bluuebunny/crossref_metadata_2025 huggingface dataset:
Bartonella quintana Infection in Canada: A Retrospective Laboratory Study and Systematic Review of the Literature
Authors: Boodman Carl , Lindsay Leslie R. , Dibernardo Antonia , Loomer Courtney , Keynan Yoav , Cheng Matthew P. , Yansouni Cédric P. , Gupta Nitin , Coatsworth Heather
Published: December 2024

Background:Bartonella quintana is a body-louse-borne bacterium. Canadian B. quintana disease has been reported primarily in populations experiencing homelessness and in Indigenous communities with limited access to water. We sought to understand the epidemiology of B. quintana in Canada. Methods: This study combined an analysis of laboratory data from Canada’s National Microbiology Laboratory (NML) with a systematic review of the literature. Laboratory data included quantitative polymerase chain reaction (qPCR) cycle threshold values and indirect immunofluorescent antibody titers with the year and province of the sample acquisition. For the systematic review, we searched PubMed, Scopus, Embase, and Web of Science for articles published before 15 July 2024, with terms related to B. quintana in Canada. Results: Thirty-three individuals with qPCR-positive B. quintana were documented in seven provinces and one territory. The number of cases increased over time (p-value = 0.005), with the greatest number of cases being reported in 2022 and 2023. The percent positivity for the B. quintana qPCR performed at the NML increased over time (p-value = 0.036). The median immunoglobulin G titer demonstrated a sustained increase starting in 2017. The systematic review identified fourteen individuals with qPCR-positive B. quintana (none had a qPCR performed at the NML) and seven probable cases of B. quintana disease. Four of these twenty-one individuals from the systematic review died (19%). All fatalities were attributed to endocarditis. Conclusions: The detection of B. quintana disease in seven provinces and one territory suggests that B. quintana has a national distribution. B. quintana disease is increasingly diagnosed in Canada, indicating ongoing transmission across geographic settings.
N-Glycoside of Indolo[2,3-a]pyrrolo[3,4-c]carbazole LCS1269 Exerts Anti-Glioblastoma Effects by G2 Cell Cycle Arrest and CDK1 Activity Modulation: Molecular Docking Studies, Biological Investigations, and ADMET Prediction
Authors: Kalitin Nikolay , Koroleva Natalia , Lushnikova Anna , Babaeva Maria , Samoylenkova Nadezhda , Savchenko Ekaterina , Smirnova Galina , Borisova Yulia , Kostarev Alexander , Karamysheva Aida , Pavlova Galina
Published: December 2024

Background/Objectives: Indolo[2,3-a]pyrrolo[3,4-c]carbazole scaffold is successfully used as an efficient structural motif for the design and development of different antitumor agents. In this study, we investigated the anti-glioblastoma therapeutic potential of glycosylated indolocarbazole analog LCS1269 utilizing in vitro, in vivo, and in silico approaches. Methods: Cell viability was estimated by an MTT assay. The distribution of cell cycle phases was monitored using flow cytometry. Mitotic figures were visualized by fluorescence microscopy. Quantitative RT-PCR was used to evaluate the gene expression. The protein expression was assessed by Western blotting. Molecular docking and computational ADMET were approved for the probable protein target simulations and predicted pharmacological assessments, respectively. Results: Our findings clearly suggest that LCS1269 displayed a significant cytotoxic effect against diverse glioblastoma cell lines and patient-derived glioblastoma cultures as well as strongly suppressed xenograft growth in nude mice. LCS1269 exhibited more potent anti-proliferative activity toward glioblastoma cell lines and patient-derived glioblastoma cultures compared to conventional drug temozolomide. We further demonstrated that LCS1269 treatment caused the severe G2 phase arrest of cell cycle in a dose-dependent manner. Mechanistically, we proposed that LCS1269 could affect the CDK1 activity both by targeting active site of this enzyme and indirectly, in particular through the modulation of the Wee1/Myt1 and FOXM1/Plk1 signaling pathways, and via p21 up-regulation. LCS1269 also showed favorable pharmacological characteristics in in silico ADME prediction in comparison with staurosporine, rebeccamycin, and becatecarin as reference drugs. Conclusions: Further investigations of LCS1269 as an anti-glioblastoma medicinal agent could be very promising.
Biocompatible Carbon Dots/Polyurethane Composites as Potential Agents for Combating Bacterial Biofilms: N-Doped Carbon Quantum Dots/Polyurethane and Gamma Ray-Modified Graphene Quantum Dots/Polyurethane Composites
Authors: Marković Zoran , Dorontić Sladjana , Jovanović Svetlana , Kovač Janez , Milivojević Dušan , Marinković Dragana , Mojsin Marija , Todorović Marković Biljana
Published: December 2024

