bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Radiation Shielding Capabilities with Epoxy-Resin Composites Reinforced with Coral-Derived Calcium Carbonate Fillers
Authors: Tochaikul Gunjanaporn , Tanadchangsaeng Nuttapol , Panaksri Anuchan , Moonkum Nutthapong
Published: January 2025

This study investigates the development of epoxy–resin composites reinforced with coral-derived calcium carbonate (CaCO3) fillers for enhanced radiation shielding and mechanical properties. Leveraging the high calcium content and density of coral, composites were prepared with filler weight fractions of 0%, 25%, and 50%. SEM and EDS analyses revealed that higher filler concentrations (50%) increased particle agglomeration, affecting matrix uniformity. Mechanical testing showed that while the tensile and flexural strengths decreased with the increased filler content, the compressive strength significantly improved, reaching 135 MPa at a 50% coral content. Radiation shielding evaluations demonstrated enhanced attenuation with a higher filler content, achieving 39.63% absorption at 60 kVp for the 50% coral composite. However, the shielding efficiency was notably lower compared to lead, which achieves over 99% absorption at similar energy levels. These quantitative comparisons highlight the material’s limitations in high-radiation environments but emphasize its suitability for moderate shielding applications. Despite their lower shielding efficiency, the composites provide an environmentally friendly and non-toxic alternative to lead, aligning with sustainability goals. Future work should focus on optimizing filler dispersion, mitigating agglomeration, and exploring hybrid systems to enhance the shielding efficiency and mechanical properties. The further quantitative evaluation of parameters such as Zeff and cross-sections is recommended to comprehensively assess the material’s performance.
Brewers’ Spent Grain-Derived Arabinoxylan as a Sustainable Filler for Enhanced PHBV Biocomposites
Authors: Belardi Ilary , Sarasini Fabrizio , Tirillò Jacopo , Russo Pietro , De Francesco Giovanni , Marconi Ombretta , Marrocchi Assunta
Published: January 2025

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a highly promising biodegradable and bio-based thermoplastic recognized for its environmental benefits and potential versatility. However, its industrial adoption has been limited due to its inherent brittleness and suboptimal processability. Despite these challenges, PHBV’s performance can be tailored for a wide range of applications through strategic modifications, particularly by blending it with other biodegradable polymers or reinforcing it with natural fibers and bio-based fillers. This study explores the potential of brewers’ spent grain (BSG) as a sustainable source for the development of PHBV biocomposites. The biocomposites were synthesized by incorporating arabinoxylan-bound benzoate, which can be derived from BSG, as a sustainable filler at concentrations of 4% and 10% w/w. The resulting materials were characterized using tensile testing, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The findings demonstrate that the incorporation of functionalized arabinoxylan significantly enhances the mechanical properties of PHBV, preserves its thermal stability, and increases its crystallinity (from 59.9% to 67.6%), highlighting a positive impact on both material performance and processing characteristics.
Selection of Network Parameters in Direct ANN Modeling of Roughness Obtained in FFF Processes
Authors: Buj-Corral Irene , Sivatte-Adroer Maurici , Rodero-de-Lamo Lourdes , Marco-Almagro Lluís
Published: January 2025

Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models. In this work, a study about the use of ANN to model surface roughness in FFF processes is presented. The main objective of the paper is discovering how key ANN parameters (specifically, the number of neurons, the training algorithm, and the percentage of training and validation datasets) affect the accuracy of surface roughness predictions. To address this question, 125 3D printing experiments were conducted changing orientation angle, layer height and printing temperature, and measuring average roughness Ra as response. A multilayer perceptron neural network model with backpropagation algorithm was used. The study evaluates the effect of three ANN parameters: (1) number of neurons in the hidden layer (4, 5, 6 or 7), (2) training algorithm (Levenberg–Marquardt, Resilient Backpropagation or Scaled Conjugate Gradient), and (3) data splitting ratios (70%–15%–15% vs. 55%–15%–30%). Mean Absolute Error (MAE) was used as the performance metric. The Resilient Backpropagation algorithm, 7 neurons, and using 55% of training data yielded the best predictive performance, minimizing the MAE. Additionally, the impact of the dataset size on prediction accuracy was analysed. It was observed that the performance of the ANN gets worse as the number of datasets is reduced, emphasizing the importance of having sufficient data. This study will help to select appropriate values for the printing parameters in FFF processes, as well as to define the characteristics of the ANN to be used to model surface roughness.
Corn Stalks-Derived Hemicellulosic Polysaccharides: Extraction and Purification
Authors: Puițel Adrian Cătălin , Balan Cătălin Dumitrel , Nechita Mircea Teodor
Published: January 2025

