bluuebunny/crossref_metadata_2025 huggingface dataset:
Simple and Efficient Synthesis of N-Succinimidyl-4-[18F]fluorobenzoate ([18F]SFB)—An Important Intermediate for the Introduction of Fluorine-18 into Complex Bioactive Compounds
Authors: Orlovskaya Viktoriya V. , Fedorova Olga S. , Viktorov Nikolai B. , Krasikova Raisa N.
Published: December 2024

Background: N-succinimidyl-[18F]fluorobenzoate ([18F]SFB) is commonly prepared through a three-step procedure starting from [18F]fluoride ion. A number of methods for the single-step radiosynthesis of [18F]SFB have been introduced recently, including the radiofluorination of diaryliodonium salts and the Cu-mediated 18F-fluorination of pinacol aryl boronates and aryl tributyl stannanes, but they still have the drawbacks of lengthy product purification procedures. In the present work, two approaches for the direct labeling of [18F]SFB from diaryliodonium (DAI) salt (4) and pinacol aryl boronate (6) are evaluated, with a major focus on developing a fast and simple SPE-based purification procedure. Methods: DAI salt precursor 6 was labeled employing the common “minimalist” approach with a two-step reaction heating sequence. The Cu-mediated radiofluorination of 4 was accomplished using Bu4NOTf as a phase transfer catalyst for the elution of [18F]fluoride, followed by radiofluorination in the same solvent. Several types of SPE cartridges were tested in the elution and SPE procedures. Results: The Cu-mediated 18F-fluorination of the pinacol aryl boronate precursor afforded a higher RCC of 56 ± 3% (n = 7), making it better suited for the one-pot synthesis of [18F]SFB. SPE-based purification was achieved using cation exchange and reverse-phase polymer resin cartridges, connected in series. In a full-batch test, [18F]SFB was obtained with an RCY of 30% (n. d. c.), RCP > 99%, Am 96–155 GBq/µmol, and a synthesis time of ≤35 min. Conclusions: Compared to other published methods, [18F]SFB production via the Cu-mediated radiofluorination of pinacol aryl boronate precursor provides significant time and cost savings, coupled with an ease of implementation.
Frequency and Polarization Response of Multipolar Gold Nanoantennae
Authors: Moreno Juan R. , Ramírez-Elías Miguel G. , Rosales-Gallegos Israel A. , García-Ramírez Eliseo , Chiwo Fernando S. , Cano-Figueroa Martha Angélica , Arcos-Gutiérrez Hugo , Garduño Isaías E. , Domínguez Carlos A. , Aboytes-González Jesús A. , González Francisco J.
Published: December 2024

Multipolarized nanoantennae are promising for terahertz applications due to their ability to support multiple resonance and polarization sensitivity. This study investigates the behavior of multipolarized gold nanoantennae placed on a Si-SiO2 substrate and compares their performance with antennae in air. The results show that the resonance frequency shifts from 27 THz in the air to 26 THz on the SiSiO2 substrate. SEM and FTIR analysis confirmed the antenna dimensions and optical response, with experimental reflectance spectra revealing resonance at 21 THz and 26 THz. The multi-polarized antenna demonstrated stable performance across different polarization angles compared to traditional dipole antennae, making it suitable for various terahertz applications. These findings provide insights into optimizing nanoantenna design for advanced sensors and communication systems.
Biomimetic 3D Hydrogels with Aligned Topography for Neural Tissue Engineering
Authors: Severs Liza J. , Katta Anjali , Cates Lindsay N. , Dewees Dane M. , Hoagland Riana T. , Horner Philip J. , Hofstetter Christoph P. , Khaing Zin Z.
Published: December 2024

Spinal cord trauma leads to the destruction of the highly organized cytoarchitecture that carries information along the axis of the spinal column. Currently, there are no clinically accepted strategies that can help regenerate severed axons after spinal cord injury (SCI). Hydrogels are soft biomaterials with high water content that are widely used as scaffolds to interface with the central nervous system (CNS). Here, we examine a simple and reproducible method that results in consistently aligned fibrils within 3D matrices using thermally gelling biomimetic polymers. A collagen type I (Col)-based thermally gelling hydrogel system was used in combination with two other native extracellular matrix proteins: laminin I (LN) and hyaluronic acid (HA). Gelling kinetics for all gel types (Col, Col LN, Col HA) showed that at 37 °C, all three hydrogels formed gels consistently. A method of aspiration and ejection was used to produce Col-based hydrogels containing aligned fibrils. In vitro, embryonic spinal cord neurons survived and produced processes aligned to collagen fibrils. Next, we implanted either non-aligned or aligned hydrogels after a bilateral dorsal hemisection of the thoracic spinal cord at T7/T8. Pan neuronal antibody-positive fibrils were found within all implants; aligned hydrogels supported neurite growth along the parallel direction of the implanted hydrogels. Combined, our in vitro and in vivo data indicate that thermally gelling biomimetic hydrogels can produce aligned matrices through a method of aspiration and ejection, and this presents a novel platform for regenerative therapies for the CNS.
Modulated Mechanical Properties of Epoxy-Based Hybrid Composites via Layer-by-Layer Assembly: An Experimental and Numerical Study
Authors: Jeon Hee-Chang , Kim Young-Seong
Published: December 2024

