bluuebunny/crossref_metadata_2025 huggingface dataset:
Dimensions of Cellulose Nanocrystals from Cotton and Bacterial Cellulose: Comparison of Microscopy and Scattering Techniques
Authors: Grachev Vladimir , Deschaume Olivier , Lang Peter R. , Lettinga Minne Paul , Bartic Carmen , Thielemans Wim
Published: March 2024

Different microscopy and scattering methods used in the literature to determine the dimensions of cellulose nanocrystals derived from cotton and bacterial cellulose were compared to investigate potential bias and discrepancies. Atomic force microscopy (AFM), small-angle X-ray scattering (SAXS), depolarized dynamic light scattering (DDLS), and static light scattering (SLS) were compared. The lengths, widths, and heights of the particles and their respective distributions were determined by AFM. In agreement with previous work, the CNCs were found to have a ribbon-like shape, regardless of the source of cellulose or the surface functional groups. Tip broadening and agglomeration of the particles during deposition cause AFM-derived lateral dimensions to be systematically larger those obtained from SAXS measurements. The radius of gyration determined by SLS showed a good correlation with the dimensions obtained by AFM. The hydrodynamic lateral dimensions determined by DDLS were found to have the same magnitude as either the width or height obtained from the other techniques; however, the precision of DDLS was limited due to the mismatch between the cylindrical model and the actual shape of the CNCs, and to constraints in the fitting procedure. Therefore, the combination of AFM and SAXS, or microscopy and small-angle scattering, is recommended for the most accurate determination of CNC dimensions.
Recent Development of Nanomaterials for Chemical Engineering
Authors: Cao Meiwen
Published: March 2024

There has been an explosive growth in research on nanomaterials since the late 1980s and early 1990s [...]
In Vivo Assessment of Hepatic and Kidney Toxicity Induced by Silicon Quantum Dots in Mice
Authors: Cristian Roxana-Elena , Balta Cornel , Herman Hildegard , Trica Bogdan , Sbarcea Beatrice G. , Hermenean Anca , Dinischiotu Anca , Stan Miruna S.
Published: March 2024

In the last decade, silicon-based quantum dots (SiQDs) have attracted the attention of researchers due to their unique properties for which they are used in medical applications and in vivo imaging. Detection of cytotoxic effects in vivo is essential for understanding the mechanisms of toxicity, a mandatory step before their administration to human subjects. In this context, we aimed to evaluate the in vivo hepatic and renal acute toxicity of SiQDs obtained by laser ablation. The nanoparticles were administrated at different doses (0, 1, 10, and 100 mg of QDs/kg of body weight) by intravenous injection into the caudal vein of Swiss mice. After 1, 6, 24, and 72 h, the animals were euthanatized, and liver and kidney tissues were used in further toxicity tests. The time- and dose-dependent effects of SiQDs on the antioxidant defense system of mice liver and kidney were investigated by quantifying the activity of antioxidant enzymes (catalase, superoxide dismutase, glutathione peroxidase, glutathione reductase, and glutathione S-transferase) in correlation with the morphological changes and inflammatory status in the liver and kidneys. The results showed a decrease in the activities of antioxidant enzymes and histopathological changes, except for superoxide dismutase, in which no significant changes were registered compared with the control. Furthermore, the immunohistochemical expression of TNF-α was significant at doses over 10 mg of QDs/kg of body weight and were still evident at 72 h after administration. Our results showed that doses under 10 mg of SiQDs/kg of b.w. did not induce hepatic and renal toxicity, providing useful information for further clinical trials.
Assessing Muscle Mass in the Orthopedic Clinical Setting: Application of the Ultrasound Sarcopenia Index in Elderly Subjects with a Recent Femoral Fracture
Authors: Di Lenarda Luca , Buoite Stella Alex , Ratti Chiara , Ruggiero Luca , Bernard Monica , Cavarzerani Luisa Priscamaria , Canton Gianluca , Murena Luigi
Published: March 2024