Nowadays, agricultural biomass is one the most valuable sources of natural polysaccharides. In addition to primary agricultural goods, agricultural waste is abundant, diverse, and renewable and can also be utilized as raw material for the production of polysaccharides and their derivatives. The extraction and purification of agri-waste-derived polysaccharides involves multiple processes that can vary depending on the type of raw material and the specific polysaccharides targeted. This study proposes a particular pathway from corn waste to hemicellulosic polysaccharides, which involves alkaline treatment and several physicochemical separation/purification phases using precipitation and ion exchange resins (Purolite A400, Purolite A100+, Purolite C100H). The ion exchange separation stage was optimized to retain most of the acid-soluble lignin derivatives from the extraction liquors. The process parameters considered for optimization included the solid (resin) liquid (black liquor pH 4.5) ratio, contact time, and temperature. These ranged from 0.05 to 0.15 g·mL−1, 30 to 180 min, and 20 to 50 °C, respectively. The chemical composition of the separated hemicelluloses varied from 44.43 to 75.28% for xylan, 2.43 to 3.93% for glucan, 1.86 to 2.44% for galactan and 8.93 to 12.68% for arabinan. The total carbohydrate content increased from 57.65 to 96.3%.
A Simple Way to Incorporate Carbon Nanotubes in Polymers: Nanostructured Road Marking Paint
Authors: de Souza Iara Alves Martins , Almeida Marconi Oliveira de , Soncim Sérgio Pacífico , da Silva Mercês Coelho , de Morais Evandro Augusto , Carvalho Jaqueline do Carmo Lima , Filho Francisco Moura , Geraldo Viviany
Published: January 2025

This study reports a new and simple method for applying and dispersing carbon nanotubes (CNTs) in polymers, without prior chemical treatment or functionalization. The process is innovative, fast, and carried out at room temperature, without the need for specialized labor, just using the cavitation energy of ultrasonic bath to enhance properties such as impermeability and high electrical conductivity. The time of the dispersion process is 30 min and diverse concentrations of carbon nanotubes (0.1%, 0.5%, and 2%) in the total road marking paint mass were employed. This study also demonstrates the effect of the nanostructured paint applied to roadways, based on the macro texture (sand test) and in the microstructure pattern obtained (British Pendulum Tester). In addition, this study demonstrates that achieving an effective dispersion of CNTs in road marking paints produces a conductive and thermally stable paint, which also serves as a promising waterproof layer, expanding its applications in road maintenance, for example. The results from the sample with a 2% CNT content revealed enhancements in polymer conductivity in contrast to the reference, coupled with increases of up to 20% in impermeability to water and glycerin. No alteration in wettability is noted in automotive oil upon the introduction of CNTs, implying that nanostructured road paints could augment safety, traffic flow efficiency, and the environmental sustainability of future transport systems.
Drug Release Kinetics of PLGA-PEG Microspheres Encapsulating Aclacinomycin A: The Influence of PEG Content
Authors: Costa Mariana Sousa , Ramos Ana M. , Cardoso M. Margarida
Published: January 2025

The present study evaluates the effect of PEG content on the characteristics of poly(lactic-co-glycolic acid)-polyoxyethylene (PLGA-PEG) microspheres loaded with a small molecular weight drug on the polymer matrix degradation behavior of the polymeric matrix and drug release profile. Aclacinomycin A (ACM) was encapsulated in PLGA-PEG microspheres with varying PEG content (0%, 5%, 10%, or 15%) using the oil-in-water solvent evaporation method. Microspheres were obtained with sizes ranging from 45–70 mm, drug loading around 1.3% and encapsulation efficiencies between 48–70%. The produced microspheres were further characterized in terms of degradation behavior and drug release kinetics. The results showed that while PEG content had minimal impact on drug loading and microsphere size, it significantly influenced the degradation behavior of the microspheres and its weight in the release process. In vitro drug release profiles exhibited a three-phase pattern for all PLGA-PEG microspheres with faster and more extensive ACM release compared to PLGA microspheres, being the release improved with the PEG content increase. The Corrigan model was successfully applied to the release data yielding burst-phase kinetic constants (kb) between 0.082–0.288 and degradation/erosion kinetic constants (k) between 0.054–0.093 day−¹, both of which increased with higher PEG content.
Automated Tomato Defect Detection Using CNN Feature Fusion for Enhanced Classification
Authors: Alzahrani Musaad
Published: January 2025

Tomatoes are among the most widely cultivated and consumed vegetable crops worldwide. They are usually harvested in large quantities that need to be promptly and accurately classified into healthy and defective categories. Traditional methods for tomato classification are labor-intensive and prone to human error. Therefore, this study proposes an approach that leverages feature fusion from two pre-trained convolutional neural networks (CNNs), VGG16 and ResNet-50, to enhance classification performance. A comprehensive evaluation of multiple individual and hybrid classifiers was conducted on a dataset of 43,843 tomato images, which is heavily imbalanced toward the healthy class. The results showed that the best-performing classifier on fused features achieved an average precision (AP) and accuracy of 0.92 and 0.97, respectively, on the test set. In addition, the experimental evaluation revealed that fused features improved classification performance across multiple metrics, including accuracy, AP, recall, and F1-score, compared to individual features of VGG16 and ResNet-50. Furthermore, the proposed approach was benchmarked against three standalone CNN models, namely MobileNetV2, EfficientNetB0, and DenseNet121, and demonstrated superior performance in all evaluated metrics. These findings highlight the efficacy of deep feature fusion in addressing class imbalance and improving automated tomato defect detection.
Methods of Capture and Transformation of Carbon Dioxide (CO2) with Macrocycles
Authors: Sanabria Edilma , Maldonado Mauricio , Matiz Carlos , Ribeiro Ana , Esteso Miguel
Published: January 2025