Background: Pathogen bacteria appear and survive on various surfaces made of steel or glass. The existence of these bacteria in different forms causes significant problems in healthcare facilities and society. Therefore, the surface engineering of highly potent antimicrobial coatings is highly important in the 21st century, a period that began with a series of epidemics. Methods: In this study, we prepared two types of photodynamic polyurethane-based composite films encapsulated by N-doped carbon quantum dots and graphene quantum dots irradiated by gamma rays at a dose of 50 kGy, respectively. Further, we investigated their structural, optical, antibacterial, antibiofouling and biocompatibility properties. Results: Nanoelectrical and nanomechanical microscopy measurements revealed deviations in the structure of these quantum dots and polyurethane films. The Young’s modulus of elasticity of the carbon and graphene quantum dots was several times lower than that for single-walled carbon nanotubes (SWCNTs) with chirality (6,5). The electrical properties of the carbon and graphene quantum dots were quite similar to those of the SWCNTs (6,5). The polyurethane films with carbon quantum dots were much more elastic and smoother than the films with graphene quantum dots. Antibacterial tests indicated excellent antibacterial activities of these films against a wide range of tested bacteria, whereas the antibiofouling activities of both composite films showed the best results against the Staphylococcus aureus and Escherichia coli biofilms. Biocompatibility studies showed that neither composite film exhibited any cytotoxicity or hemolysis. Conclusions: Obtained results indicate that these composite films could be used as antibacterial surfaces in the healthcare facilities.
Glucosinolates Mediated Regulation of Enzymatic Activity in Response to Oxidative Stress in Brassica spp.
Authors: Gantait Aishmita , Masih Sam A. , Addesso Rosangela , Maxton Ann , Sofo Adriano
Published: December 2024

Brassica crops are vital as they supply essential minerals, antioxidants, and bioactive substances like anthocyanins, glucosinolates, and carotenoids. However, biotic and abiotic elements that cause oxidative stress through heavy metals and other eco-toxicants pose a risk to Brassica plants. Increased generation of Reactive Oxygen Species (ROS) causes oxidative stress, which damages biomolecules and interferes with plant growth, productivity, and cellular equilibrium. Plants producing Brassica need an intricate enzyme defence mechanism to fend off oxidative stress. All the enzymes that have been addressed are found in mitochondria, peroxisomes, chloroplasts, and other cell components. They are in charge of removing ROS and preserving the cell’s redox balance. Additionally, Brassica plants use secondary metabolites called Glucosinolates (GLs), which have the capacity to regulate enzymatic activity and act as antioxidants. By breaking down compounds like sulforaphane, GLs boost antioxidant enzymes and provide protection against oxidative stress. To develop methods for improving agricultural crop stress tolerance and productivity in Brassica, it is necessary to comprehend the dynamic interaction between GL metabolism and enzymatic antioxidant systems. This highlights the possibility of maximizing antioxidant defences and raising the nutritional and commercial value of Brassica across the globe by utilizing genetic diversity and environmental interactions.
From Division to Death: Metabolomic Analysis of Nicotiana tabacum BY-2 Cells Reveals the Complexity of Life in Batch Culture
Authors: Puzanskiy Roman K. , Kirpichnikova Anastasia A. , Bogdanova Ekaterina M. , Prokopiev Ilya A. , Shavarda Alexey L. , Romanyuk Daria A. , Vanisov Sergey A. , Yemelyanov Vladislav V. , Shishova Maria F.
Published: December 2024

Tobacco BY-2 cell culture is one of the most widely used models in plant biology. The main advantage of BY-2 suspension cultures is the synchronization of cell development and the appearance of polar elongation. In batch culture, BY-2 cells passed through the lag, proliferation, elongation, and stationary phases. During this process, the composition of the growth medium changed dramatically. Sucrose was rapidly eliminated; hexose first accumulated and then depleted. The medium’s pH initially decreased and then rose with aging. As a result of the crosstalk between the internal and external stimuli, cells pass through complicated systemic rearrangements, which cause metabolomic alterations. The early stages were characterized by high levels of amino acids and sterols, which could be interpreted as the result of synthetic activity. The most intense rearrangements occurred between the proliferation and active elongation stages, including repression of amino acid accumulation and up-regulation of sugar metabolism. Later stages were distinguished by higher levels of secondary metabolites, which may be a non-specific response to deteriorating conditions. Senescence was followed by some increase in fatty acids and sterols as well as amino acids, and probably led to self-destructive processes. A correlation analysis revealed relationships between metabolites’ covariation, their biochemical ratio, and the growth phase.
The bHLH Transcription Factor PhbHLH121 Regulates Response to Iron Deficiency in Petunia hybrida
Authors: Pan Liru , Huang Chengcheng , Li Ruiling , Li Yanbang
Published: December 2024