Background: Sarcopenia poses a risk factor for falls, disability, mortality, and unfavorable postoperative outcomes. Recently, the Ultrasound Sarcopenia Index (USI) has been validated to assess muscle mass, and this study aimed to apply the USI in the clinical setting. Methods: This prospective observational study included 108 patients aged >65 years, hospitalized for proximal femoral traumatic fracture. Patients were divided into two groups based on anamnestic data: patients with independent walking (IW) and patients requiring walking aid (WA) before admission. All the participants received an ultrasound examination. Other parameters evaluated were handgrip strength, limb circumferences, nutrition (MNA), and activity of daily living (ADL) scores. Results: Fifty-six IW patients (83 ± 6 y; 38 females) and 52 WA patients (87 ± 7 y; 44 females) were recruited. The USI was significantly higher in the IW group compared to the WA group (p = 0.013, Cohen’s d = 0.489). Significant correlations were found between the USI and other sarcopenia-associated parameters, such as handgrip strength, MNA, ADLs, other muscle ultrasound parameters, and limb circumferences. Conclusion: The application of the USI in the orthopedic surgery setting is feasible and might support the diagnosis of sarcopenia when combined with other measures of strength and function.
Role of Food Digestion and Digestive System in the Nutritional, Functional and Health Properties of Food Bioactives
Authors: Fernández-Tomé Samuel
Published: March 2024

The impact of food components on the human digestive system is an important area of research in the fields of nutrition and food science [...]
Early Infant Feeding Practices and Associations with Growth in Childhood
Authors: Clayton Priscilla K. , Putnick Diane L. , Trees Ian R. , Ghassabian Akhgar , Tyris Jordan N. , Lin Tzu-Chun , Yeung Edwina H.
Published: March 2024

Early infant growth trajectories have been linked to obesity risk. The aim of this study was to examine early infant feeding practices in association with anthropometric measures and risk of overweight/obesity in childhood. A total of 2492 children from Upstate KIDS, a population-based longitudinal cohort, were included for the analysis. Parents reported breastfeeding and complementary food introduction from 4 to 12 months on questionnaires. Weight and height were reported at 2–3 years of age and during later follow-up at 7–9 years of age. Age and sex z-scores were calculated. Linear mixed models were conducted, adjusting for maternal and child sociodemographic factors. Approximately 54% of infants were formula-fed at <5 months of age. Compared to those formula-fed, BMI- (adjusted B, −0.23; 95% CI: −0.42, −0.05) and weight-for-age z-scores (adjusted B, −0.16; −0.28, −0.03) were lower for those exclusively breastfed. Infants breastfed for ≥12 months had a lower risk of being overweight (aRR, 0.33; 0.18, 0.59) at 2–3 years, relative to formula-fed infants. Compared to introduction at <5 months, the introduction of fruits and vegetables between 5 and 8 months was associated with lower risk of obesity at 7–9 years (aRR, 0.45; 0.22, 0.93). The type and duration of breastfeeding and delayed introduction of certain complementary foods was associated with lower childhood BMI.
The Relationship between Birth Weight and the Risk of Overweight and Obesity among Chinese Children and Adolescents Aged 7–17 Years
Authors: Shi Jiaqi , Guo Qiya , Fang Hongyun , Cheng Xue , Ju Lahong , Wei Xiaoqi , Zhao Liyun , Cao Qiuye , Yuan Xiaolin , He Li
Published: March 2024

Obesity is a major public health issue in children and adolescents. Our study aimed to examine the impacts of birth weight on overweight and obesity among Chinese children and adolescents. Using data from the China National Nutrition and Health Surveillance of Children and Lactating Mothers in 2016–2017, we included 10,041 participants aged 7–17 years. According to birth weight, participants were categorized into six groups, and the birth weight category of 3000 to 3499 g was chosen as the reference group, containing the largest number of children. Logistic regression analyses were used to investigate the association of birth weight with the risk of being obese at 7 to 17 years of age in multivariable-adjusted models. A restricted cubic spline was utilized to show the odds ratios (ORs) of obesity at different birth weight levels. The adjusted ORs for overweight were 0.98 (95%CI 0.63, 1.53), 1.02 (95%CI 0.84, 1.25), 1.34 (95%CI 1.16, 1.55), 1.72 (95%CI 1.35, 2.18), and 1.17 (95%CI 0.71, 1.96) in several birth weight groups, compared with group C (3000–3499 g). The adjusted ORs for obesity were 0.82 (95%CI 0.48, 1.40), 0.77 (95%CI 0.60, 0.98), 1.33 (95%CI 1.13, 1.57), 1.97 (95%CI 1.53, 2.53), and 2.01 (95%CI 1.27, 3.19). Furthermore, children in the post-pubertal stage had a slightly higher risk of overweight and obesity than those in the pre-pubertal and pubertal stage. Moreover, these associations were stronger among boys. The lower part of normal birth weight range is associated with a lower risk of overweight and obesity in children and adolescents. However, higher levels of birth weight increase risk.
Mediterranean Diet Adherence and Health-Related Quality of Life during Pregnancy: Is the Mediterranean Diet Beneficial in Non-Mediterranean Countries?
Authors: Flor-Alemany Marta , Sandborg Johanna , Migueles Jairo H. , Söderström Emmie , Henström Maria , Marín-Jiménez Nuria , Baena-García Laura , Aparicio Virginia A. , Löf Marie
Published: March 2024

