bluuebunny/crossref_metadata_2025 huggingface dataset:
Flood Hazard Assessment Using Weather Radar Data in Athens, Greece
Authors: Bournas Apollon , Baltas Evangelos
Published: January 2025

Weather radar plays a critical role in flash flood forecasting, providing an effective and comprehensive guide for the identification of possible flood-prone areas. However, the utilization of radar precipitation data remains limited in current research and applications, particularly in addressing flash flood hazards in complex environments such as in Athens, Greece. To address this gap, this study introduces the Gridded Flash Flood Guidance (GFFG) method, a short-term flash flood forecasting and warning technology based on radar precipitation and hydrological model coupling, and implements it in the region of Athens, Greece. The GFFG system improves upon the traditional flash flood guidance (FFG) concept by better integrating the weather radar dataset’s spatial and temporal flexibility, leading to increased resolution results. Results from six flood events underscore its ability to identify high-risk areas dynamically, with urban regions frequently flagged for flooding unless initial soil moisture conditions are low. Moreover, the sensitivity analysis of the system showed that the most crucial parameter apart from rainfall input is the soil moisture conditions, which define the amount of effective rainfall. This study highlights the significance of incorporating radar precipitation and real-time soil moisture assessments to improve flood prediction accuracy and provide valuable flood risk assessments.
Bridging Disciplines with Photogrammetry: A Coastal Exploration Approach for 3D Mapping and Underwater Positioning
Authors: Karaki Ali Alakbar , Ferrando Ilaria , Federici Bianca , Sguerso Domenico
Published: January 2025

Conventional methodologies often struggle in accurately positioning underwater habitats and elucidating the complex interactions between terrestrial and aquatic environments. This study proposes an innovative methodology to bridge the gap between these domains, enabling integrated 3D mapping and underwater positioning. The method integrates UAV (Uncrewed Aerial Vehicles) photogrammetry for terrestrial areas with underwater photogrammetry performed by a snorkeler. The innovative aspect of the proposed approach relies on detecting the snorkeler positions on orthorectified images as an alternative to the use of GNSS (Global Navigation Satellite System) positioning, thanks to an image processing tool. Underwater camera positions are estimated through precise time synchronization with the UAV frames, producing a georeferenced 3D model that seamlessly joins terrestrial and submerged landscapes. This facilitates the understanding of the spatial context of objects on the seabed and presents a cost-effective and comprehensive tool for 3D coastal mapping, useful for coastal management to support coastal resilience.
Attempts to Determine the Velocity Variations in Ocean Geostrothrophic Currents Based on GRACE Solutions
Authors: Rzepecka Zofia , Birylo Monika
Published: January 2025

The paper shows the idea of using GRACE solutions to determine the velocity of the Atlantic geostrophic currents at a depth of 2000 m, where the assumptions of geostrophic motion are met. The relationship between the water velocity variations and the OBP, determined based on the L2 GRACE solutions, is shown. The velocities obtained were compared with the results of a direct ocean current measurement program, the RAPID AMOC, carried out in the Atlantic area. The upper North Atlantic Deep Water layer transport was used for comparisons performed. An analysis of the obtained results, their summary, and discussion are shown. The research covers the period from November 2004 to March 2022. As a result of flow calculations through the parallel, a satisfactory agreement was obtained between the GRACE and RAPID AMOC fitted functions. The correlation between the GRACE and RAPID AMOC time series observations exceeded the significance level.
Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images
Authors: Wei Bo , Zhang Yili , Liu Linshan , Zhang Binghua , Gong Dianqing , Gu Changjun , Li Lanhui , Paudel Basanta
Published: January 2025

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R2 was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas.
Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data
Authors: Kombate Arifou , Fotso Kamga Guy Armel , Goïta Kalifa
Published: January 2025

Quantifying forest carbon storage to better manage climate change and its effects requires accurate estimation of forest structural parameters such as canopy height. Variables from remote sensing data and machine learning models are tools that are being increasingly used for this purpose. This study modeled the canopy height of forest–savanna mosaics in the Sudano–Guinean zone of Togo. Relative heights were extracted from GEDI and ICESat-2 products, which were combined with optical, radar, and topographic variables for canopy height modeling. We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). The best-performing result was obtained from variables extracted from GEDI data (r = 0.84; RMSE = 4.15 m; MAE = 2.36 m) and compared to ICESat-2 (r = 0.65; RMSE = 5.10 m; MAE = 3.80 m). Models that were developed during this study can be applied over large areas in forest–savanna mosaics, enhancing forest dynamics monitoring in line with REDD+ objectives. This study provides valuable insights for future spaceborne LiDAR and other remote sensing data applications in similar complex ecosystems and offers local decision-makers a robust tool for forest management.
Knowledge Distillation-Enhanced Behavior Transformer for Decision-Making of Autonomous Driving
Authors: Zhao Rui , Fan Yuze , Li Yun , Zhang Dong , Gao Fei , Gao Zhenhai , Yang Zhengcai
Published: January 2025

Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL’s policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments. On the other hand, the complexity of these environments leads to low sample efficiency in RL, making it difficult to efficiently learn driving policies. To address these challenges, we propose an innovative Knowledge Distillation-Enhanced Behavior Transformer (KD-BeT) framework. Building on the successful application of Transformers in large language models, we introduce the Behavior Transformer as the policy network in RL, using observation–action history as input for sequential decision-making, thereby leveraging the Transformer’s contextual reasoning capabilities. Using a teacher–student paradigm, we first train a small-capacity teacher model quickly and accurately through IL, then apply knowledge distillation to accelerate RL’s training efficiency and performance. Simulation results demonstrate that KD-BeT maintains fast convergence and high asymptotic performance during training. In the CARLA NoCrash benchmark tests, KD-BeT outperforms other state-of-the-art methods in terms of traffic efficiency and driving safety, offering a novel solution for addressing real-world autonomous driving tasks.
EBR-YOLO: A Lightweight Detection Method for Non-Motorized Vehicles Based on Drone Aerial Images
Authors: Zhou Meijia , Wan Xuefen , Yang Yi , Zhang Jie , Li Siwen , Zhou Shubo , Jiang Xueqin
Published: January 2025

Modern city construction focuses on developing smart transportation, but the recognition of the large number of non-motorized vehicles in the city is still not sufficient. Compared to fixed recognition equipment, drones have advantages in image acquisition due to their flexibility and maneuverability. With the dataset collected from aerial images taken by drones, this study proposed a novel lightweight architecture for small objection detection based on YOLO framework, named EBR-YOLO. Firstly, since the targets in the application scenario are generally small, the number of Backbone layers is reduced, and the AZML module is proposed to enrich the detail information and enhance the model learning capability. Secondly, the C2f module is reconstructed using part of the convolutional PConv to reduce the network’s computational volume and improve the detection speed. Finally, the downsampling operation is reshaped by combining with the introduced ADown module to further reduce the computational amount of the model. The experimental results show that the algorithm achieves an mAP of 98.9% and an FPS of 89.8 on the self-built dataset of this paper, which is only 0.2% and 0.3 lower compared to the original YOLOv8 network, respectively, and the number of parameters is 70% lower compared to the baseline, which ensures the accuracy and computational speed of the model while reducing its computational volume greatly. At the same time, the model generalization experiments are carried out on the UCAS-AOD and CARPK datasets, and the performance of the model is almost the same as the baseline.
Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks
Authors: Deng Zhongliang , Liao Yicheng
Published: January 2025

This research presents an intelligent beam-hopping-based grant-free random access (GFRA) architecture designed for secure Internet of Things (IoT) communications in Low Earth Orbit (LEO) satellite networks. In light of the difficulties associated with facilitating extensive device connectivity while ensuring low latency and high reliability, we present a beam-hopping GFRA (BH-GFRA) scheme that enhances access efficiency and reduces resource collisions. Three distinct resource-hopping schemes, random hopping, group hopping, and orthogonal group hopping, are examined and utilized within the framework. This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. Also, a kind of activity mechanism is proposed based on the constraints of the spatio-temporal distribution of devices. We assess the system’s performance through a thorough mathematical analysis. Furthermore, we ascertain the access delay and success rate to evaluate its capability to serve a substantial number of IoT devices under satellite–terrestrial delay and interference of massive connections. The suggested method demonstrably improves connection, stability, and access efficiency in 6G IoT satellite networks, meeting the rigorous demands of next-generation IoT applications.
Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector Machine
Authors: Kamal Saleha , Alhasson Haifa F. , Alnusayri Mohammed , Alatiyyah Mohammed , Aljuaid Hanan , Jalal Ahmad , Liu Hui
Published: January 2025