Iron (Fe) is an essential micronutrient for plants. Due to the low Fe bioavailability in cultivated soils, Fe deficiency is a widespread agricultural problem. In this study, we present the functional characterization of a petunia (Petunia hybrida) basic-helix-loop-helix transcription factor PhbHLH121 in response to Fe shortage. Real-time PCR revealed that the expression of PhbHLH121 in petunia roots and shoots was downregulated under Fe-limited conditions. CRISPR/Cas9-edited phbhlh121 mutant plants were generated to investigate the functions of PhbHLH121 in petunia. Loss-of-function of PhbHLH121 enhanced petunia tolerance to Fe deficiency. Further investigations revealed that the expression level of several structural genes involved in Fe uptake in petunia, such as IRT1 and FRO2, was higher in phbhlh121 mutants compared to that in wild-type under Fe-limited conditions, and the expression level of several genes involved in Fe storage and Fe transport, such as VTL2, FERs and ZIF1, was lower in phbhlh121 mutants compared to that in wild-type under Fe-deficient conditions. Yeast one-hybrid assays revealed that PhbHLH121 binds to the G-box element in the promoter of genes involved in Fe homeostasis. Yeast two-hybrid assays revealed that PhbHLH121 interacts with petunia bHLH IVc proteins. Taken together, PhbHLH121 plays an important role in the Fe deficiency response in petunia.
Coordinated Dispatch of Power Generation and Spinning Reserve in Power Systems with High Renewable Penetration
Authors: Yuan Bo , Zhou Jun , Lu Gang , Liu Dazheng , Xia Peng , Wu Cong
Published: December 2024

The spinning reserve demand in high renewable penetration power systems is increasing significantly due to the stochastic and unpredictable nature of renewable power. This paper defines the expected load not supplied ratio (ELNSR) and models uncertainty factors such as unit forced outage rates, load and wind power output prediction errors based on probability density functions. It derives a quantitative relationship between system operating reserves and the ELNSR and uses this quantitative relationship as a constraint for power generation scheduling. Based on this, a coordinated generation and reserve scheduling model for power systems with large-scale wind power is established. Case study results demonstrate that the proposed model can balance both economy and reliability, coordinate the output allocation between wind power and thermal power, and provide an optimized allocation scheme for operating reserves among thermal power units to meet corresponding reliability requirements.
Offshore Wind Farm Generation Simulation and Capacity Value Evaluation Considering Resonance Zone Control
Authors: Li Bo , Wang Yuxue , Jiang Jianjian , Yu Yanghao , Cai Xiao , Zhang Ning
Published: December 2024

Offshore wind is a promising renewable energy generation technology and is arousing great attention in regards to pursuing carbon neutrality targets. Accurately simulating offshore wind generation can help to better optimize its operation and planning. It is also a concern that mechanical resonance is a threat to the wind turbines’ lifespan. In this paper, the time-series simulation of offshore wind generation with consideration of resonance zone (RZ) control is investigated. The output model for multiple wind farms with different spatial correlations is proposed. Additionally, the capacity value (CV) of the joint wind farms is also evaluated through a reliability-based model. The case study illustrates the offshore wind power output simulation and CV results under different farm correlation scenarios and RZ control strategies. It is shown that strong spatial correlation brings great synchronicity in wind farms’ output and results in a lower CV. The RZ control in wind simulation is validated and proven to have a marginal impact on the total output when multiple wind farms are evaluated together.
The Gourd as a Spiritual and Cultural Symbol Among the Yi People in Southwest China
Authors: Chen Ling
Published: December 2024

In the Yi ethnic group in southwest China, the gourd is a prominent symbol, collectively imbued with imaginative and spiritual significance. It maintains a profound connection to the Yi people’s way of life, manifested in both ancient mythological narratives and contemporary social practices. Given the pivotal role of the gourd in the Great Flood, reconstituting the world and birthing humanity, this study delves into its intricate associations with fertile imagery and thinking, provides an interpretation of the gourd’s mythological archetype, elucidates religious practices, and explores the cultural interpretations conveyed through gourd imagery. The cultural traditions of the gourd within Yi culture operate through both mythological narratives and lived practices, forming an integrated cognitive framework through which people perceived and interpreted the world. Presently, research specifically focusing on the gourd symbol in Yi mythology and rituals is limited. This article, offering a perspective grounded in classical textual interpretation, aims to understand the culture of the Yi people. It provides a comprehensive analysis of the gourd’s symbolic significance and enriches broader discussions on cultural diversity in contemporary society.
Bayesian Inference for Post-Processing of Remote-Sensing Image Classification
Authors: Camara Gilberto , Assunção Renato , Carvalho Alexandre , Simoes Rolf , Souza Felipe , Carlos Felipe , Souza Anielli , Rorato Ana , Dal’Asta Ana Paula
Published: December 2024

