bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental and Theoretical Analysis of Dopamine Polymerization on the Surface of Cellulose Nanocrystals and Its Reinforcing Properties in Cellulose Acetate Films
Authors: Souza Amanda Lélis de , Oliveira Arthur Vinicius de Abreu , Ribeiro Laisse Dias , Moraes Allan Robledo Fialho e , Jesus Meirielly , Santos Joana , Oliveira Taila Veloso de , Soares Nilda de Fátima Ferreira
Published: January 2025

The study of natural materials inspires sustainable innovations, with biomimetics excelling in surface modification. Polydopamine (PDA) offers a promising approach for modifying cellulose nanocrystals (CNC), enhancing their compatibility with hydrophobic polymers by improving interfacial adhesion. In this work, the modification of CNC with PDA (CNC@PDA) significantly enhanced the compatibility between the nanocargoes and the cellulose acetate (CA) matrix. The CNC@PDA complex formation was suggested through a combination of FTIR analysis, particle size distribution measurements and ζ-potential analysis. However, the exact mechanism behind dopamine polymerization on the surface of CNC remains a subject of ongoing debate among researchers due to its complexity. This study hypothesized the formation of modified CNC through this process. Furthermore, this study provided a satisfactory investigation of the antimicrobial activity of CNC@PDA in response to bacterial strains (E. coli, P. aeruginosa, S. aureus and L. plantarum) in view of the hypothesis of the possible generation of reactive oxygen species (ROS). Additionally, the incorporation of CNC@PDA CA films was analyzed to assess its effect as a mechanical reinforcement agent. The results showed an improvement in mechanical properties, with the 1% CNC@PDA film exhibiting the best balance between tensile strength and flexibility.
Development of a Chitosan-Silver Nanocomposite/β-1,3-Glucan/Hyaluronic Acid Composite as an Antimicrobial System for Wound Healing
Authors: Yao Cheng-Jung , Yang Shu-Jyuan , Shieh Ming-Jium , Young Tai-Horng
Published: January 2025

An ideal wound dressing should be biocompatible, exhibit high antibacterial activity, and promote blood coagulation in the wound. In this study, we used chitosan as a multifunctional template to synthesize silver nanoparticles embedded in chitosan (Ag NP@CHI), which were then combined with β-1,3-glucan/hyaluronic acid (HA) to form an Ag NP@CHI/β-1,3-glucan/HA composite material with biocompatibility, wound healing-promoting properties, and antibacterial activity. A high concentration of chitosan led to the formation of smaller crystalline structures of Ag NPs and improved their dispersion within the chitosan matrix, but decreased their antibacterial potency. The Ag NP@CHI prepared with 1.0 mg/mL chitosan had the smallest particle size and good antibacterial activity. Compared to Ag NP@CHI, the prepared Ag NP@CHI/β-1,3-glucan/HA composite significantly enhanced biocompatibility, cell migration, hemocompatibility, and blood coagulation, with a minor reduction in antibacterial efficiency due to restricted ionic silver release and diffusion. With its high biocompatibility, hemocompatibility, promotion of blood coagulation and wound healing, and antibacterial efficiency, Ag NP@CHI@β-1,3-glucan/HA demonstrates potential as a wound healing composite in the future.
Development of Digital Twins for Continuous Processes: Concept Description of Virtual Mass Balance Based on the Tennessee Eastman Process
Authors: Fudyma Jakub , Kura Łukasz , Gębicki Jacek
Published: January 2025

In this work, the authors describe a concept called Virtual Mass Balance and its applicability to oil and gas fields. This study relies on data modelling and data engineering to enrich operational data and achieve digitized Material Balance Tables for monitoring and benchmarking purposes. A data-driven set of stream properties allow for the calculation of newly proposed soft sensors, which are subsequently used for predictive functionalities. The implementation of this concept has proven popular in an industry set of tools, AVEVA PI. The concept could be classified as an Enriched Digital Twin. Incorporating the DEXPI Process model and deploying proposed soft sensors could, on the one hand, combine both asset and operational characteristics. On the other hand, its simplicity and moderate level of detail can be much easier to implement and would show flexible applicability at various corporate levels. The application of the Tennessee Eastman Process proves its flexibility. The presented soft sensors exhibit good monitoring capabilities and, furthermore, allow for the application of data-driven artificial intelligence methods without a reduction in model clarity for operational staff.
Research on Mass Prediction of Maize Kernel Based on Machine Vision and Machine Learning Algorithm
Authors: Yu Yang , Fan Chenlong , Li Qibin , Wu Qinhao , Cheng Yi , Zhou Xin , He Tian , Li Hao
Published: January 2025