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop coherent and robust systems that efficiently perform HAR. Although there have been many efficient systems developed to date, still, there are many issues to be addressed. There are several elements that contribute to the complexity of the task, making it more challenging to detect human activities, i.e., (i) poor lightning conditions; (ii) different viewing angles; (iii) intricate clothing styles; (iv) diverse activities with similar gestures; and (v) limited availability of large datasets. However, through effective feature extraction, we can develop resilient systems for higher accuracies. During feature extraction, we aim to extract unique key body points and full-body features that exhibit distinct attributes for each activity. Our proposed system introduces an innovative approach for the identification of human activity in outdoor and indoor settings by extracting effective spatio-temporal features, along with a Multi-Class Support Vector Machine, which enhances the model’s performance to accurately identify the activity classes. The experimental findings show that our model outperforms others in terms of classification, accuracy, and generalization, indicating its efficient analysis on benchmark datasets. Various performance metrics, including mean recognition accuracy, precision, F1 score, and recall assess the effectiveness of our model. The assessment findings show a remarkable recognition rate of around 88.61%, 87.33, 86.5%, and 81.25% on the BIT-Interaction dataset, UT-Interaction dataset, NTU RGB + D 120 dataset, and PKUMMD dataset, respectively.
Influence of the Transducer-Mounting Method on the Radiation Performance of Acoustic Sources Used in Monopole Acoustic Logging While Drilling
Authors: Wang Jiale , Che Xiaohua , Qiao Wenxiao , Tao Shengyue , Zhao Qiqi
Published: January 2025

Transducers used in acoustic logging while drilling (ALWD) must be mounted on a drill collar, and their radiation performance is dependent on the employed mounting method. Herein, the complex transmitting voltage response of a while-drilling (WD) monopole acoustic source was calculated through finite-element harmonic-response analysis. Subsequently, the acoustic pressure waveform radiated by the source driven by a half-sine excitation voltage signal was calculated using the complex transmitting voltage response. The calculation results were compared with those obtained using finite-element transient analysis to verify the accuracy of the calculation method. The influence of transducer-mounting methods on the radiation performance of the monopole acoustic source was examined by modifying the material and structural dimensions of the coupling medium between the transducer and drill collar as well as the material and thickness of the protective cover. Numerical simulations were performed, and a transducer-mounting method suitable for ALWD was proposed based on the simulation results. Results showed that soft rubber (as the coupling material; thickness = 2 mm) enabled the WD monopole acoustic source to radiate robust acoustic energy in an infinite fluid. Increasing the height of the coupling material enhanced the radiated acoustic energy and reduced axial vibrations on the drill collar. The radiated acoustic pressure signal was unaffected by a steel protective cover (thickness = 0.5 mm). Conversely, increasing the cover thickness reduced the energy of the radiated acoustic signal. With increasing pulse width of the half-sine excitation voltage signal, the amplitude of the radiated acoustic pressure of the transducer initially increased and then declined, reaching a maximum at a pulse width that was 0.6 times the resonant period. Overall, the findings help in designing acoustic-source structures and excitation signals for ALWD tools.
Research on the Flotation Mechanism of Microemulsion Collector Enhanced Removal of Dyeing Impurities from Phosphogypsum
Authors: Yu Xiaosheng , Deng Lijun , Shen Changpan , Li Huiyong , Li Jingchao , Cao Yijun , Zhou Guoli , Li Guosheng
Published: January 2025

Phosphogypsum is an industrial byproduct that is limited in its high-value application due to the presence of dyeing impurities (such as organic matter and carbon black). The flotation method has been verified to be effective in separating these dyeing impurities from gypsum. In this study, microemulsion was used as the collector method of dyeing impurities for their separation from gypsum. The results of flotation tests showed that the microemulsion collector exhibited excellent collection capability and selectivity under natural pH conditions (pH = 1.5). With a microemulsion collector consumption of 400 g/t, purified gypsum of 65.1% whiteness, 95.74% yield, and 97.01% recovery was obtained. The purified gypsum of 65.1% whiteness, 95.74% yield, 97.01 recovery obtained by a used microemulsion collector amount of 400 g/t was better than using the same dosage of kerosene collector. The dispersion behavior of the microemulsion collector was studied by low-temperature transmission electron microscopy. The microemulsion collector demonstrated superior dispersibility, as it forms nano-oil droplets with an average size of 176.83 nm in the pulp, resolving issues associated with poor dispersibility observed in traditional kerosene collectors. Additionally, the nano-oil droplets effectively adsorbed onto the surface of dyeing impurities through hydrogen bonding, enhancing their hydrophobicity. Therefore, the microemulsion collector holds great potential for application in flotation whitening processes involving phosphogypsum.
Staff Views Towards the Sexuality of Adults with Mild Intellectual and Developmental Disabilities: The Role of Experience and Job Position
Authors: Correa Ana Belén , Castro Ángel , Gil-Llario María Dolores
Published: January 2025