A key component of remote-sensing image analysis is image classification, which aims to categorize images into different classes using machine-learning methods. In many applications, machine-learning classifiers assign class probabilities to each pixel. These class probabilities serve as input for post-processing techniques that aim to improve the results of machine-learning algorithms. This paper proposes a new post-processing algorithm based on an empirical Bayes approach. We employ non-isotropic neighborhood definitions to capture the impact of borders between land classes in the statistical model. By incorporating expert knowledge, the algorithm improves the consistency of the classified map. This technique has proven its efficacy for large-scale data processing using image time-series analysis. The proposed method is a key component of a time-first, space-based approach for big Earth-observation data processing. It is available as open source as part of the R package sits.
LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion
Authors: Shi Zongwen , Fan Junfu , Du Yujie , Zhou Yuke , Zhang Yi
Published: December 2024

Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). This network enhances the clarity of the edge segmentation, detail resolution, and the visualization and accuracy of the contours by delving into the spatial details of the remote sensing images. The LULC-SegNet incorporates DDPM decoder features into the LULC segmentation task, utilizing machine learning clustering algorithms and spatial attention to extract continuous DDPM semantic features. The network addresses the potential loss of spatial details during feature extraction in convolutional neural network (CNN), and the integration of the DDPM features with the CNN feature extraction network improves the accuracy of the segmentation boundaries of the geographical features. Ablation and comparison experiments conducted on the Circum-Tarim Basin Region LULC Dataset demonstrate that the LULC-SegNet improved the LULC semantic segmentation. The LULC-SegNet excels in multiple key performance indicators compared to existing advanced semantic segmentation methods. Specifically, the network achieved remarkable scores of 80.25% in the mean intersection over union (MIOU) and 93.92% in the F1 score, surpassing current technologies. The LULC-SegNet demonstrated an IOU score of 73.67%, particularly in segmenting the small-sample river class. Our method adapts to the complex geophysical characteristics of remote sensing datasets, enhancing the performance of automatic semantic segmentation tasks for land use and land cover changes and making critical advancements.
Analyses of the 2016–2023 Dust Storms in China Using Himawari-8 Remote Sensing Observations
Authors: Luo Nana , Hu Chaonan , Piao Xingguang , Chen Ming , Yan Xing
Published: December 2024

The March 2021 dust storm in China degraded air quality across a wide area of Asia. Atmospheric circulation and meteorological factors play an important role in the occurrence of dust storms. To understand whether decreasing or increasing these factors can mitigate dust storms, this study utilizes remote sensing imagery data from the Himawari-8/-9 satellites to understand spatial and temporal variations in China’s 2016–2023 dust storms. Our findings are as follows: (1) in 2016–2023, dust storms covered northern China, with Xinjiang, Inner Mongolia, Gansu, and Ningxia being high-frequency areas; (2) the origins of the dust storms are northwest of Mongolia and Xinjiang, with upper air masses originating from Siberia and concentrating in central-west Inner Mongolia and northern Gansu; (3) dew point temperature, wind speed, cloud cover, and atmospheric circulation are important determinants of the occurrences of dust storms. Analyzing trends and influential factors of dust storms is important as this provides a scientific basis for decision-making in dust storm management.
Satellite Image Time-Series Classification with Inception-Enhanced Temporal Attention Encoder
Authors: Zhang Zheng , Zhang Weixiong , Meng Yu , Zhao Zhitao , Tang Ping , Li Hongyi
Published: December 2024

In this study, we propose a one-branch IncepTAE network to extract local and global hybrid temporal attention simultaneously and congruously for fine-grained satellite image time series (SITS) classification. Transformer and the temporal self-attention mechanism have been the research focus of SITS classification in recent years. However, its effectiveness seems to diminish in the scenario of fine-grained classification among similar categories, for example, different crop types. Theoretically, most of the existing methods focus on only one type of temporal attention, either global attention or local attention, but actually, both of them are required to achieve fine-grained classification. Even though some works adopt two-branch architecture to extract hybrid attention, they usually lack congruity between different types of temporal attention and hinder the expected discriminating ability. Compared with the existing methods, IncepTAE exhibits multiple methodological novelties. Firstly, we insert average/maximum pooling layers into the calculation of multi-head attention to extract hybrid temporal attention. Secondly, IncepTAE adopts one-branch architecture, which reinforces the interaction and congruity of different temporal information. Thirdly, the proposed IncepTAE is more lightweight due to the use of group convolutions. IncepTAE achieves 95.65% and 97.84% overall accuracy on two challenging datasets, TimeSen2Crop and Ghana. The comparative results with existing state-of-the-art methods demonstrate that IncepTAE is able to achieve superior classification performance and faster inference speed, which is conducive to the large-area application of SITS classification.
Detection of Flight Target via Multistatic Radar Based on Geosynchronous Orbit Satellite Irradiation
Authors: Dong Jia , Liu Peng , Wang Bingnan , Jin Yaqiu
Published: December 2024