The yield assessment process during maize harvesting is a necessary means to ensure farmers’ economic benefits and stable agricultural production. Predicting the mass of maize kernels is an important condition for yield detection. This study proposes a maize kernel mass prediction model based on machine vision and machine learning algorithms to determine whether the kernels are broken. By extracting the geometric features of maize kernels, a phenotypic feature dataset of maize kernels was constructed. Subsequently, popular machine learning algorithms were used to establish regression models for maize kernel mass, achieving quantitative prediction of maize kernel mass. The results indicate that the PLSR (Partial Least Squares Regression) and RF (Random Forest) algorithms are suitable for constructing mass prediction models for broken and unbroken kernels, respectively. The models established by the two algorithms achieved R-values of 0.941 and 0.925, respectively. Field trial results show that there is a strong linear relationship between the predicted maize kernel mass using the constructed model and the actual kernel mass. Therefore, this method can serve as an accurate, objective, and efficient detection method for maize yield.
Methylene Blue Removal Using Activated Carbon from Olive Pits: Response Surface Approach and Artificial Neural Network
Authors: Ozcelik Tijen Over , Altintig Esra , Cetinkaya Mehmet , Ak Dilay Bozdag , Sarici Birsen , Ates Asude
Published: January 2025

This study evaluated the efficiency of methylene blue (MB) removal by using activated carbon produced from olive pits. The activated carbon (OPAC) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET). The adsorption process was optimized in two stages using factorial design. Based on the existing literature, the first stage selected the most influential variables (reaction time, dosage, pH, and dye concentration). Response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption of MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, various independent variables were employed, including initial dye concentrations ranging from 25 to 125 mg/L, adsorbent dosages ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R2 value (R2 = 0.9804) indicates that regression can effectively forecast the response of the adsorption process within the examined range. Thermodynamic studies were performed for three different temperatures between 293 and 303 K. Isothermal analysis parameters and negative Gibbs free energy indicate that the process is spontaneous and favorable. The data best fit the Langmuir model. This research showcases the effectiveness of optimizing and predicting the color removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption using OPAC as a viable primary treatment method for the removal of color from wastewater-containing dyes.
Research on Unmanned Aerial Vehicle Intelligent Maneuvering Method Based on Hierarchical Proximal Policy Optimization
Authors: Wang Yao , Jiang Yi , Xu Huiqi , Xiao Chuanliang , Zhao Ke
Published: January 2025

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs.
Statistics of Smoke Sphericity and Optical Properties Using Spaceborne Lidar Measurements
Authors: Midzak Natalie , Yorks John E. , Zhang Jianglong
Published: January 2025

Smoke particles from biomass burning events are typically assumed to be spherical despite previous observations of non-spherical smoke. As such, large uncertainties exist in some physical and optical parameters used in lidar aerosol retrievals, including depolarization and lidar ratio of non-spherical smoke aerosols. In this analysis, using NASA’s Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data during the biomass burning season over Africa from 2015 to 2017, we studied the frequency and distribution of non-spherical smoke particles to compare with findings of smoke particle non-sphericity from the Cloud-Aerosol Transport System (CATS) lidar. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non-spherical smoke particles, which might otherwise be misclassified as desert dust, polluted dust, or dusty marine by the CALIOP standard aerosol typing algorithm. Then, the relationships between smoke particle sphericity, lidar ratio, and relative humidity are analyzed for CATS and CALIOP observations over Africa. Approximately 18% of smoke layers observed by CALIOP over Africa are non-spherical (depolarization ratio > 0.075) and agree with spatial distributions of non-spherical smoke found in CATS observations. A dependance of lidar ratio on relative humidity was found for layers of spherical smoke over Africa in both CATS and CALIOP data; however, no such dependance was evident for the depolarization ratio and layer relative humidity. While the supplemental smoke aerosol typing algorithm presented in this analysis was targeted only for specific biomass burning regions during biomass burning seasons and is not meant for global operational use, it presents one potential method for improved backscatter lidar aerosol typing. These results suggest that a dynamic lidar ratio, based on layer-relative humidity for spherical smoke, could be used to reduce uncertainties in smoke aerosol extinction retrievals for future backscatter lidars.
Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends
Authors: Bao Chunling , Yang Yonghui , Bagan Hasi , Wang Qinxue , Te Terigelehu , Uudus Bayarsaikhan , Yong Mei , Liao Tanghong
Published: January 2025

