bluuebunny/crossref_metadata_2025 huggingface dataset:
Compact and Highly Isolated Continuous Scanning Dual-Polarized Holographic Antenna Using a Pillbox Feeding Structure
Authors: Park Chan Yeong , Yoon Ick-Jae , Yoon Young Joong
Published: April 2024

In this paper, we propose a novel approach to realize a compact and highly isolated dual-polarized holographic antenna using a pillbox feeding structure. The proposed antenna feeds dual orthogonal surface waves with low distortion phase distribution and high isolation through a compact three-layer pillbox feeding structure. This antenna also consists of a shared aperture dual-polarized hologram pattern calculated to radiate the objective wave in the desired direction without increasing the antenna size. As a result, the proposed holographic antennas (HA) have a compact size and support forward-to-backward continuous scanning with minimal gain degradation. The simulated and measured results are in good agreement, validating the efficiency of the proposed antenna design, which has the ability to scan the beam direction from +18° to −25°, passing through the broadside within the frequency range of 21–27 GHz. Finally, the proposed antenna has a broadside gain of 18.5 dBi in each polarization and a gain variation of less than 2 dB within the operating bandwidth.
Adaptive Whale Optimization Algorithm–DBiLSTM for Autonomous Underwater Vehicle (AUV) Trajectory Prediction
Authors: Guo Shufang , Zhang Jing , Zhang Tianchi
Published: April 2024

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.
Raising an Eye at Facial Muscle Morphology in Canids
Authors: Sexton Courtney L. , Diogo Rui , Subiaul Francys , Bradley Brenda J.
Published: April 2024

The evolution of facial muscles in dogs has been linked to human preferential selection of dogs whose faces appear to communicate information and emotion. Dogs who convey, especially with their eyes, a sense of perceived helplessness can elicit a caregiving response from humans. However, the facial muscles used to generate such expressions may not be uniquely present in all dogs, but rather specifically cultivated among various taxa and individuals. In a preliminary, qualitative gross anatomical evaluation of 10 canid specimens of various species, we find that the presence of two facial muscles previously implicated in human-directed canine communication, the levator anguli occuli medialis (LAOM) and the retractor anguli occuli lateralis (RAOL), was not unique to domesticated dogs (Canis familiaris). Our results suggest that these aspects of facial musculature do not necessarily reflect selection via human domestication and breeding. In addition to quantitatively evaluating more and other members of the Canidae family, future directions should include analyses of the impact of superficial facial features on canine communication and interspecies communication between dogs and humans.
Extended Review Concerning the Integration of Electrochemical Biosensors into Modern IoT and Wearable Devices
Authors: Bocu Razvan
Published: April 2024

Electrochemical biosensors include a recognition component and an electronic transducer, which detect the body fluids with a high degree of accuracy. More importantly, they generate timely readings of the related physiological parameters, and they are suitable for integration into portable, wearable and implantable devices that are significant relative to point-of-care diagnostics scenarios. As an example, the personal glucose meter fundamentally improves the management of diabetes in the comfort of the patients’ homes. This review paper analyzes the principles of electrochemical biosensing and the structural features of electrochemical biosensors relative to the implementation of health monitoring and disease diagnostics strategies. The analysis particularly considers the integration of the biosensors into wearable, portable, and implantable systems. The fundamental aim of this paper is to present and critically evaluate the identified significant developments in the scope of electrochemical biosensing for preventive and customized point-of-care diagnostic devices. The paper also approaches the most important engineering challenges that should be addressed in order to improve the sensing accuracy, and enable multiplexing and one-step processes, which mediate the integration of electrochemical biosensing devices into digital healthcare scenarios.
Latent Profile Analysis of Suicidal Ideation in Chinese Individuals with Bipolar Disorder
Authors: Pan Yanmeng , Wang Huaizhi , Geng Yimeng , Lai Jianbo , Hu Shaohua
Published: April 2024

Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.
Sustainable Impact of Green Building on the Eco-Economic Efficiency of the Construction Industry: Evidence from China
Authors: Lu Wei , Zhang Juan , Tam Vivian W. Y.
Published: April 2024