As a special microwave detection system, multistatic radar has obvious advantages in covert operation, anti-jamming, and anti-stealth due to its configuration of spatial diversity. As a high-orbit irradiation source, a geosynchronous orbit satellite (GEO) has the advantages of a low revisit period, large beam coverage area, and stable power of ground beam compared with traditional passive radar irradiation sources. This paper focuses on the key technologies of flight target detection in multistatic radar based on geosynchronous orbit satellite irradiation with one transmitter and multiple receivers. We carry out the following work: Firstly, we aim to address the problems of low signal-to-noise ratio (SNR) and range cell migration of high-speed cruise targets. The Radon–Fourier transform constant false alarm rate detector-range cell migration correction (RFT-CFAR-RCMC) is adopted to realize the coherent integration of echoes with range cell migration correction (RCM) and Doppler phase compensation. It significantly improves the SNR. Furthermore, we utilize the staggered PRF to solve the ambiguity and obtain multi-view data. Secondly, based on the aforementioned target multi-view detection data, the linear least square (LLS) multistatic positioning method combining bistatic range positioning (BR) and time difference of arrival positioning (TDOA) is used, which constructs the BR and TDOA measurement equations and linearizes by mathematical transformation. The measurement equations are solved by the LLS method, and the target positioning and velocity inversion are realized by the fusion of multistatic data. Finally, using target positioning data as observation values of radar, the Kalman filter (KF) is used to achieve flight trajectory tracking. Numerical simulation verifies the effectiveness of the proposed process.
Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale
Authors: Bozorgi Mahsa , Cristóbal Jordi , Pàmies-Sans Magí
Published: December 2024

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ETd) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ETBR and ETRES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ETd across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ETRES, by a mean bias error (MBE) of 0.96 mm.day−1, an average mean root square (RMSE) of 1.47 mm.day−1, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day−1, 1.60 mm.day−1, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought.
Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography
Authors: Kuznetsov Oleksandr , Frontoni Emanuele , Chernov Kyrylo , Kuznetsova Kateryna , Shevchuk Ruslan , Karpinski Mikolaj
Published: December 2024

This paper presents an extensive investigation into the application of artificial intelligence, specifically Convolutional Neural Networks (CNNs), in image steganography detection. We initially evaluated the state-of-the-art steganalysis model, SRNet, on various image steganography techniques, including WOW, HILL, S-UNIWARD, and the innovative Spread Spectrum Image Steganography (SSIS). We found SRNet’s performance on SSIS detection to be lower compared to other methods, prompting us to fine-tune the model using SSIS datasets. Subsequent experiments showed significant improvement in SSIS detection, albeit at the cost of minor performance degradation as to other techniques. Our findings underscore the potential and adaptability of AI-based steganalysis models. However, they also highlight the need for a delicate balance in model adaptation to maintain effectiveness across various steganography techniques. We suggest future research directions, including multi-task learning strategies and other machine learning techniques, to further improve the robustness and versatility of steganalysis models.
Technological Characterization of Cobalt and Nickel Ores from Greece for Metal Recovery
Authors: Eljoudiani Amina , Sampaio Carlos Hoffmann , Oliva Josep , Veras Moacir Medeiros , Alfonso Pura , Anticoi Hernan , Tampouris Stylianos , Cortina Jose Luis , Escalante Percy Rodrigo
Published: December 2024

Greece has mineral resources in which strategic elements such as nickel (Ni) and cobalt (Co) are present. In this study, characterizations of the metal concentrations of three laterite deposits, as well as minerals from a kiln originating from a mineral processing plant, were carried out. The aim of this research was to integrate technological characterization data on the quantities of cobalt (Co) and nickel (Ni) present in the lateritic profiles of three mineral deposits from Greece and in kiln dust. The study was conducted at the Mineral Processing Laboratory of UPC/Bases Manresa. Aliquots were constituted for particle-size distribution tests by wet sieving followed by laser granulometry. Scanning electron microscopy (SEM) was used to determine the mineralogical compositions. Sink–float tests were conducted to estimate the particle liberation. Mineral liberation analysis (MLA) was performed to understand the liberation and the association of minerals in the samples.
Relationship of Individual Athlete External Load, Session Rating of Perceived Exertion, and Athlete Playing Status Across a Collegiate Women’s Basketball Season
Authors: Brown Faith S. A. , Fields Jennifer B. , Jagim Andrew R. , King Erica L. , Baker Robert E. , Miller Angela , Jones Margaret T.
Published: December 2024