Dust storms, characterized by their rapid movement and high intensity, present significant challenges across atmospheric, human health, and ecological domains. This study investigates the spatiotemporal variations in dust intensity (DI) and its driving factors in Mongolia from 2001 to 2022, using data from ground observations, reanalysis, remote sensing satellites, and statistical analyses. Our findings show an increasing DI trend at approximately two-thirds of the monitoring stations, with DI rising at an average rate of 0.8 per year during the study period. Anthropogenic factors dominate as the primary drivers in regions such as Forest, Meadow Steppe, Typical Steppe, Desert Steppe, and the Gobi Desert. For example, GDP significantly impacts Forest and Meadow Steppe areas, contributing 25.89% and 14.11% to influencing factors of DI, respectively. Population emerges as the key driver in Typical Grasslands (20.77%), Desert Steppe (26.65%), and the Gobi Desert (37.66%). Conversely, climate change is the dominant factor in the Alpine Meadow regions of southern–central Hangay Uul, with temperature (20.69%) and relative humidity (20.67%) playing critical roles. These insights are vital for Mongolian authorities: promoting green economic initiatives could mitigate DI in economically active Desert Steppe regions, while climate adaptation strategies are essential for climate-sensitive Alpine Meadows. The findings also provide valuable guidance for addressing environmental issues in other arid and semi-arid regions worldwide.
A Submerged and Buried Mesolithic Site off Svanemøllen Harbor, Copenhagen, Denmark: Acoustic Detection (Human-Altered Lithic Detection) and Verification by Means of Coring
Authors: Boldreel Lars Ole , Grøn Ole , Tayong Rostand Boumda , Madsen Bo , Bennike Ole , Andersen Morten Sparre
Published: January 2025

Teledyne Chirp III high-resolution seismic data were acquired over a three-year period at a water depth of 6.0–9.0 m, with a clear concentration of acoustic “haystack” features, in the dredged Svanemøllen Harbor, Copenhagen. The recordings show haystacks related to preserved shallow basins and rivers in the paleo-coastal setting. Eleven short vibrocores were retrieved below these pronounced haystacks and a sandy interval, underlain by clayey till and overlain by harbor mud, which represent the basin configuration. Two cores contained four pieces of knapped flint in the sandy interval (statistical density of around 230 pieces per square meter), while the remaining cores did not reach the desired depth. Finite Element (FE) modeling revealed that the small pieces of knapped flint were resonant and that the acoustic impedance of the flint was high. The Svanemøllen Harbor site is a hitherto unknown buried Stone Age site, and this is the first time ever that an unknown submerged, buried Stone Age site has been acoustically detected (using Teledyne Chirp III), verified by means of coring and modeled using FE modeling. The Innomar and Geopulse data acquired at the site did not register any haystacks. Due to the global sea level rise after the Last Glacial Maximum, a significant part of the world’s submerged Stone Age sites must be expected to be buried in seafloor sediments.
Enhancing Laser-Induced Breakdown Spectroscopy Quantification Through Minimum Redundancy and Maximum Relevance-Based Feature Selection
Authors: Wang Manping , Lu Yang , Liu Man , Cui Fuhui , Gao Rongke , Wang Feifei , Chen Xiaozhe , Yu Liandong
Published: January 2025

Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples.
Giant Aerosol Observations with Cloud Radar: Methodology and Effects
Authors: Gumà-Claramunt Pilar , Madonna Fabio , Amodeo Aldo , Bauer-Pfundstein Matthias , Papagiannopoulos Nikolaos , Rosoldi Marco , Pappalardo Gelsomina
Published: January 2025

In this study, we present an innovative methodology for the identification of giant aerosols using cloud radar. The methodology makes use of several insects studies in order to separate radar-derived atmospheric plankton signatures into the contributions of insects and giant aerosols. The methodology is then applied to a 6-year-long cloud radar dataset in Potenza, South Italy. Forty giant aerosol events per year were found, which is in good agreement with the site’s climatological record. A sensitivity study on the effects of the giant aerosols on three atmospheric variables and under different atmospheric stability conditions showed that the presence of giant aerosols (a) increased the aerosol optical depth in all the atmospheric stability conditions, (b) decreased the Ångström exponent for the highest and lowest stability conditions and had the opposite effect for the intermediate stability condition, and (c) increased the accumulated precipitation in all the atmospheric conditions, especially in the most unstable ones.
Use of Multi-Feature Extraction and Transfer Learning to Identify Urban Villages in China
Authors: Shu Yuqing , Cai Zhongliang , Li Guie , Yan Qingwu , Li Bozhao , Si Wencai , Qiao Dongxiang
Published: January 2025