Addressing the global warming challenge, the carbon emissions reduction potential of green building (GB) is garnering increasing attention. However, the extent to which GB can impact the eco-economic efficiency (EE) of the construction industry remains unclear. To fill this gap, based on panel data for 30 regions of China from 2003 to 2018, in this study, a comprehensive analysis of the EE and sustainable impact of GB is conducted using a super slacks-based measure (SBM-DEA), panel vector autoregression (PVAR) and threshold models. The findings reflect that only about 10% of provinces have achieved EE effectiveness, with pure technical effectiveness being a significant driving force behind this. The average comprehensive, pure technical, and scale EE show a 62.23%, 46.42%, and 31.91% improvement potential regarding the efficiency frontier. The EE in the Eastern Region is relatively high, while the Western Region surpassed the Central Region in regards to EE in the areas of scale and pure technical efficiency. EE is significantly reduced in the current period (year) when it is subject to the positive impact of a standard deviation from the existing economic level. This impact was the strongest in period 1, then gradually disappeared until period 6, aligning with the Kuznets curve’s theoretical assumption. GB awareness negatively impacted the current period, but is expected to gradually show a positive effect after period 2. The urbanization, green building awareness, and green building coverage make a very small contribution to the EE, accounting for 8.6%, 1.6%, and 9%, respectively, with early EE and economic level identified as the primary variables affecting the current EE. The impact of GB on EE exhibits a threshold effect, with the ecological effect of GB significant only in cities with economic levels higher than 11.063. This research contributes to the existing knowledge of the eco-economic mechanism of GB and provides insights for government policies, promoting the sustainable development of the GB market.
An Investigation of the Behaviors and Characteristics of the Interfacial Bonding Capacity between Cement Emulsified Asphalt Composite Binder and Reclaimed Asphalt Pavement
Authors: Zheng Kangfeng , Yang Qing , Qiu Xin , Xu Wenyi , Liu Huiqiong , Chen Zijun
Published: April 2024

The interfacial bonding capacity between cement emulsified asphalt composite binder (CEACB) and reclaimed asphalt pavement (RAP) plays a critical role in improving the pavement performance of cold recycled asphalt emulsion mixtures (CRAEMs). This study aims to investigate the formation and development of the interfacial bonding capacity between CEACB and RAP. First, the dynamic wettability and the spreading behaviors of CEACB on RAP surfaces were explored according to the surface free energy theory. Second, digital image processing (DIP) technology was employed to recognize interfacial failure patterns. Lastly, the influence of internal and external factors on the interfacial bonding capacity between CEACB and RAP during the curing process was analyzed via grey relational analysis (GRA). The results indicate that a moderate cement content with a mass ratio of asphalt to cement equivalent to 1.0 can significantly enhance the wettability of CEACB on RAP surfaces. By appropriately prolonging the curing time and controlling the curing temperature, it is possible to increase the bonding strength between CEACB and RAP. Additionally, a strong correlation exists between initial wettability and ultimate bonding capacity during the bonding strength curing process. The good wettability that developed in the initial stage of interfacial strength formation relates to the decreased spalling rate of CEACB on the RAP surface. This study is not only devoted to understanding the mechanisms that can enhance CRAEM performance but also provides important guidance for practical engineering applications of cold recycled asphalt pavements.
Research on Lightweight Method of Segment Beam Point Cloud Based on Edge Detection Optimization
Authors: Dong Yan , Yang Haotian , Yin Mingjun , Li Menghui , Qu Yuanhai , Jia Xingli
Published: April 2024

In order to reduce the loss of laser point cloud appearance contours by point cloud lightweighting, this paper takes the laser point cloud data of the segment beam of the expressway viaduct as a sample. After comparing the downsampling algorithm from many aspects and angles, the voxel grid method is selected as the basic theory of the research. By combining the characteristics of the normal vector data of the laser point cloud, the top surface point cloud edge data are extracted and the voxel grid method is fused to establish an optimized point cloud lightweighting algorithm. The research in this paper shows that the voxel grid method performs better than the furthest point sampling method and the curvature downsampling method in retaining the top surface data, reducing the calculation time and optimizing the edge contour. Moreover, the average offset of the geometric contour is reduced from 2.235 mm to 0.664 mm by the edge-optimized voxel grid method, which has a higher retention. In summary, the edge-optimized voxel grid method has a better effect than the existing methods in point cloud lightweighting.
A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation
Authors: Liu Zhentao , Chiu Yu-Chiao , Chen Yidong , Huang Yufei
Published: April 2024

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen’s interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.
A Systematic Review of Prognostic Factors in Patients with Cancer Receiving Palliative Radiotherapy: Evidence-Based Recommendations
Authors: Tam Alexander , Scarpi Emanuela , Maltoni Marco Cesare , Rossi Romina , Fairchild Alysa , Dennis Kristopher , Vaska Marcus , Kerba Marc
Published: April 2024

(1) Background: Prognostication in patients with cancer receiving palliative radiotherapy remains a challenge. To improve the process, we aim to identify prognostic factors in this population from the literature and offer evidence-based recommendations on prognostication in patients undergoing palliative radiotherapy for non-curable or advanced cancers. (2) Methods: A systematic review was performed on the medical literature from 2005 to 2023 to extract papers on the prognosis of palliative radiotherapy patients with advanced cancer. The initial selection was performed by at least two authors to determine study relevance to the target area. Studies were then classified based on type and evidence quality to determine final recommendations. (3) Results: The literature search returned 57 papers to be evaluated. Clinical and biological prognostic factors were identified from these papers to improve clinical decision making or construct prognostic models. Twenty prognostic models were identified for clinical use. There is moderate evidence supporting (i) evidence-based factors (patient, clinical, disease, and lab) in guiding decision making around palliative radiation; (ii) that certain biological factors are of importance; (iii) prognostication models in patients with advanced cancer; and that (iv) SBRT or re-irradiation use can be guided by predictions of survival by prognostic scores or clinicians. Patients with more favorable prognoses are generally better suited to SBRT or re-irradiation, and the use of prognostic models can aid in this decision making. (4) Conclusions: This evaluation has identified several factors or tools to aid in prognosis and clinical decision making. Future studies should aim to further validate these tools and factors in a clinical setting, including the leveraging of electronic medical records for data availability. To increase our understanding of how causal factors interact with palliative radiotherapy, future studies should also examine and include prediction of response to radiation as an outcome.
Deregulation of New Cell Death Mechanisms in Leukemia
Authors: Favale Gregorio , Donnarumma Federica , Capone Vincenza , Della Torre Laura , Beato Antonio , Carannante Daniela , Verrilli Giulia , Nawaz Asmat , Grimaldi Francesco , De Simone Maria Carla , Del Gaudio Nunzio , Megchelenbrink Wouter Leonard , Caraglia Michele , Benedetti Rosaria , Altucci Lucia , Carafa Vincenzo
Published: April 2024

Hematological malignancies are among the top five most frequent forms of cancer in developed countries worldwide. Although the new therapeutic approaches have improved the quality and the life expectancy of patients, the high rate of recurrence and drug resistance are the main issues for counteracting blood disorders. Chemotherapy-resistant leukemic clones activate molecular processes for biological survival, preventing the activation of regulated cell death pathways, leading to cancer progression. In the past decade, leukemia research has predominantly centered around modulating the well-established processes of apoptosis (type I cell death) and autophagy (type II cell death). However, the development of therapy resistance and the adaptive nature of leukemic clones have rendered targeting these cell death pathways ineffective. The identification of novel cell death mechanisms, as categorized by the Nomenclature Committee on Cell Death (NCCD), has provided researchers with new tools to overcome survival mechanisms and activate alternative molecular pathways. This review aims to synthesize information on these recently discovered RCD mechanisms in the major types of leukemia, providing researchers with a comprehensive overview of cell death and its modulation.
Photoelectrocatalytic Reduction of Cr(VI) in Wastewater with a CuBi2O4 Thin Film Photocathode
Authors: An Sai , Wang Ying , Qiao Huajian , Xiu Hao , Liu Deyu , Kuang Yongbo
Published: April 2024

Photoelectrocatalytic approaches show promise for contaminate removal in wastewater through redox reactions. However, the direct treatment of very low concentration heavy metals is a challenging task. Copper bismuth oxide is considered as a potential photocathode material due to its appropriate bandgap width and excellent light absorption properties. In this work, we utilize copper bismuth oxide photoelectrodes with micrometer-scale pores to achieve the efficient and complete reduction of micromolar-level hexavalent chromium(VI) in wastewater. In a continuous 180 min experiment, the reduction rate of 5 µM hexavalent chromium reached 97%, which is an order lower than the drinking standard. Such a process was facilitated by the unique hierarchical microstructure of the oxide thin film and the porous morphology. On the other hand, the structural evolution during the operation was analyzed. A surface passivation was observed, suggesting the possible long-term practical application of this material. This study serves as an important reference for the application of photoelectrocatalysis in addressing Cr(VI) pollution in wastewater, with implications for improving water quality and environmental protection.
Insights into Early Phases of Phycocyanin Crystal Formation via SONICC Spectroscopy
Authors: Pechkova Eugenia , Ghisellini Paola , Fiordoro Stefano , Rando Cristina , Eggenhöffner Roberto
Published: April 2024

This research delves into the early nucleation stages of phycocyanin, a protein pivotal for its fluorescent properties and crystalline stability and holding considerable potential for biotechnological applications. The paper contrasts traditional crystallization methods with the innovative Langmuir–Blodgett nanotemplate approach, aiming to enhance molecular assembly and nucleation processes. The study employs Langmuir–Blodgett nanotemplates alongside second-order nonlinear imaging of chiral crystal (SONICC) spectroscopy. This combination is designed to orderly organize phycocyanin molecules and provide a sensitive visualization of early-stage crystal formation, capturing the intricate dynamics of protein crystallization. The experiments were conducted under controlled conditions, where surface pressure was maintained at 26 mN/m and barrier speed at 70 cm/min to optimize the monolayer formation at the air–water interface. The Langmuir–Blodgett method, compared to traditional vapor diffusion techniques, shows improvements in the uniformity and efficiency of nucleation. The sensitivity of SONICC spectroscopy significantly enhances the visualization of the nucleation process, revealing a more structured and uniform crystalline assembly in the early stages of formation. This method demonstrates a substantial improvement in nucleation dynamics, leading to a more orderly growth process and potentially larger, well-ordered crystals. Integrating Langmuir–Blodgett nanotemplates with SONICC spectroscopy offers a significant step in understanding protein crystallization processes with insights into the nucleation and growth of protein crystals and broad implications for refining crystallography methodologies of protein-based biomaterials, contributing to the advancement of structural biology and materials science.
Two 5-Methoxyindole Carboxylic Acid-Derived Hydrazones of Neuropharmacological Interest: Synthesis, Crystal Structure, and Chemiluminescent Study of Radical Scavenging Properties
Authors: Anastassova Neda , Hristova-Avakumova Nadya , Rusew Rusi , Shivachev Boris , Yancheva Denitsa
Published: April 2024

Given the importance of molecular structure in pharmacological activity and interaction with biological receptors, we conducted a study on the 3,4-dihydroxybenzaldehyde hydrazone derivative of 5-methoxy-indole carboxylic acid (5MICA) and a newly synthesised analogue bearing a 2-methoxy-4-hydroxyphenyl ring using single-crystal X-ray diffraction. We studied the ability of the two compounds to scavenge hypochlorite ions using luminol-enhanced chemiluminescence and their potential to modulate oxidative damage induced by iron on the biologically significant molecules lecithin and deoxyribose in order to evaluate possible antioxidant and prooxidant effects. The X-ray study revealed highly conserved geometry and limited rotation and deformation freedom of the respective indole and phenyl fragments. Interestingly, a conformational difference between the two independent molecules in the asymmetric unit of 3b was found. The X-ray study revealed a combination of hydrogen bonding interactions, short contacts, and π–π stacking stabilizing the specific three-dimensional packing of the molecules of 3a and 3b in the crystal structures. The three-dimensional packing of the molecules of 3b produced a zigzag layering projected along the c-axis. Both compounds effectively decreased luminol-dependent chemiluminescence in model systems with KO2-produced superoxide. They displayed opposite effects when applied in a xanthine/xanthine oxidase system. The hydrazones of 5MICA do not trigger a prooxidant effect or subsequent toxicity under conditions of iron-induced oxidative stress. The 3,4-dihydroxy-substituted derivative demonstrated excellent radical scavenging properties in all model systems, making it the lead compound for the development of compounds with combined neuroprotective and antioxidant properties.
Enhancing Regular Expression Processing through Field-Programmable Gate Array-Based Multi-Character Non-Deterministic Finite Automata
Authors: Zhang Chuang , Tang Xuebin , Peng Yuanxi
Published: April 2024

This work investigates the advantages of FPGA-based Multi-Character Non-Deterministic Finite Automata (MC-NFA) for enhancing regular expression processing over traditional software-based methods. By integrating Field-Programmable Gate Arrays (FPGAs) within a data processing framework, our study showcases significant improvements in processing efficiency, accuracy, and resource utilization for complex pattern matching tasks. We present a novel approach that not only accelerates database and network security applications, but also contributes to the evolving landscape of computational efficiency and hardware acceleration. The findings illustrate that FPGA’s coherent access to main memory and the efficient use of resources lead to considerable gains in processing times and throughput for handling regular expressions, unaffected by expression complexity and driven primarily by dataset size and match location. Our research further introduces a phase shift compensation technique that elevates match accuracy to optimal levels, highlighting FPGA’s potential for real-time, accurate data processing. The study confirms that the benefits of using FPGA for these tasks do not linearly correlate with an increase in resource consumption, underscoring the technology’s efficiency. This paper not only solidifies the case for adopting FPGA technology in complex data processing tasks, but also lays the groundwork for future explorations into optimizing hardware accelerators for broader applications.
Set-Up for Measuring Thermal Parameters of Power Semiconductor Devices
Authors: Górecki Krzysztof , Ptak Przemysław , Górecki Paweł , Data Aleksander
Published: April 2024

In order to determine the junction temperature of semiconductor devices operating at different power supply and cooling conditions, their thermal parameters are needed. This article describes an original measurement set-up enabling the determination of thermal parameters of power semiconductor devices. In contrast to other set-ups described in the literature, this set-up makes it possible to measure thermal parameters which characterize the efficiency of the removal generated due to a self-heating phenomenon, as well as the parameters characterizing mutual thermal couplings. The presented set-up makes use of an indirect electrical method to determine the junction temperature of diodes, bipolar and unipolar transistors and IGBTs. The methods used to measure the self and transfer transient thermal impedances of these devices and the construction of the set-up are described. The influence of selected factors on the accuracy of the measurements is analyzed. Examples of the measurement results of thermal parameters (self and transfer transient thermal impedances) of power semiconductor devices operating at different cooling conditions are presented. The obtained research results are discussed.
Applications of the TL-Based Fault Diagnostic System for the Capacitor in Hybrid Aircraft
Authors: Skowron Maciej , Oliszewski Stanisław , Dybkowski Mateusz , Jarosz Jeremi Jan , Pawlak Marcin , Weisse Sebastien , Valire Jerome , Wyłomańska Agnieszka , Zimroz Radosław , Szabat Krzysztof
Published: April 2024

The article concerns the problem of capacitor diagnosis in a hybrid aircraft. Capacitors are one of the most commonly damaged components of electrical vehicle drive systems. The result of these failures is an increase in voltage ripple. Most known analytical methods are based on frequency spectrum analysis, which is time-consuming and computationally complex. The use of deep neural networks (DNNs) allows for the direct use of the measurement signal, which reduces the operating time of the overall diagnostic system. However, the problem with these networks is the long training process. Therefore, this article uses transfer learning (TL), which allows for the secondary use of previously learnt DNNs. To collect data to learn the network, a test bench with the ability to simulate a capacitor failure was constructed, and a model based on it was made in the MATLAB/Simulink environment. A convolutional neural network (CNN) structure was developed and trained by the TL method to estimate the capacitance of the capacitor based on signals from the Simulink-designed model. The proposed fault diagnostic method is characterised by a nearly 100% efficiency in determining capacitance, with an operating time of about 10 ms, regardless of load and supply voltage.
Feature Sparse Choosing VIT Model for Efficient Concrete Crack Segmentation in Portable Crack Measuring Devices
Authors: Zhang Xiaohu , Huang Haifeng , Cai Meng
Published: April 2024

Concrete crack measurement is important for concrete buildings. Deep learning-based segmentation methods have achieved state-of-art results. However, the model size of these models is extremely large which is impossible to use in portable crack measuring devices. To address this problem, a light-weight concrete crack segmentation model based on the Feature Sparse Choosing VIT (LTNet) is proposed by us. In our proposed model, a Feature Sparse Choosing VIT (FSVIT) is used to reduce computational complexity in VIT as well as reducing the number of channels for crack features. In addition, a Feature Channel Selecting Module (FCSM) is proposed by us to reduce channel features as well as suppressing the influence of interfering features. Finally, Depthwise Separable Convolutions are used to substitute traditional convolutions for further reducing computational complexity. As a result, the model size of our LTNet is extremely small. Experimental results show that our LTNet could achieve an accuracy of 0.887, 0.817 and 0.693, and achieve a recall of 0.882, 0.805 and 0.681 on three datasets, respectively, which is 3–8% higher than current mainstream algorithms. However, the model size of our LTNet is only 2 M.
Power Amplifier Modeling Framework for Front-End-Aware Next-Generation Wireless Networks
Authors: Kostrzewska Kornelia , Kryszkiewicz Pawel
Published: April 2024

Next-generation wireless systems require increased spectral and energy efficiency in terminals. This can only be achieved if the nonlinear characteristics of the radio front-end, primarily the power amplifier (PA), are known and considered while designing algorithms or optimizing radio links. PA datasheets contain limited information, and wideband PA measurements typically require advanced equipment and software. This paper proposes a measurement and signal processing framework that enables the acquisition of nonlinear PA characteristics using generic complex sample transmitters and receivers, e.g., software-defined radio (SDR). This paper also proposes a multistage signal calibration and synchronization procedure that allows obtaining nonlinear PA characteristics that are non-distorted by other phenomena. The correctness and usefulness of the proposed framework are demonstrated by measuring three PAs, each under varying supply voltage and carrier frequency. At the same time, the high variability of the obtained nonlinear characteristic justifies the need for PA measurements to obtain awareness of nonlinear characteristics.
Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things
Authors: Jan Syed Roohullah , Ghaleb Baraq , Tariq Umair Ullah , Ali Haider , Sabrina Fariza , Liu Lu
Published: April 2024

The Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach’s exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion.
Median Absolute Deviation for BGP Anomaly Detection
Authors: Romo-Chavero Maria Andrea , Cantoral-Ceballos Jose Antonio , Pérez-Díaz Jesus Arturo , Martinez-Cagnazzo Carlos
Published: April 2024

The stability and reliability of the global Internet infrastructure heavily rely on the Border Gateway Protocol (BGP), a crucial protocol that facilitates the exchange of routing information among various Autonomous Systems, ensuring seamless connectivity worldwide. However, BGP inherently possesses a susceptibility to abnormal routing behaviors, potentially leading to significant connectivity disruptions. Despite extensive efforts, accurately detecting and effectively mitigating such abnormalities persist as tough challenges. To tackle these, this article proposes a novel statistical approach employing the median absolute deviation under certain constraints to proactively detect anomalies in BGP. By applying advanced analysis techniques, this research offers a robust method for the early detection of anomalies, such as Internet worms, configuration errors, and link failures. This innovative approach has been empirically validated, achieving an accuracy rate of 90% and a precision of 95% in identifying these disruptions. This high level of precision and accuracy not only confirms the effectiveness of the statistical method employed but also marks a significant step forward for enhancing the stability and reliability of the global Internet infrastructure.
A New Generation of Collaborative Immersive Analytics on the Web: Open-Source Services to Capture, Process and Inspect Users’ Sessions in 3D Environments
Authors: Fanini Bruno , Gosti Giorgio
Published: April 2024

Recording large amounts of users’ sessions performed through 3D applications may provide crucial insights into interaction patterns. Such data can be captured from interactive experiences in public exhibits, remote motion tracking equipment, immersive XR devices, lab installations or online web applications. Immersive analytics (IA) deals with the benefits and challenges of using immersive environments for data analysis and related design solutions to improve the quality and efficiency of the analysis process. Today, web technologies allow us to craft complex applications accessible through common browsers, and APIs like WebXR allow us to interact with and explore virtual 3D environments using immersive devices. These technologies can be used to access rich, immersive spaces but present new challenges related to performance, network bottlenecks and interface design. WebXR IA tools are still quite new in the literature: they present several challenges and leave quite unexplored the possibility of synchronous collaborative inspection. The opportunity to share the virtual space with remote analysts in fact improves sense-making tasks and offers new ways to discuss interaction patterns together, while inspecting captured records or data aggregates. Furthermore, with proper collaborative approaches, analysts are able to share machine learning (ML) pipelines and constructively discuss the outcomes and insights through tailored data visualization, directly inside immersive 3D spaces, using a web browser. Under the H2IOSC project, we present the first results of an open-source pipeline involving tools and services aimed at capturing, processing and inspecting interactive sessions collaboratively in WebXR with other analysts. The modular pipeline can be easily deployed in research infrastructures (RIs), remote dedicated hubs or local scenarios. The developed WebXR immersive analytics tool specifically offers advanced features for volumetric data inspection, query, annotation and discovery, alongside spatial interfaces. We assess the pipeline through users’ sessions captured during two remote public exhibits, by a WebXR application presenting generative AI content to visitors. We deployed the pipeline to assess the different services and to better understand how people interact with generative AI environments. The obtained results can be easily adopted for a multitude of case studies, interactive applications, remote equipment or online applications, to support or accelerate the detection of interaction patterns among remote analysts collaborating in the same 3D space.
In-Plane Mechanical Characterization of a Kevlar® Composite
Authors: Canceco de la Cruz Rene Alejandro , Carreño Gallardo Caleb , Diaz Diaz Alberto , Zuñiga Aviles Luis Adrian , Plascencia Barrera Gabriel , Herrera Ramirez Jose Martin
Published: April 2024

Polymer-based composites are widely used in the automotive, security, aeronautical and space industries, to mention a few. This is because of their good mechanical properties, which are similar to those of metals but with the attraction of being lightweight. Kevlar® is extensively used as a reinforcement in the security industry owing to its good ballistic properties. This investigation presents a mechanical characterization based on in-plane quasi-static tensile testing of Kevlar® 29/phenolic resin with a polyvinyl butyral composite using a universal testing system. The methodology developed for the preparation of the coupons is based on pressure, temperature and time. As a result of this work, elastic moduli (EL and ET), Poisson’s ratio (νLT), shear modulus (GLT) and strengths (XT, YT, S) were obtained. It is worth mentioning that there is scarce or no characterization of this material in the literature, and those studies that do characterize it do not present the coupons’ thermoforming conditions or the reasons for the coupons’ dimensions (width, length and thickness).
Predicting Wildfire Ember Hot-Spots on Gable Roofs via Deep Learning
Authors: Al-Bashiti Mohammad Khaled , Nguyen Dac , Naser M. Z. , Kaye Nigel B.
Published: April 2024

Ember accumulation on and around homes can lead to spot fires and home ignition. Post wildland fire assessments suggest that this mechanism is one of the leading causes of home destruction in wildland urban interface (WUI) fires. However, the process of ember deposition and accumulation on and around houses remains poorly understood. Herein, we develop a deep learning (DL) model to analyze data from a series of ember-related wind tunnel experiments for a range of wind conditions and roof slopes. The developed model is designed to identify building roof regions where embers will remain in contact with the rooftop. Our results show that the DL model is capable of accurately predicting the position and fraction of the roof on which embers remain in place as a function of the wind speed, wind direction, roof slope, and location on the windward and leeward faces of the rooftop. The DL model was augmented with explainable AI (XAI) measures to examine the extent of the influence of these parameters on the rooftop ember coverage and potential ignition.
The Impact of Financial Development on Renewable Energy Consumption: The Case of Vietnam and Other ASEAN Members
Authors: Van Nguyen Chien
Published: April 2024

The purpose of this study was to evaluate the impact of financial development and renewable energy consumption in Vietnam and some selected countries in Southeast Asia. After researching over the period from 1970 to 2022, using quantitative analyses, including the ordinary least squares (OLS), fixed effects method (FEM), and random effects method (REM), and measuring the Driscoll–Kraay standard errors to assess cross-dependence between countries as well as a Dynamic Ordinary Least Squares (DOLS) estimation analysis to evaluate the robustness of the research, the research results confirm that financial development has a negative impact on renewable energy consumption, which reflects the important role of fossil energy sources in meeting energy consumption demand. Similarly, increased per capita income negatively affects renewable energy consumption. This study also confirms the positive impact of foreign direct investment on renewable energy use.
A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide
Authors: Virág Dávid , Schlosser Gitta , Borbély Adina , Gellén Gabriella , Papp Dávid , Kaleta Zoltán , Dalmadi-Kiss Borbála , Antal István , Ludányi Krisztina
Published: April 2024

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.
Expanded Geographical Distribution of Coleomegilla maculata lengi (Coleoptera: Coccinellidae) in North America
Authors: Hesler Louis , Brust Mathew
Published: April 2024

Several species of lady beetle native to North America have declined in abundance in the last few decades, often accompanied by contractions in their geographic ranges. Coleomegilla maculata lengi is a lady beetle native to North America that is an important predator of pests in various agroecosystems. Its reported range spans the eastern half of the USA, with no sustained decline in abundance or contraction of its range reported. Indeed, we recently collected several individuals of this lady beetle in central USA roughly 500 km beyond the western edge of its reputed range. We hypothesized that new records could indicate either that previous range characterization failed to include pre-existing collection records further west or that C. maculata lengi has recently expanded its geographic range. To test these hypotheses, we searched several institutional insect collections and digital databases for records and found many earlier records of C. maculata lengi beyond its reputed geographic range, clearly showing that the previous characterization of its geographic distribution in North America was substantially underestimated. In addition, we report a new state record of C. maculata lengi from Wyoming, USA, that further indicates its geographic range expansion in North America. We discuss new records of C. maculata lengi in light of declines in native coccinelline lady beetle species in North America.
Correlation between Subjective Nasal Patency and Nasal Capacity in Young Adults: A Pilot Study with a Prototype Device—A Nasoorospirometer
Authors: Zasadzińska-Stempniak Katarzyna , Zajączkiewicz Hanna , Kukwa Andrzej
Published: April 2024

Background: Nasal airway obstruction (NAO) is characterised by high resistance in the nasal cavity with a collapsible and narrowed upper airway and is an integral part of OSA pathophysiology. The literature demonstrates that the identification of high-risk OSA in the young adult population leads to the prevention of later health consequences. A nasoorospirometer is a prototype device that measures nasal capacity during inspiration. The basis for measurement is a Wheatstone bridge and a thermal anemometer. The parameters are recorded via hot wire anemometry (HTA) with velocity measurements in the airflow field. Therefore, this pilot study aimed to test the feasibility of the device by examining a young adult sample. The secondary aim was to determine whether subjective NAO correlates with nasal capacity and whether NAO corresponds with anthropometric parameters and individual risk of OSA. Methods: A group of 31 participants (mean age 24.9 years) underwent a thorough laryngological examination. The nasoorospirometer was used to measure objective NAO (nasal capacity), the NOSE scale was used to gain subjective NAO evaluation, and the Berlin Questionnaire for the risk of OSA. Results: A correlation analysis confirmed no significant associations between the subjective and objective measures (p > 0.05). Higher BMI and neck circumference are associated with lower NAO and higher nasal patency in the population of young adults (r: 0.32–0.45; p < 0.05). The risk of OSA showed no statistically significant association (p > 0.05). Conclusions: We presented three methods of NAO assessment: subjective participant evaluation, objective nasoosopirometry, and objective laryngological assessment. However, the use of a nasoorospirometer with anthropometric measures in young adults needs to be verified in future studies.
Pituitary Apoplexy: An Updated Review
Authors: Iglesias Pedro
Published: April 2024

Pituitary apoplexy (PA) is an acute, life-threatening clinical syndrome caused by hemorrhage and/or infarction of the pituitary gland. It is clinically characterized by the sudden onset of headache. Depending on the severity, it may also be accompanied by nausea, vomiting, visual disturbances, varying degrees of adenohypophyseal hormone deficiency, and decreased level of consciousness. Corticotropic axis involvement may result in severe hypotension and contribute to impaired level of consciousness. Precipitating factors are present in up to 30% of cases. PA may occur at any age and sometimes develops during pregnancy or the immediate postpartum period. PA occurs more frequently in men aged 50–60, being rare in children and adolescents. It can develop in healthy pituitary glands or those affected by inflammation, infection, or tumor. The main cause of PA is usually spontaneous hemorrhage or infarction of a pituitary adenoma (pituitary neuroendocrine tumor, PitNET). It is a medical emergency requiring immediate attention and, in many cases, urgent surgical intervention and long-term follow-up. Although the majority of patients (70%) require surgery, about one-third can be treated conservatively, mainly by monitoring fluid and electrolyte levels and using intravenous glucocorticoids. There are scoring systems for PA with implications for management and therapeutic outcomes that can help guide therapeutic decisions. Management of PA requires proper evaluation and long-term follow-up by a multidisciplinary team with expertise in pituitary pathology. The aim of the review is to summarize and update the most relevant aspects of the epidemiology, etiopathogenesis, pathophysiology, clinical presentation and clinical forms, diagnosis, therapeutic strategies, and prognosis of PA.