External (EL) and internal (IL) load are commonly used methods used to quantify training load in team sports. Playing time and playing position may influence the training loads for specific athletes throughout a season. The purpose of the current study was to evaluate the effect of athlete playing status and individual in-season practices on EL and IL across a collegiate women’s basketball season. Female basketball athletes were classified as high-minute (HMA; ≥15 min/game) or low-minute (LMA; <15 min/game) and wore microsensors during 53 practices for a total of 583 data points. EL was obtained via an inertial measurement unit (IMU) device that contained a triaxial accelerometer to obtain three-dimensional positioning data. IL and strength training (ST) load were determined via session rating of perceived exertion (sRPE) to create a daily summated value. Descriptive statistics indicate that athletes experienced individual differences in EL, ST, and IL throughout the season. A growth model showed that HMAs experienced higher EL than LMAs at the start of the season for practices (90.21 AU). Across all athletes, IL increased across the season (40.11 AU) and for each 1 unit of change in EL, IL increased by 1.04 AU. Repeated measures correlations identified a large relationship between IL and EL (r = 0.51, p < 0.001). A location-scale model indicated that the within-person variability of IL across all athletes was 3.29 AU but was not due to athlete playing status. It is recommended to base in-season training on individual loads and game demands to promote athlete readiness and improved sport performance.
Advanced Control Strategies for Cleaner Energy Conversion in Biomass Gasification
Authors: Velilla-Díaz Wilmer , Barrios Johann Monroy , Villegas Jonathan Fábregas , Palencia-Díaz Argemiro
Published: December 2024

The escalating climate crisis necessitates urgent and decisive action to mitigate greenhouse gas emissions. Gasification stands out as a highly adaptable process for energy conversion, capable of handling a wide range of feedstocks, from coal to biomass. The process plays a significant role in improving sustainability by converting these feedstocks into synthesic gas (syngas), which can be used as a cleaner energy source or as a building block for producing various chemicals. The utilization of syngas obtained through gasification not only reduces the reliance on fossil fuels but also helps in reducing greenhouse gases (GHGs), thereby contributing to a more sustainable energy landscape. To maintain optimal operational conditions and ensure the quality and safety of the product, an effective control system is crucial in the gasification process. This paper presents a comparative analysis of three control strategies applied to a numerical model of rice husk gasification: classical control, fuzzy logic control, and dynamic matrix control. The analysis is based on a comprehensive model that includes the equations necessary to capture the dynamic behavior of the gasification process across its various stages. The goal is to identify the most effective control strategy, and the performance of each control strategy is evaluated based on the integral of the absolute value of the error (IAE). The results indicatethat fuzzy logic control consistently outperforms classical control techniques, demonstrating superior disturbance rejection, enhanced stability, and overall improved control accuracy. These findings highlight the importance of selecting an appropriate advanced control strategy to optimize sustainable gasification processes.
Correction: Ma et al. Assessment of Crop Water Footprint and Actual Agricultural Water Consumption in Arid Inland Regions: A Case Study of Aksu Region. Sustainability 2024, 16, 2911
Authors: Ma Jiali , Zhang Pei , Deng Xiaoya , Lai Xiaoying , Ren Cai , Zhang Ji , Liu Jing , Zhang Yanfei , Long Aihua
Published: December 2024

The authors would like to make the following corrections to the published paper [...]
Enhancing Fish Passage Efficiency: Lessons from UHE Porto Primavera’s Fish Ladder
Authors: Makrakis Sergio , Sanz-Ronda Francisco Javier , Bravo-Cordoba Francisco Javier , de Assumpção Hartmann Lucileine , Fortunato Wane Cristina Picanço , Borghezan Hudson Luiz Moret , Dias João Henrique Pinheiro , Makrakis Maristela Cavicchioli
Published: December 2024