Urban villages (UVs) are the most typical urban informal settlements in China, and the study of an effective identification method for UVs can help to provide a reference for the development of locally adapted UV transformation policies. In order to reduce the cost of labeling and enhance transferability, this study integrates remote sensing and social sensing data and applies sample migration from a labeled area to a less labeled area based on the theory of transfer learning. There are two main results of this study: (1) This study constructed a feature system for UV identification based on multi-feature extraction using a block as a unit, and experiments based on Tianhe District achieved an overall accuracy of 90% and a kappa coefficient of 0.76. (2) Using Tianhe District as the source domain and Jiangan District as the target domain, samples from the source domain were reused based on the KMM, TCA, and CORAL algorithms. The CORAL+RF algorithm showed the best performance, where its overall accuracy reached 97.06% and its kappa coefficient reached 0.89, and its overall accuracy reached 91.17% and its kappa coefficient reached 0.67 in the case of no target domain labeling. To sum up, the identification method for UVs proposed in the present study provides theoretical references for identification methods for UVs in different geographical areas.
Spatiotemporal Patterns of Chlorophyll-a Concentration in a Hypersaline Lake Using High Temporal Resolution Remotely Sensed Imagery
Authors: Ramsey R. Douglas , Brothers Soren M. , Cobo Melissa , Wurtsbaugh Wayne A.
Published: January 2025

The Great Salt Lake (GSL) is the largest saline lake in the Western Hemisphere. It supports billion-dollar industries and recreational activities, and is a vital stopping point for migratory birds. However, little is known about the spatiotemporal variation of phytoplankton biomass in the lake that supports these resources. Spectral reflectance provided by three remote sensing products was compared relative to their relationship with field measurements of chlorophyll a (Chl a). The MODIS product MCD43A4 with a 500 m spatial resolution provided the best overall ability to map the daily distribution of Chl a. The imagery indicated significant spatial variation in Chl a, with low concentrations in littoral areas and high concentrations in a nutrient-rich plume coming out of polluted embayment. Seasonal differences in Chl a showed higher concentrations in winter but lower in summer due to heavy brine shrimp (Artemia franciscana) grazing pressure. Twenty years of imagery revealed a 68% increase in Chl a, coinciding with a period of declining lake levels and increasing local human populations, with potentially major implications for the food web and biogeochemical cycling dynamics in the lake. The MCD43A4 daily cloud-free images produced by 16-day temporal composites of MODIS imagery provide a cost-effective and temporally dense means to monitor phytoplankton in the southern (47% surface area) portion of the GSL, but its remaining bays could not be effectively monitored due to shallow depths, and/or plankton with different pigments given extreme hypersaline conditions.
A Global-Scale Overlapping Pixels Calculation Method for Whisk-Broom Payloads with Multi-Module-Staggered Longlinear-Array Detectors
Authors: Du Xinwang , Wu Chao , Liang Quan , Zhao Lixing , Xu Yixuan , Guo Junhong , Li Xiaoyan , Chen Fansheng
Published: January 2025

A multi-module staggered (MMS) long-linear-array (LLA) detector is presently recognized as an effective and widely adopted means of improving the field of view (FOV) of in-orbit optical line-array cameras. In particular, in terms of low-orbit whisk-broom payloads, the MMS LLA detector combined with the one-dimensional scanning mirror is capable of achieving both large-swath and high-resolution imaging. However, because of the complexity of the instantaneous relative motion model (IRMM) of the whisk-broom imaging mechanism, it is really difficult to determine and verify the actual numbers of overlapping pixels of adjacent detector sub-module images and consecutive images in the same and opposite scanning directions, which are exceedingly crucial to the instrument design pre-launch as well as the in-orbit geometric quantitative processing and application post-launch. Therefore, in this paper, aiming at addressing the problems above, we propose a global-scale overlapping pixels calculation method based on the IRMM and rigorous geometric positioning model (RGPM) of the whisk-broom payloads with an MMS LLA detector. First, in accordance with the imaging theory and the specific optical–mechanical structure, the RGPM of the whisk-broom payload is constructed and introduced elaborately. Then, we qualitatively analyze the variation tendency of the overlapping pixels of adjacent detector sub-module images with the IRMM of the imaging targets, and establish the associated overlapping pixels calculation model based on the RGPM. And subsequently, the global-scale overlapping pixels calculation models for consecutive images of the same and opposite scanning directions of the whisk-broom payload are also built. Finally, the corresponding verification method is presented in detail. The proposed method is validated using both simulation data and in-orbit payload data from the Thermal Infrared Spectrometer (TIS) of the Sustainable Development Goals Satellite-1 (SDGSAT-1), launched on 5 November 2021, demonstrating its effectiveness and accuracy with overlapping pixel errors of less than 0.3 pixels between sub-modules and less than 0.5 pixels between consecutive scanning images. Generally, this method is suitable and versatile for the other scanning cameras with a MMS LLA detector because of the similarity of the imaging mechanism.
Nighttime Tweek Characteristics in Mid–Low Latitudes: Insights from Long-Term VLF Observations in China
Authors: Wang Qingshan , Ni Binbin , Feng Jingyuan , Gu Xudong , Xu Wei , Wang Shiwei , Hu Mengyao , Ma Wenchen , Cheng Wen , Wu Yufeng , Zhang Junjie
Published: January 2025

