bluuebunny/crossref_metadata_2025 huggingface dataset:
The Use of Diss Fibers to Enhance Lime Plaster Durability
Authors: Megna Bartolomeo , Fiore Vincenzo , Badagliacco Dionisio , Sanfilippo Carmelo , Valenza Antonino
Published: July 2024

The use of natural fibres is investigated in recent years in cement, lime or geopolymer mortars to increase their toughness, resistance, tensile strength and durability. These fibres, derived from sources such as jute, hemp, sisal, and flax, are added to the mortar mix to enhance its mechanical properties and reduce cracking. The choice of the natural source strongly depends on local availability of the plant, its use and harvesting. In this paper the fibres of Diss (Ampelodesmos Mauritanicus, Poir) were used as reinforcement for mortars prepared by using waste materials, such as waste glass powder and air hardening lime obtained by the kilning of marble slurry. The samples underwent artificial and natural ageing and were characterized by measuring apparent density, water sorption coefficient, compression and bending stress, indicating a positive role of fibres addiction in increasing mortars durability.
Anti-collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm
Authors: Zhang Chaoxia , Chen Zhihao , Li Xingjiao , Zhao Ting
Published: July 2024

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.
Evaluating Order Allocation Sustainability Using a Novel Z Number ITARA-CoCoSo Approach
Authors: Lin Kuan-Yu , Yeng Louis Cheng-Lu , Lin Yi-Kuei
Published: July 2024

The United Nations' sustainable development goals have underscored the importance of enhancing supply chain sustainability while appropriately distributing orders. This study proposes a novel framework involving Z-number, game theory, indifference threshold-based attribute ratio analysis (ITARA), and combined compromise solution method (CoCoSo) to evaluate the sustainability of suppliers and order allocations. To better reflect the decision makers’ current choices for the sustainability of assessed suppliers and order allocations; enhance the comprehensiveness of decision-making, the importance parameter of the supplier is obtained through game theory objectively for transforming supplier performance into order allocation performance. The Z-numbers are involved in ITARA (so-called ZITARA) and CoCoSo (so-called ZCoCoSo) to overcome the issue of information uncertainty in the process of expert evaluation. ZITARA and ZCoCoSo are applied to assess the objective weight of criteria and the ranking of assessed order allocations, respectively. Afterwards, a case study of China’s company is illustrated the utility of the proposed framework and inform their decision-making process regarding the orders to be assigned to the assessed suppliers.
Effect of Ceria doping on the Mechanical Properties and Phase Stability of Partially Samaria-Stabilized Zirconia Crystals
Authors: Borik Mikhail , Chislov Artem , Kulebyakin Alexej , Lomonova Elena , Milovich Filipp , Myzina Valentina , Pankratov Vladimir , Poselennov Alexandr , Ryabochkina Polina , Tabachkova Nataliya , Sidorova Nataliya , Zakharov Dmitriy , Kiselev Dmitriy
Published: July 2024

The effect of ceria doping of (ZrO2)1-x(Sm2O3)x crystals on their phase composition, microhardness and fracture toughness has been studied. The (ZrO2)0.995-х(Sm2O3)х(СеО2)0.005 crystals (where x = 0.032, 0.037 and 0.04) have been grown using directional melt crystallization in cold skull. The phase composition and structure of the crystals have been studied using X-ray diffraction, TEM and Raman spectroscopy. The mechanical properties such as microhardness and fracture toughness have been studied using Vickers indentation. It has been shown that the (ZrO2)0.995-х(Sm2O3)х(СеО2)0.005 solid solution crystals contain both Се4+ and Се3+ ions. Phase analysis data suggest that СеО2 doping increases the tetragonality degree of the transformable t phase and reduces the tetragonality degree of the non-transformable t' phase as compared to the (ZrO2)1-x(Sm2O3)x crystals. As a result, the t→m phase transition triggered by the indentation-induced stress in the СеО2 doped crystals is more intense and covers greater regions. СеО2 doping of the solid solutions increases the fracture toughness of all the crystals studied whereas the microhardness of the crystals changes but slightly. СеО2 doping of the (ZrO2)1-х(Sm2O3)х solid solutions in the experimental concentration range does not improve the high-temperature phase stability of the crystals and does not prevent high-temperature degradation of their fracture toughness.
Adaptive Load Balancing Approach to Mitigate Network Congestion in VANETS
Authors: Haider Syed Ehsan , Khan Muhammad Faizan , Saeed Yousaf
Published: July 2024