In this study, epoxy-based composites were fabricated using a layer-by-layer assembly technique, and their mechanical properties were systematically evaluated. The inclusion of cellulose nanocrystals led to variations in the mechanical properties of the composites. These modified properties were assessed through tensile and flexural tests, with each layer cast to enhance strength. Due to the inherent characteristics of epoxy, a single specimen was fabricated through chemical bonding, even post-curing. This approach demonstrated that a three-layer structure, developed using the layer-by-layer method, exhibited improved elastic and flexural moduli compared to a single-layer composite. This improvement aligns with theoretical predictions, which suggest that stiffness increases when stiffer materials are positioned farther from the neutral axis in a layered structure. Furthermore, numerical analysis validated changes in stress distribution across each layer. Consequently, this method enables the production of composites with superior mechanical properties while minimizing the quantity of cellulose nanocrystals required.
A New Polymeric Hybrid Auxetic Structure Additively Manufactured by Fused Filament Fabrication 3D Printing: Machine Learning-Based Energy Absorption Prediction and Optimization
Authors: Hasanzadeh Rezgar
Published: December 2024

The significance of this paper is an investigation into the design, development, and optimization of a new polymeric hybrid auxetic structure by additive manufacturing (AM). This work will introduce an innovative class of polymeric hybrid auxetic structure by the integration of an arrow-head unit cell into a missing rib unit cell, which will be fabricated using fused filament fabrication (FFF) technique, that is, one subset of AM. The auxetic performance of the structure is validated through the measurement of its negative Poisson’s ratio, confirming its potential for enhanced energy absorption. A chain of regression, linear, and quadratic polynomial machine learning algorithms are used to predict and optimize the energy absorption capability at variant processing conditions. Amongst them, the polynomial regression model stands out with an R-squared value of 92.46%, reflecting an excellent predictive capability for energy absorption of additively manufactured polymeric hybrid auxetic structure. The optimization technique revealed that the printing speed of 80 mm/s and layer height of 200 µm were the critical values to achieve a maximum amount of energy absorption at 5.954 kJ/m2, achieved at a printing temperature of 244.65 °C. Such results also contribute to the development of AM, since they show not only the potential for energy absorption of polymeric hybrid auxetic structures but also how effective machine learning is in the optimization of the AM process.
Exploring Hypofractionated Radiotherapy Efficacy in Prostate Cancer: In Vitro Insights
Authors: du Plessis Peter , Nkosi Pauline Busisiwe , Nair Shankari , Akudugu John
Published: December 2024

The rising incidence of prostate cancer necessitates innovative treatment approaches, particularly as diseases such as the COVID-19 pandemic can disrupt traditional cancer care. This study aims to evaluate the impact of hypofractionated versus conventionally fractionated radiotherapy on prostate cancer cell lines in vitro. Prostate cancer cell lines (PC-3 and DU-145) were exposed to varying doses of radiation alongside non-cancerous BPH-1 cells. We assessed radiation effects on cell proliferation, viability, colony formation, DNA repair, migration, invasion, and cytotoxicity. The results demonstrated that the prostate cell lines exhibited varying responses, with hypofractionation favourably impacting aggressive PC-3 cells while preserving non-cancerous cells. In contrast, conventional fractionation led to increased invasion and cytotoxicity in both prostate cancerous cell lines. These findings advocate for personalised radiation therapy approaches that enhance treatment efficacy by considering the distinct behaviours of differing prostate cancer subtypes.
The Myth of Islamist Victimhood: Unpacking the Myths of Realities Behind the Narrative
Authors: Erturk Omer F.
Published: December 2024

The enduring narrative of Islamist victimhood, particularly for those who call for a just governance system on behalf of devout Muslim society, which they claim has been systematically repressed and forced underground by the Kemalists, has been prominently echoed in both Turkish and Western scholarship from the late 1990s to the early 2010s. Scholars argued that loosening the hegemonic Kemalist grip on state institutions would promote democracy and civil rights; however, when Islamists took control of the state apparatus in the 2010s, it became evident that this support contributed to the rise of an autocratic electoral regime with Islamist overtones, worsening democracy, which became more repressive than ever after the Kemalist threat was removed. Despite the prominence of these victimhood narratives, the origin and credibility of these claims have not been critically and systematically examined. Through a detailed analysis of biographies, official texts, and personal memoirs, this study assesses the validity of the foundational Islamist victimhood narratives. The findings challenge the narrative of widespread repression, demonstrating that many religious figures continued to operate openly under the Kemalist regime, using the closure of Sufi lodges as a tool to construct a narrative of collective victimhood for political leverage. This study is significant because it highlights how academia can fall into similar “victimhood traps” elsewhere in the world by amplifying victimhood narratives without fully understanding their dynamics or verifying their factual basis, ultimately worsening the situation.
From Ethical Naturalism to Aquinas’ Notion of Natural Law: A Non-Trivial Convergence?
Authors: Asla Mariano , Paladino María Soledad
Published: December 2024