An improved method for identifying nighttime tweek signals in WHU VLF measurements was developed by redesigning the extraction process and validated through comparison with World-Wide Lightning Location Network (WWLLN) data. Using the enhanced method, 1,728,032 tweek signals were identified from four years (2018–2021) of VLF data, forming the most comprehensive tweek dataset for the mid–low latitude region in China. Statistical analysis reveals distinct nighttime variations in tweek occurrence rates, which increase from 18:00 LT to 20:00 LT, remain high until 04:00 LT, and gradually decrease towards sunrise. Seasonal differences in propagation distance are evident, ranging from ~2000 km in summer to ~4000 km in winter, corresponding to the seasonal shift of lightning activity. The cutoff frequency showed apparent daily and seasonal fluctuations, and the trends of daily variation are opposite between winter and summer. The annual variation in cutoff frequency presents a pattern different from previous cognition, with a minimum of 1.62 kHz in summer and a maximum of 1.68 kHz in winter, influenced by the magnetic cyclotron frequency at ionospheric reflection points. These findings improve the understanding of nighttime tweek characteristics and ionospheric dynamics in East Asia, offering valuable insights for ionospheric research and VLF communication systems.
Rhizobium Inoculants Mitigate Corn Herbicide Residual Effects on Soybean Germination
Authors: Maphalala Ncomiwe , Richardson Alaina , Sastre Sabrina Quevedo , Correa Aricia Ritter , Souza Fernanda Reolon de , Tseng Te Ming
Published: January 2025

Corn residual herbicides offer a practical approach to comprehensive weed management throughout the growing season. However, the use of residual pre-emergence herbicides can have a negative impact on crops grown in succession or within a rotation. A study was carried out to determine the effect of the residual activity of selected corn herbicides on soybeans. The objective of the study was to evaluate the impact of these herbicides on the germination of inoculated soybean seeds. Experiments were conducted in greenhouse conditions to check the carryover effect on soybean germination. Treatment combinations of two pre-herbicides and two inoculants were applied: atrazine (2241 g ai ha−1), mesotrione (105 g ai ha−1), and Bradyrhizobium japonicum, Bradyrhizobium japonicum + Bacillus subtilis, respectively. A randomized complete block design evaluated six treatment combinations, including the control. All treatments, except uninoculated treatments, presented efficacy in reducing the carryover effects of corn residual herbicides on the germination of soybeans. An increase in final germination percentage was observed with Bradyrhizobium japonicum + Bacillus subtilis co-inoculation plus atrazine (24% increase) and Bradyrhizobium japonicum plus mesotrione treatment combinations (19% increase). Inoculating soybean seeds with rhizobium bacteria can reduce the carryover effects on the germination of soybean seeds grown in soil applied with atrazine and mesotrione.
Green Chemistry Method for Analyzing Bisphenol A in Milk
Authors: Encerrado Manriquez Angela M. , Lee Wen-Yee
Published: January 2025

A simple, fast, green, and sensitive method for determining Bisphenol A (BPA) levels in commercial milk was developed using a solventless sample preparation technique known as stir bar sorptive extraction, coupled with thermal desorption–gas chromatography/mass spectrometry. BPA was selected due to its ubiquitous presence in the environment and its classification as an endocrine-disrupting chemical of concern (i.e., its ability to mimic hormone functions). Studies have reported that BPA can leach into various food sources, including milk, a dietary staple for infants. It is critical to have an effective and efficient process for monitoring the presence of BPA in milk to protect children’s health. Current detection methods for BPA in milk are lengthy and tedious and tend to require the use of organic solvents for the extraction of BPA. This optimized “green” method provides an effective alternative for BPA detection in a challenging matrix, e.g., milk. Factors such as pH (1.5, 6, and 13), temperature (70–80 °C), and sonication (1 h, 2 h, and 3 h) were studied with a BPA-spiked whole milk sample (final concentration of 8 ppb) to optimize the extraction efficiency without the use of solvents. The developed methodology improves BPA recovery from whole milk by over 50%, with a detection limit in the parts per trillion range (45 ng/L). The sample preparation developed in this report rendered a more sensitive option for analyzing BPA in milk, with a limit of detection in the parts per trillion range (compared to low ppb) even though the recovery performance is not as good as in reported studies (54% vs. >85%); nonetheless, it provides a green alternative for future studies assessing BPA exposure through dairy products.
Tuning the Inlet Flow Pattern of Cyclones for Boosted Particle Rotation Behaviors with High Purification Performances of Oily Sludge
Authors: Ye Rui , Zheng Wan , Shi Bi
Published: January 2025

Cyclone separation is a widely utilized separation technique, which enables the self-rotation behaviors of particles in the internal flow field, in order to realize high-performance separation of mixtures. Oily sludges are solid wastes generated by the shale gas industry, which need to be properly treated for environmental protection. In the present investigation, we demonstrated that tuning the inlet flow pattern of the cyclone from linear flow to vortex flow is an effective approach to boost the rotation speed of oily sludge particles for obtaining significantly improved separation effects. Numerical simulations were carried out to investigate the influences of inlet flow pattern on the rotation behaviors of particles, which manifested in the rotation speed of particles being evidently increased up to 4500 rad/s when the inlet flow was tuned from a unidirectional pattern into vortex pattern. The effective rotation zone’s area was also found to increase significantly, with the area of the effective rotation zone enlarged by up to 400%. Further separation experiments on oily sludge were carried out using a cyclone equipped with a worm shell that generated vortex inlet flow with rotating blades. Separation results confirmed that the oily sludge was successfully purified by the cyclone equipped with a worm shell, which provided an extremely high oil removal percentage of 99.9%, showing a 49.1% enhancement in oil removal capability over the individual cyclone separation. Our investigations demonstrated an effective method for realizing oily sludge treatment and oil resource recovery by conventional cyclone separation.
Unspoken, Unseen, Unheard: Using Arts-Based and Visual Research Methods to Gain Insights into Lived Experiences of Suicide in Young Adults
Authors: Smit Jude , Colucci Erminia , Marzano Lisa
Published: January 2025

Suicide is often referred to as a silent killer, and the need to break down barriers and build bridges to communication and understanding remains of vital importance. Working within the field of further and higher education for more than 18 years with students experiencing suicidal thoughts, feelings, and behaviours has highlighted how often deep pain, grief, and trauma go unspoken, unseen, and unheard. Societal and cultural stigma, judgement, misunderstanding, and assumptions remain, all of which silence and can lead to a negative sense of self, others, and a person’s experience of being in the world. This article shows how using arts-based and visual research methods, as part of a mixed methods study, can offer unique insights into the inner world of lived experiences. It draws on analysis of 62 artworks made by 20 students between the ages of 16 and 25 with personal experiences of attempted suicide. These included two-dimensional pieces, sculpture, photography, poetry, and digital art. The research methodology is also discussed, including a 5/6-step approach to the analysis of visual data and data synthesis that has been created to ensure a robust, socially contextualised, and framed analysis. This follows polytextual thematic analysis using a multimodal approach and draws on visual social semiotics. Analysis of visual and arts-based data has revealed aspects of meaning that would otherwise not have been identified. This has led to the development of a model that can help us better understand the cycle of stigma and judgement and how we may be able to break it. This article demonstrates how a creative approach provides a means to share some of the complexity of feelings in a relatable way that has the capacity to bridge the divide between what is hidden and what is seen, bringing this human experience out of the shadows. It aims to honour everyone whose experiences have gone unseen, unspoken, and unheard, as well as the research participants’ wish for their artworks to be shared as a way to challenge the stigma that silences. It further hopes to demonstrate the power of arts-based and visual methods in research whilst also acknowledging some of their limitations so that they can be used more widely with under-represented, marginalised, and silenced voices.
“Intolerant Television”—The Coverage on Antisemitic Events in Italian Television News in 2019–2022
Authors: Buoncompagni Giacomo
Published: January 2025