Rapid industrialization and the indiscriminate use of fossil fuels have generated an impact that is affecting the climate worldwide. Among the substances that are causing climate change are several gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and sulphur hexafluoride (SF6), among others. Particularly, carbon dioxide is one of the substances that has attracted the most attention from researchers, as it is responsible for more than three quarters of greenhouse gases. Because of this, many efforts have been directed towards the capture of CO2, its separation, adsorption and transformation into products that are less harmful to the environment or that even have added value in the industry. For this purpose, the use of different types of macrocycles has been explored mainly in the last 5 years. This review seeks to present the advances that have occurred in recent years in the capture and transformation of CO2 by different methods, to finally focus on the capture and transformation through macrocycle systems such as azacompounds, heterometallic macrocycles, calixpyrrols, modified cyclodextrins and metallic porphyrins, among others.
Photodiode Signal Patterns: Unsupervised Learning for Laser Weld Defect Analysis
Authors: Ozkat Erkan Caner
Published: January 2025

Laser welding, widely used in industries such as automotive and aerospace, requires precise monitoring to ensure defect-free welds, especially when joining dissimilar metallic thin foils. This study investigates the application of machine learning techniques for defect detection in laser welding using photodiode signal patterns. Supervised models, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF), were employed to classify weld defects into sound welds (SW), lack of connection (LoC), and over-penetration (OP). SVM achieved the highest accuracy (95.2%) during training, while RF demonstrated superior generalization with 83% accuracy on validation data. The study also proposed an unsupervised learning method using a wavelet scattering one-dimensional convolutional autoencoder (1D-CAE) network for anomaly detection. The proposed network demonstrated its effectiveness in achieving accuracies of 93.3% and 87.5% on training and validation datasets, respectively. Furthermore, distinct signal patterns associated with SW, OP, and LoC were identified, highlighting the ability of photodiode signals to capture welding dynamics. These findings demonstrate the effectiveness of combining supervised and unsupervised methods for laser weld defect detection, paving the way for robust, real-time quality monitoring systems in manufacturing. The results indicated that unsupervised learning could offer significant advantages in identifying anomalies and reducing manufacturing costs.
Disassembly and Its Obstacles: Challenges Facing Remanufacturers of Lithium-Ion Traction Batteries
Authors: Ohnemüller Gregor , Beller Marie , Rosemann Bernd , Döpper Frank
Published: January 2025

Lithium-ion batteries are major drivers to decarbonize road traffic and electric power systems. With the rising number of electric vehicles comes an increasing number of lithium-ion batteries reaching their end of use. After their usage, several strategies, e.g., reuse, repurposing, remanufacturing, or material recycling can be applied. In this context, remanufacturing is the favored end-of-use strategy to enable a new use cycle of lithium-ion batteries and their components. The process of remanufacturing itself is the restoration of a used product to at least its original performance by disassembling, cleaning, sorting, reconditioning, and reassembling. Thereby, disassembly as the first step is a decisive process step, as it creates the foundation for all further steps in the process chain and significantly determines the economic feasibility of the remanufacturing process. The aim of the disassembly depth is the replacement of individual cells to replace the smallest possible deficient unit and not, as is currently the case, the entire battery module or even the entire battery system. Consequently, disassembly sequences are derived from a priority matrix, a disassembly graph is generated, and the obstacles to non-destructive cell replacement are analyzed for two lithium-ion traction battery systems, to analyze the distinctions between battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) battery systems and identify the necessary tools and fundamental procedures required for the effective management of battery systems within the circular economy.
Simulation and Local Parametric Sensitivity Analysis of a Computational Model of Fructose Metabolism
Authors: Buljan Ivona , Benković Maja , Jurina Tamara , Sokač Cvetnić Tea , Valinger Davor , Gajdoš Kljusurić Jasenka , Jurinjak Tušek Ana
Published: January 2025