Is it possible for Aquinas’s moral theology to engage constructively with contemporary moral naturalism? The proposed question has already been a subject of scholarly interest, eliciting various responses. Some authors emphasize the difficulties and contradictions, while others identify potential affinities. This paper explores the extent to which Aquinas’s concept of Natural Law might align, albeit partially, with a form of moral non-reductive, non-non-cognitivist ethical naturalism. Specifically, it aims to assess how his view of Natural Law, rooted in practical reason and natural inclinations, incorporates a teleological understanding of nature, which provides a foundational basis for this consonance. Within this framework, moral virtue emerges as central in bridging bio-psychological facts with ethical principles. Aquinas’s assertion that gratia non tollit naturam, sed perficit “grace does not destroy nature but perfects it” (ST I. q1. a8) further underscores the continuity between nature and normativity, suggesting that while grace and revelation introduce true novelty, they enhance rather than supplant the natural moral order.
Weakly Supervised Instance Segmentation in Aerial Images via Comprehensive Spatial Adaptation
Authors: Xu Jingting , Luo Peng , Mu Dejun
Published: December 2024

Weakly supervised instance segmentation (WSIS) only employs image-level supervision to identify instance class labels and create segmentation masks, drawing increasing attention. Currently, existing WSIS methods primarily focus on activating the most discriminative regions and then inferring the entire instance by analyzing inter-pixel relationships within those regions. However, these identification regions are typically concentrated in limited but critical regions or are mistakenly activated in the background region, making it challenging to address scale variations among instances. Furthermore, different aerial instances often appear in close proximity, resulting in the merging of multiple instances of the same class. To tackle these challenges, we propose a comprehensive approach called Comprehensive Spatial Adaptation Segmentation (CSASeg). Specifically, the self-adaptive spatial-aware enhancement network (SSE) identifies extensive regions by analyzing spatial consistency within the class semantic map. Then, we develop a multi-level projection field (MPF) module to significantly enhance instance-level discrimination through deep-to-shallow residual estimation. Additionally, a foreground enhancement module is incorporated into SSE to reduce background noise while enhancing foreground details, significantly increasing the effectiveness of instance analysis. Extensive experiments conduct on three challenging datasets, iSAID, NWPU VHR-10.v2, and SSDD, demonstrate the competitiveness of our proposed approach.
Dynamic Emotion Recognition and Expression Imitation in Neurotypical Adults and Their Associations with Autistic Traits
Authors: Wang Hai-Ting , Lyu Jia-Ling , Chien Sarina Hui-Lin
Published: December 2024

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication. While many studies suggest that individuals with ASD struggle with emotion processing, the association between emotion processing and autistic traits in non-clinical populations is still unclear. We examine whether neurotypical adults’ facial emotion recognition and expression imitation are associated with autistic traits. We recruited 32 neurotypical adults; each received two computerized tasks, the Dynamic Emotion Recognition and Expression Imitation, and two standardized measures: the Chinese version AQ and the Twenty-Item Prosopagnosia Index (PI-20). Results for the dynamic emotion recognition showed that happiness has the highest mean accuracy, followed by surprise, sadness, anger, fear, and disgust. For expression imitation, it was easiest to imitate surprise and happiness, followed by disgust, while the accuracy of imitating sadness, anger, and fear was much lower. Importantly, individual AQ scores negatively correlated with emotion recognition accuracy and positively correlated with PI-20. The AQ imagination, communication sub-scores, and PI-20 positively correlated with the expression imitation of surprise. In summary, we found a significant link between recognizing emotional expressions and the level of autistic traits in non-clinical populations, supporting the concept of broader autism phenotype.
The Relationship Between Daily Activity Level, Posture Distribution, Stepping Patterns, and Cadence in the BCS70 Cohort
Authors: Speirs Craig , Ahmadi Matthew , Hamer Mark , Stamatakis Emmanuel , Granat Malcolm
Published: December 2024

This study investigated the relationship between stepping-defined daily activity levels, time spent in different postures, and the patterns and intensities of stepping behaviour. Using a thigh-mounted triaxial accelerometer, physical activity data from 3547 participants with seven days of valid data were analysed. We classified days based on step count and quantified posture and stepping behaviour, distinguishing between indoor, community, and recreation stepping. The results indicated significant differences in time spent in upright (2.5 to 8.9 h, p < 0.05), lying (8.0 to 9.1 h, p < 0.05), and sedentary (7.0 to 13.0 h, p < 0.05) postures across activity levels. At higher daily activity levels (10,000–15,000 steps), individuals tended to spend approximately equal time in each posture (8 h lying, 8 h sitting, and 8 h upright). The study found that at lower stepping-defined activity levels, step volumes were driven primarily by indoor stepping, while at higher activity levels, outdoor and recreation stepping were larger contributors. Additionally, stepping classified as indoor had significantly slower cadences compared to outdoor stepping. These findings suggest that the composition and intensity of stepping behaviours vary significantly with daily activity volumes, providing insights that could enhance public health messaging and interventions aimed at promoting physical activity.
G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection
Authors: Bai Jimeng , Cao Guohua
Published: December 2024

