bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolic Insights into Neuropsychiatric Illnesses and Ketogenic Therapies: A Transcriptomic View
Authors: Sahay Smita , Pulvender Priyanka , Reddy Madhu Vishnu Sankar Reddy Rami , McCullumsmith Robert Erne , O’Donovan Sinead Marie
Published: June 2024

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances and the therapeutic effects of metabolic ketogenic therapy remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies.
Noncommutative Donoho-Stark-Elad-Bruckstein-Ricaud-Torresani Uncertainty Principle
Authors: Krishna K. Mahesh
Published: June 2024

Let $\{\tau_n\}_{n=1}^\infty$ and $\{\omega_m\}_{m=1}^\infty$ be two modular Parseval frames for a Hilbert C*-module $\mathcal{E}$. Then for every $x \in \mathcal{E}\setminus\{0\}$, we show that \begin{align}\label{UE} \|\theta_\tau x \|_0 \|\theta_\omega x \|_0 \geq \frac{1}{\sup_{n, m \in \mathbb{N}} \|\langle \tau_n, \omega_m\rangle \|^2}. \end{align} We call Inequality (\ref{UE}) as \textbf{Noncommutative Donoho-Stark-Elad-Bruckstein-Ricaud-Torr\'{e}sani Uncertainty Principle}. Inequality (\ref{UE}) is the noncommutative analogue of breakthrough Ricaud-Torr\'{e}sani uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2013]}. In particular, Inequality (\ref{UE}) extends Elad-Bruckstein uncertainty principle \textit{[IEEE Trans. Inform. Theory, 2002]} and Donoho-Stark uncertainty principle \textit{[SIAM J. Appl. Math., 1989]}.
Wrinkled TiNAgNW Nanocomposites for High-Performance Flexible Electrodes on TEMPO-Oxidized Nanocellulose
Authors: Gence Loïk , Quero Franck , Escalona Miguel , Wheatley Robert , Seifert Birger , Díaz-Droguett Donovan , Retamal María José , Wallentowitz Sascha , Volkmann Ulrich , Bhuyan Heman
Published: June 2024

In this study, we present a novel method for fabricating semi-transparent electrodes by combining silver nanowires (AgNW) with titanium nitride (TiN) layers, resulting in conductive nano-composite coatings with exceptional electro-mechanical properties. These nano-composites were deposited on cellulose nanopaper (CNP) using a plasma-enhanced pulsed laser deposition (PE-PLD) technique at low temperatures (below 200°C). Repetitive bending tests demonstrate that incorporating AgNW into TiN coatings significantly enhances the microstructure, increasing the electrodes electro-mechanical robustness by up to four orders of magnitude compared to commercial PET/ITO substrates. Furthermore, the optical and electrical conductivities can be optimized by adjusting the AgNW network density and TiN synthesis temperature. Our results also indicate that the nanocomposite electrodes exhibit improved stability in air and superior adhesion compared to bare AgNW coatings. These findings are crucial for advancing bio-compatible flexible electronics and could lead to the development of new wearable and implantable medical devices.
Region-Related Differences in Short-Term Synaptic Plasticity and Synaptotagmin-7 in Male and Female Hippocampus of a Rat Model of Fragile X Syndrome
Authors: Tsotsokou Giota , Miliou Athina , Trompoukis George , Leontiadis Leonidas J. , Papatheodoropoulos Costas
Published: June 2024