Communicative misunderstandings, cultural misinterpretations, and tribal hatreds are not phenomena that emerge and develop only in the digital world. Within platforms, conflicts explode and circulate mainly in crisis situations, but the relationship (constructive or destructive) with the similar and the different, as well as the narration of the symbolic meanings of specific cultural events, originate first and foremost in interpersonal relationships, institutional political contexts, and the representations (and consumption) of traditional media, such as the television space. Italian television is still one of the reference means of communication for the majority of the population, a figure that has been recorded especially during the recent pandemic emergency despite the significant collapse in advertising investments. Hatred, especially anti-Semitic hatred, is increasingly present in the information ecology, linked to nationalist narratives or aimed at restoring traditional values and fuelling an already highly polarised political debate in a now “dense” public sphere. In particular, during the health crisis, television journalists found it very difficult to report in depth on cases of discrimination or COVID-19.
Component Degradation in Lithium-Ion Batteries and Their Sustainability: A Concise Overview
Authors: Mansir Ibrahim B. , Okonkwo Paul C.
Published: January 2025

Researchers are presently involved in the creation of materials for high-voltage lithium-ion batteries, with a particular emphasis on their practical uses. However, it is important to acknowledge that the components of lithium-ion batteries frequently undergo substantial loss of capacity during the cycling process, which hinders their potential for becoming commercially viable. Lithium-ion battery electrodes can degrade due to electrochemical surface reactions that occur with the electrolyte at the interface between the electrode and electrolyte, as well as from structural degradation within the electrode particles. The presence of structural degradation can be ascribed to the development of imperfections or unstable states, which are expedited by electrochemical processes arising from the electrolyte if unstable states emerge near the electrode/electrolyte contact. Underlying degradation mechanisms can enhance improvements in the electrochemical properties of the electrodes. This paper presents a comprehensive analysis of the various degradation mechanisms that impact the components of lithium-ion batteries to improve energy efficiency. It also discusses innovative methodologies used to analyze the degradation phenomena that occur at the surface of the electrode and within individual battery components.
Spatial Temporal Differences in Carbon Emissions from Land-Use Change and Carbon Compensation in Gansu Province, China
Authors: Liu Qiang , Zhao Shenglong , Wang Lixia
Published: January 2025

This study employs land use and energy consumption data from Gansu Province spanning the years 2005 to 2020 and utilizes models to estimate carbon emissions and the corresponding carbon compensation values. The research calculated the carbon emissions and compensation for various administrative regions over different time periods, revealing the following insights: (1) There is a notable increasing trend in net carbon emissions due to land-use changes in Gansu Province, characterized by an initial swift rise, followed by a more gradual growth pattern. Construction land is identified as the primary contributor to carbon emissions, increasing from 26 million tons in 2005 to 55.3 million tons in 2020, which is an increase of 1.80 times; meanwhile, forested areas, as significant carbon sinks, show a slight increase in carbon absorption from 2.33 million tons in 2005 to 2.35 million tons in 2020. (2) The municipalities with high net carbon emissions are predominantly Lanzhou, Qingyang City, and Jiuquan City, which are marked by high levels of carbon emissions and low compensation rates. In contrast, regions with lower net carbon emissions are mainly found in the Gannan Tibetan Autonomous Prefecture, where emissions are minimal and compensation rates are relatively high. A similarity in the spatial distribution patterns of both net carbon emission intensity and total net carbon emissions is observed among these cities. Alterations in land use have a significant impact on regional carbon emissions. Investigating the spatiotemporal variations of land-use change and carbon compensation in Gansu Province is essential for comprehending the dynamics of regional carbon emissions, developing effective emission reduction strategies, and fostering low-carbon development.
Optimal Strategy and Performance for a Closed-Loop Supply Chain with Different Channel Leadership and Cap-and-Trade Regulation
Authors: Zhang Yuhao , Zhang Qian , Hu Ren , Yang Man
Published: January 2025