Dams severely affect aquatic environments and block the longitudinal migration of fish. In order to mitigate the negative effects generated by these developments, fish passes, or fishways, are implemented in dams with the purpose of restoring river connectivity and allowing the movement of migrants. Nevertheless, fishways in neotropical areas often face design and construction issues that can reduce their efficiency and selectively disadvantage species with limited swimming capabilities. This study analyzes how a fish ladder on the Paraná River influences the black armored catfish (Rhinelepis aspera), a benthic, long-distance migratory species important to commercial fisheries. A total of 200 individuals were PIT-tagged and monitored for four months. The results showed that although many fish successfully located the fishway, only a small portion (3.5%) managed to complete the ascent. The interaction between the hydraulic characteristics of the fishway and the fish condition factor played a significant role in ascent performance. Our findings underscore the importance of assessing fishway suitability for benthic neotropical species to support conservation efforts in the Upper Paraná River Basin. To improve passage rates for R. aspera, we recommend optimizing flow conditions by adjusting orifice and notch configurations, incorporating roughness elements, and modifying resting pool designs. These adaptations would reduce energy expenditure for ascending fish, enhancing fishway performance and contributing to the sustainability of migratory species in this region.
Obligation to Opportunity: Exploring the Symbiosis of Corporate Social Responsibility, Green Innovation, and Organizational Agility in the Quest for Environmental Performance
Authors: Abro Qurat-ul-ain , Laghari Azhar Ali , Yin Jianhua , Qasim Muhammad , Hussain Azhar , Soomro Azra , Hisbani Faiza , Ashraf Anila
Published: December 2024

This study investigates the intricate relationship between CSR, green innovation, and environmental performance within the context of China’s manufacturing industries. Given the pressing environmental challenges faced by this sector, understanding how CSR practices correlate with sustainable innovations is critical for stakeholders aiming to enhance environmental outcomes. This was a survey-based study using a questionnaire and the five-point Likert scale; items were adopted from previous studies. Sampling was drawn through random sampling. Utilizing a sample of 327 respondents, this research employs SPSS and Structural Equation Modeling with Partial Least Squares (SEM-PLS) as analytical tools. The findings reveal a robust positive correlation between CSR practices and green innovation, as evidenced by a path coefficient of 0.704. These data support the stakeholder theory, which posits that organizations attentive to stakeholder expectations are more inclined to adopt sustainable practices. Furthermore, this study underscores the mediating role of green innovation in the relationship between CSR and environmental performance, highlighting its importance in aligning organizational strategies with sustainability-oriented stakeholder interests. This conclusion aligns with the existing literature emphasizing CSR’s significance in improving environmental performance through innovative approaches. However, an unexpected finding emerged: there exists a weak negative relationship between green innovation and organizational agility (−0.080). This suggests that, while firms strive for sustainable innovations, they may inadvertently compromise their flexibility in responding to evolving market demands. By addressing these dynamics, this research contributes valuable insights into how CSR can effectively spur green innovation and promote sustainable practices within China’s manufacturing sector. This study fills a gap in the existing literature by elucidating the mechanisms that connect CSR with enhanced environmental performance while also recognizing the potential trade-offs associated with innovation strategies. Also, the exploration of agility, which is least investigated, can also open various doors towards sustainability and the adaptation of new changes. Future research is encouraged to further explore these relationships across different industries and delve deeper into the mechanisms linking CSR to improved environmental outcomes, ultimately guiding organizations in balancing sustainability efforts with market responsiveness.
Analysis and Future Projections of Land Use and Land Cover Changes in the Hindon River Basin, India Using the CA-Markov Model
Authors: Singh Ritu , Rai Suresh Chand , Mishra Prabuddh Kumar , Abdelrahman Kamal , Fnais Mohammed S.
Published: December 2024

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, and urbanization is responsible for these developments. Monitoring and mapping of changes in land cover and land use is essential to the sustainable development and management of the area. The study attempts to track changes in LULC pattern for the years 2002, 2013, and 2023 in the Hindon River Basin, a major tributary of the Yamuna River, using remote sensing and geographic information system techniques. Images obtained from Landsat data were employed to extract historical land use and land cover maps. Additionally, the CA-Markov model was implemented to forecast future land use and land cover patterns. This study examines the historical and predicted LULC in the area. Field observations and site-specific interviews were used to confirm and determine the ground realities. High-resolution images were used to evaluate the accuracy of the classified map. According to the results, the agricultural land decreased from 60.98% in 2002 to 54.70% in 2050, while built-up areas increased from 12.95% to 21.25% during the same period. By 2050, vegetation is predicted to increase to 2.58%, whereas surface water, fallow land, barren areas, and dry water bodies are predicted to decrease to 0.58%, 18.87%, 1.20%, and 0.83%, respectively. The rapid pace of urbanization is facilitating economic growth within the country; however, this development is occurring at the expense of the natural landscape, which subsequently diminishes the overall quality of human life. In order to maintain sustainable development in the Hindon Basin, proper urban planning is essential. Important policy implications for the sustainable management of land use and conservation in the Hindon River basin are highlighted by the study’s research and findings.
Transformation Through Servitization: How Buffer Resources and Social Capital Support Transformation in Manufacturing Companies
Authors: Xie Xiaoya , Zhang Yu
Published: December 2024