Staff attitudes towards the sexuality of adults with mild intellectual and developmental disabilities may influence how the sexuality of service users is dealt with. The present study aimed to examine these attitudes in a Spanish context. A total of 102 staff members from service facilities for adults with intellectual and developmental disabilities, including direct-care and professional staff, responded to an online version of the Attitudes Towards the Sexuality of Individuals with Intellectual Disabilities Scale. In general, the participants demonstrated an understanding that adults with intellectual and developmental disabilities have sexuality. They reported favourable attitudes towards some self- and other-oriented sexual behaviours and sex education. Intermediate scores and variability on some items revealed concerns about these adults’ ability to control their sexual urges, perceptions of sexual abuse, pornography use, and contraception. Some differences appeared in relation to socio-demographic variables, experience and job position. Although generally favourable, differences in dispositional attitudes, such as talking about sexuality, were related to years of experience in the field. Professional staff reported greater acceptance of some sexual behaviours compared to direct-care staff. These findings are relevant to understanding variability in staff perceptions. The need for support to respond to service users’ sexuality, especially among direct-care professionals, is discussed.
A Study on the Characteristics and System Construction of Urban Disaster Resilience in Shanghai: A Metropolis Perspective
Authors: Dong Damin , Yu Zeyu , Xu Jianzhong
Published: January 2025

As climate change intensifies, cities face growing risks. Natural disasters underscore the vulnerabilities inherent in urban systems. Resilience provides a dynamic and developmental approach to urban disaster management. This study focuses on Shanghai to examine its urban disaster resilience by analyzing compounded disaster scenarios, the impacts on vulnerable entities, and spatial differentiation within the city. Specifically, it explores the internal mechanisms of resilience across three dimensions, functional, procedural, and systemic, forming the foundation for the model. A three-dimensional urban disaster resilience model is then constructed. The first-level indicators afford the overall framework, the second-level indicators emphasize resilience based on resources and geographical endowments, and the third-level indicators are tailored to the current political, economic, cultural, and social characteristics of the region. Using actual collected data, along with the Analytic Hierarchy Process (AHP) and expert judgment methodologies, the resilience indicator system for Shanghai can scientifically reflect the city’s capacity to withstand disasters and offers a foundation for developing targeted solutions. The results highlight the framework’s potential generalizability to other metropolises and its contributions to global resilience research.
The Nonlinear Effect of the Built Environment on Bike–Metro Transfer in Different Times and Transfer Flows Considering Spatial Dependence
Authors: Zhang Yuan , Meng Yining , Chen Xiao-Jian , Liu Huiming , Gong Yongxi
Published: January 2025

Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems.
Estimating Large Global Significances with a New Monte Carlo Extrapolation Method
Authors: Chen Liangliang , Chen Yufei , Bauer Gerry , Spiegel Leonard G. , Hu Zhen , Yi Kai
Published: January 2025

In particle physics, it is needed to evaluate the possibility that excesses of events in mass spectra are due to statistical fluctuations as quantified by the standards of local and global significances. Without prior knowledge of a particle’s mass, it is especially critical to estimate its global significance. The usual approach is to count the number of times a significance limit is exceeded in a collection of simulated Monte Carlo (MC) “toy experiments”. To demonstrate this conventional method for global significance, we performed simulation studies according to a recent Compact Muon Solenoid (CMS) result to show its effectiveness. However, this counting method is not practical for computing large global significances. To address this problem, we developed a new “extrapolation” method to evaluate the global significance. We compared the global significance estimated by our new method with that of the conventional approach, and verified its feasibility and effectiveness. This method is also applicable for cases where only small toy MC samples are available. In this approach, the significance is calculated based on p-values, assuming symmetrical Gaussian distributions.
Novel View Synthesis with Depth Priors Using Neural Radiance Fields and CycleGAN with Attention Transformer
Authors: Qin Yuxin , Li Xinlin , Zu Linan , Jin Ming Liang
Published: January 2025

Novel view synthesis aims to generate new perspectives from a limited number of input views. Neural Radiance Field (NeRF) is a key method for this task, and it produces high-fidelity images from a comprehensive set of inputs. However, a NeRF’s performance drops significantly with sparse views. To mitigate this, depth information can be used to guide training, with coarse depth maps often readily available in practical settings. We propose an improved sparse view NeRF model, ATGANNeRF, which integrates an enhanced U-Net generator with a dual-discriminator framework, CBAM, and Multi-Head Self-Attention mechanisms. The symmetric design enhances the model’s ability to capture and preserve spatial relationships, ensuring a more consistent generation of novel views. Additionally, local depth ranking is employed to ensure depth consistency with coarse maps, and spatial continuity constraints are introduced to synthesize novel views from sparse samples. SSIM loss is also added to preserve local structural details like edges and textures. Evaluation on LLFF, DTU, and our own datasets shows that ATGANNeRF significantly outperforms state-of-the-art methods, both quantitatively and qualitatively.
From Seed to Tree—The Performance of Taxonomy in the Year 2024
Authors: Harzhauser Mathias
Published: January 2025

“Welcome to the New Journal Taxonomy”, wrote Enric Sayas in his editorial in January 2021 [...]
Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface
Authors: Bonini Neto Alfredo , de Queiroz Alexandre , da Silva Giovana Gonçalves , Gifalli André , de Souza André Nunes , Garbelini Enio
Published: January 2025

Technical power losses in power systems are unavoidable, caused by factors such as transformer impedance, conductor resistance, equipment inefficiencies, line reactance, and phase imbalances. Reducing these losses is essential for improving system efficiency. This study introduces an innovative approach using Artificial Neural Networks (ANN) combined with the graphical interface to predict complete curves of real and reactive power losses in power systems under various contingencies. The key advantage of this methodology is its speed, allowing quick estimation of power loss curves both in normal and contingency conditions, whether mild or severe. ANN models excel at capturing the nonlinear behavior of power systems, eliminating the need for iterative methods commonly used in traditional approaches. The results showed that the ANN performed effectively, with a mean squared error during training below the specified threshold. For samples not included in the training set, the network accurately estimated 99% of the real and reactive power losses within the specified range, with residuals around 10−3 and an overall accuracy rate of 99% between the desired and obtained outputs. Additionally, a Graphical User Interface (GUI) was implemented to facilitate user interaction, allowing for easy visualization of power-loss predictions and real-time adjustments.
The Role of Indoxyl Sulfate in Exacerbating Colorectal Cancer During Chronic Kidney Disease Progression: Insights into the Akt/β-Catenin/c-Myc and AhR/c-Myc Pathways in HCT-116 Colorectal Cancer Cells
Authors: Ichisaka Yu , Takei Chihiro , Naito Kazuma , Higa Manami , Yano Shozo , Niwa Toshimitsu , Shimizu Hidehisa
Published: January 2025

Epidemiological studies suggest an increased risk of colorectal cancer (CRC) aggravation in patients with chronic kidney disease (CKD). Our previous study demonstrated that indoxyl sulfate, a uremic toxin whose concentration increases with CKD progression, exacerbates CRC through activation of the AhR and Akt pathways. Consequently, indoxyl sulfate has been proposed to be a significant link between CKD progression and CRC aggravation. The present study aimed to investigate the roles of c-Myc and β-Catenin, which are hypothesized to be downstream factors of indoxyl sulfate-induced AhR and Akt activation, in CRC cell proliferation and EGF sensitivity in HCT-116 CRC cells. Indoxyl sulfate significantly induced CRC cell proliferation at concentrations exceeding 62.5 µM, a process suppressed by the c-Myc inhibitor 10058-F4. Indoxyl sulfate activated the Akt/β-Catenin/c-Myc pathway as evidenced by the Akt inhibitor MK2206, which decreased both β-Catenin and c-Myc protein levels, and the β-Catenin inhibitor XAV-939, which reduced c-Myc protein levels. The AhR antagonist CH223191 also inhibited c-Myc upregulation, indicating involvement of the AhR/c-Myc pathway. MK2206 partially attenuated the indoxyl sulfate-induced AhR transcriptional activity, suggesting that Akt activation influences the AhR/c-Myc pathway. MK2206, CH223191, and 10058-F4 suppressed the increase in EGFR protein levels induced by indoxyl sulfate, indicating that the Akt/β-Catenin/c-Myc and AhR/c-Myc pathways enhance the sensitivity of HCT-116 CRC cells to EGF. These findings indicate that the elevation of indoxyl sulfate levels in the blood, due to CKD progression, could worsen CRC by promoting the proliferation of CRC cells and enhancing EGF signaling. Therefore, indoxyl sulfate could potentially serve as a therapeutic target for CRC aggravation in patients with CKD.
Influence of Apis mellifera syriaca Bee Venom on Nociception and Inflammatory Cytokine Profiles in Experimental Hyperalgesia
Authors: Ayoub Mohamad , Fayjaloun Salma , Roufayel Rabih , El Obeid Dany , Fajloun Ziad , Rima Mohamad , Karam Marc
Published: January 2025

Hyperalgesia is a condition marked by an abnormal increase in pain sensitivity, often occurring in response to tissue injury, inflammation, or prolonged exposure to certain medications. Inflammatory mediators, such as cytokines IL-1β, IL-6, and TNF-α, play a central role in this process, amplifying pain perception. Developing effective treatments that address the underlying mechanisms of hyperalgesia is an active field of research. Apis mellifera syriaca venom demonstrated potential immunomodulatory activity associated with cytokine release in vivo. Therefore, the aim of this study is to evaluate the effect of Apis mellifera syriaca bee venom (AmsBV) on pain sensitivity in a formalin-induced hyperalgesia mice model and to evaluate the potential role of cytokines associated with the nociception of pain. The hotplate test, used to measure pain latency, showed that hypersensitivity to pain was induced in formalin-injected male mice only, with no changes in females, suggesting a sex-based response to formalin. When applied, AmsBV reduced pain sensitivity in males, suggesting pain relief potential. At the molecular level, AmsBV was able to reduce pro-inflammatory interleukin IL-4 and cytokine IFN-γ, emphasizing its immunomodulatory potential. Interestingly, the venom restored anti-inflammatory IL-10 levels that were significantly decreased in hyperalgesia males. Together, these findings highlight the therapeutic potential for AmsBV in managing inflammation and reducing pain, particularly hyperalgesia.
Rabies Disease and Prophylaxis Knowledge Among Turkish Medical Students: Insights from a Cross-Sectional Study
Authors: Pervane Vasfiye Demir , Erten Bucaktepe Pakize Gamze , İnce Fatma Meral , Demir Dicle , Koç Simanur
Published: January 2025

Rabies is a fatal infectious disease that can be prevented with vaccination. The aim of this study was to evaluate the level of rabies knowledge among medical faculty students. This cross-sectional study included students in the medical faculty of a university hospital. The level of rabies knowledge was evaluated with a questionnaire evaluating 70 parameters. A total of 892 students participated in this study. Medical students knew that dogs and cats transmit rabies at high rates (96.9% and 87.4%, respectively) but understood less about other animals. Pregnancy (45.2%), being >65 years of age, having a chronic disease, and being immunosuppressed were indications for rabies vaccine, with rates between 31.4 and 37.4%. In total, 64.3% of respondents stated that the nearest health center should be approached to seek medical care, without first touching the wound. While indications for vaccination were correctly reported to be high after bites or scratches (74.2–94.6%), such indications were considered to be lower for other types of contact (46.2–66.6%). Indications for rabies immunoglobulin administration were correctly recognized at rates between 15.9% and 57.8%. Overall, the mean rabies knowledge level was 41.82 (Max. score 70). There was a statistically significant relationship between the total and subgroup rabies knowledge scores and class level, having taken a rabies course, a history of dog bites among the respondents or their family members, and possessing information about rabies. It was determined that the rabies knowledge levels among the students were insufficient. Having received lessons about rabies or a history of being bitten by an animal with suspected rabies were found to be important factors for increased knowledge about rabies.
Latin America’s Dengue Outbreak Poses a Global Health Threat
Authors: de Almeida Michelle Teixeira , Merighi Davi Gabriel Salustiano , Visnardi Aline Biazola , Boneto Gonçalves Cauê Augusto , Amorim Vitor Martins de Freitas , Ferrari Anielle Salviano de Almeida , de Souza Anacleto Silva , Guzzo Cristiane Rodrigues
Published: January 2025

Dengue fever, caused by the dengue virus (DENV), poses a significant global health challenge, particularly in tropical and subtropical regions. Recent increases in indigenous DENV cases in Europe are concerning, reflecting rising incidence linked to climate change and the spread of Aedes albopictus mosquitoes. These vectors thrive under environmental conditions like temperature and humidity, which are increasingly influenced by climate change. Additionally, global travel accelerates the cross-border spread of mosquito-borne diseases. DENV manifests clinically in a spectrum from asymptomatic cases to severe conditions like dengue hemorrhagic fever and dengue shock syndrome, influenced by viral serotype and host factors. In 2024, Brazil experienced a fourfold increase in dengue cases compared to 2023, accompanied by higher mortality. Conventional control measures, such as vector control, community engagement, and vaccination, proved insufficient as climate change exacerbated mosquito proliferation, challenging containment efforts. In this regard, our review analyzes prevention measures and therapeutic protocols during the outbreak while addressing DENV transmission dynamics, clinical presentations, and epidemiological shifts. It also evaluates diagnostic strategies combining clinical assessment with serological and molecular testing, providing information to improve diagnostic and preventive measures. The global expansion of dengue-endemic regions, including outbreaks in Europe, highlights the urgent need for enhanced surveillance, proactive interventions, and international collaboration to mitigate the growing threat of Dengue and other arboviruses like West Nile, Zika, Chikungunya, Oropouche, and Yellow Fever viruses.
Transcriptional Systems Vaccinology Approaches for Vaccine Adjuvant Profiling
Authors: Pellegrina Diogo , Wilson Heather L. , Mutwiri George K. , Helmy Mohamed
Published: January 2025

Adjuvants are a diverse group of substances that can be added to vaccines to enhance antigen-specific immune responses and improve vaccine efficacy. The first adjuvants, discovered almost a century ago, were soluble crystals of aluminium salts. Over the following decades, oil emulsions, vesicles, oligodeoxynucleotides, viral capsids, and other complex organic structures have been shown to have adjuvant potential. However, the detailed mechanisms of how adjuvants enhance immune responses remain poorly understood and may be a barrier that reduces the rational selection of vaccine components. Previous studies on mechanisms of action of adjuvants have focused on how they activate innate immune responses, including the regulation of cell recruitment and activation, cytokine/chemokine production, and the regulation of some “immune” genes. This approach provides a narrow perspective on the complex events involved in how adjuvants modulate antigen-specific immune responses. A comprehensive and efficient way to investigate the molecular mechanism of action for adjuvants is to utilize systems biology approaches such as transcriptomics in so-called “systems vaccinology” analysis. While other molecular biology methods can verify if one or few genes are differentially regulated in response to vaccination, systems vaccinology provides a more comprehensive picture by simultaneously identifying the hundreds or thousands of genes that interact with complex networks in response to a vaccine. Transcriptomics tools such as RNA sequencing (RNA-Seq) allow us to simultaneously quantify the expression of practically all expressed genes, making it possible to make inferences that are only possible when considering the system as a whole. Here, we review some of the challenges in adjuvant studies, such as predicting adjuvant activity and toxicity when administered alone or in combination with antigens, or classifying adjuvants in groups with similar properties, while underscoring the significance of transcriptomics in systems vaccinology approaches to propel vaccine development forward.
Hydrogen and Oxygen Isotopes as Key Monitoring Indexes for Deep Confined Brine Mining: Insights from Mahai Salt Lake, Qaidam Basin
Authors: Zhang Minglu , Hu Yufei , Wang Nan , Zhao Yanjun , Wang Qiang , Wang Zhaoxu
Published: January 2025

Salt lake brine-type potassium-lithium deposits play an important role as a strategic mineral resource that is in short supply in China and the rest of the world. In the case of long-term, high-intensity exploitation of brine resources, if large-scale active faults are encountered, these faults will form advantageous channels for brine migration. In some special cases, the surface fresh water leaks into these advantageous channels and will soon produce a large amount of fresh water in the brine mining well area, resulting in the destruction of the mining well and in the reduction in the potassium lithium grade of the brine. Based on the monitoring of the chemical components and hydrogen and oxygen isotopes of brine in mining wells, combined with the principle of stable isotope conservation, the influence of freshwater leakage accidents on brine extraction wells was quantitatively calculated. The amount of fresh water added to brine by accident is approximately 78%. This further indicates that it is necessary to strengthen long-term monitoring of the chemical components of confined brine to ensure sustainable and stable production.
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
Authors: Hooper Annalise Sara , Christofides Sarah R. , Windsor Fredric M. , Watson Sophie E. , Kille Peter , Perkins Rupert G.
Published: January 2025

Geosmin and 2-methylisoborneol (2-MIB) are secondary bacterial metabolites that create an earthy-musty taste and odour (T&O) in drinking water. Both compounds exhibit low odour thresholds and are the leading causes of customer complaints to water companies worldwide. Water companies must predict spikes in T&O concentrations early to intervene before these compounds reach the treatment works. Cyanobacteria are key producers of T&O in open waters, yet the influence of broader microbial and algal communities on cyanobacterial T&O events remains unclear. This study identified T&O risk indicator taxa using next-generation sequencing of bacterial (16S rRNA) and algal (rbcL) communities in three reservoirs in Wales, UK. Ordination analysis of these communities revealed clustering according to assigned T&O concentration levels, identifying T&O signature communities. Random Forest (RF) analyses accurately classified samples for high and low concentrations of geosmin and 2-MIB, demonstrating the biological consortium’s predictive power. Based on shared ecological traits of bacterial and algal taxa, we propose five categories corresponding to different magnitudes of T&O risk. Indicator taxa in T&O risk categories can then be used to predict T&O events, empowering water companies first to optimise treatment response and, importantly, to determine triggers before an event to evidence preventative intervention management.
Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria
Authors: Eludire Oluwadamilare , Faloye Oluwaseun , Alatise Michael , Ajayi Ayodele , Oguntunde Philip , Badmus Tayo , Fashina Abayomi , Adeyeri Oluwafemi , Olorunfemi Idowu , Ogunrinde Akinwale
Published: January 2025

The accurate assessment of water availability throughout the cassava cropping season (the initial, developmental, mid-season, and late stages) is crucial for mitigating the impacts of climate change on crop production. Using the Mann–Kendall Test, we investigated the trends in rainfall and cassava crop evapotranspiration (ETc) within the Cross River basin in Nigeria. Reference evapotranspiration (ETo) was based on two approaches, namely Artificial Neural Network (ANN) modelling and three established empirical models—the Penman–Monteith (considered the standard method), Blaney–Morin–Nigeria (BMN), and Hargreaves–Samani (HAG) models. ANN predictions were performed by using inputs from BMN and HAG parameters, denoted as BMN-ANN and HAG-ANN, respectively. The results from the ANN models were compared to those obtained from the Penman–Monteith method. Remotely sensed meteorological data spanning 39 years (1979–2017) were acquired from the Climatic Research Unit (CRU) to estimate ETc, while cassava yield data were acquired from the International Institute of Tropical Agriculture (IITA), Ibadan. The study revealed a significant upward trend in cassava crop ETc over the study period. Additionally, the ANN models outperformed the empirical models in terms of prediction accuracy. The BMN-ANN model with a Tansig activation function and a 3-3-1 architecture (number of input neurons, hidden layers, and output neurons) achieved the highest performance, with a coefficient of determination (R2) of 0.9890, a root mean square error (RMSE) of 0.000056 mm/day, and a Willmott’s index of agreement (d) of 0.9960. There is a decreasing trend in cassava yield in the region and further analysis indicated potential average daily water deficits of approximately −1.1 mm/day during the developmental stage. These deficits could potentially hinder root biomass, yield, and overall cassava yield in the Cross River basin. Our findings highlight the effectiveness of ANN modelling for irrigation planning, especially in the face of a worsening climate change scenario.
Using a YOLO Deep Learning Algorithm to Improve the Accuracy of 3D Object Detection by Autonomous Vehicles
Authors: Murendeni Ramavhale , Mwanza Alfred , Obagbuwa Ibidun Christiana
Published: January 2025

This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency.
Life Writing on Sex and Relationships: Australian LGBTQ+ Youth’s Sexual Subjectivities
Authors: Bellamy Roz , Bourne Adam , Fisher Christopher
Published: January 2025

Narrative therapy and expressive writing interventions have been found to improve health and wellbeing among vulnerable populations; however, few have been conducted with young lesbian, gay, bisexual, transgender, and queer people, and those of other diverse sexual orientations and gender identities (LGBTQ+). This study aimed to determine how young LGBTQ+ people in Australia might make meaning of themselves and their experiences through life writing and the implications for health and wellbeing. A study was conducted with 20 LGBTQ+-identifying participants, aged 16 to 20, in or around Melbourne, Australia. The participants attended one of two writing workshops held at a public library in Melbourne in October 2019 and February 2020, where they were taught creative nonfiction writing techniques. They were asked to submit pieces of life writing (“narratives”) and reflections about participating in the study. Fifteen participants submitted one or more narratives in response to a prompt, and 10 participants provided reflections. Thematic analysis was used to code the data and identify themes that emerged. The participants’ processes of meaning-making were analysed using a performativity lens. This article explores the theme “sex and relationships” that emerged and the related sub-themes. The participants’ writing provided insights into sociocultural norms, discourses, and tropes, including those present in school-based sexuality education and in the media, that may impact the developing sexual subjectivities of young LGBTQ+ people in Australia. Engaging in life writing provided an outlet for developing self-knowledge, agency, and subjectivity. The findings can be used to promote the use of creative, youth-centred and queer-affirming practices and interventions for LGBTQ+ youth in school and community settings, including peer support and community health approaches. They indicate the need for de-pathologising the medicalised risk- and prevention-based interventions for LGBTQ+ youth in health and education research and policy, particularly around sexual and mental health.