In industrial applications, robotic arm grasp detection tasks frequently suffer from inadequate accuracy and success rates, which result in reduced operational efficiency. Although existing methods have achieved some success, limitations remain in terms of detection accuracy, real-time performance, and generalization ability. To address these challenges, this paper proposes an enhanced grasp detection model, G-RCenterNet, based on the CenterNet framework. First, a channel and spatial attention mechanism is introduced to improve the network’s capability to extract target features, significantly enhancing grasp detection performance in complex backgrounds. Second, an efficient attention module search strategy is proposed to replace traditional fully connected layer structures, which not only increases detection accuracy but also reduces computational overhead. Additionally, the GSConv module is incorporated during the prediction decoding phase to accelerate inference speed while maintaining high accuracy, further improving real-time performance. Finally, ResNet50 is selected as the backbone network, and a custom loss function is designed specifically for grasp detection tasks, which significantly enhances the model’s ability to predict feasible grasp boxes. The proposed G-RCenterNet algorithm is embedded into a robotic grasping system, where a structured light depth camera captures target images, and the grasp detection network predicts the optimal grasp box. Experimental results based on the Cornell Grasp Dataset and real-world scenarios demonstrate that the G-RCenterNet model performs robustly in grasp detection tasks, achieving accurate and efficient target grasp detection suitable for practical applications.
PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph
Authors: Hamed Naeima , Rana Omer , Orozco-terWengel Pablo , Goossens Benoît , Perera Charith
Published: December 2024

Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.
Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies
Authors: Vakitbilir Nuray , Islam Abrar , Gomez Alwyn , Stein Kevin Y. , Froese Logan , Bergmann Tobias , Sainbhi Amanjyot Singh , McClarty Davis , Raj Rahul , Zeiler Frederick A.
Published: December 2024

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.
The Role of Wood Storage in Reducing Fossil Fuel Consumption in Forestry Operations: A Sustainable Approach
Authors: Zanuncio Antonio Jose Vinha , de Castro Vinícius Resende , Barbosa Raiana Augusta Grandal Savino , Carvalho Amélia Guimarães , Winter Shoraia Germani , Lopes Olivia Pereira , Morais Roberta Barbosa , Guimarães Dandara Paula Silva , Carneiro Angélica de Cássia Oliveira , Soares Alvaro Augusto Vieira , Araujo Solange de Oliveira , Sampietro Jean Alberto
Published: December 2024

Forest operations consume a large amount of fossil fuels. Storing wood in the field reduces the weight of the wood, optimizing transportation and reducing fuel consumption. However, there are few studies linking these variables. The objective was to evaluate the drying of logs with different diameters and assess the fossil fuel consumption considering the mass of dry wood transported. Eucalyptus logs were divided into two diameter classes: 6 ≤ D1 ≤ 11 cm and 11.1 ≤ D2 ≤ 14 cm. These logs were arranged in a stack, divided into base, intermediate, and top positions, and subjected to drying. The wood moisture values were correlated with the amount of transported dry wood, fuel consumption, and costs. These calculations were based on a vehicle with a load capacity of 41.6 Mg, a fuel consumption rate of 1.82 L/km, and a road cost of 0.51 R$·Mg−1·km−1. The moisture content was lower in smaller-diameter logs located at the top of the storage stack and higher in larger-diameter logs in the intermediate position and in both diameter classes at the bottom position after 365 days of drying. Storage for 365 days, on average, increased the dry wood transport capacity by 90%, resulting in similar gains in fuel economy and the final cost of transportation. Storing wood outdoors has proven effective in reducing its moisture content, resulting in lower fossil fuel consumption during forestry operations, reducing environmental impact, and generating economic gains.
Model-Supported Groundwater Table Control on the Vistula River Plain—Methodological Approach
Authors: Brandyk Andrzej , Oleszczuk Ryszard , Majewski Grzegorz , Barszcz Mariusz , Rozbicka Katarzyna
Published: December 2024