This research utilized a mathematical model of fructose metabolism within the CellDesigner software package to investigate the effects of varying dietary fructose intake on fat metabolism. By simulating different meal patterns with varying levels of fructose, the model provided valuable insights into the relationship between fructose consumption and hepatic triglyceride accumulation. The results demonstrated a clear correlation between increased fructose intake and elevated hepatic triglycerides. Additionally, a local parametric sensitivity analysis identified glyceraldehyde-3-phosphate and pyruvate as key regulatory factors in this process. Importantly, the model accurately simulated changes in fructose concentration and its metabolites, validating its predictive capabilities. These findings underscore the importance of systems biology in elucidating the complex mechanisms underlying nutrition-related diseases. By integrating computational modeling with experimental data, researchers can gain a deeper understanding of how dietary factors influence metabolic pathways and contribute to health outcomes. Ultimately, systems biology holds the promise of enabling personalized nutrition recommendations tailored to individual needs and genetic predispositions.
Reasonable Coal Pillar Width and Control Technology for Gob-Side Entry Driving in Deep Irregular Working Face
Authors: Yin Shuaifeng , Zhao Xubo , Wang En , Yan Yitao , Han Kanglei , Ma Jun , Wang Yibo
Published: January 2025

Aiming to address the challenges of determining the coal pillar’s width and managing the significant deformation of the surrounding rock in the deep gob-side entry driving, the limiting equilibrium zone theory, employing the operational area of Dongpang Mine 21110 as the engineering setting, states that a coal pillar’s appropriate width in the gob-side entry driving falls between 7.9 and 9.8 m. The pattern of vertical stress distribution and the extent of the plastic zone in the roadway for coal pillar widths of 7.0 m, 8.0 m, 9.0 m, and 10.0 m are analyzed, respectively, investigated using the numerical simulation method of FLAC3D. The acceptable coal pillar width in the deep gob-side entry driving is 8.0 m. Combined with the roadway surrounding rock borehole inspection results, the fracture development condition of the roadway’s full-face surrounding rock is determined, and the asymmetric aberration characteristics, with significant surrounding rock damage depth at the coal pillar flank location, are obtained. Based on the theoretical calculations, an integrated proposal for a “non-symmetrical bolt and cable anchor” coupling support scheme for the surrounding rock in the gob-side entry driving is put forward. This was applied at the Dongpang coal mine site. Engineering practice shows that leaving an 8.0 m coal pillar width and adopting the “non-symmetrical bolt and cable anchor” support system design can control the deformation of the surrounding rock in the track entry at a reasonable range, which ensures the stability of the surrounding rock in the gob-side entry driving.
Unplugging Youth: Mobile Phone Addiction, Social Impact, and the Call for Digital Detox
Authors: Salepaki Asimenia , Zerva Anna , Kourkouridis Dimitris , Angelou Ioannis
Published: January 2025

This study investigates the impact of excessive mobile phone use on young individuals aged 14–35 in Kastoria, Greece. It explores how smartphone addiction affects daily life and social interactions. A survey with 30 closed-ended questions was conducted among 150 respondents from May to August 2019. Data were analyzed using descriptive statistics and chi-square tests, focusing on gender and employment status differences. Most respondents use their phones for over 5 h daily, primarily for social media and communication. Findings reveal high addiction rates, reduced productivity, and disrupted sleep. Women reported more difficulty disconnecting and a greater need for detoxification than men. Students and unemployed individuals showed higher addiction symptoms and negative productivity impacts compared to employed respondents. Excessive mobile phone use among young people poses significant challenges, including addiction and adverse effects on productivity, relationships, and sleep, influenced by gender and employment status.
Chemoselective Transfer Hydrogenation over MgO as the Catalyst: Acetylnaphthalenes, Diacylbenzenes, Acetophenone, Benzaldehyde, and Various Aliphatic Ketones as Hydrogen Acceptors
Authors: Gliński Marek , Dubinin Olga , Rostek Klaudia , Waniek Patrycja
Published: January 2025

Liquid and vapor phase transfer hydrogenation with 2-alkanols as hydrogen donors in the presence of MgO as a catalyst was studied. A series of dicarbonyl compounds as well as the equimolar mixtures of various monocarbonyl compounds were used as hydrogen acceptors in order to determine the chemoselectivity (ChS) in the reduction of their carbonyl groups. Thus, 1,4-diacetylbenzene was reduced to 1-(4-acetylphenyl)-1-ethanol with 89% ChS and 1,3-diacetyl-4,6-dimethylbenzene with 100% ChS. Mesitylene diacyl derivatives were unreactive in the studied reaction. CTH of an equimolar mixture of benzaldehyde and acetophenone gave benzyl alcohol and 1-PhEtOH with yields of 91 and 3%, respectively (97% ChS). An equimolar mixture of acetophenone and 6-undecanone underwent CTH with yields of the corresponding alcohols of 89 and 2%, respectively, with 98% ChS towards 1-PhEtOH. Significant differences in reactivity in CTH were reported for an equimolar mixture of regioisomeric 1- and 2-acetylnaphthalenes. The yields of the corresponding alcohols were 20 and 68% with a ChS of 77% towards 2-NphCH(OH)Me. In the case of CTH of 3-oxo-2,2-dimethylbutanal and 2,4-bis(spirocyclohexyl)-1,3-cyclobutanedione with 2-propanol, only the solvolysis of the substrates was observed. The products were methyl isopropyl ketone and isopropyl formate for the former diketone and 1-(cyclohexylcarbonyl)-1-(carboisopropoxy)cyclohexane for the latter.
Eco-Friendly and Complex Processing of Vanadium-Bearing Waste for Effective Extraction of Valuable Metals and Other By-Products: A Critical Review
Authors: H. Ibrahim Ahmed , Lyu Xianjun , E. Sharafeldin Hani , ElDeeb Amr B.
Published: January 2025

Achieving the New World Sustainability Vision 2030 leads to enacting environmental restrictions, which aim to partially or totally reduce the negative impacts of different forms of waste and develop alternative technologies for eco-friendly and cost-effective utilization. Solid waste is a hazardous waste with many environmental and economic problems resulting from its storage and disposal. However, at the same time, these wastes contain many valuable elements. One of these solid wastes is heavy oil fly ash “HOFA” generated in power stations using heavy oil as fuel. HOFA is produced annually in massive amounts worldwide, the storage of which leads to the contamination of water resources by the contained heavy metals, resulting in many cancerogenic diseases. At the same time, these ashes contain many valuable metals in significant amounts, such as vanadium “V” and nickel “Ni” that can be extracted effectively compared to their low content and difficulty processing in their main ores. Hence, recycling these types of wastes reduces the environmental adverse effects of their storage and the harmful elements in their composition. This paper critically reviews the world resources of vanadium-bearing waste and various approaches described in the literature for recovering V, Ni, as well as other valuable metals from (HOFA) and other wastes, including pyro- and hydro-metallurgical processes or a combination. Hydro-metallurgical processes include alkaline or acidic leaching using different reagents followed by chemical precipitation, solvent extraction, and ion exchange to extract individual elements. The pyro-metallurgical processes involve the non-salt or salt roasting processes followed by acidic or alkaline leaching processes. The operational parameters and their impact on the efficiency of recovery are also discussed. The digestion mixtures of strong mineral acids used to dissolve metal ions in HOFA are also investigated. Bioleaching is a promising eco-friendly technology for recovering V and Ni through appropriate bacteria and fungi. Oxidation leaching is also a promising environmentally friendly approach and more effective. Among all these processes, the salt roasting treatment showed promising results concerning the cost, technological, and environmental effectiveness. The possibility of complex processing of HOFA has also been investigated, proposing innovative technology for completely utilizing this waste without any remaining residue. Effective zeolite for wastewater treatment has been formulated as a good alternative for conserving the available water resources.
Religious Symbolism and Political Resistance: Navalny’s Martyr Narrative in the Russian Opposition
Authors: Beliakova Nadezhda , Timofeeva Zhanna
Published: January 2025

The death of Russian dissident Alexey Navalny in a far-northern colony after three years of imprisonment was declared a martyrdom by the Russian opposition, reflecting the sentiments of a part of Russian society. This unexpected reference to martyr narrative by a largely secular and anti-clerical opposition has set a significant precedent when religious discourse is instrumentalized not only by official propaganda but also by its critics. The authors posit that the construction of a martyr narrative is deeply political, being a part of contemporary ideological war. This article argues that Alexey Navalny’s death and its subsequent sacralization by opposition figures represent a significant moment in contemporary Russian politics. By adopting religious narratives and symbols, the opposition not only mobilizes support but also challenges the regime’s monopoly on ideological and religious discourse. However, this strategic use of martyrdom carries inherent contradictions, reflecting the opposition’s ambivalent relationship with religion and the Russian Orthodox Church.
Astronomy and Feng Shui in Ming Urban Planning: A Satellite and Paleo-Magnetic Based Analysis
Authors: Baratta Norma Camilla , Magli Giulio
Published: January 2025

Chinese urban planning has a millenary history. According to the ancient classic texts, it was based on principles related to the cosmic order as well as on traditional ideas related to the feng shui doctrines. The problem of understanding the extent to which such ideas were put into practice is still open, partially due to the overwhelming number of cities founded or re-founded in the more than 2100 years of Chinese imperial history. In the present work, a new, comprehensive analysis of this issue is presented, with a focus on the Ming period. We construct a database which includes 238 towns—virtually all the cities founded or re-founded by the Ming dynasty—using tools based on satellite imagery to investigate orientations and relationships with natural features, and historical paleomagnetic models to investigate magnetic orientation. The results show the existence of three main “families” of towns, of which one strictly adheres to “cosmic” principles, while the other two are mainly connected to the environment. Magnetic orientation turns out, instead, to be related to the imperial capitals only.
High-Resolution High-Squint Large-Scene Spaceborne Sliding Spotlight SAR Processing via Joint 2D Time and Frequency Domain Resampling
Authors: Ren Mingshan , Zhang Heng , Yu Weidong
Published: January 2025

A frequency domain imaging algorithm, featured as joint two-dimensional (2D) time and frequency domain resampling, used for high-resolution high-squint large-scene (HHL) spaceborne sliding spotlight synthetic aperture radar (SAR) processing is proposed in this paper. Due to the nonlinear beam rotation during HHL data acquisition, the Doppler centroid varies nonlinearly with azimuth time and traditional sub-aperture approaches and two step approach fail to remove the inertial Doppler aliasing of spaceborne sliding spotlight SAR data. In addition, curved orbit effect and long synthetic aperture time make the range histories difficult to model and introduce space-variants in both range and azimuth. In this paper, we use the azimuth deramping and 2D time-domain azimuth resampling, collectively referred to as preprocessing, to eliminate the aliasing in Doppler domain and correct the range-dependent azimuth-variants of range histories. After preprocessing, the squint sliding spotlight SAR data could be considered as equivalent broadside strip-map SAR during processing. Frequency domain focusing, mainly involves phase multiplication and resampling in 2D frequency and RD domain, is then applied to compensate for the residual space-variants and achieve the focusing of SAR data. Moreover, in order to adapt higher resolution and larger scene cases, the combination of the proposed algorithm and partitioning strategy is also discussed in this paper. Processing results of simulation data and Gaofen-3 experimental data are presented to demonstrate the feasibility of the proposed methods.
Spatiotemporal Variations in Compound Extreme Events and Their Cumulative and Lagged Effects on Vegetation in the Northern Permafrost Regions from 1982 to 2022
Authors: Dong Yunxia , Liu Guimin , Wu Xiaodong , Wang Lin , Xu Haiyan , Yang Sizhong , Wu Tonghua , Abakumov Evgeny , Zhao Jun , Cui Xingyuan , Shao Meiqi
Published: January 2025

The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound extreme events. Many climate-related hazards in these areas are driven by such compound events, significantly affecting the stability and functionality of vegetation ecosystems. However, the cumulative and lagged effects of compound extreme events on vegetation remain unclear, which may lead to an underestimation of their actual impacts. This study provides a comprehensive analysis of the spatiotemporal variations in compound extreme events and the vegetation response to these events in the northern permafrost regions from 1982 to 2022. The primary focus of this study is on examining the cumulative and lagged effects of compound extreme climate events on the Kernel Normalized Difference Vegetation Index (kNDVI) during the growing seasons. The results indicate that in high-latitude regions, the frequency of extreme high temperature–precipitation compound events and high temperature–drought compound events have increased in 58.0% and 67.0% of the areas, respectively. Conversely, the frequency of extreme low temperature–drought compound events and extreme low temperature–precipitation compound events has decreased in 70.6% and 57.2% of the areas, with the high temperature–drought compound events showing the fastest increase. The temporal effects of compound extreme events on kNDVI vary with vegetation type; they produce more cumulative and lagged effects compared with single extreme high-temperature events and fewer effects compared with single extreme precipitation events, with compound events significantly affecting forest and grassland ecosystems. Notably, extreme high temperature–precipitation compound events exhibit the strongest cumulative and lagged effects on vegetation, while extreme low temperature–drought compound events influence wetland and shrubland areas within the same month. This study underscores the importance of a multivariable perspective in understanding vegetation dynamics in permafrost regions.
Integrating Sensor Technologies with Conversational AI: Enhancing Context-Sensitive Interaction Through Real-Time Data Fusion
Authors: Kush Joseph C.
Published: January 2025

This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models’ comprehension of surroundings, user contexts, and physical conditions. Lastly, this article delves into the scientific principles supporting sensor technologies, data processing methods, and their fusion with generative models such as ChatGPT to develop adaptable, dynamic systems that engage with humans intelligently in real time. Some of the specific topics that are investigated include the science behind sensor networks and acquiring real-time data, how ChatGPT can analyze sensor data to generate dialogue that is sensitive to context, instances in healthcare (such as using wearable sensors along with AI chatbots for patient treatment), and smart homes (interaction with AI assistants driven by sensors). These subjects will prove advantageous for researchers in sensor technology as well as AI development, showcasing interdisciplinary progress in smart systems.
A PFM-Based Calibration Method for Low-Power High-Linearity Digital Pixel
Authors: Cheng Yu , Liu Jionghan , Wang Xiyuan , Hou Hongyu , Jiang Qian , Chang Yuchun
Published: January 2025

The nonlinearity problem of digital pixels restricts the reduction in power consumption at the pixel-level circuit. The main cause of nonlinearity is discussed in this article and low power consumption is attained by reducing the static current in capacitive transimpedance amplifiers (CTIAs) and comparators. Linearity was successfully improved through the use of an off-chip calibration method. A 64 × 64 array prototype digital readout integrated circuit (DROIC) was fabricated using a 0.18 μm 1P6M CMOS process. Experimental results indicated that the post-calibration linearity reached 99.6% with an input current of up to 1.5 μA. The static power consumption per digital pixel was 6 μW.
Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images
Authors: Liu Shukuan , Cao Shi , Lu Xia , Peng Jiqing , Ping Lina , Fan Xiang , Teng Feiyu , Liu Xiangnan
Published: January 2025

Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification. This paper proposes ConvNeXt-U, a lightweight deep learning network that efficiently extracts fragmented cropland while reducing computational requirements and saving costs. ConvNeXt-U retains the U-shaped structure of U-Net but replaces the encoder with a simplified ConvNeXt architecture. The decoder remains unchanged from U-Net, and the lightweight CBAM (Convolutional Block Attention Module) is integrated. This module adaptively adjusts the channel and spatial dimensions of feature maps, emphasizing key features and suppressing redundant information, which enhances the capture of edge features and improves extraction accuracy. The case study area is Hengyang County, Hunan Province, China, using GF-2 remote sensing imagery. The results show that ConvNeXt-U outperforms existing methods, such as Swin Transformer (Acc = 85.1%, IoU = 79.1%), MobileNetV3 (Acc = 83.4%, IoU = 77.6%), VGG16 (Acc = 80.5%, IoU = 74.6%), and ResUnet (Acc = 81.8%, IoU = 76.1%), achieving an IoU of 79.5% and Acc of 85.2%. Under the same conditions, ConvNeXt-U has a faster inference speed of 37 images/s, compared to 28 images/s for Swin Transformer, 35 images/s for MobileNetV3, and 0.43 and 0.44 images/s for VGG16 and ResUnet, respectively. Moreover, ConvNeXt-U outperforms other methods in processing the boundaries of fragmented cropland, producing clearer and more complete boundaries. The results indicate that the ConvNeXt and CBAM modules significantly enhance the accuracy of fragmented cropland extraction. ConvNeXt-U is also an effective method for extracting fragmented cropland from remote sensing imagery.
Three-Dimensional Scanning Virtual Aperture Imaging with Metasurface
Authors: Ou Zhan , Liang Yuan , Cai Hua , Wang Guangjian
Published: January 2025

Metasurface-based imaging is attractive due to its low hardware costs and system complexity. However, most of the current metasurface-based imaging systems require stochastic wavefront modulation, complex computational post-processing, and are restricted to 2D imaging. To overcome these limitations, we propose a scanning virtual aperture imaging system. The system first uses a focused beam to achieve near-field focal plane scanning, meanwhile forming a virtual aperture. Secondly, an adapted range migration algorithm (RMA) with a pre-processing step is applied to the virtual aperture to achieve a 3D high-resolution reconstruction. The pre-processing step fully exploits the feature of near-field beamforming that only a time delay is added on the received signal, which introduces ignorable additional calculation time. We build a compact prototype system working at a frequency from 38 to 40 GHz. Both the simulations and the experiments demonstrate that the proposed system can achieve high-quality imaging without complex implementations. Our method can be widely used for single-transceiver coherent systems to significantly improve the imaging depth of field (DOF).
Displacement Measurement Based on the Missing-Order Talbot Effect
Authors: Song Liuxing , Zhao Kailun , Wang Xiaoyong , He Jinping , Tian Guoliang , Yang Shihua , Li Yaning
Published: January 2025

Displacement measurement is a crucial application, with laser-based methods offering high precision and being well established in commercial settings. However, these methods often come with the drawbacks of significant size and exorbitant costs. We introduce a novel displacement measurement method that utilizes the missing-order Talbot effect. This approach circumvents the need to measure contrast in the Talbot diffraction field, opting instead to leverage the displacement within the missing-order Talbot diffraction pattern. Our method only requires parallel light, an amplitude grating, and a detector to achieve displacement measurement. The measurement dynamic range can be adjusted by altering the grating period and the wavelength of the incident light. Through careful simulation and experimental validation, our method exhibits a correlation coefficient R surpassing 0.999 across a 30 mm dynamic range and achieves a precision superior to 3 μm.
Monitoring Stresses Caused by Gaseous Pollutants: How Can They Affect a Fruit-Feeding Butterfly Community (Lepidoptera: Nymphalidae) in the Caatinga?
Authors: dos Santos Silva Gustavo , dos Santos da Silva Maicon , de Mates Eloito Caires , Silva Wesley Gil Oliveira , da Costa Daniela Ribeiro , Braga Laura , de Sá Neto Raymundo José , de Oliveira Soares Filho Avaldo , dos Santos Mateus Pereira , Leite Suzany Aguiar , Castellani Maria Aparecida
Published: January 2025

This study evaluated the effects of gaseous pollutants and vegetation on the structure of fruit-feeding butterfly communities (some subfamilies of Nymphalidae) in a Caatinga area in Brumado, BA, between 2016 and 2018. Two transects were established: Transect “I” (presence of pollutant plumes) and Transect “II” (absence), encompassing a forest fragment and pasture. Bait traps were installed in each transect, and the butterfly communities were analyzed using faunistic indices, including species richness, Shannon diversity index, abundance, and dominance. The canopy opening was also assessed. The composition of fruit-feeding butterfly communities was influenced by both pollutants and vegetation. Gaseous pollutants increased butterfly abundance, diversity, and species richness, though species dominance remained unaffected. Notably, the abundance of Hamadryas februa was particularly sensitive to pollutant exposure. Conversely, increased canopy opening was negatively associated with butterfly abundance and diversity. A relationship between canopy opening and the presence of gaseous pollutants may reflect changes in the abundance and diversity of fruit-feeding butterfly species in the study region. Long-term community monitoring is important, as interannual differences in population fluctuations are common. A better understanding of the patterns found is essential to for devise devising conservation strategies for frugivorous butterfly communities in mining ventures.
Sustainability: A Concept in Flux? The Role of Multidisciplinary Insights in Shaping Sustainable Futures
Authors: Saruchera Fanny
Published: January 2025

This study explores the multifaceted concept of sustainability through a multidisciplinary lens, integrating perspectives from natural sciences, social sciences, humanities, economics, engineering, technology, psychology, and health. Drawing on a systematic review of the literature and empirical data from the Sustainable African futures Sustainability School, this research integrates disciplinary insights and aligns them with the United Nations’ Sustainable Development Goals (SDGs). The findings reveal significant variations in how different disciplines define, prioritise, and approach sustainability, highlighting the need for transdisciplinary approaches to develop holistic and actionable solutions. This study identifies limitations in multidisciplinary methods, such as potential biases and siloed discussions, and advocates for research that fosters dynamic exchanges across various disciplines. Practical recommendations are provided for corporate sustainability initiatives and governmental policies that align with the SDGs, offering pathways to address the interconnected global challenges of sustainable development. By advancing collaborative transdisciplinary approaches, this study contributes to the shaping of sustainable futures through integrated and inclusive strategies.
Green Skills Are Not Enough: Three Levels of Competences from an Applied Perspective
Authors: Wegenberger Oliver , Ponocny Ivo
Published: January 2025

The world of work is increasingly recognizing sustainability needs and issues, which require skills and competences. Accordingly, concepts like green skills and sustainability competences have been discussed in the literature. However, it remains unclear what green skills are and what differentiates them from other concepts such as sustainability competences. The aim of this paper is to address the question of what green skills are in order to facilitate the application of them by bringing the concept closer to practice. A qualitative approach was employed, utilizing semi-structured interviews with experts engaged in sustainability matters across various sectors. In line with previous research, the term green skills is seen as a complex and ambiguous one. Based on the findings of the present study, we propose to differentiate three levels of skills and competences, delineated by their specificity: general sustainability competences, intermediate cross-sectional competences, and specific green skills. Additionally, experts underscored the pivotal role of personal values and interests.
Data Storytelling and Decision-Making in Seaport Operations: A New Approach Based on Business Intelligence
Authors: Gonçalves Marco , Salgado Cátia , de Sousa Amaro , Teixeira Leonor
Published: January 2025

Seaports are experiencing several challenges due to the explosive growth of the maritime shipping business, which has led to the need for digitalized operations and more effective solutions. This article provides a comprehensive exploration of the process used to create a reliable business intelligence solution by analyzing the container delivery and pick-up services flow in one of Portugal’s largest maritime container ports, using the CRISP-DM methodology. The solution, built with Microsoft Power BI®, provides the capability to identify and address data anomalies and present key performance indicators in visually dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into the current and future operational status, thereby facilitating well-informed and adaptable decision-making, representing the main practical contributions. As a theoretical contribution, this study advances research by covering a gap in the literature and establishing the foundations for future business intelligence applications within the maritime industry, with a focus on addressing data dispersion challenges, enhancing logistics flow analysis, and reducing port congestion. The manuscript is structured into seven sections: introduction, literature review, port challenges, methodology, tool development, SWOT analysis, and conclusion.
Progress and Trends in Research on Soil Nitrogen Leaching: A Bibliometric Analysis from 2003 to 2023
Authors: Liu Getong , Sun Jiajun , Liu Chenfeng , Shi Huading , Fei Yang , Wang Chen , Zhang Guilong , Wang Hongjie
Published: January 2025

An in-depth discussion on the research progress and trends in soil nitrogen leaching is essential for the development of agricultural sustainability. However, not enough attention has been paid to the progress and future trends of soil nitrogen leaching research. Using software such as VOSviewer and CiteSpace, bibliometric analyses of a total of 2767 documents in the Web of Science Core Collection were conducted; the documents considered were published over the last 20 years (2003–2023). The results are as follows: (1) The research output on soil nitrogen leaching has been increasing steadily, showing a single-discipline dominance in agronomy, but a trend of multidisciplinary cross-research has gradually begun to emerge in recent years. (2) There has been close cooperation between authors, countries, and institutions; the main cooperation includes research on modelling and management, and research related to nitrogen management practices and soil types has a high international profile. (3) The research components at the heart of soil nitrogen leaching are nitrogen cycling in soils, soil properties, water environments and crops, greenhouse gas formation and emissions, and agronomic management practices and the research hotspot has gradually changed to applied research. (4) Increasing the considerations of management measures, deepening the related research on soil microorganisms, and constructing a complete evaluation system constitute the main future research directions. This study can provide valuable references for the sustainable development of agriculture.