Servitization has profound implications for the sustainable development of manufacturing, the economy, and the environment. Drawing upon the literature on servitization, resource-based theory, and social capital theory, this study examines the influence of diverse forms of slack resources in manufacturing firms on their transition to a service-oriented business model, as well as the threshold effects of two types of social capital in this process. By conducting an empirical study, using the sample of 538 listed companies in the Chinese manufacturing sector, we confirm that both absorbed and unabsorbed slack resources have a positive effect on servitization, with the former playing a more significant role. Moreover, as political social capital increases and exceeds a specific threshold, the positive influence of both forms of slack resources on servitization is enhanced. Conversely, the positive effect of absorbed slack resources is negated when business social capital exceeds a certain threshold. In addition, the heterogeneity analysis shows that both types of slack resources significantly promote servitization in non-high-tech firms, while their effects are insignificant in high-tech firms; both types of slack resources effectively promote servitization when the industry is highly competitive, and vice versa; for the eastern region, both types of slack resources significantly promote servitization, while their effects are insignificant in the central region, and the negative effects of unabsorbed slack resources are significant in the western region. This study innovatively integrates the dual factors of intra-organizational slack resources and extra-organizational social capital, offering deeper insights and more concrete practical guidance for enterprises in formulating their servitization strategies.
Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management
Authors: Calle Müller Claudia , Lagos Leonel , Elzomor Mohamed
Published: December 2024

Natural disasters cause extensive infrastructure and significant economic losses, hindering sustainable development and impeding social and economic progress. More importantly, they jeopardize community well-being by causing injuries, damaging human health, and resulting in loss of life. Furthermore, communities often experience delayed disaster response. Aggravating the situation, the frequency and impact of disasters have been continuously increasing. Therefore, fast and effective disaster response management is paramount. To achieve this, disaster managers must proactively safeguard communities by developing quick and effective disaster management strategies. Disruptive technologies such as artificial intelligence (AI), machine learning (ML), and robotics and their applications in geospatial analysis, social media, and smartphone applications can significantly contribute to expediting disaster response, improving efficiency, and enhancing safety. However, despite their significant potential, limited research has examined how these technologies can be utilized for disaster response in low-income communities. The goal of this research is to explore which technologies can be effectively leveraged to improve disaster response, with a focus on low-income communities. To this end, this research conducted a comprehensive review of existing literature on disruptive technologies, using Covidence to simplify the systematic review process and NVivo 14 to synthesize findings.
Unpacking the Relationship Between Empowerment Leadership and Electricity Worker’s Unsafe Behavior: A Multi-Moderated Mediation Approach
Authors: Arhim Ali , Alzubi Ahmad , Iyiola Kolawole , Banje Faith Umene
Published: December 2024

Ensuring workplace safety in high-risk sectors is critical to achieving sustainable productivity and occupational health, particularly in industries prone to unsafe practices. Drawing on social exchange theory (SET), this study examines the impact of empowerment leadership (EL) on electricity workers’ unsafe behaviors (EWUBs) in Jordan, focusing on the mediating roles of safety motivation (SM) and work engagement (WE), as well as the moderating role of the error management climate (EMC). A quantitative approach was employed, collecting data from 409 electricity workers across various regions of Jordan. The data were analyzed using structural equation modeling (SEM) employing SmartPLS 4 to assess the relationships of these variables and AMOS 24.0 to compute the study measurement model’s internal consistency and construct validity. The results demonstrate that empowerment leadership significantly reduces electricity workers’ unsafe behaviors through increased safety motivation and work engagement. Furthermore, the error management climate moderates the relationship between empowerment leadership and work engagement (Estimate = 0.238, t = 7.783, <0.001) is stronger when the error management climate is high and weaker but also insignificant when the error management climate is low (Estimate = 0.045, t = 1.015, >0.05). The research highlights the crucial role of empowerment leadership in promoting safety motivation and work engagement, which (Estimate = 0.238, t = 7.783, <0.001) is stronger and essential for minimizing unsafe behavior in high-risk industries like electricity. The findings highlight the pivotal role of shaping employees’ unsafe behavior and offers practical implications for policymakers and institutions aiming to promote employees’ safety behavior. Future studies also emphasize fostering an error management climate to reinforce these effects and organizations should focus on leadership development and creating a supportive error management climate to maximize safety outcomes.