At present, a sustainable and wise management of water resources requires more insight into drainage/irrigation practices in river valleys. Since efficient sub-irrigation, based on reliable hydrometeorological forecasts, has been extensively considered with respect to water saving, the proper modeling tools were subsequently developed. An original, conceptual model for the management of drainage/irrigation systems was presented, taking into account the water inflow and storage in the soil profile. The aim was to propose a relatively simple procedure with parameters that relate to easily obtainable variables, e.g., groundwater table depth in the form of uncomplicated equations. The results of this tool were compared with the groundwater heads simulated using the recognized, common Modflow model. The comparisons proved a close match of the modeled variables and point at possibilities to calibrate it on polder areas.
Study of Microalgae Biofixation with Bacteria Carbonic Anhydrase for Carbon Capture and Utilization
Authors: Chan Shui-Shing , Chan Kwan-Shing , Leung Shu-Kei , Lam Wai-Yu Vivian , Kwok Ho-Pan , Yau Tze-Yee Jasmine , Wong Sum-Yi Sammie , Chan Cho-Yin
Published: December 2024

Climate change has been significantly affecting human activities due to the accumulation of greenhouse gases, such as carbon dioxide. Biofixation of carbon dioxide (CO2) has been investigated to reduce the atmospheric CO2 level and slow the rapid increase in the global temperature. Carbon capture and utilization (CCU) can be performed by either physio-chemical or biological methods. The latter takes place in ambient temperature and mild conditions, such that there is no need for high pressure and high energy consumption nor hazardous chemicals. Biofixation by microalgae has been utilized to capture CO2 and the microalgae biomass collected after the process can be further utilized in renewable biofuel generation. On the other hand, microbial enzymes, such as carbonic anhydrase (CA), have been investigated to speed up the whole biofixation process by increasing the conversion rate of CO2 into bicarbonate (HCO3−) in a culture medium and the latter can be readily used by microalgae to increase CO2 removal. In this study, in the presence of 20% CO2 (v/v) gas in air and 5 mL CA enzyme extract (0.5 mg mL−1 protein), we can significantly increase the biofixation rate using marine green microalgae, Tetraselmis sp. Results showed that the biofixation rate can be increased from 0.64 g L−1 day−1 (no CA and at 0.04% CO2) to 4.26 g L−1 day−1. The effects of different experimental conditions such as pH, nutrient levels and working CO2 concentration levels on Tetraselmis sp. growth and CO2 biofixation (CO2 removal) rate have been investigated. This study demonstrates a new alternative approach for effective carbon capture and utilization (CCU) using microalgae which can be applied to achieve the goal of carbon neutrality.
A Hybrid Method of Cooling and Heating Consumption Prediction for Six Types of Buildings Based on Machine Learning
Authors: Chi Baibing , Li Yashuai , Zhou Dawei
Published: December 2024

Sustainable development is a vital strategy that is being implemented in China. To achieve sustainable development in terms of building energy efficiency, accurately estimating the amount of energy that buildings will consume is crucial. A theoretical framework for machine learning-based building energy consumption prediction is presented in this study; six different types of building information models in five major thermal design zones of China were used for gathering information and forming a database. The suggested prediction model’s distinctive feature is that nine factors affecting building energy consumption in three aspects, including macro-view, middle-view, and micro-view aspects, are proposed, eight machine learning techniques are employed to predict building energy consumption, and the factors influencing energy consumption are identified. Two standard measures were employed to evaluate the framework’s performance: the coefficient of determination (R2) and the root mean square error (RMSE). It was found that the accuracies of all eight models were above 90%. Among them, the kNN model and GBRT have the best prediction results. Using the optimal GBRT model, the feature importance ranking was obtained. The proposed machine learning prediction model informs similar studies and can be applied to predict different buildings’ cooling and heating loads accurately.
Sparse Sensor Fusion for 3D Object Detection with Symmetry-Aware Colored Point Clouds
Authors: Wang Lele , Zhang Peng , Li Ming , Zhang Faming
Published: December 2024

Multimodal fusion-based object detection is the foundational sensing task in scene understanding. It capitalizes on LiDAR and camera data to boost the robust results. However, there are still great challenges in establishing an effective fusion mechanism and performing accurate and diverse feature interaction fusion. In particular, the relationship construction between the two modalities has not been comprehensively exploited, leading to sensor data utilization deficiencies and redundancies. In this paper, a novel 3D object-detection framework, namely a symmetry-aware sparse sensor fusion detection network (2SFNet), is proposed. This framework was designed to leverage point clouds and RGB images. The 2SFNet consists of three submodules, filtered colored point cloud generation, pseudo-image generation, and a dilated feature fusion network, to solve these problems. Firstly, filtered colored point cloud generation constructs non-ground colored point cloud (NCPC) data by employing an early fusion strategy and a ground-height-filtering module, selectively retaining only object-related information. Subsequently, 2D grid encoding is used on the reduced colored data. Finally, the processed colored data are fed into the improved PillarsNet architecture, which now has expanded receptive fields to enhance the fusion effect. This design optimizes the fusion process by ensuring a more balanced and effective data representation, aligning with the symmetry concept that underlies the model’s functionality. Experiments and evaluations were conducted on the KITTI dataset to present the effectuality, particularly for categories characterized by sparse point clouds. The results indicate that the symmetry-aware design of the 2SFNet leads to an improved performance when compared to other multimodal fusion networks, and alleviates the phenomenon caused by highly obscured and crowded scenes.
Lorentz Invariance in Relativistic Particle Mechanics
Authors: Hill James M.
Published: December 2024

The notion of invariance under the Lorentz transformation is fundamental to special relativity and its continuation beyond the speed of light. Theories and solutions with this characteristic are stronger and more powerful than conventional theories or conventional solutions because the Lorentz-invariant approach automatically embodies the conventional approach. We propose a Lorentz-invariant extension of Newton’s second law, which includes both special relativistic mechanics and Schrödinger’s quantum wave theory. Here, we determine new general expressions for energy–momentum, which are Lorentz-invariant. We also examine the Lorentz-invariant power-law energy–momentum expressions, which include Einstein’s energy relation as a particular case.
Occurrence, Bioaccumulation, and Human Exposure Risk of the Antiandrogenic Fluorescent Dye 7-(Dimethylamino)-4-methylcoumarin and 7-(Diethylamino)-4-methylcoumarin in the Dongjiang River Basin, South China
Authors: Lai Yufeng , Huang Yin , Yang Danlin , Xue Jingchuan , Chen Runlin , Peng Rundong , Zhang Siying , Li Yufei , Yang Guochun , Liu Yuxian
Published: December 2024

Recently, 7-diethylamino-4-methylcoumarin (DEAMC) has been identified as a potent antiandrogenic compound in the surface water; however, little is known about the antiandrogenic potentials of other synthetic coumarins and their occurrence in the aquatic ecosystem. In this study, for the first time, we observed that 7-dimethylamino-4-methylcoumarin (DAMC) elicited androgen receptor (AR) antagonistic activity with a 50% inhibitory concentration (IC50) of 1.46 µM, which is 14.3 times more potent than that observed for DEAMC (IC50 = 20.92 µM). We further collected abiotic (water and sediment) and biotic (plant, plankton, and fish) samples (n = 208) from a subtropical freshwater ecosystem, the Dongjiang River basin, in southern China, and determined the concentrations of the two coumarins in these samples. Overall, DAMC was the predominant compound found in the sediment, plant, algae, zooplankton, and fish muscle samples, with median concentrations at 0.189, 0.421, 0.832, 0.798, and 0.335 ng/g dry wt. (DW), respectively, although it was not detected in any surface water sample. For DEAMC, the median concentrations observed in the surface water, sediment, plant, algae, zooplankton, and fish muscle samples were 0.105 ng/L, 0.012, 0.051, 0.009, 0.008, and 0.181 ng/g DW, respectively. The bioaccumulation factor (BAF) values of DAMC and DEAMC in the algae, zooplankton, and fish muscle exceeded 5000 L/kg, suggesting that the two coumarins may have significant bioaccumulation potentials in aquatic biota. Additionally, the mean daily intake (EDI) of coumarins through fish consumption was estimated as 0.19 ng/kg BW/day for male toddlers. This is the first field study to illustrate the antiandrogenic potential of DAMC and document the widespread occurrence of the two synthetic coumarins in aquatic ecosystems.
Resonance of Gravitational Axion-like Particles
Authors: Gamboa Jorge , Méndez Fernando
Published: December 2024

The motion of gravitational axion-like particles (ALPs) around a Kerr black hole is analyzed, paying attention to the resonance and distribution of spectral radiation. We first discuss the computation of gR˜μνρρσRμνρσ and its implications with Pontryagin’s theorem, and then a detailed analysis of Teukolsky’s master equation is carried out. After carefully analyzing the Teukolsky master equation, we show that this system exhibits resonance when ω≳μ, where μ is the mass of the ALP, while the homogeneous part of the solution exhibits the superradiance. A skew-normal distribution can approximate the energy distribution of the resonant modes, and we give explicit expressions for its lifetime.
Emergency Capacity Pool to Respond to Unconventional Emergencies Based on Principal–Agent Theory
Authors: Jin Na , Tan Fuyou , Wang Haiyan , Sang Ao , Wang Shipeng
Published: December 2024

To address the conflict of interest between the government and enterprises regarding urban emergency transportation resources in unconventional emergencies and to enhance resource allocation and response efficiency. This paper proposes a collaborative government–enterprise model for emergency transport capacity reserves and develops an incentive model based on principal–agent theory. First, by comprehensively considering enterprise characteristics, high-quality enterprises are selected to collaborate with the government in building an emergency capacity pool of social vehicles. Second, to address potential conflicts of interest between the government and enterprises within the emergency capacity pool, this paper uses principal–agent theory to analyze the interest game process under information asymmetry, constructs a corresponding incentive model, and determines the government’s optimal incentive coefficient, the enterprise’s optimal actual capacity supply ratio, and the benefit distribution between both parties. Finally, numerical simulations and sensitivity analyses were used to verify the model’s applicability. The findings reveal that transport effort cost, economic requisition compensation, and government supervision cost influence the optimal decisions and outcomes in government–enterprise interactions. This study provides theoretical guidance and managerial insights for coordinating emergency transport scheduling between the government and enterprises during unconventional emergencies.
Phylogenetic and Pathogenic Analysis of H5N1 and H5N6 High Pathogenicity Avian Influenza Virus Isolated from Poultry Farms (Layer and Broiler Chickens) in Japan in the 2023/2024 Season
Authors: Nishiura Hayate , Kumagai Asuka , Mine Junki , Takadate Yoshihiro , Sakuma Saki , Tsunekuni Ryota , Uchida Yuko , Miyazawa Kohtaro
Published: December 2024

During the 2023–2024 winter, 11 high pathogenicity avian influenza (HPAI) outbreaks caused by clade 2.3.4.4b H5N1 and H5N6 HPAI viruses were confirmed in Japanese domestic poultry among 10 prefectures (n = 10 and 1, respectively). In this study, we aimed to genetically and pathologically characterize these viruses. Phylogenetic analysis revealed that H5N1 viruses were classified into the G2d-0 genotype, whereas the H5N6 virus was a novel genotype in Japan, designated as G2c-12. The G2c-12 virus shared PB2, PB1, PA, HA, and M genes with previous G2c viruses, but had NP and NS genes originating from avian influenza viruses in wild birds abroad. The N6 NA gene was derived from an H5N6 HPAI virus that was different from the viruses responsible for the outbreaks in Japan in 2016–2017 and 2017–2018. Experimental infections in chickens infected with H5N1(G2d-0) and H5N6(G2c-12) HPAI viruses showed no significant differences in the 50% chicken lethal dose, mean death time, or virus shedding from the trachea and cloaca, or in the histopathological findings. Different genotypes of the viruses worldwide, their introduction into the country, and their stable lethality in chickens may have triggered the four consecutive seasons of HPAI outbreaks in Japan.
Vaccination Coverage at Birth in Brazil: Spatial and Temporal Trends in the Impact of COVID-19 on Uptake of BCG and Hepatitis B Vaccines
Authors: Alves Yan Mathias , Berra Thaís Zamboni , Tavares Reginaldo Bazon Vaz , Zini Nathalia , Ferreira Quézia Rosa , Andrade Licia Kellen de Almeida , Tártaro Ariela Fehr , Pelodan Maria Eduarda Pagano , Vigato Beatriz Fornaziero , Silveira Beatriz Kuroda , Assumpção Ana Luiza Brasileiro Nato Marques , Popolin Marcela Antunes Paschoal , Curvo Patricia Abrahão , Protti-Zanatta Simone , Serrano-Gallardo Maria Del Pilar , Arcêncio Ricardo Alexandre , Palha Pedro Fredemir , Ballestero Jaqueline Garcia de Almeida
Published: December 2024

Introduction: Vaccines are a significant public health achievement, which are crucial for child survival and disease control globally. In Brazil, the National Immunization Program (PNI) manages vaccination schedules, including essential vaccines like BCG and Hepatitis B, administered at birth. Despite achieving over 95% coverage for years, vaccination rates have declined since 2016, a trend exacerbated by the COVID-19 pandemic. This study aims to analyze spatial and temporal trends in BCG and Hepatitis B vaccination coverage at birth, identify areas with spatial variation in these trends, classify the identified trends, and investigate the pandemic’s impact on vaccination adherence. Methods: This is an ecological study with real-world data from Brazil, focusing on vaccination coverage from 2014 to 2023. Utilizing the Spatial Variation in Temporal Trends (SVTT) technique, the study identifies municipalities’ vaccination trends. It also employs time series analysis and Interrupted Time Series methods to evaluate the pandemic’s impact on vaccination rates, using data from the PNI and the Information System on Live Births (SINASC). Results: Between January 2014 and December 2023, Brazil administered 25,902,207 doses of the BCG vaccine to children at birth, with 3911 municipalities (70.24%) showing declining trends, particularly in Florianópolis. Similarly, 22,962,434 doses of the Hepatitis B vaccine were administered, with 3284 municipalities also experiencing declines. Conclusions: It is crucial that public health policies be reevaluated to address regional disparities in vaccination coverage, particularly in more vulnerable areas. Focused interventions, such as awareness campaigns, improved access to vaccination services, and strengthened monitoring, are fundamental to reversing this trend.
Application of NIPOS-SVM Model for Evaluation of Water Richness of Coal Seam Roof Aquifer—A Case Study of the Xinhu Coal Mine in Huaibei, China
Authors: Xing Maolin , Wang Qiqing , Xu Jie , Li Wenping
Published: December 2024

The sandstone roof of coal seams, with its high porosity and developed fissures, serves as a favorable reservoir for groundwater. Predicting and assessing the water-bearing capacity of the sandstone roof in coal seams is crucial for the rational development of coal tunnels, ensuring safe and efficient production in mining areas. This study targets the Cenozoic bottom aquifer of the No. 81 mining area of the Xinhu Coal Mine. By analyzing the geological and hydrogeological conditions of the mining area, it was found that the primary water-bearing strata of the coal seam roof are the Permian sandstone fracture waters. Key factors for evaluating the water richness of the sandstone aquifer were identified as aquifer thickness, aquifer depth, core recovery rate, coal seam dip angle, brittleness–plasticity ratio, and the sand–mud interlayer index. A novel particle swarm optimization algorithm incorporating improved sine chaos mapping (NIPSO) to enhance the support vector machine (SVM), thereby constructing the NIPSO-SVM model, was applied for quantitative evaluation of water richness in the study area. Experimental results indicated that the NIPSO-SVM model has high accuracy and practical engineering application value in predicting water richness, which is significant for ensuring the safe production of coal mines.
Práticas sustentáveis na manutenção de edifícios
Authors:   , Domingos Juliana Silva
Published: December 2024

O constante aumento da população nos meios urbanos e a consequente busca por moradia resultam em um número crescente de residências, dentre elas, a maior demanda por edifícios. Durante a fase de operação desses edifícios, para garantir o seu bom estado de funcionamento são realizadas atividades de manutenção. A fase de operação de um edifício provoca grande impacto ambiental, pelo consumo de recursos naturais e geração de resíduos. Com a finalidade de apresentar práticas sustentáveis que possam minimizar esse impacto, o estudo toma como objetivo identificar práticas sustentáveis para edifícios residenciais em operação, que possam ser integradas as atividades de manutenção desse edifício. A metodologia condutora do estudo é a Design Science Research (DSR), estruturada em quatro fases: conscientização, sugestão e desenvolvimento, avaliação e conclusão. A conscientização uniu revisão bibliográfica nos referenciais das principais certificações ambientais aplicadas no Brasil, com revisão sistemática de literatura (RSL) para levantamento de práticas sustentáveis para edifícios. Na etapa seguinte, de sugestão e desenvolvimento, construiu-se um instrumento para coleta de dados. A realização dessa coleta ocorreu em empresas construtoras e/ou incorporadoras atuantes no mercado de edifícios de médio e alto padrão no município de Juiz de Fora - MG. Em seguida realizou-se a avaliação qualitativa dos resultados da coleta de dados. Por fim, a quarta e última etapa, de conclusão, apresenta a compilação das práticas sustentáveis levantadas, como por exemplo a utilização de infraestrutura para captação de águas pluviais e sensores de presença ou fotocélulas e descreve os fatores: consciência ambiental e recursos financeiros, como base, para que a aplicação de práticas sustentáveis na manutenção de edifícios se torne possível e bem sucedida. A presente pesquisa desenvolveu um material que pode servir de base para a tomada de decisão de empresas e profissionais da construção e gestores de edifícios.
As ruas como espaços de sociabilidade: relações entre a configuração da malha urbana e o potencial de vitalidade da via pública em Juiz de Fora-MG
Authors:   , Moreira Lídia Maia
Published: December 2024

Desde o princípio de formação das cidades, anunciada pela sedentarização do homem e seu estabelecimento em um local específico, a forma coletiva de habitar se apresenta como um componente sustentador da sociedade. E, por isso, a cidade carrega o papel fundamental de promover espaços propícios à sociabilidade dos indivíduos. Nessa concepção, os espaços públicos, como praças, parques e jardins, tiveram especial atenção nos princípios do planejamento urbano. Todavia, durante um longo período de tempo, as ruas, principal integrante do sistema de espaços públicos de uma cidade, por inúmeras razões, teve sua importância reduzida à mero canal de circulação, reprimindo a vitalidade das vias públicas. Diante desse contexto, este trabalho objetivou construir uma reflexão acerca da importância da sociabilidade das ruas como fato urbano essencial para o cumprimento da função social da cidade, considerando a relação existente entre duas variáveis: a configuração da malha urbana e o potencial de vitalidade das vias públicas. Para isso, foi desenvolvida uma análise empírica na Região de Planejamento Centro da cidade de Juiz de Fora-MG a fim de determinar a relação entre essas duas variáveis. Fez-se uso da Teoria da Sintaxe Espacial para o estudo da configuração urbana, considerando as medidas sintáticas de Integração, Conectividade e Escolha e, para avaliar as condições de vitalidade das vias foram observados seis elementos, a saber: 1) uso do solo; 2) interface público/privado; 3) calçada; 4) travessia; 5) mobiliário urbano; e, 6) vegetação. Como resultado principal, chegou-se à conclusão de que, no objeto de estudo em específico, parece existir uma relação direta entre as duas variáveis de análise. Todavia, apesar da existência dessa relação, é preciso reconhecer que a discussão sobre a importância das ruas como espaços de sociabilidade é muito mais complexa do que, simplesmente, avaliar o arranjo da malha urbana e os elementos físicos que compõem as ruas. É preciso que essa discussão seja tratada, também, por uma perspectiva social, de modo a compreender as relações existentes em um ambiente tão complexo quanto os espaços públicos de uma cidade, considerando, principalmente, as percepções e atuações na escala humana.