Cap-and-trade is widely recognized as an effective mechanism for curbing carbon emissions, and it significantly influences the operational decisions within supply chains. This study investigates a three-echelon closed-loop supply chain (CLSC) consisting of one original equipment manufacturer, one traditional retailer, and one independent third-party collector. The manufacturer invests in cleaner technologies to produce green products and remanufactures new products from used items recycled by the third-party collector. Considering different channel power structures, three Stackelberg game models are developed, and their optimal solutions are derived using the backward induction. Additionally, the combined effects of remanufacturing-related and carbon-related parameters on economic and environmental benefits as well as social welfare are investigated under different settings. Moreover, the derived results are validated via numerical simulation. The findings indicate that: (1) Each channel member is incentivized to act as the leader role within the CLSC to maximize profits. (2) A loose cap-and-trade regulation is conducive to enhancing the emission abatement rate, collection rate, and overall performance for the CLSC. (3) The retailer-led model is the best option for capturing more economic benefits and social welfare, while the third party-led model can always achieve the best environmental performance regardless of carbon trading price. These research findings can provide valuable insights for policymakers and decision makers engaged in CLSC.
Developing the Recovery Gap Index: A Comprehensive Tool for Assessing National Disaster Recovery Capacities
Authors: Borre Alessandro , Ghizzoni Tatiana , Trasforini Eva , Ottonelli Daria , Rudari Roberto , Ferraris Luca
Published: January 2025

The increasing frequency and severity of extreme natural events, along with their escalating impacts, highlight the urgent need for robust tools to assess and strengthen national capacities for disaster preparedness and recovery. In this context, this paper introduces one of these tools, called the Recovery Gap Index (RGI), a comprehensive composite index designed to quantify and evaluate the post-extreme natural event response and recovery capabilities at the country level. The tool addresses the need for a systematic approach to quantify resilience and evaluate the impacts of consecutive events on vulnerable areas. The RGI synthesizes data from three well-established indices: the World Risk Index, INFORM, and Global Infrastructure Risk Model and Index (GIRI), covering critical dimensions related to sociodemographic factors, infrastructure, governance, technology, and economic resilience. By extracting key parameters from these diverse indices and aggregating them, the RGI provides a detailed assessment of each nation’s ability to manage the aftermath of extreme natural events. The index findings reveal significant regional disparities in recovery capacities, with European countries exhibiting stronger resilience, whereas many African and Asian nations face substantial challenges. Furthermore, this study proposes several potential future enhancements, such as the incorporation of early warning systems and insurance coverage metrics, aimed at improving its accuracy and practical application. The RGI aims to be a valuable tool for policymakers, disaster management professionals, and stakeholders, enabling them to make informed decisions and implement targeted interventions to further enhance global disaster resilience.
Happy Workers, Healthy Business: The Impact of Sustainable Human Resource Management and Workplace Happiness on Employee Engagement of Women Employees in Tea Plantations
Authors: Usha M. , Ramkumar N.
Published: January 2025

As globalization accelerates and environmental awareness expands, it is imperative for organizations to align their business practices with sustainable development goals. This research examines the substantial connection between sustainable human resource management (SHRM) practices and employee happiness, with a focused emphasis on their direct impact on employee engagement. Quantitative research has been used for this particular research, employing both descriptive and causal research methods. It effectively analyzes a sample of 473 women workers from one of India’s largest tea plantations. The results clearly indicate a strong and positive relationship between SHRM practices, employee happiness, and employee engagement. Industries, particularly the tea plantation sector, must prioritize initiatives that foster employee engagement. By deliberately cultivating a work environment that promotes sustained joy and fulfillment, tea plantations can significantly enhance employee commitment and build long-term loyalty. These findings provide critical theoretical insights into the undeniable role of SHRM and employee happiness in driving employee engagement forward.
Toward Sustainable Development: The Impact of Green Fiscal Policy on Green Total Factor Productivity
Authors: Luo Peikai , Zhang Chenchu , Cheng Bohui
Published: January 2025

Green fiscal policy draws worldwide attention from policymakers as a potential mechanism that contributes to sustainable development. However, although many studies have discussed the economic consequences of green fiscal policy, there is still a lack of studies that systematically quantify the productivity impacts of green fiscal policy. Therefore, to fill this gap, China’s Energy Conservation and Emission Reduction Fiscal Policy Pilot (ECER) and difference-in-differences (DID) identification method were chosen to explore the impact of green fiscal policy on green total factor productivity (GTFP). We find that ECER significantly enhances urban GTFP, and it holds after a series of robustness tests. Moreover, we explore the mediating mechanisms that may explain this effect: government environmental regulation, green technology innovation, and industrial structure optimization. Further analysis shows that the positive effect of ECER is more significant when (1) government transparency is high; (2) government financial autonomy is high; (3) government digital transformation is high; (4) the city’s resource endowment is high; and (5) the city’s economic development level is high. Overall, our study provides new insights into the economic consequences of green fiscal policy.
Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction
Authors: Liu Yongkang , Gu Yi , Long Yuwei , Zhang Qinyu , Zhang Yonggang , Zhou Xu
Published: January 2025

Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy.