal recycling practices is limited. Soil and groundwater samples for five important and representative informal e-waste recycling processes and one related oil process were collected and analyzed to determine heavy metal-, PBDEs, PCBs, CBs, and PAHs concentrations. Contamination indices were used to assess and compare the effects of informal recycling processes, thereby providing a geochemical evaluation of soil conditions. As a result, the manual dismantling of CRT and ICT devices is the major source of heavy metal pollution. Furthermore, the burning of e-waste plastic for waste reduction purposes and the oil collection process substantially contribute to the input of organic pollutants. Regulatory measures for CRT and ICT recycling would result in an 85% reduction of heavy metals and the enforcement of a compulsory collection system for plastic and oil would eliminate 86% of the organic pollutants.
Strategic Planning, Transformational Leadership and Organization Performance: Driving Forces for Sustainability in Higher Education in Nigeria
Authors: Habeeb Yusuf Olatunji , Eyupoglu Serife Zihni
Published: May 2024

Leadership is crucial to moving society and organizations from one stage to another. Strategic planning is required to sustain the quality of education due to the changing features of students and the construction of many higher educational institutions in Nigeria. However, because higher education is evolving, leaders’ strategic thinking constantly shifts. This study examines the effect of strategic planning on transformational leadership towards attaining the organizational performance of higher education institutions in Nigeria. This study used a sample of 388 staff from 48 state universities in Nigeria. The findings indicate that strategic planning positively influences transformational leadership and organizational performance. Moreover, transformational leadership positively mediates strategic planning and organizational performance. We empirically validate the role of strategic planning in enhancing the performance of higher education institutions. Also, strategic-planning-focused and transformational leadership processes can be critical for facilitating the proper guidelines to improve higher education institutions’ performance.
Noise Pollution Studies in the Arab World: A Scientometric Analysis and Research Agenda
Authors: Bouzir Tallal Abdel Karim , Berkouk Djihed , Barrigón Morillas Juan Miguel , Rey-Gozalo Guillermo , Montes González David
Published: May 2024

This review analyzed scientific publications on noise pollution in 22 Arab countries from 1983 to 2022. The objective of this study was to assess the current state of research on noise pollution in the Arab world, identify trends and gaps in the literature, and subsequently formulate a thorough research agenda. A comprehensive search of the Scopus database was conducted using relevant search terms and inclusion criteria. Out of 249 results, 104 studies were selected. The data extraction from the 104 studies includes the country where the study was conducted/designed; the publication year; the journal of publication; sponsorship details; and the research methodology used. VOSviewer software (VOSviewer 1.6.18) was used to visualize the literature data and co-occurrence networks, collaborations, and research fronts. The analysis revealed that Saudi Arabia, Jordan, and Kuwait were the most active countries in noise pollution research, while Sudan and Libya had the lowest number of publications in this field. Additionally, countries like Somalia, Syria, Yemen, and Mauritania had no publications on this subject. Research on noise pollution in this region mainly focuses on noise in urban zones and workplaces, particularly in Saudi Arabia, Kuwait, and Jordan. Topics vary across other Arab countries. The results highlight the increasing significance of noise pollution research in the Arab world, particularly evident in recent periods. However, scientific output from Arab countries remains significantly limited compared to other global contexts. This study emphasizes the pressing requirement for increased investment in noise pollution research, aiming to achieve knowledge of the acoustic situation in Arab countries in order to develop strategies to mitigate population exposure to noise pollution, improve the well-being of citizens, and protect public health.
Urban Transformation: Analyzing the Combined Forces of Vacant Building Occupancy and Socio-Economic Dynamics
Authors: Santos Teresa , Ramalhete Filipa
Published: May 2024

In the last decades, historic European cities such as Lisbon have faced the challenge of aging dwellings, infrastructures, and a growing number of vacant buildings. These vacant spaces represent both a problem and an opportunity for the cities. While they detract from the city’s aesthetics and safety, they also offer a chance for renewal. Strategic reuse can address housing shortages, boost businesses, and revitalize neighborhoods. This study examines Lisbon’s efforts to revitalize vacant buildings from 2009 to 2022, with projections extending into the next five to ten years. Analyzing data on building use and reoccupation, the study reveals significant progress. A detailed survey of 1674 vacant buildings in 2009 shows many have been transformed. Then, through a GIS-based analysis, the immediate social and economic impacts of the requalification process are assessed, and two future development scenarios are evaluated. By 2022, 60% of these buildings (999) were reoccupied, expanding housing options and driving economic growth. Shops saw a 166% increase in occupancy, highlighting a thriving commercial sector. Additionally, 27% of the reoccupied buildings now hold tourism activities, including short-rental accommodation and hotels. Short- and long-term scenarios are proposed based on a comprehensive survey that captured the status, function, and preservation conditions of the vacant buildings within the city. These scenarios are planning tools for exploring potential future trajectories of urban development, allowing policymakers to anticipate issues, mitigate risks, and make informed decisions, shaping a more sustainable and resilient Lisbon.
Chemical Modification of Commercial Fabrics by Photoinduced Grafting Tannic Acid to Produce Antioxidant and Antibacterial Textiles
Authors: Fouilloux Julie , Abbad Andaloussi Samir , Langlois Valérie , Dammak Lasâad , Renard Estelle
Published: May 2024

The goal of this study was to provide antioxidant and antibacterial properties to different types of fabrics via tannic acid (TA) covalent grafting. To that extent, TA was first methacrylated using glycidylmethacrylate. TA derivatives were characterized using infrared spectroscopy and 1H NMR to assess the degree of acrylation. Antioxidant and antibacterial properties of TA were preserved after chemical modification. The coating process was studied using infrared spectroscopy (IR), weight gain, and radical scavenging activity (RSA) measurements. To covalently bond TA to raw polypropylene (PP) and PP coated with chitosan, photoinduced grafting was performed. The process was optimized and resulted in fabrics with enough tannic acid to provide strong antioxidant activity, with RSA ranging at 95%. The antibacterial activity was assessed against E. coli and S. aureus, the main strains responsible for nosocomial infections. Results revealed a substantial reduction of bacterial contamination for PP samples coated with chitosan, with stronger activity against E. coli, attributed to hydrophobic repellence. This study highlights the benefits of using tannic acid to obtain antioxidant and antibacterial fabrics.
A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things
Authors: Alsamrai Osama , Redel-Macias Maria Dolores , Pinzi Sara , Dorado M. P.
Published: May 2024

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.
Comment on Verteramo Chiu, L.J.; Gomez, M.I. A Tale of Two Strawberries: Conventional and Organic Open-Field Production in California. Sustainability 2023, 15, 14363
Authors: Muramoto Joji , Bolda Mark P.
Published: May 2024

We appreciate Verteramo and Gomez’s Life Cycle Analysis (LCA) work on conventional and organic strawberry production systems in California [...]
Energetic Comparison between Pneumatic and Traditional Disintegration in the Vinification of Negroamaro Grapes
Authors: Giametta Ferruccio , Catalano Filippo , Perone Claudio , Bianchi Biagio
Published: May 2024

This study compares the energetic and functional aspects of pneumatic and traditional disintegration methods during the vinification of Negroamaro grapes to produce ready-to-drink wine, focusing on sustainability and energy efficiency in winemaking. It addresses the critical need to reducing costs and environmental impact in the wine industry through improved energy efficiency and sustainable practices. The experimental tests conducted reveal that the pneumatic system exhibits advantages in terms of energy consumption, production time, and thermal homogenization during fermentation compared to the traditional system. Results indicate that the pneumatic system requires significantly lower energy consumption and shorter operating times during fermentation and pressing phases while maintaining consistent wine quality, highlighting its potential for more efficient and sustainable winemaking practices.