This study aimed to examine the association of Mediterranean diet (MD) adherence and MD components with health-related quality of life (HRQoL) in pregnant women from Spain and Sweden. A total of 138 pregnant women from Spain (age: 32.9 ± 4.6 years old) and 302 pregnant women from Sweden (age: 31.3 ± 4.1 years old) were included. MD adherence was assessed with the Mediterranean food pattern (i.e., a MD index) at the 14–16th gestational weeks. HRQoL was assessed with the Spanish and Swedish versions of the 36-item Short-Form Health Survey (SF-36 and RAND-36, respectively) at the 14–16th and 34–37th gestational weeks. A greater MD adherence was associated with better physical functioning, bodily pain, vitality, emotional role, and mental health in cross-sectional associations (2nd trimester) in the Spanish sample (all p < 0.05). Furthermore, a greater MD adherence was associated with lower bodily pain in both Spanish and Swedish samples (both p < 0.05) in the 3rd trimester. The associations of MD adherence with pain seem to be explained by a greater intake of fiber, fish, fruits, nuts, and legumes (all p < 0.05). A greater MD adherence, driven by a higher intake of fiber, fish, fruits, nuts, and legumes, was associated with lower pain throughout pregnancy in both Mediterranean and non-Mediterranean populations.
Ceftazidime/Tobramycin Co-Loaded Chitosan-Coated Zein Nanoparticles against Antibiotic-Resistant and Biofilm-Producing Pseudomonas aeruginosa and Klebsiella pneumoniae
Authors: Campos Luís André de Almeida , Neto Azael Francisco Silva , Scavuzzi Alexsandra Maria Lima , Lopes Ana Catarina De Souza , Santos-Magalhães Nereide Stela , Cavalcanti Isabella Macário Ferro
Published: March 2024

This study aimed to co-encapsulate ceftazidime and tobramycin in zein nanoparticles coated with chitosan and to characterize and evaluate the antibacterial and antibiofilm activity against antibiotic-resistant Pseudomonas aeruginosa and Klebsiella pneumoniae. Zein nanoparticles, synthesized using the nanoprecipitation method, were characterized by their particle size (Ø), polydispersity index (PDI), zeta potential (ζ), pH, and encapsulation efficiency (%EE). The chitosan coating provided stability, and physicochemical analyses revealed chemical interactions, efficient drug encapsulation, and thermal stability. The release kinetics demonstrated controlled release in simulated gastric and intestinal pH. The antibacterial activity, assessed by minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), indicated effectiveness against both pathogens. Antibiofilm assays, conducted using the crystal violet method, demonstrated the inhibition and eradication of biofilms. The chitosan-coated zein nanoparticles with CAZ and/or TOB exhibited Ø (315–335 nm), PDI (<0.2), ζ (+40 to +50 mV), pH (5), and %EE (>55%). Notably, the co-encapsulation formulation (CAZ–TOB–ZNP–CH) showed enhanced antibacterial and antibiofilm activities compared to the individual formulations. These findings suggest that the developed nanoparticles present a promising alternative for treating respiratory and intestinal infections caused by antibiotic-resistant and biofilm-producing P. aeruginosa and K. pneumoniae.
Screening for Active Compounds of Acorus calamus against SARS-CoV-2 Viral Protease and Mechanism Prediction
Authors: Huang Yuting , Li Zhaoxing , Ma Yuan , Wu Qianqian , Kong Jianping , Zhao Lijuan , Li Shunxiang , Li Juan
Published: March 2024

COVID-19, caused by SARS-CoV-2, has emerged as the most destructive emerging infectious disease of the 21st century. Vaccination is an effective method to combat viral diseases. However, due to the constant mutation of the virus, new variants may weaken the efficacy of vaccines. In the current field of new coronavirus research, viral protease inhibitors have emerged as a highly regarded therapeutic strategy. Nevertheless, existing viral protease inhibitors do not fully meet the therapeutic needs. Therefore, this paper turned to traditional Chinese medicine to explore new active compounds. This study focused on 24 isolated compounds from Acorus calamus L. and identified 8 active components that exhibited significant inhibitory effects on SARS-CoV-2 PLpro. Among these, the compound 1R,5R,7S-guaiane-4R,10R-diol-6-one demonstrated the best inhibitory activity with IC50 values of 0.386 ± 0.118 μM. Additionally, menecubebane B and neo-acorane A exhibited inhibitory activity against both Mpro and PLpro proteases, indicating their potential as dual-target inhibitors. The molecular docking results confirmed the stable conformations of these compounds with the key targets and their good activity. ADMET and Lipinski’s rule analyses revealed that all the small molecule ligands possessed excellent oral absorption properties. This study provides an experimental foundation for the discovery of promising antiviral lead compounds.
State-of-the-Art Review on Inhalable Lipid and Polymer Nanocarriers: Design and Development Perspectives
Authors: Costabile Gabriella , Conte Gemma , Brusco Susy , Savadi Pouria , Miro Agnese , Quaglia Fabiana , d’Angelo Ivana , Ungaro Francesca
Published: March 2024

Nowadays, the interest in research towards the local administration of drugs via the inhalation route is growing as it enables the direct targeting of the lung tissue, at the same time reducing systemic side effects. This is of great significance in the era of nucleic acid therapeutics and personalized medicine for the local treatment of severe lung diseases. However, the success of any inhalation therapy is driven by a delicate interplay of factors, such as the physiochemical profile of the payload, formulation, inhalation device, aerodynamic properties, and interaction with the lung fluids. The development of drug delivery systems tailored to the needs of this administration route is central to its success and to revolutionize the treatment of respiratory diseases. With this review, we aim to provide an up-to-date overview of advances in the development of nanoparticulate carriers for drug delivery to the lung tissue, with special regard concerning lipid and polymer-based nanocarriers (NCs). Starting from the biological barriers that the anatomical structure of the lung imposes, and that need to be overcome, the current strategies to achieve efficient lung delivery and the best support for the success of NCs for inhalation are highlighted.
Decreases and Pronounced Geographic Variability in Antibiotic Prescribing in Medicaid
Authors: Aguilar Alexia G. , Canals Priscilla C. , Tian Maria , Miller Kimberly A. , Piper Brian J.
Published: March 2024

Antibiotic resistance is a persistent and growing concern. Our objective was to analyze antibiotic prescribing in the United States (US) in the Medical Expenditure Panel System (MEPS) and to Medicaid patients. We obtained MEPS prescriptions for eight antibiotics from 2013 to 2020. We extracted prescribing rates per 1000 Medicaid enrollees for two years, 2018 and 2019, for four broad-spectrum (azithromycin, ciprofloxacin, levofloxacin, and moxifloxacin) and four narrow-spectrum (amoxicillin, cephalexin, doxycycline, and trimethoprim-sulfamethoxazole) antibiotics. Antibiotic prescriptions in MEPS decreased from 2013 to 2020 by 38.7%, with a larger decline for the broad (−53.7%) than narrow (−23.5%) spectrum antibiotics. Antibiotic prescriptions in Medicaid decreased by 6.7%. Amoxicillin was the predominant antibiotic, followed by azithromycin, cephalexin, trimethoprim-sulfamethoxazole, doxycycline, ciprofloxacin, levofloxacin, and moxifloxacin. Substantial geographic variation in prescribing existed, with a 2.8-fold difference between the highest (Kentucky = 855/1000) and lowest (Oregon = 299) states. The South prescribed 52.2% more antibiotics (580/1000) than the West (381/1000). There were significant correlations across states (r = 0.81 for azithromycin and amoxicillin). This study identified sizable disparities by geography in the prescribing rates of eight antibiotics with over three-fold state-level differences. Areas with high prescribing rates, particularly for outpatients, may benefit from stewardship programs to reduce potentially unnecessary prescribing.
Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data
Authors: Poudyal Chudamani
Published: March 2024

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to a MLE become significantly limited when dealing with grouped loss severity data, with only a handful of methods, like least squares, minimum Hellinger distance, and optimal bounded influence function, available. This paper introduces a novel robust estimation technique, the Method of Truncated Moments (MTuM), pecifically designed to estimate the tail index of a Pareto distribution from grouped data. Inferential justification of the MTuM is established by employing the central limit theorem and validating it through a comprehensive simulation study.
Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia
Authors: Eisfelder Christina , Boemke Bruno , Gessner Ursula , Sogno Patrick , Alemu Genanaw , Hailu Rahel , Mesmer Christian , Huth Juliane
Published: March 2024

Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management.
Hyperspectral Image Classification Based on Mutually Guided Image Filtering
Authors: Zhan Ying , Hu Dan , Yu Xianchuan , Wang Yufeng
Published: March 2024

Hyperspectral remote sensing images (HSIs) have both spectral and spatial characteristics. The adept exploitation of these attributes is central to enhancing the classification accuracy of HSIs. In order to effectively utilize spatial and spectral features to classify HSIs, this paper proposes a method for the spatial feature extraction of HSIs based on a mutually guided image filter (muGIF) and combined with the band-distance-grouped principal component. Firstly, aiming at the problem that previously guided image filtering cannot effectively deal with the inconsistent information structure between the guided and target information, a method for extracting spatial features using muGIF is proposed. Then, aiming at the problem of the information loss caused by a single principal component as a guided image in the traditional GIF-based spatial–spectral classification, a spatial feature-extraction framework based on the band-distance-grouped principal component is proposed. The method groups the bands according to the band distance and extracts the principal components of each set of band subsets as the guide map of the current band subset to filter the HSIs. A deep convolutional neural network model and a generative adversarial network model for the filtered HSIs are constructed and then trained using samples for HSIs’ spatial–spectral classification. Experiments show that compared with the traditional methods and several popular spatial–spectral HSI classification methods based on a filter, the proposed methods based on muGIF can effectively extract the spatial–spectral features and improve the classification accuracy of HSIs.
A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions
Authors: Rahman Md Habibur , Sejan Mohammad Abrar Shakil , Aziz Md Abdul , Tabassum Rana , Baik Jung-In , Song Hyoung-Kyu
Published: March 2024

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.
Aesthetic and Thermal Suitability of Highly Glazed Spaces with Interior Roller Blinds in Najran University Buildings, Saudi Arabia
Authors: Qahtan Abdultawab M.
Published: March 2024

Highly glazed spaces are visually appealing and trendy, but effectively managing their temperature in hot arid climates remains a significant challenge. This study evaluates the effectiveness of dark-tinted double low-E glass with internal roller blinds in reducing heat gain in glazed spaces in hot arid climates and investigates architects’ perspectives on these facades. It combines field measurements and a survey to assess the balance between thermal control and aesthetics in such environments. This study reveals that the current glazing significantly attenuates solar radiation ingress, evidenced by a marked indoor-–outdoor temperature differential (ΔT) of approximately 9.2 °C. The mean radiant temperature registers at 1.5 °C above the indoor air temperature, which can be attributed to the glazing’s propensity to absorb and retain solar heat, resulting in an inner glass surface temperature of 43 °C. The implementation of adjustable blinds has a dynamic influence on the heat transfer coefficient (HTC), effectively modulating the temperature by impeding natural convection currents. With the blinds retracted, the HTC stands at an average of 7.1 W/m2K, which diminishes to 5 W/m2K when the blinds are 50% closed and further reduces to 4.2 W/m2K when the blinds are fully closed (100%). Survey results suggest that architects prioritise glazed facades for aesthetics (52%) while facing challenges in thermal and energy efficiency (44%). Future studies should concentrate on developing novel glazing systems that integrate solutions for visual appeal, lighting and thermal efficiency in glazed facades, particularly in hot arid climates.
Alleviating Cd Stress in Sunflower (Helianthus annuus) through the Sodium Silicate Application
Authors: Wu Haoying , Wang Xiyuan , Gao Haifeng , Chen Jiao , Zhang Tingting
Published: March 2024

Sunflower has substantial potential for the remediation of heavy metals in soil, but its efficiency in Cd-contaminated soil is limited, with high concentrations of Cd causing stress in plants. Exogenous Si enhances plant tolerance to heavy metals, but the mechanism for enhancing the tolerance of oil sunflower under Cd stress is not known. In potting experiments, sunflowers were grown in soil with a Cd concentration of 11.8 mg/kg soil (the maximum value in the Zhundong coal mining area of Xinjiang) and five Si treatment levels (0, 50, 250, 500, and 1500 mg/kg soil). Exogenous Si improved sunflower development, gas exchange characteristics, and antioxidant enzyme activities in sunflower compared with the Cd-only control; exogenous Si application increased Cd concentrations in sunflower roots, stems, and leaves, and Cd was mainly concentrated in sunflower roots. Sunflower biomass increased by 13.83–114.18%, and gas exchange parameters increased by 16.95–36.03%, 30.06–66.82%, and 9.77–14.71%, respectively, as compared to the control. With the increase in sodium silicate concentrations, sunflower antioxidant enzyme activities increased by 8.81–150.28%, 91.35–351.55%, and 35.11–54.69%, respectively, and MDA content decreased by 3.34–25.14% as compared to Si0. Moreover, exogenous Si increased Cd uptake and minimized Cd stress in sunflowers at the seedling and blooming stages, and it potentially facilitated the phytoremediation of Cd-contaminated soils through enriched plants such as sunflower, as well as contributing to the achievement of sustainable development of the soil environment.
Mothers’ Breastfeeding Satisfaction: Key to Environmental Sustainability
Authors: Murillo-Llorente Maria Teresa , Asins-Cubells Adalberto , Pérez-Murillo Javier , Palau-Ferrè Alma , Legidos-García Maria Ester , Llorca-Colomer Francisco , Tomás-Aguirre Francisco , Perez-Bermejo Marcelino
Published: March 2024

Breastfeeding is the natural way to feed a baby in the first months of life. It is an option with a low environmental impact, as it is a natural and renewable food that does not pollute. It is the optimal food for the infant, with nutritional and immunological advantages that reduce the prevalence of pregnancy disorders and provide benefits for the infant, the mother, and the environment. Its premature abandonment may have medium- and long-term consequences, as the use of formula milk is much more polluting. A descriptive, observational, cross-sectional study was carried out between December 2020 and January 2021, involving 96 breastfeeding women. Sociodemographic and obstetric variables were collected. A validated Spanish version of the Breastfeeding Self-efficacy Scale-Short Form (BSES-SF) was used. Factors that may lead to the early cessation of breastfeeding among the mothers that were studied are a high level of education, a lack of preparation for childbirth, a lack of security, a lack of confidence in preparation for childbirth, a lack of self-confidence, discomfort with breastfeeding in the presence of others, and a low level of maternal satisfaction. Married women and women whose partners support breastfeeding are significantly more satisfied with exclusive breastfeeding than single and divorced women. Adequate and protocolised care must be provided. Education and support for parents to allow for optimal breastfeeding and reduce the environmental impact of formula feeding is essential.
“The Greatest Benefit Is to Think Differently”: Experiences of Developing and Using a Web-Based Tool for Decision-Making under Deep Uncertainty for Adaptation to Sea Level Rise in Municipalities
Authors: Carlsson Kanyama Annika , Zapico Jorge Luis , Holmberg Chatarina , Wikman-Svahn Per
Published: March 2024

The need for handling the deep uncertainty surrounding the future climate has led to various novel and robust approaches for decision-making under deep uncertainty (DMDU) when adapting to climate change. Here, an online and self-explanatory web-based tool was developed and tested with civil servants from five municipalities in Sweden challenged by rising sea levels. The municipalities used the tool by themselves and were then interviewed about the usability of the tool, the perceived urgency of climate change adaptation, and the possibilities for municipalities for handling the flexible solutions that are at the core of DMDU. Results were that the civil servants reported that the urgency of climate change adaptation has increased, that the tool changed their perspectives compared to traditional planning methods, and that changes in laws and regulations to accommodate decisions about flexible solutions were recommended.
Car-Following Strategy Involving Stabilizing Traffic Flow with Connected Automated Vehicles to Reduce Particulate Matter (PM) Emissions in Rainy Weather
Authors: Li Renjie , Qin Yanyan
Published: March 2024

On highways, it is commonplace to observe car-following behavior among vehicles. Unfortunately, this behavior results in significant particulate matter (PM) emissions, which greatly contribute to environmental pollution. Additionally, adverse weather conditions such as rain can negatively affect vehicles’ car-following behavior and have further influences on their PM emissions. The technology of connected automated vehicles (CAVs) offers a promising solution for mitigating these negative influences. This paper investigates the effect of various rainy weather conditions on PM emissions during car-following behavior on highways and proposes a CAV car-following strategy to reduce these emissions. Firstly, we employed a calibrated car-following model of traditional vehicles to perform simulation experiments, examining characteristics of PM emissions under four levels of rain and two simulation scenarios. Secondly, based on the relationship between PM emissions and speed fluctuations, we proposed a CAV car-following strategy by stabilizing traffic flow to smooth speed fluctuations. The proposed CAV car-following strategy was then validated through simulation experiments, and its effectiveness in reducing PM emissions under rainy conditions was assessed. The results indicate that higher speed fluctuations during car-following behavior lead to more PM emissions in rainy weather. By utilizing the proposed car-following strategy, CAVs can significantly reduce PM emissions in rain conditions, with average reductions of 41.07%, 59.46%, 49.60%, and 71.66% under very light rain, light rain, moderate rain, and heavy rain conditions, respectively. The findings of this paper facilitate the assessment of PM emissions fluctuations in different rainy weather conditions, which in turn can contribute to the development of more effective PM emissions control strategies. The proposed CAV car-following strategy can smooth speed fluctuations, and improve traffic flow stability, thus reducing PM emissions in rainy weather. It has the potential to mitigate environmental pollution from the transportation sector.
How to Change Entrepreneurs’ Attitudes toward a Circular Economy: An Exploratory Framework to Reduce the Gap between Circular Intentions and Circular Actions
Authors: Fonrouge Cecile
Published: March 2024

Entrepreneurs are aware of the importance of having practices for circularizing their resources. However, the rare previous research specifically in entrepreneurship shows a gap between intentions and implementation with numerous barriers (structural, contextual, and cultural). This article proposes a first framework to reduce this gap between intentions and circular actions. These solutions are based on the theory of attitude changes, which states that changes in individuals’ attitudes are more significant if they are preceded by an inexpensive act (Binding Communication Approach). As this is a still little-explored subject that mixes circular entrepreneurship and environmental psychology, the research method is exploratory, based on an analysis of previous writings in these two distinct fields, with the proposal of a first formalization starting from the example of solutions based on inexpensive acts (communities of practices, prizes, and tools). Conclusions are proposed regarding the characteristics of this type of preparatory act necessary to change entrepreneur attitude toward circular economy practices.
Sustainability of the Food Industry: Ecological Efficiency and Influencing Mechanism of Carbon Emissions Trading Policy in China
Authors: Sheng Jiping , Gao Xiaoge , Sun Yongqi
Published: March 2024

As an important factor affecting ecological sustainability, the food industry’s ecological efficiency (EE) deserves great attention and control. In 2013, China implemented a carbon emissions trading policy (CETP) to limit carbon emissions from various industries to promote the optimization of the ecological environment. To explore the ecological sustainability of China’s food industry, identify the factors affecting the EE of the food industry, and study the influence heterogeneity and influencing mechanisms, the impact of the CETP on the food industry, which emits high volumes of greenhouse gasses, requires evaluation. Many scholars have studied the policy’s effect from the perspective of EE, but they have ignored the food industry, which is the main carbon emitting sector, and there is a lack of heterogeneity analyses of the influencing factors. This study reviewed the implementation process and characteristics of the CETP in the past decades. Using provincial panel data from 2003 to 2019, this study measured the EE in the food industry through the difference-in-difference model, evaluated the emission reduction and economic effects of the CETP on the food industry, characterized the heterogeneity of the policy’s effectiveness, and analysed its mechanism using three-stage mediating regression. The results showed that (1) the CETP significantly affected the food industry’s EE, which increased by 38.3% on average in experimental provinces compared with non-experimental provinces. (2) For the food industry, the policy’s effect was most significant in the food manufacturing and tobacco subsectors, and these subsectors in the experimental provinces increased by 66.0% and 39.7%, respectively; meanwhile, the policy’s effect was not significant in agriculture and subsidiary food processing and beverage manufacturing. By industrial area, the policy’s effects were significantly higher in the eastern region compared with the central and western regions. The influence on the food industry’s EE in the eastern region was close to 150%, while in the central and western regions, it was not significant. (3) The CETP promoted the food industry’s EE by improving energy consumption structure and technological innovation. The proportion of coal consumption decreased by 6.34% on average, and the technological innovation level increased by 25.1% on average in the experimental provinces’ food industries. The research findings indicate that the CEPT is a good practice and worth spreading. For food industry enterprises with high carbon emissions, attention should be paid to low-carbon transformation through technological upgrading and management optimization. For policymakers, targeted policies are needed to establish a national unified carbon trading market so that the national carbon emissions can be controlled, and the gap between regional carbon emissions can be narrowed.
Assessing Restorative Community Development Frameworks—A Meso-Level and Micro-Level Integrated Approach
Authors: Hampton Midas , O’Hara Sabine , Gearin Elizabeth
Published: March 2024

This study critically examines existing frameworks aimed at establishing restorative practices in community development by conducting a comparative analysis of a meso-level development framework called the Five Pillars of Development and a micro-level framework implemented by a grassroots initiative. Both frameworks were implemented in Washington, DC, and both seek to address the enduring negative externalities disinvested communities face and suggest the need for restorative practices in community development. Restorative development practices trace their roots to the field of restorative economics, which is a subfield of sustainable development. It argues that sustainability must also address the sins of the past and restore systems and systems components that have been negatively impacted by past development practices. This study aims to discern the adaptability of the more broadly applicable meso-level Five Pillars framework in capturing nuanced micro-level frameworks like the one devised by the grassroots organization in Ward 8. By identifying potential gaps, this study proposes strategies to enhance the utility of the more transferable meso-level framework, particularly for communities lacking resources to formulate their own micro-level framework. This study, therefore, contributes valuable insights and recommendations for bridging potential disparities between the meso-level and micro-level frameworks, as demonstrated in the Ward 8 case. The overarching objective is to enrich the community development field by presenting a restorative framework that refines existing meso-level approaches and facilitates opportunities for micro-level applications.
Achieving Net Zero Condominiums through Energy Community Sharing
Authors: Trevisan Riccardo , Ladu Mara , Ghiani Emilio , Balletto Ginevra
Published: March 2024

The European energy transition process is geared toward improving the economic viability of the energy sector through its democratization, which includes enabling citizens to generate, share, and sell energy produced by renewable sources. The current directives have led to the creation of energy communities and collective self-consumption groups to engage and raise awareness among citizens, with the goal of achieving social, economic, and environmental benefits through shared renewable energy generation and consumption. In the near future, more and more of these initiatives are anticipated; therefore, innovative technological tools are necessary to assist their growth path. This research introduces a multi-criteria techno-economic simulation framework that enables the evaluation of several investment scenarios for various plant sizes and energy prices. The findings are useful during the investment planning phase as they help guide decision-making toward the objectives of economic, energy, and environmental sustainability. To evaluate the methodology, a case study of a collective self-consumption group located in a smart building in Italy is proposed. The results are discussed from statistical, technical, economic, and financial standpoints, demonstrating how the proposed approach can contribute to the development of collective self-consumption groups, risk hedging, and the goal of developing energy self-sufficiency based on the net-zero energy building concept.
A Neopragmatic Perspective on the Processual Nature of Landscape—Coastal Land Loss in Louisiana in the Context of Scientific Findings, Social Patterns of Interpretation, and Individual Experience
Authors: Hinz Lena , Weber Anna-Maria , Koegst Lara , Kühne Olaf
Published: March 2024

The changes on the Louisiana coast due to land loss can be understood as a process, and the social construction of these processes is highly complex. Due to this complexity, we will examine these social patterns of interpretation as well as individual experiences of coastal land loss in Louisiana within a neopragmatic meta-theoretical framework using several methods, data, researcher perspectives, forms of representation, and theories, with a special focus on the construction of coastal land loss by the media. For this purpose, comments below a YouTube video on a hurricane event on Grand Isle, Louisiana, as well as on-site interviews with people affected by coastal land loss, were qualitatively analyzed. The results were interpreted with the help of various theories such as the theory of three landscapes, Dahrendorf’s conflict theory, Bourdieu’s theory of social capital, and Luhmann’s autopoietic systems theory. The research reveals patterns of interpretation, categorization, and evaluation of processes from an internal and external perspective that are highly morally charged.
Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN
Authors: Fu Wenlong , Yang Ke , Wen Bin , Shan Yahui , Li Shuai , Zheng Bo
Published: March 2024

As vital equipment in modern industry, the health state of rotating machinery influences the production process and equipment safety. However, rotating machinery generally operates in a normal state most of the time, which results in limited fault data, thus greatly constraining the performance of intelligent fault diagnosis methods. To solve this problem, this paper proposes a novel fault diagnosis method for rotating machinery with limited multisensor fusion samples based on the fused attention-guided Wasserstein generative adversarial network (WGAN). Firstly, the dimensionality of collected multisensor data is reduced to three channels by principal component analysis, and then the one-dimensional data of each channel are converted into a two-dimensional pixel matrix, of which the RGB images are obtained by fusing the three-channel two-dimensional images. Subsequently, the limited RGB samples are augmented to obtain sufficient samples utilizing the fused attention-guided WGAN combined with the gradient penalty (FAWGAN-GP) method. Lastly, the augmented samples are applied to train a residual convolutional neural network for fault diagnosis. The effectiveness of the proposed method is demonstrated by two case studies. When training samples per class are 50, 35, 25, and 15 on the KAT-bearing dataset, the average classification accuracy is 99.9%, 99.65%, 99.6%, and 98.7%, respectively. Meanwhile, the methods of multisensor fusion and the fused attention mechanism have an average improvement of 1.51% and 1.09%, respectively, by ablation experiments on the WT gearbox dataset.
A Mini-Review of the Kinetic Energy Partition Method in Quantum Mechanics
Authors: Chen Yu-Hsin , Wu I-Huan , Chao Sheng D.
Published: March 2024

Based on the idea of adiabatic symmetry, we present a novel basis set expansion method—the kinetic energy partition (KEP) method—for solving quantum eigenvalue problems. Broken symmetry is responsible for quantum entanglement in many-body systems via parametric non-adiabatic corrections. Starting from simple one-particle-in-one-dimension problems, we gradually increase the complexity in the number of particles and the interaction patterns. Our goal in the mini-review is to advocate for the utility of the KEP method in front-line research, in particular for research beginners in quantum many-body problems.
Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China
Authors: Wu Yinghan , Xu Jia , Liu Ziqi , Han Bin , Yang Wen , Bai Zhipeng
Published: March 2024

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies.