bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Land Use and Land Cover on the Quality of Surface Waters in the Miranda River Watershed, Brazilian Pantanal
Authors: Carvalho Wesley dos Santos , Filho Fernando Jorge Corrêa Magalhães , Rodrigues Lucia Ribeiro , Calheiros Cristina Sousa Coutinho
Published: May 2024

Water quality assessment stands as a fundamental step in water resource management. In addition to substances and organisms present in the water, land use and land cover are two factors affecting also water quality. This study analyzed the correlation and influence between water quality and land use and land cover in the Miranda River Basin (MRB) and the natural wetlands in the Pantanal Biome. Using a watershed-wide approach, results suggested that water quality management policies can be considered in terms of sub-basins (smaller planning units). The Water Quality Index (WQI) was considered GOOD or EXCELLENT throughout the MRB. It was observed that the natural wetlands have the capacity to contribute to improve WQI, with reductions in thermotolerant coliforms and turbidity; however, high values were found for thermotolerant coliforms, a temporal increasing trend of biochemical oxygen demand (BOD), and a reduction in dissolved oxygen and total phosphorus. Conversely, it was found in these flooded areas a trend of decrease in dissolved oxygen and an increase in BOD concentrations. Natural wetlands play important hydrological and ecological roles in water quality, storing, removing, and cycling nutrients. They are highly relevant areas for defining watershed management and conservation strategies, environmental protection, and providing ecosystem services.
The Carotenoid Composition of Larvae Feed Is Reflected in Adult House Fly (Musca domestica) Body
Authors: Lahmi Li-Or , Harari Ayelet , Shaish Aviv , Tsurim Ido
Published: May 2024

Carotenoids are common and diverse organic compounds with various functional roles in ani-mals. Except for certain aphids, mites, and gall midges, all animals acquire necessary carotenoids only through their diet. The House fly (Musca domestica) is a cosmopolitan pest insect that populates diverse habitats. Its larvae feed on organic substrates that may vary in carotenoid composition according to their specific content. We hypothesized that the carotenoid composition in the adult House fly’s body would reflect its composition in the larval feed. We reared house fly larvae on substrates that differed in carotenoid composition and characterized the carotenoid composition of the emerging adults. Our results indicate that the carotenoid composition of adult House flies is related to the carotenoid composition in its natal substrate, but does not directly reflect it. We suggest that these findings may be developed to identify sources of House fly in-festations. We also recommend that care should be taken when considering the rearing substrates of House fly larvae used for animal feed.
DeClEx-Processing Pipeline for Critical Healthcare Application
Authors: Shinde Gaurav , Goniguntla Sai Charan , Shirur Prajwal , Hambaba Ahmed
Published: May 2024

Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.
Open Reduction and Internal Fixation with Plate and Screw Versus Triplanar External Fixation in the Surgical Treatment of Calcaneal Fractures: A Retrospective Cohort Study
Authors: Pavone Vito , Sapienza Marco , Carnazza Michela , Vaccalluzzo Marco Simone , Leotta Giulia , Sergi Francesco , Mobilia Giuseppe , Di Via Danilo , Testa Gianluca
Published: May 2024

Background: The surgical approach to treating calcaneal fractures, specifically Sanders types II, III, and IV, is still a subject of controversy. The decision to opt for open reduction and internal fix-ation (ORIF) or minimally invasive osteosynthesis (MIOS) remains uncertain owing to varying complication rates. This study intends to examine each treatment’s pros and cons in relation to the incidence of complications. Methods: In this retrospective study, we examined 70 patients treated between January 2018 and September 2022: 50 received ORIF, and 20 underwent MIOS. We evaluated functional outcomes with the Maryland Foot Score and the Creighton-Nebraska Health Foundation Assessment Scale (CNHFAS). We also assessed the amount of anatomical restoration using Böhler’s and Gissane’s angles, determined by radiographic checks, and the frequency of postoperative complications. Results: A significant reduction in the risk of reinter-vention was observed in patients with high anesthetic risks, polytraumatized individuals, and those with comorbidities who underwent MIOS. The chi-square test analysis of postoperative complications highlighted no substantial differences between the ORIF and MIOS groups in rela-tion to tendinitis, chronic pain and swelling, algodystrophy, and surgical wound infections (p > 0.20 for all). Conclusion: ORIF is more suitable for patients who have high motor function re-quirements, a long-term activity schedule and can endure potential additional procedures. This preference is because ORIF provides better anatomical restoration and features a lower osteoar-thritis occurrence rate.
Attitude Survey regarding to Tourist Attractions of a County in Western Hungary
Authors: Darabos Ferenc , Kundi Viktória , Kőmíves Csaba
Published: May 2024

Preserving and passing on folk traditions and gastronomic culture to the next generations was an important task of the older age group in every historical era. The most important objective of this study is to reveal people's attitudes regarding to the values of gastronomy and culture in a West Hungarian county. The combined presence of the Szigetköz Nature Park and the World Heritage sites (Fertő-Hanság National Park, Pannonhalma Abbey) in the offer requires re-sponsible, environmentally, socially and economically sustainable tourism planning. In the framework of a quantitative research (questionnaire survey), the tourists’ knowledge of gas-tronomy values and also their knowledge regarding to the architectural, cultural and natural attractions will be analysed. Four hypotheses were formulated in the study. The data collec-tion took place between September 1. 2023 and October 30. 2023 (N=666). The sample is not representative, the respondents were selected arbitrarily and randomly. The most important novelty of the study is that in contrast to previous studies, in this study, tourists' visits to different destinations were measured, while previous studies focused on tourists' interests.
Linearized Expressions of 3D Rotational Motion
Authors: Liu Yinlong
Published: May 2024

Rotation motion in a three-dimensional physical world refers to an angular displacement of an object around a specific axis in $\mathbb{R}^3$. It is typically formulated as a non-linear and non-convex process due to the nonlinearity and nonconvexity of $\mathbb{SO}(3)$. However, this paper proposes a new perspective that the 3D rotation motion can be expressed by the linear equation without dropping any constraints and increasing any singularities. Moreover, two frequent cases, i.e., $\angle\left(\mathbf{R}\boldsymbol{x},\boldsymbol{y}\right)=0(\pi)$ and $\angle\left(\mathbf{R}\boldsymbol{x},\boldsymbol{y}\right)=\frac{\pi}{2}$, in computer vision and robotics that can be expressed linearly are deeply discussed in this paper.
Retrieval of the Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model
Authors: Alam Mir Md Tasnim , Simic Milas Anita , Gašparović Mateo , Osei Henry Poku
Published: May 2024

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.
On the Raleigh-Ritz Variational Method. Non-orthogonal Basis Set
Authors: Fernández Francisco
Published: May 2024

We overview the main equations of the Rayleigh-Ritz variational method and discuss their connection with the problem of simultaneous diagonalization of two Hermitian matrices.
Rubber-Based Agroforestry Systems Associated with Food Crops: A Solution for Sustainable Rubber and Food Production?
Authors: Cahyo Andi Nur , Dong Ying , Taryono Taryono , Nugraha Yudhistira , Junaidi Junaidi , Sahuri Sahuri , Penot Eric , Hairmansis Aris , Purwestri Yekti Asih , Akbar Andrea , Asywadi Hajar , Ardika Risal , Prasetyo Nur Eko , Agustina Dwi Shinta , Alam Taufan , Oktavia Fetrina , Subandiyah Siti , Montoro Pascal
Published: May 2024

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A library was set-up consisting of 415 scientific references. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber producing countries, in particular in Indonesia, Thailand, China and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only 4 papers are devoted to plant physiology and breeding. The discussion section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops.
Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction
Authors: Alizadegan Hamed , Radmehr Arian , Karimi Hossein , Asghari Ilani Mohsen
Published: May 2024

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.
Robustness of Bayesian Random Forest in High-Dimensional Analysis with Missing Data
Authors: Olaniran Oyebayo Ridwan , Alzahrani Ali Rashash R.
Published: May 2024

The challenge of missing data in scientific research prompts researchers to decide between imputing incomplete data or discarding observations, where discarding can lead to information loss. Various methods exist, from simple deletion to sophisticated approaches like Multiple Imputation (MI). However, these methods often fall short with high-dimensional datasets. Multiple Imputation by Chained Equations (MICE) and Random Forest (RF) proximity imputation offer promising alternatives. Therefore, in this paper, we propose integrating MICE with Bayesian random forest (BRF) to enhance imputation accuracy and predictive power, particularly in high-dimensional analyses. Our approach combines MICE’s efficiency with BRF’s robustness, offering a comprehensive solution to missing data challenges. By way of example, we provide empirical evaluations to validate its effectiveness using synthetic data of various missing data scenarios. The results from the simulations showed that the combination of BRF and MICE offered a promising strategy for high-dimensional analysis in the presence of missing data.
Detection of Low-Concentration Biological Samples Based on a QBIC Terahertz Metamaterial Sensor
Authors: Dong Bing , Wei Bo , Wei Dongshan , Ke Zhilin , Ling Dongxiong
Published: May 2024

Quasi-bound state in the continuum (QBIC) can effectively enhance the interaction of terahertz (THz) wave with matters due to the tunable high-Q property, which has a strong potential application in detection of low-concentration biological samples in the THz band. In this paper, a novel THz metamaterial sensor with a double-chain separated resonant cavity structure based on QBIC is designed and fabricated. The process of excitation of the QBIC mode is verified and the structural parameters are optimized after considering the ohmic loss by simulations. The simulated refractive index sensitivity of the sensor is up to 544 GHz/RIU, much higher than those of recently reported THz metamaterial sensors. Sensing sensitivity of the proposed metamaterial sensor is confirmed in experiment by detecting low-concentration lithium citrate (LC) and bovine serum albumin (BSA) solutions. The limits of detection (LoDs) are obtained to be 0.0025 mg/mL (12 μM) for LC and 0.03125 mg/mL (0.47 μM) for BSA, respectively, both of which excel over most of reported results in previous studies. These results indicate that the proposed THz metamaterial sensor has excellent sensing performances and can well be applied to the detection of low concentration biological samples.
An Improved Large Kernel-Based Remote Sensing Land Cover Segmentation Algorithm
Authors: Liu Guohong , Liu Cong , Wu Xianyun , Li Yunsong , Zhang Xiao , Xu Junjie
Published: May 2024

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.
Food Safety Knowledge, Attitudes, and Practices (KAP) Among Dairy Retail Vendors in Amman, Jordan
Authors: Al-Slaihat Abed , Saleh Mohammed , Alawadi Ahmad , Almomani Hamzeh , Alsakran Shatha , Alsheikh Dina , Qatham Raghad , Lee Youngseung
Published: May 2024

Food safety in dairy shops has gained increasing attention. Consequently, this study aims to evaluate dairy handlers' knowledge of food safety practices, assess compliance with food safety codes regarding facility maintenance and personal hygiene, and understand retail dairy shops' capacity to implement food safety standards. This research involved surveys and interviews with 110 dairy retail workers to gauge their grasp of food safety principles, their recognition of its importance, and the obstacles they encounter in implementing proper food safety measures. Among the participants, 93% were males under 35 years old, with experience ranging from 1 to 8 years.
The study revealed that while over 83% of participants were familiar with standard procedures ensuring dairy product safety, only 37% understood “HACCP” in relation to food safety. Additionally, 42% believed that freezing food at -18°C eliminates food bacteria, and 56% thought food contamination could be identified by taste, indicating misconceptions among respondents. Results showed that 90% of survey participants had a strong awareness of food safety, with 98% emphasizing the importance of regular inspection of production areas for cleanliness to ensure a safe dairy production environment. However, less than 45% had received formal training on dairy-specific food safety practices, and only 53.6% had a documented system for correctly labeling and providing production dates for dairy products. These findings have significant implications for public health and food safety policies in Jordan. Furthermore, the study provides valuable insights to enhance food safety practices among dairy retail vendors.
A Survey of Computer Vision Algorithm Applied in Driverless Robotic Vehicles with Sensing Capability
Authors: Chen Lu , Li Gun , Xie Weisi , Tan Jie , Li Yang , Pu Junfeng , Chen Lizhu , Gan Decheng , Shi Weimin
Published: May 2024

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.
Expedient Syntheses of Alkyl and Aryl Thioethers using Xanthates as Thiol-Free Reagents
Authors: Nie Jinli , He Ziqing , Xie Sijie , Li Yibiao , He Runfa , Chen Lu , Luo Xiai
Published: May 2024

Thioethers are critical in the fields of pharmaceuticals and organic synthesis, but most of the methods for synthesizing alkyl thioethers employ foul-smelling thiols as starting materials or generate them as by-products. Additionally, most thiols are air-sensitive and are easily oxidized to produce disulfides under atmospheric conditions; thus, a novel method for synthesizing thioethers is necessary. This paper reports a simple, effective, green method for synthesizing dialkyl or alkyl aryl thioether derivatives using odorless, stable, low-cost ROCS2K as a thiol surrogate. This transformation offers a broad substrate scope and good functional group tolerance with excellent selectivity. The reaction likely proceeds via xanthate intermediates, which can be readily generated via the nucleophilic substitution of alkyl halides or aryl halides with ROCS2K under transition-metal-free and base-free conditions.
Advanced Laser Plasma Diagnostics for a Modular High-Repetition Rate Plasma Electron Accelerator
Authors: Greb Christian , Aktan Esin , Adam Roman , Dickson Alex , Sire Cédric , Nefedova Viktoria E. , Sylla Francois , Lopez-Martens Rodrigo , Schneider Claus Michael , Faure Jérôme , Büscher Markus
Published: May 2024

We present a laser-plasma electron accelerator module designed to be driven by high repetition rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet. The core methods involve comprehensive metrological assessments, including rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatio-temporal distribution analyses of the laser system. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We demonstrate the full functionality of the laser-plasma accelerator module and perform target density and laser-plasma characterizations. Our work is a first step towards the wide dissemination of fully integrated laser-plasma accelerator technology.
Security Challenges in 6G Networks: A Game Theory-based Intrusion Detection with Network Slicing and AI/ML Perspectives
Authors: Gupta Mantisha , Jha Rakesh Kumar , Sabraj Manish
Published: May 2024

The vast network of interconnected devices of the Internet of Things poses significant security challenges, necessitating the upgrade of current wireless networks to the 6G standard and an improved intrusion detection system for host networks. Thus this article therefore underscores a game theory concept to analyze the security impediments in wireless communication systems, where a host network bandwidth is jeopardized by the intruder while acting as a legitimate user. While game theory can help with the impending security concerns, network slicing is saliently proposed for large and complicated networks, precipitating a layer wise fragmentation of the concerned network and effectively addressing the security issues in different layers of the IoT framework. However, with increasing network complexity, network security issues incorporating traditional measures become increasingly cumbersome. Therefore, this paper also briefly mentions some of the ML techniques that can be used to classify and segregate valid users from the compromised nodes in the host network. The paper discusses the potential of incorporating intelligence into wireless communication systems to enhance the host network security. The paper further aids in analyzing existing security issues in different layers of wireless communication systems, underscoring network slicing for further classification of intruders from user nodes and training the host network using ML/AI per the user application specification.
Experimental Granulomatous Amebic Encephalitis Caused by Acanthamoeba castellanii
Authors: Borges Samuel da Luz , de Macedo Eberson da Silva , da Silva Felipe Alexandre Vinagre , Ataíde Brenda Jaqueline de Azevedo , Mendes Nívia de Souza Franco , Passos Adelaide da Conceição Fonseca , de Moraes Suellen Alessandra Soares , Herculano Anderson Manoel , Oliveira Karen Renata Herculano Matos , Bahia Carlomagno Pacheco , Dolabella Silvio Santana , Batista Evander de Jesus Oliveira
Published: May 2024

Acanthamoeba genus amoebae can affect humans with diseases such as Granulomatous Amebic Encephalitis (GAE), a highly lethal neuroinfection. Several aspects about the disease still need to be elucidated. Animal models of GAE have allowed advances in these aspects. This work tested Wistar rats (Rattus norvegicus albinus) as an animal model of GAE. For this, 32 animals were infected with 1x106 A. castellanii trophozoites, T4 genotype. Ameba recovery tests were carried out on plates of agar, vascular extravasation assays, behavioral tests and histopathological technique with H/E staining. Data were submitted to linear regression analysis, one-way ANOVA and Tukey's test, performed in the GraphPad Prism® 8.0 program, with a significance level of p<0.05. The results revealed the efficiency of the model. There was recovery of amoebae from the liver, lungs and brain of infected animals, significant encephalic vascular extravasation and behavioral changes in these animals, but not in the control animals. However, not all infected animals showed positive histopathology for the analyzed organs. The nervous tissue was the least affected, demonstrating the role of the BBB in the defense of the CNS. Supported by the demonstrated evidence, we confirm the difficulty, but also the feasibility of using rats as an animal model of GAE.
Epidemiology of Pediatric Cardiomyopathy in a Mediterranean Population
Authors: Bagkaki Alena , Parthenakis Fragiskos , Chlouverakis Gregory , Anastasakis Aris , Papagiannis Ioannis , Galanakis Emmanuil , Germanakis Ioannis
Published: May 2024

Background. Our knowledge regarding the epidemiology of pediatric cardiomyopathy is based on large national population studies reporting an annual incidence of 1 case per 100 000 children with a higher incidence observed in infancy and among selected populations. The aim is to document the epidemiology of pediatric cardiomyopathy in a Mediterranean population. Methods. Children younger than 18 years of age, living on the Mediterranean island of Crete, Greece and evaluated during 20 years since the establishment of tertiary pediatric cardiology services (2002-2022) were included in this retrospective study. Results. A total of 40 children were included, corresponding to an average annual incidence of pediatric cardiomyopathy of 1.59 cases (95% CI: 1.4-2,3) and a prevalence of 26 cases per 100.000 children. In decreasing order of frequency most cases corresponded to dilated cardiomyopathy (50%) followed by hypertrophic (42.5%), arrhythmogenic (5%) and restrictive (2.5%). An etiology was identified in 40%, including a genetic diagnosis in 22.5%. Conclusion. The incidence of pediatric cardiomyopathy in the Mediterranean island of Crete is higher compared with that reported previously for other Caucasian populations. Further study is needed to investigate the exact prevalence and specific genetic factors associated with the epidemiology of pediatric cardiomyopathy in Mediterranean populations.
Useful Clinical Criteria for Identifying Neonates with Congenital Cytomegalovirus Infection at Birth in the Context of an Expanded Targeted Screening Program
Authors: Poletti de Chaurand Valeria , Scandella Gaia , Zicoia Marianna , Arienti Francesca , Fernicola Federica , Lanteri Laura , Guglielmi Diletta , Carli Anna , Vasarri Maria Viola , Iozzi Lucia , Cavallero Annalisa , Malandrin Sergio Maria Ivano , Locatelli Anna , Ventura Maria Luisa , Sinelli Mariateresa , Ornaghi Sara
Published: May 2024

Cytomegalovirus (CMV) is the leading infectious cause of brain defects and neurological dysfunctions, including sensorineural hearing loss (SNHL). A targeted screening in neonates failing the hearing screen is currently recommended in Italy according to national guidelines. However, SNHL may not be present at birth; also, congenital CMV (cCMV) may manifest with subtle signs other than SNHL. Therefore, inclusion of additional criteria for cCMV screening appears clinically valuable. Starting January 2021, we have implemented an expanded targeted cCMV screening at our center, with testing in case of maternal CMV infection during pregnancy, inadequate antenatal care, maternal HIV infection or immunosuppression, birthweight and/or head circumference.
A Conceptual Device Turning Quantum-Mechanical Interactions into Macroscopically Observable Events
Authors: Müller J Gerhard
Published: May 2024

The paper is concerned with the concept of elementary observations (EO). EOs are experimental answers to the question whether a matter-instrument interaction had taken place at the micro-scale of quantum phenomena. Focusing on the specific case of photon detection, we show that during their lifetimes EOs proceed through the four phases of initiation, detection, erasure and reset. Once generated, the observational value of EOs can be measured in units of the Planck quantum of physical action h=4.136×10^(-15) eVs. Once terminated, each unit of entropy of size k_B=8.617×10^(-5) eV⁄K, which had been created in the instrument during the observational phase, needs to be removed from the instrument to ready it for a new round of photon detection. This withdrawal of entropy takes place at an energetic cost of at least two units of the Landauer minimum energy bound of E_La=ln⁡〖(2) k_B T_D 〗 for each unit of entropy of size k_B.
Fibropapillamatosis: A Review of the Disease with Attention to the Situation Northern Coast of Brazil
Authors: da Cruz Karoline Petrini Pinheiro , Gattamorta Marco Aurélio , Matushima Eliana Reiko , Salvarani Felipe Masiero
Published: May 2024

Fibropapillomatosis in sea turtles is a potentially debilitating and fatal disease for which there is still a lack of knowledge, especially for specific regions of Brazil. The diagnosis is made through the observation of clinical manifestations, and despite its association with Chelonid Alphaherpesvirus 5 (ChHV5) as the etiological agent, the expression of the disease may also be related to immunological and environmental factors caused by anthropic degradation of the environment. Thus, this review aims to elucidate what is known about this disease globally and especially in various regions of Brazil, promoting a better understanding of its evolution, spatiotemporal prevalence and relationship with human activities. Furthermore, the review explores the molecular biology of ChHV5, including its genomic structure, replication cycle, and mechanisms of pathogenesis. The role of environmental factors, such as temperature and pollution, in modulating ChHV5 infection and FP development is also discussed. Additionally, the review summarizes current diagnostic methods for detecting ChHV5 infection in sea turtles, highlighting the importance of early detection and monitoring for effective disease management and conservation efforts. Finally, the review outlines future research directions aimed at improving our understanding of ChHV5 and developing strategies for FP control and prevention in sea turtle populations.
A Genomewide Association Study of Oxypurinol Concentrations in Patients Treated with Allopurinol
Authors: Meloche Maxime , Provost Sylvie , Leclair Grégoire , Oussaïd Essaïd , St-Jean Isabelle , Jutras Martin , Gaulin Marie-Josée , Lemieux Perreault Louis-Philippe , Valois Diane , Mongrain Ian , Busseuil David , Rouleau Jean-Lucien , Tardif Jean-Claude , Dubé Marie-Pierre , de Denus Simon
Published: May 2024

Cohort studies have identified several genetic determinants that could predict the clinical response to allopurinol. However, they have not been commonly used for genome-wide investigations to identify genetic determinants on allopurinol metabolism and concentrations. We conducted a genome-wide association study of a prior cross-sectional investigation of patients from the Montreal Heart Institute Biobank undergoing allopurinol therapy. Four endpoints were investigated, namely plasma concentrations of oxypurinol, the active metabolite of allopurinol, allopurinol, and allopurinol-riboside, as well as allopurinol daily dosing. A total of 439 participants (mean age 69.4 years; 86.4% male) taking allopurinol (mean daily dose 194.5 mg) and who had quantifiable oxypurinol concentrations were included in the genome-wide analyses. Participants presented multiple comorbidities and received concomitant cardiovascular medications. No association achieved the predefined genome-wide threshold values for any of the endpoints (all p>5 × 10-8). Our results are consistent with prior finding regarding the difficulty in identifying genetic determinants of drug concentrations or pharmacokinetics of allopurinol and its metabolites, and allopurinol daily dosing. Given the size of this genome-wide study, collaborative investigations involving larger and diverse cohorts may be required to further identify pharmacogenomic determinants of allopurinol and measure their clinical relevance to personalize allopurinol therapy.
Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey
Authors: Tenzin Sangay , Rassau Alexander , Chai Douglas
Published: May 2024

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.
Empowering New-Quality Productivity Development through Artificial Intelligence: Impact Mechanisms and Spatial Spillover
Authors: Gao Xuesong , Liu Xuefang
Published: May 2024

Constructing an innovation system centered on new-quality productivity is key to achieving China's technological independence and high-quality development, especially against the backdrop of rapid advancements in artificial intelligence. This article utilizes provincial panel data from China spanning 2010 to 2022 and applies kernel density estimation and the Moran's I index to study the spatiotemporal evolution of artificial intelligence and new-quality productivity. The findings indicate a clustering trend in the development of both factors across regions. Causal empirical analysis using a two-way fixed-effects model reveals that artificial intelligence positively influences new-quality productivity, with conclusions validated by robustness and endogeneity tests.
Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Authors: Zheng Haohua , Zhang Jianchen , Li Heying , Wang Guangxia , Guo Jianzhong , Wang Jiayao
Published: May 2024

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.
Process of Object-Oriented Design of Gear Size Ranges. Part II: Synthesis
Authors: Czader Wiesław , Drewniak Józef , Zhauyt Algazy , Rysiński Jacek , Alipbayev Kuanysh , Orazaly Yerkin
Published: May 2024

Design process can be divided into stages of analysis and synthesis. This paper presents the application of the object-oriented technique to the synthesis as well as theoretical considerations concerning to design of gear size ranges. The synthesis consists in composition (aggregation) of single details into units – according to the rule: “from details up to general view”. The basics of the object-oriented synthesis were formulated. Furthermore, the consecutive stages of synthesis were formulated. The interactions between these stages were discussed. Some aspects were described in details e.g.: creation of interfaces for performance of the embodiment phase the designed artefact via CAD system drawing with specification (dimensions are still parametrically set). The final step of the procedure is creation of so called substantial (concrete) form of artifacts i.e. final assignment of dimensions which stored in the special data base. The final stage of the design process is issuing of technical documentation (technical drawings) created automatically via CAD system. The considerations are terminated by the list of advantages connected with the object-oriented approach to design (in comparison to the traditional way).
Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds
Authors: Li WenJie , Tang Biyu , Hou Zhen , Wang Hongbo , Bing Zongyu , Yang Qiong , Zheng Yongqiang
Published: May 2024

Crop phenotyping data collection is the basis for precision agriculture and smart decisionmaking applications. In this study, a dynamic slicing and reconstruction canopy volume (DR) algorithm is proposed to accurately estimate citrus canopy volume. The algorithm dynamically slices nearby slices based on their proportional area change and density difference, subsequently conducting AS reconstruction and volume calculation for each slice using an iterative mean point spacing as the α-value. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal. In conclusion, we recommend selecting appropriate operating environments for each algorithm based on their principles, geometric properties, volumetric values, running time, and linear relationships with one another to guide orchard mechanization and intelligent operation.
INTRODUCING THE CONCEPT OF THE "GEOMETRIC STANDARD OF LIVING PREMISES" IN CREATING A MODEL OF ITS SUSTAINABLE DEVELOPMENT
Authors:   Bydgoszcz University of Technology, Faculty of Construction and Environmental Engineering, Poland Ukrainian Academy of Architecture, Ukraine, Durmanov Volodymyr
Published: May 2024

Most contemporary housing is characterized by increasing visual quality, but that has not stopped the short period of its existence of new ones and the growing number of empty or temporarily used apartments around the world. The article aims to propose using the concept of the "geometric standard of living premises" acquiring knowledge about the living conditions of modern households and their activities and behaviors in the existing premises of the state. As evidenced by the practice of its use in pilot environmental studies, its use can be fruitful in creating local models of stable geometric development of the environment.
Implementation of Waterfall Method in Model Development to Improve Learning Quality of Computer Network Courses
Authors: Sallu Sulfikar , Harsono Yhonanda , Fajarianto Otto
Published: May 2024

This research aims to improve the learning quality of Computer Network course through the implementation of Waterfall method in the development of learning model. Waterfall method, with its focus on systematic and sequential approach in software development, is adapted to design and implement effective learning structure. This study uses qualitative research design with data collection through observation, interview, and documentation. Data analysis was conducted using content analysis method to evaluate the effectiveness of Waterfall-based learning model implementation. The results show that the implementation of Waterfall method facilitates structured planning, systematic development of learning materials, and continuous evaluation, which overall contribute to the improvement of learning quality. The developed learning model encourages students' active participation and improves the understanding of key concepts in Computer Networking. This research confirms that the Waterfall method can be effectively used outside the context of software development, particularly in improving the quality of learning in the academic field.