Fragile X syndrome (FXS) is an intellectual developmental disorder characterized, inter alia, by deficits in short-term processing of neural information such as sensory processing and working memory. The primary cause of FXS is the loss of fragile X messenger ribonucleoprotein (FMRP), which is profoundly involved in synaptic function and plasticity. Short-term synaptic plasticity (STSP) may play important roles in functions that are affected by FXS. Recent evidence points to a crucial involvement of presynaptic calcium sensor synaptotagmin-7 in STSP. However, how the loss of FMRP affects STSP and synaptotagmin-7 have been insufficiently studied. Further-more, males and females are affected differently by FXS, but the underlying mechanisms remain elusive. The aim of the present study is to investigate possible changes in STSP and the expres-sion of synaptotagmin-7 along the hippocampus of adult males and females in the Fmr1 knock-out (KO) rat model of FXS. We found that paired-pulse ratio and frequency facilita-tion/depression, two forms of STSP, as well as the expression of synaptotagmin-7, are normal in adult KO males, but paired-pulse ratio is increased in the ventral hippocampus of KO females. Furthermore, we found no gender-related but robust region-dependent difference in STSP. AM-PA receptors are similarly expressed in the two hippocampal segments of the two genotypes and in both genders. Also, basal excitatory synaptic transmission is higher in males compared with females. Interestingly, we found more than twofold higher levels of synaptotagmin-7, not syn-aptotagmin-1, in the dorsal compared with the ventral hippocampus in males of both genotypes and in wild type females. These results point to the susceptibility of the female ventral hippo-campus to FMRP loss. Importantly, the different levels of synaptotagmin-7 that parallel the higher score of synaptic facilitation in the dorsal vs ventral hippocampus suggest that synapto-tagmin-7 may play a pivotal role in defining the striking difference in STSP along the long axis of the hippocampus.
Poultry Meat Safety from the Point of View of European and Slovak Legislation
Authors: Nagyová Alena , Korim Peter , Nagy and Jozef , Takáčová Daniela
Published: June 2024

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.
Modern Interpretation of Banjarese Traditional House Design: A Case Study of Bubungan Tinggi’s Model
Authors: Agusniansyah Nursyarif , Sarbini Gusti Novi
Published: June 2024

This the term "Bubungan Tinggi" (High Roof) in this study refers to the banjarese traditional house. A significant artifact in South Kalimantan is called Bubungan Tinggi. With its unique typology and anatomy, this particular building has evolved into an iconic structure that symbolizes the collective memory of the Banjarese people.This research aims to showcase the versatility of the Bubungan Tinggi design in modern architecture by exploring different transformation models. By applying various techniques and approaches, the study provides insights into how this traditional Banjarese character can be adapted for new house designs.The Bubungan Tinggi's transformation process reveals characteristics in its fundamental components, which are distinguished by their geometric objects. Experimental design is the methodology employed in this study. This methodology shows that a variety of transformation models may be carried out by introducing and identifying the Bubungan Tinggi, observing objects in case studies, using transformation techniques and their application, and creating transformation models.This research produced a variety of modeling results that illustrated new interpretations of bubungan tinggi's in modern house design. The incorporation of traditional elements like the bubungan tinggi in modern house design can help preserve cultural heritage while also creating unique and innovative architectural solutions.
Adilights: A Novel Automotive Lighting System for Enhanced Safety
Authors: Badgujar Chetan
Published: June 2024

The increasing complexity of vehicular traffic and the prevalence of accidents, particularly rear-end collisions, necessitate enhancements in automotive safety technologies. Adilights, an innovative automotive lighting system, addresses this need by introducing a dynamic visual communication method between vehicles. This system features a novel design that employs variable-intensity brake lights configured in an upside-down "U" shape to indicate braking severity. The intensity and pattern of the lights change in response to the pressure applied to the brake pedal, providing clear and immediate feedback to following drivers. This design not only improves the perceptibility of braking signals, particularly in dense traffic and poor visibility conditions but also aims to reduce the frequency of rear-end collisions significantly. The paper details the development and testing of Adilights, discusses its integration with current vehicular systems, and evaluates its effectiveness through simulated scenarios. The introduction of Adilights marks a step forward in the use of adaptive lighting systems for enhanced vehicular communication and road safety.\cite{Lee2007RearEnd}
Development of a Cost-driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation
Authors: Leal Wagner Coelho , Godinho Marcelo Oliveira , Kraemer Rodrigo Antonio Sbardeloto , Cardoso Beatriz Batista , Neto Durval da Silva , Dobes Mauricio Ibarra
Published: June 2024

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.
Wavelet-Based Stochastic Finite Element Analysis of Steel Girders
Authors: Kamiński Marcin , Guminiak Michał , Knitter-Piątkowska Anna , Kawa Olga
Published: June 2024

The paper presents the problem of damage detection in steel girders. Static displacements at the selected point of the structure play the role of measured variables. Structural response signal decomposition is performed according to the Mallat pyramid algorithm, which is used to perform the discrete wavelet transform (DWT). This procedure allows us to quite well determine the location of structural damage. The geometry and the placement of any defective part of the structure may have a random character. It can be assumed that the random processes occurring in the broadly understood structure mechanics are Gaussian in nature. The first four probabilistic moments are estimated using three approaches independent: semi-analytical (SAM), perturbative (SPT), and Monte-Carlo simulations (MCS). The semi-analytical random approach seems to be the most optimal due to the necessary computation time. The incorporation of the mathematical stochastic apparatus into the classical (deterministic) analysis of the statics of the structure makes it possible to estimate the reliability measures of the analyzed girder.
Hybrid PVP/Battery/Fuel Cell Wireless Charging Station Using High-Frequency Optimized Inverter Technology for Electric Vehicles
Authors: Baccouche Gaith , Chehab Mohamed Haikel , Ben Salah Chokri , Tlija Mehdi , Rabhi Abdelhamid
Published: June 2024

The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.
Differential Regulation of Hemichannels and Gap Junction Channels by RhoA GTPase and Actin Cytoskeleton: A Comparative Analysis of Cx43 and Cx26
Authors: Jara Oscar , Maripillán Jaime , Momboisse Fanny , Cárdenas Ana María , García Isaac E. , Martínez Agustín D.
Published: June 2024

Connexins (Cxs) are transmembrane proteins that assemble into gap junction channels (GJCs) and hemichannels (HCs). Previous research supports the involvement of Rho GTPases and actin microfilaments in the trafficking of Cxs, formation of GJCs plaques, and regulation of channel activity. Nonetheless, it remains uncertain whether distinct types of Cxs HCs and GJCs respond differently to Rho GTPases or changes in actin polymerization/depolymerization dynamics. Our investigation revealed that inhibiting RhoA, a small GTPase that controls actin polymerization, or disrupting actin microfilaments with cytochalasin B (Cyto-B), resulted in reduced GJCs plaque size at appositional membranes and increased transport of HCs to non-appositional plasma membrane regions. Notably, these effects were consistent across different Cx types, since Cx26 and Cx43 exhibited similar responses despite having distinct trafficking routes to the plasma membrane. Functional assessments showed that RhoA inhibition and actin depolymerization decreased the activity of Cx43 GJCs while significantly increasing HCs activity. However, the functional status of GJCs and HCs composed of Cx26 remained unaffected. This study supports the hypothesis that RhoA, through its control of the actin cytoskeleton, facilitates the transport of HCs to appositional cell membranes for GJCs formation while simultaneously limiting the positioning of free HCs at non-appositional cell membranes, independently of Cx type. This dynamic regulation promotes intercellular communication and reduces non-selective plasma membrane permeability through a Cx-type dependent mechanism, whereby Cx43 HCs and GJCs are affected, but Cx26 channels are not.
Rheology of Suspensions Thickened by Cellulose Nanocrystals
Authors: Pal Rajinder , Pattath Karthika
Published: June 2024

The steady rheological behavior of suspensions of solid particles thickened by cellulose nanocrystals is investigated. Two different types and sizes of particles are used in the preparation of suspensions, namely TG hollow spheres of Sauter mean diameter of 69 µm and Solospheres S-32 of Sauter mean diameter of 14 µm. The nanocrystal concentration varies from 0 to 3.5 wt% and the particle concentration varies from 0 to 57.2 vol%. The influence of salt (NaCl) concentration and pH on the rheology of suspensions is also investigated. The suspensions generally exhibit shear-thinning behavior. The degree of shear-thinning is stronger in suspensions of smaller size particles. The experimental viscosity data are adequately described by a power-law model. The variations of power-law parameters (consistency index and flow behavior index) under different conditions are determined and discussed in detail.
Calculation of Low-Lying Electronic Excitations of Magnesium Monofluoride: How Well Do Coupled-Cluster Methods Work?
Authors: Horbatsch Marko
Published: June 2024

Magnesium monofluoride is a polar molecule amenable to laser cooling which has caused renewed interest in its spectroscopy.
In this work we consider the case of three low-lying electronic excitations, namely $\rm X \, {}^{2}\Sigma \to A \, {}^{2}\Pi, \ \to B \, {}^{2}\Sigma, \ \to C \,{}^{2}\Sigma$,
using well developed quantum chemistry approaches, i.e., without reference to the spin-orbit splitting of the $\rm A \, {}^{2}\Pi$ states.
Accurate experimental data for these transitions have been available for over 50 years. Here we explore the linear response method at the level of CC2 theory, as well as
equation of motion methods at the level of CCSD and CC3, using two families of basis sets.
Excellent agreement is obtained for the first three transitions when using the correlation-consistent basis sets and extrapolation to the complete basis limit within EOM-CC3 (at a relative precision of $10^{-4}$), and qualitative agreement for the other two methods.
The purpose of this paper is to serve as a guide in how to approach the accurate calculation of excitations in polar diatomic molecules.
Reduction of Xenogeneic Epitopes on Porcine Endothelial Cells by Periodate Oxidation
Authors: Thom Jonas , Roters Nathalie , Schuemann Slavica , Andrée Birgit , Buettner Falk F. R. , Hilfiker Andres , Goecke Tobias , Ramm Robert
Published: June 2024

Background: Patterns of humoral immune responses represent a major hurdle in terms of pig-to-human xenotransplantation approaches. The best-known xenogeneic glycan antigens pre-sent in pigs are the αGal (Galili-antigen) and the non-human sialic acid Neu5Gc. As there are further differences between porcine and human cellular surface glycosylation, a much broader range of glycan epitopes with xeno-reactive relevance can be anticipated. Therefore, we set-out to chemically modify porcine cellular surface glycans in a global approach by applying sodium peri-odate (NaIO4) oxidation. Methods: Porcine endothelial cells were exposed to oxidation with 1 to 5 mM NaIO4 for different time periods at 37°C or 4°C and under static or dynamic conditions. The impact on cellular sur-vival was determined applying live/dead-assays. Oxidation of αGal-epitopes was assessed by flu-orescence microscopy-based quantification of isolectin-B4 (IL-B4) staining. Overall immunogenic-ity of porcine cells was determined by human serum antibody binding. Results: Treatment of porcine endothelial cells and tissues with NaIO4 led to a reduced binding of the αGal-specific IL-B4 and/or human serum antibodies. NaIO4 revealed to be cytotoxic when performed at elevated temperature and for prolonged time. However, by applying 2 mM NaIO4 for 60 min at 4°C a high extent of cellular viability and a relevant reduction of detectable αGal epitope were observed. No differences were detected irrespectively on whether the cells were oxi-dized under static or flow conditions. Conclusion: Glycan epitopes on living cells can be oxidized with NaIO4 while maintaining their viability. Accordingly, this strategy holds promise to prevent immune reactions mediated by pre-formed anti-glycan antibodies.
Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework
Authors: Alwakeel Ahmed M. , Alnaim Abdulrahman
Published: June 2024

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages blockchain’s inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine learning models like SVM, KNN, and Random Forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard’s effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-Score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.
Developments in the Use of Indocyanine Green (ICG) Fluorescence in Colorectal Surgery
Authors: Khalafi Shayan , Botero Fonnegra Cristina , Reyes Ana , Hui Vanessa W
Published: June 2024

Indocyanine Green (ICG) has significantly advanced minimally invasive surgery. It is widely recognized for its ability to visualize blood vessel patency in real-time across various surgical specialties. While its primary use in colorectal surgery is to evaluate anastomoses for leaks, nu-merous other applications have been documented in the literature.

In this review, we aim to explore both established and emerging applications of ICG fluorescence in colorectal surgery, with the goal of improving patient outcomes. This includes preoperative tumor marking and the detection of metastatic disease. Some applications, such as lymphatic mapping, require further research to determine their impact on clinical practices. Conversely, oth-ers, like the intraoperative localizations of ureters, necessitate additional procedures and are not yet widely accepted by the surgical community. However, the development of alternative com-pounds could offer better solutions. Future research should focus on areas like quantitative ICG and protocol standardization in prospective multicenter studies.
Dual-input Dual-stream Network with Two-fold Margin Loss for Improved Brain Tumor Detection on MRI Images
Authors: Tole Kevin , Kaloi Mumtaz Ali , Ali Riaz , Hajano Nazim Hussain , Chan Abdul Sattar Sattar , Babbar Irfan Ali , Ali Asif
Published: June 2024

Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.
Deciphering the Plastome and Molecular Identity of Six Medicinal Amomum Species
Authors: Zhao Ying , Kipkoech Amos , Li Zhi-Peng , Yang Jun-Bo
Published: June 2024

The genus Amomum encompasses six medicinal species that are extensively utilized and have a significant historical background. Due to their morphological similarities, however, the presence of counterfeit and substandard products remains a challenge. Accurate plant identification is therefore essential to address these issues. This study utilized 11 newly sequenced samples along with extensive data from NCBI to perform molecular identification of these six species. The plastomes of Amomum displayed a typical quadripartite structure with conserved gene content, yet showed independent variations in the SC/IR boundary shifts at both inter- and intra-specific levels. Our approach incorporated ITS, ITS1, ITS2, complete plastomes, matK, rbcL, and psbA-trnH sequences for molecular identification, which effectively differentiated the six medicinal species within the genus Amomum, as confirmed by distance-based and phylogenetic tree analyses. Among these, the ITS, ITS1, and complete plastomes sequences demonstrated the highest identification success rate (3/6), followed by ITS2, matK, and psbA-trnH (1/6). In contrast, rbcL failed to identify any species. This research successfully established a reliable molecular identification method for Amomum plants, to protect wild plant resources and promote the sustainable use of medicinal plants and restrict the exploitation of these resources.
Horizontal-Vertical Interpretation of Mangrove Using Data Unmanned Aerial Vehicles in Tongke-tongke Indonesia
Authors: Salle Supriady , Soma Andang Suryana , Paembonan Samuel Arung , Nursaputra Munajat , Arif Astuti , Rahmat Syaeful
Published: June 2024

This research explores the application of Support Vector Machine (SVM) and Digital Surface Models (DSM) derived from Unmanned Aerial Vehicle (UAV) data for a vertical-horizontal classification of mangrove ecosystems in Tongke-tongke and Samataring Village. The study focuses on integrating high-resolution DSM subtracted from Digital Terrain Models (DTM) to classify mangrove vegetation into sixteen distinct vertical-horizontal classes based on vegetation type and elevation. The analysis revealed that older mangroves, particularly those within the 25-35 meters elevation range, dominate the landscape, accounting for 43.40% of the total classified area. This dominance suggests a mature and ecologically stable mangrove ecosystem, which is crucial for biodiversity conservation and carbon storage. The study demonstrates the effectiveness of SVM and UAV technologies in providing detailed and accurate ecological assessments of mangrove forests. The classification results contribute valuable insights into the structural diversity and health of mangrove ecosystems, facilitating targeted conservation and management strategies. Additionally, the research highlights the utility of advanced remote sensing and machine learning techniques in enhancing the precision of environmental monitoring and the assessment of carbon sequestration capabilities within mangrove ecosystems. The findings underscore the potential of UAV-based remote sensing for ecological studies, offering a replicable method for similar assessments in other regions, thereby supporting global environmental management and climate action initiatives.
Comprehensive Analysis of Various Parkinson-Like Events and Induced Drugs
Authors: Kikegawa Mami , Sone Hideko , Uesawa Yoshihiro
Published: June 2024

Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Phar-maceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with associated drug trends and characteristics. Our findings indicate that similar to　Parkinson’s disease, age-related differences affect the onset of these events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, side effects can vary depending on the site of action. These insights underline the importance of swift identification of the drugs suspected of causing these events and implementation of measures to reduce their side effects.
The Roles of Alpha, Beta, and Functional Diversity Indices in the Ecological Connectivity between Two Sub-Antarctic Macrobenthic Assemblages
Authors: Nykol Jara , Americo Montiel , Benjamin Cáceres
Published: June 2024

The study of ecological connectivity is a global priority due to the important role it plays in the conservation of diversity. However, few studies in this context have focused on marine benthic ecosystems. To address this issue, the present work determines the ecological connectivity between two sub-Antarctic macrobenthic assemblages through assessment of the α-, β-, and functional diversity indices. Samples were collected using a van Veen grab at stations located in Bahia Inútil and Seno Almirantazgo. The ecological analysis was based on a total of 113 invertebrate taxa. The mean abundance values were lower in Bahia Inútil (888.9 ± 26.8 ind m-2) than in Seno Almirantazgo (1358.6 ± 43.4 ind m-2). While the mean α-diversity values showed significant differences between assemblages, β- and functional diversity indices presented no significant differences. These results indicate that, despite the distance (56 km) separating the two basins from each other, there is a high degree of connectivity at the functional level between the assemblages, due to the high number of shared species and their functional traits. The species most responsible for this observation were the polychaetes Capitela capitata and Aricidia (Acmira) finitima, as well as the bivalves Nucula pisum and Yoldiella sp. 1. In terms of functional biodiversity, species characterized as omnivorous and with lecithotrophic larval development were mostly responsible for connectivity between assemblages. These results suggest the importance of including β- and functional diversity indices as criteria in the future planning of marine protected areas for the maintenance of marine ecosystem integrity.
Coastal Adaptation Pathways and Tipping Points for Typical Mediterranean Beaches Under Future Scenarios
Authors: Jr. Agustín Sánchez-Arcilla , Gracia Vicente , Sánchez-Arcilla Agustín
Published: June 2024

Coastal zones experience increasing climatic and human pressures, which lead to growing risks and tipping points (TPs) under future scenarios and natural resource scarcity. To avoid crossing TPs with irreversible coastal losses, this paper proposes the development of adaptation pathways based on advanced coastal oceanography and engineering knowledge that enables an objective assessment of evolving coastal risks. These pathways feature sequential interventions, combining conventional engineering works with nature-based solutions (NbS), steered by simulations and observations as a function of available coastal resources (space and sediment) and risks for infrastructure and socioeconomic assets. Such an adaptation has been developed for urban and peri-urban Mediterranean beaches, considering conventional coastal engineering together with NbS. Both types of interventions are assessed in terms of key physical variables, which serve to evaluate performance and estimate TPs. This analysis supports the new coastal protection and management plan promoted by the local government and the coastal adaptation plan of the central government. The approach and results illustrate the potential of adaptation pathways for beach sustainability, enhancing the compatibility between short/long-term coastal protection objectives under present/future climate and management scenarios. The adaptation pathways development underpins an increasing stakeholder cooperation to achieve shared decisions for coastal sustainability.
Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities
Authors: Alwakeel Ahmed M. , Alnaim Abdulrahman
Published: June 2024

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in Smart Cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G Smart Cities IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low Round-Trip Time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.
Long-Term Validation of Aeolus Wind Products in the Brazilian Amazon
Authors: Yoshida Alexandre Calzavara , Venturini Patricia Cristina , Lopes Fábio Juliano da Silva , Landulfo Eduardo
Published: June 2024

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler Wind Lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth in a polar sun-synchronous orbit at about 320 km altitude with a repeat cycle of 7 days, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. Exceeding scientific expectations, Aeolus, initially designed for a 3-year lifetime, provided global coverage of wind profiles for almost five years. In this long-term validation study, we assessed the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products by collecting radiosonde data from three stations in the Brazilian Amazon: Cruzeiro do Sul (7.62° S, 72.67° W), Porto Velho (8.76° S, 63.91° W), and Rio Branco (10.00° S, 67.80° W). Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data, launched daily at 12:00 UTC from October 2018 to March 2023. Considering all data collected during the mission period, Pearson’s coefficient (R) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy, revealing a strong correlation between Aeolus and radiosonde winds. No significant biases were detected, with values of -0.14 m/s for Rayleigh-clear and -0.40 m/s for Mie-cloud, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was -0.95 m/s, exceeding the mission requirements.
A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems
Authors: Kose Habib Taha , Nunez-Yanez Jose , Piechocki Robert , Pope James
Published: June 2024

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.
IMAGE CLASSIFIER FOR FAST SEARCH IN LARGE DATABASES
Authors: Filatov Valerii , Filatova Anna , Povoroznyuk Anatolii , Omarov Shakhin
Published: June 2024

Relevance. The avalanche-like growth in the amount of information on the Internet necessitates the development of effective methods for quickly processing such information in information systems. Clustering of news information is carried out by taking into account both the morphological analysis of texts and graphic content. Thus, an urgent task is the clustering of images accompanying textual information on various web resources, including news portals. The subject of study is an image classifier that exhibits low sensitivity to increased information in databases. The purpose of the article is to enhance the efficiency of searching for identical images in databases experiencing a daily influx of 10-12 thousand images, by developing an image classifier. Methods used: mathematical modeling, content-based image retrieval, two-dimensional discrete cosine transform, image processing methods, decision-making methods. The following results were obtained. An image classifier has been developed with low sensitivity to increased database information. The properties of the developed classifier have been analyzed. The experiments demonstrated that clustering information based on images using the developed classifier proved to be sufficiently fast and cost-effective in terms of information volumes and computational power requirements.
MODELING THE DISTRIBUTION OF EMERGENCY RELEASE PRODUCTS AT A NUCLEAR POWER PLANT UNIT
Authors: Biliaieva Viktoriia , Levchenko Larysa , Myshchenko Iryna , Tykhenko Oksana , Kozachyna Vitalii
Published: June 2024

Despite the fact that much attention is paid to the safe operation of nuclear power plants, there is a possibility of an accident with the release of radionuclides. This is especially true in Ukraine, where there is a threat of the damage to nuclear reactors as a result of military operations. It is impossible to research the distribution of products emergency releases radioactive substances in laboratory conditions. Therefore, the only tool for the development predicting of an accident is the modeling the spread of a radionuclides cloud. The purpose of the research is a modeling the distribution of emergency release products in a nuclear power plant unit, suitable for the operative assessment of a development an accident. Results of the research: The mathematical model of the distribution emission products of a nuclear power plant has been developed, which takes into account the value of the initial activity of emission products, the rate of the settling radioactive particles, the wind speed components, the intensity changes radionuclide emission over time. The technique for solving the boundary value problem of modeling in conditions of a complex shape of the computational domain, taking into account the presence of obstacles to the spread of emission products has been developed. The use of the velocity potential equation in evolutionary form allows us to speed up the calculation process. The chosen splitting scheme of an alternating-triangular method allows to find the speed potential according to the explicit form at each splitting step. This allowed software implementation of the CFD model. The visualized models of the emission cloud distribution allow to determine the radiation situation in any place of the emission product distribution zone. The developed model makes it possible to quickly predict the development of an accident in space and time, which makes it possible to take measures to protect people from exposure in the shortest possible time. Conclusions: The obtained emission cloud propagation models and their visualization make it possible to determine the state of environmental pollution under various initial conditions during the development of the accident.
RESEARCH AND ANALYSIS OF EFFICIENCY INDICATORS OF CRITICAL INFRASTRUCTURES IN THE COMMUNICATION SYSTEM
Authors: Ibrahimov Bayram , Hasanov Arif , Hashimov Elshan
Published: June 2024

The efficiency indicators of the functioning critical information infrastructures in the communication system are analyzed based on the architectural concept of future networks. The object of the study is hardware and software complexes critical information infrastructures for special purposes. Critical information infrastructure represents information and telecommunication communication systems, the maintenance, reliability and security which are necessary for the safe operation special-purpose enterprises. In order to avoid the occurrence of various security and reliability incidents, the studied critical infrastructures communication systems require constant analysis and updating operating rules. The subject of the research is a method for calculating quality indicators of the functioning of critical information infrastructures in communication systems. In this work, using the example of a communication system based on modern technologies, the sequence of actions for analyzing threats to the security of a critical information infrastructure facility is considered. The purpose of the study is to develop a new approach for creating methods for calculating indicators of efficiency, reliability and information security systems. Based on the analysis of the work, a method for calculating efficiency indicators critical information infrastructures of communication systems is proposed and important analytical expressions for further research are obtained. As a result of the study, the main conclusions of the study were obtained, which can be implemented and used in critical infrastructures of communication systems to calculate the quality of functioning public computer and telecommunication systems.
DEEPFAKE DETECTION USING TRANSFER LEARNING-BASED XCEPTION MODEL
Authors: Rajakumareswaran Velusamy , Raguvaran Surendran , Chandrasekar Venkatachalam , Rajkumar Sugavanam , Arun Vijayakumar
Published: June 2024

Justification of the purpose of the research. In recent times, several approaches for face manipulation in videos have been extensively applied and availed to the public which makes editing faces in video easy for everyone effortlessly with realistic efforts.  While beneficial in various domains, these methods could significantly harm society if employed to spread misinformation. So, it is also vital to properly detect whether a face has been distorted in a video series. To detect this deepfake, convolutional neural networks can be used in past works. However, it needs a greater number of parameters and more computations. So, to overcome these limitations and to accurately detect deepfakes in videos, a transfer learning-based model named the Improved Xception model is suggested. Obtained results. This model is trained using extracted facial landmark features with robust training. Moreover, the improved Xception model's detection accuracy is evaluated alongside ResNet and Inception, considering model loss, accuracy, ROC, training time, and the Precision-Recall curve. The outcomes confirm the success of the proposed model, which employs transfer learning techniques to identify fraudulent videos. Furthermore, the method demonstrates a noteworthy 5% increase in efficiency compared to current systems.