Load balancing to alleviate network congestion remains a critical challenge in Vehicular Ad-Hoc Networks (VANETs). During route and response scheduling, RSUs risk being overloaded beyond their calculated capacity. Despite recent advancements like RSU-based load transfer, NP-Hard hierarchical geography routing, RSU-based MAC schemes, simplified clustering, and network activity control, a significant gap persists in employing a load-balancing server for effective traffic management. We propose a server-based network congestion handling mechanism (SBNC) in VANETs to bridge this gap. Our approach clusters RSUs within specified ranges and incorporates dedicated load balancing and network scheduler RSUs to manage route selection and request-response scheduling, thereby balancing RSU loads. We introduce three key algorithms: optimal placement of dedicated RSUs, a scheduling policy for packets/data/requests/responses, and a congestion control algorithm for load balancing. Using the VanetMobiSim library of Network Simulator-2 (NS-2), we evaluate our approach based on residual energy consumption, end-to-end delay, packet delivery ratio (PDR), and control packet overhead. Results indicate substantial improvements in load balancing through our proposed server-based approach.
Pseudo Core-Shell Permalloy (Supermalloy)@ZnFe2O4 Powders and Spark Plasma Sintered Compacts Based on Mechanically Alloyed Powders
Authors: Marinca Traian Florin , Cotojman Loredana , Popa Florin , Neamțu Bogdan Viorel , Prică Călin Virgiliu , Chicinaș Ionel
Published: July 2024

Soft magnetic composites cores were produced by Spark Plasma Sintering (SPS) from Ni3Fe@ZnFe2O4 and NiFeMo@ZnFe2O4 pseudo-core-shell powders. In the Fe-Ni alloys@ZnFe2O4 pseudo-core-shell composite powders, the core is a large nanocrystalline Permalloy or Supermalloy particle obtained by mechanical alloying, and the shell is a pseudo continuously layer of Zn ferrite particles. The pseudo-core-shell powders have been compacted by SPS at temperatures between 500-700 ºC, with a holding time of 0 minutes. Several techniques were used for the characterisation of the powders and sintered compacts: X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, magnetic hysteresis measurements (DC and AC) and electrical resistivity. The electrical resistivity is stabilised at values of about 7·10-3 Ω·m for sintering temperatures between 600-700 °C and this value is 3 orders of magnitude higher than the electrical resistivity of sintered Fe compacts. The best relative initial permeability was obtained for the Supermalloy/ZnFe2O4 composite compacts sintered at 600 ºC, which decreases linearly for the entire frequency range studied, from around 95 to 50. Up a frequency of 2,000 Hz the power losses are lower than 1.5 W/kg. At the frequency of 10 kHz, the power losses are larger, but they remain at a low level. In the case of Supermalloy/ZnFe2O4 composite compact SPS-ed at 700 °C, the specific power losses are even lower than 5 W/kg. The power losses decomposition proved that intra-particles losses give the main part of the losses.
Tangential and Anisotropic Bases in Quadratic Spaces
Authors: Leones Alexander , Hurtado Pedro , Moreno John , Pimienta Adolfo
Published: July 2024

This paper presents an explicit method for constructing tangential and anisotropic bases in quadratic spaces. We will also provide three theorems that allow us to explicitly calculate vectors that are orthogonal or tangent to given vectors or that provide necessary and sufficient conditions for the existence of such vectors. A classical theorem on quadratic space states that every finite-dimensional quadratic space has an orthogonal basis. A result that seems to be unanswered is under what conditions a finite-dimensional quadratic space has a basis consisting of isotropic vectors. A second problem is whether or not every finite-dimensional quadratic space has a basis consisting of mutually tangent vectors, where two vectors v,w are tangent if (u·v)2=(u·u)(v·v). In this paper, we attack these two problems.
Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
Authors: Mamyrbayev Orken , Pavlov Sergii , Saldan Yosip , Momynzhanova Kymbat , Zhumagulova Sholpan
Published: July 2024

For the first time, mathematical models were obtained for evaluating biomedical images using fuzzy set methods on the basis of expert knowledge bases, which made it possible to carry out complex qualitative diagnostics and increase the reliability and efficiency of diagnosis, to form a methodology for analyzing biomedical images based on the fuzzy set apparatus, which allowed to more fully assess the level of the disease for glaucoma. Aspects of practical implementation of the optical-electronic system of biomedical information processing are considered. An algorithm and an optical-electronic system of biomedical image analysis are proposed, which are used to increase the informativeness and reliability of diagnosing eye pathologies, in particular, glaucoma.
Hydrogen Bond Strengthens Acceptor Group: The Curious Case of The C-H...O=C Bond
Authors: Basu Kingshuk , Brielle Esther S. , Arkin Isaiah T.
Published: July 2024

An H-bond involves the sharing of a hydrogen atom between an electronegative atom to which it is covalently bound (the donor) and another electronegative atom serving as an electron acceptor. Such bonds represent a critically important geometrical force in biological macromolecules and, as such, have been characterized extensively. H-bond formation invariably leads to a weakening within the acceptor moiety due to the pulling exerted by the donor hydrogen. This phenomenon can be compared to a spring connecting two masses; pulling one mass stretches the spring, similarly affecting the bond between the two masses. Herein, we describe the opposite phenomenon when investigating the energetics of the C-H...O=C bond. This bond underpins the most prevalent protein transmembrane dimerization motif (GxxxG) in which a glycine C-H on one helix forms a hydrogen bond with a carbonyl in a nearby helix. We use isotope-edited FT-IR spectroscopy and corroborating computational approaches to demonstrate a surprising strengthening of the acceptor C=O bond upon binding with the glycine C-H. We show that electronic factors associated with the C-H bond strengthen the C=O oscillator by increasing the s-character of the -bond, lowering the hyperconjugative disruption of the -bond. In addition, a reduction of the acceptor C=O bond’s polarity is observed upon the formation of the C-H...O=C bond. Our findings challenge the conventional understanding of H-bond dynamics and provide new insights into the structural stability of inter-helical protein interactions.
Supplier Relationship Management as a Driver of Sustainable E-Commerce Practices
Authors: Grant Oliver
Published: July 2024

This qualitative research explores Supplier Relationship Management (SRM) as a pivotal driver of sustainable practices in the e-commerce sector. Through in-depth interviews with industry experts, practitioners, and scholars, the study investigates how SRM strategies integrate sustainability criteria into procurement processes, mitigate environmental and ethical risks, and enhance operational efficiencies. Key findings highlight the strategic importance of collaborative relationships with suppliers, technological innovations such as blockchain and data analytics in promoting transparency and accountability, and the influence of consumer preferences for sustainable products on market dynamics. Despite challenges such as regulatory complexities and global supply chain dynamics, businesses are increasingly adopting sustainable SRM practices to strengthen brand reputation, achieve cost savings, and align with evolving sustainability standards. The study underscores the interconnected nature of sustainability within e-commerce operations and calls for continued research to advance technological solutions, refine regulatory frameworks, and foster industry-wide collaboration. By embracing sustainability as a strategic imperative, businesses can navigate complexities, drive innovation, and contribute to a more resilient and sustainable future for the e-commerce industry.
The Role of Environment, Social and Governance in Shaping Firm Performance: Indonesian Evidence
Authors: Asih Kiki Nindya , Achsani Noer Azam , Novianti Tanti , Manurung Adler Haymans
Published: July 2024

The purpose of this study is to explore the effect of Environment, Social, and Governance (ESG) scores on Return on Assets (ROA), Risk-Adjusted Return on Capital (RAROC), and Tobin's Q on firm performance. This study used 37 non-financial companies listed on the IDX between 2018 and 2022 in Indonesia companies. Linear and nonlinear panel data regression methods were used on the ESG score and its pillars, resulting in eight different models. The study found a U-shaped impact of ESG scores and social and environmental pillars on ROA. There is a positive linear relationship between ESG scores and the environmental and governance pillars with RAROC, while the social pillar shows an inverted U-shaped effect. However, ESG and its environmental and social pillars have a negative linear relationship with Tobin's Q. The findings suggest the need to manage ESG initiatives strategically, aligning them with financial objectives and awareness of how ESG factors relate to profitability.
Using Citizen Science and Field Survey to Document the Introduction, Establishment, and Rapid Spread of the Bare-eyed Pigeon, Patagioenas corensis, on the Island of Saint-Martin, West Indies
Authors: Cambrone Christopher , Levesque Anthony , Cézilly Frank
Published: July 2024

Early reporting of the introduction and establishment of exotic species is of paramount importance for successful management. Here we report the introduction and rapid spread of the Bare-eyed Pigeon, Patagioenas corensis, on the binational island of Saint-Martin, West Indies. This range-restricted species naturally occurs in the arid coastal areas of Columbia and Venezuela and nearby islands. Its introduction on Saint-Martin represents an expansion of about 1000 km beyond its established native range. Using observations recorded in e-bird and results from a recent field survey, we show that since its introduction, most probably between late 2012 and early 2013, the species is expanding fast in Saint-Martin and has recently broadened its habitat to include anthropized, built areas. The expansion of Bare-eyed Pigeon on Saint-Martin and the neighbouring Leeward Islands, possibly facilitated by climate change in the future, could be a threat to both native columbid species and other bird species, through competition for resources. We therefore recommend to local authorities and stakeholders to rapidly eradicate the species or at least prevent its further spread on Saint-Martin, possibly though listing it as a game species, while it is still possible to do so.
Targeting Androgen, Thyroid Hormone, and Vitamin A and D Receptors to Treat Prostate Cancer
Authors: Hantusch Brigitte , Kenner Lukas , Stanulović Vesna S , Hoogenkamp Maarten , Brown Geoffrey
Published: July 2024

The nuclear hormone family of receptors regulates gene expression. The androgen receptor (AR) shuttles upon ligand binding and homodimerization from the cytosol into the nucleus to activate gene expression. Thyroid hormone receptors(TR), retinoic acid receptors (RAR), and the vitamin D receptor (VDR) are present in the nucleus bound to chromatin as a heterodimer with the retinoid X receptors (RXR) and repress gene expression. Ligand binding leads to transcription activation. The hormonal ligands for these receptors play crucial roles to ensure the proper conduct of very many tissues and exert effects on prostate cancer (PCa) cells. Androgens support PCa proliferation and deprivation alone or with chemotherapy is the standard therapy for PCa. RAR activation and 3,5,3'-triiodo-L-thyronine (T3) stimulation of TR support the growth of PCa cells. Ligand stimulation of VDR drives growth arrest, differentiation, and apoptosis of PCa cells. Often these receptors are explored as separate avenues to find treatments for PCa and other cancers. However, there is accumulating evidence to support receptor interactions and crosstalk of regulatory events whereby a better understanding might lead to new combinatorial treatments.
A Comprehensive Review of Semantic Segmentation and Instance Segmentation in Forestry: Advances, Challenges, and Applications
Authors: Wołk Krzysztof , Tatara Marek
Published: July 2024

This article presents a succinct overview of the progress, obstacles, and uses of semantic segmentation and instance segmentation within the forestry domain. The objective of this review is to conduct a critical analysis of the current literature pertaining to segmentation techniques and provide a methodical summary of their impact on forestry-related activities, including but not limited to tree species classification, forest inventory, and ecological monitoring such as retrieval of dominant tree species. Through the process of synthesizing pivotal discoveries from multiple studies, this comprehensive analysis provides valuable perspectives on the present status of research and highlights prospective areas for further exploration. The primary topics addressed encompass the approach employed for executing the examination, the fundamental discoveries associated with semantic segmentation and instance segmentation in the domain of forestry, and the ramifications of these discoveries for the discipline. The results indicate that the utilization of semantic segmentation methods has been efficacious in the field of forestry for the precise identification of tree species. Such methods also aid in tracking of deforested regions over the course of time by separating other land-use classes from forested regions. Additionally, the employment of instance segmentation techniques exhibits potential in the demarcation of individual trees. Instance segmentation offers promising results due to deep learning models based on forest point clouds. However, several challenges persist in the successful implementation of semantic segmentation methods such as the presence of occlusions, overlapping branches, and intricate structures hampers the accurate segmentation of trees. Additionally, instance segmentation approaches that utilize models are mostly trained by using laser scanning data based on forest types which are typically trained on specific laser scanning data and forest types that create limitations in generalization from high to low resolution point clouds. Due to this reason, the existing approaches often struggle with handling these complex structures, leading to the need for manual methods for extracting measurements from forest point clouds. The review culminates by underscoring the necessity for additional research to tackle current obstacles and augment the precision and relevance of segmentation methodologies in the field of forestry. In general, the present article provides a significant reference for scholars and professionals who are interested in utilizing segmentation techniques in the field of forestry.
Grading Evaluation of Marbling in Wagyu Beef Using Fractal Analysis
Authors: Suzuki Yuuya , Yue Bao
Published: July 2024

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Cur-rently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader's mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments.
Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars
Authors: Monti Lorenzo , Muraveva Tatiana , Clementini Gisella , Garofalo Alessia
Published: July 2024

Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep learning techniques, particularly advanced neural network architectures, in predicting photometric metallicity from time-series data. Moreover, our deep learning models demonstrated notable predictive performance over experimentation and analysis. Through GRU-based models we achieved a low mean absolute error of 0.056 and a high $R^2$ regression performance of 0.9401 measured by cross-validation, showcasing the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.
Quantum Effects on Cosmic Scales as an Alternative to Dark Matter and Dark Energy
Authors: Chen Da-Ming , Wang Lin
Published: July 2024

The spin-torsion theory is a gauge theory approach to gravity that expands upon Einstein's general relativity (GR) by incorporating the spin of microparticles. In this study, we further develop the spin-torsion theory to examine spherically symmetric and static gravitational systems that involve free-falling macroscopic particles. We posit that the quantum spin of macroscopic matter becomes noteworthy on cosmic scales. We further assume that the Dirac spinor and Dirac equation adequately capture all essential physical characteristics of the particles and their associated processes. A crucial aspect of our approach involves substituting the constant mass in the Dirac equation with a scale function, allowing us to establish a connection between quantum effects and the scale of gravitational systems. This mechanism ensures that the quantum effect of macroscopic matter is scale-dependent and diminishes locally, a phenomenon not observed in microparticles. For any given matter density distribution, our theory predicts an additional quantum term, the quantum potential energy (QPE), within the mass expression. QPE induces time dilation, distance contraction, and thus mimics a gravitational well. When applied to cosmology, our theory yields a static cosmological model. The QPE serves as a counterpart to the cosmological constant introduced by Einstein to balance gravity in his static cosmological model. The QPE also offers a plausible explanation for the origin of the Hubble redshift (traditionally attributed to the universe's expansion). The predicted luminosity distance-redshift relation aligns remarkably well with SNe Ia data from the cosmological sample of SNe Ia. In the context of galaxies, QPE functions as the equivalent of dark matter. The predicted circular velocities align well with the rotation curve data from the SPARC (Spitzer Photometry and Accurate Rotation Curves database) sample. Importantly, our conclusions in this paper are reached through a conventional approach, with the sole assumption of the quantum effects of macroscopic matter on large scales, without the need for additional modifications or assumptions.
The Effects of Monitoring Activities on Loan Defaults in Group-Based Lending Program: Evidence from Vietnam
Authors: Tri Tran Ba , Truong Loc Dong , Friday H. Swint , Pham Tien Phat
Published: July 2024

The aim of this study is to investigate the impact of delegated monitoring by the group leader and peer monitoring by group members on loan defaults in a group-based lending program in Vietnam. Data used in the study were collected from a questionnaire survey of 675 participants involved in group-based lending program in the Mekong River Delta, Vietnam. The group-based lending program employs a unique monitoring system that involves hiring the group leader to supervise the group and encouraging group members to monitor each other. The empirical findings derived from the Probit model indicated that delegated monitoring significantly reduces loan defaults, but there was no evidence supporting the effectiveness of peer monitoring within the group. Additionally, under the delegated monitoring scheme, group size plays an important role in decreasing loan defaults.
Advancements in Financial Market Predictions Using Machine Learning Techniques
Authors: Idowu Emmanuel
Published: July 2024

This study investigates the latest advancements in utilizing machine learning techniques for predicting financial market movements. Financial markets are complex systems influenced by numerous factors, making accurate predictions challenging but highly valuable. The research explores various machine learning approaches, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, to enhance the accuracy and reliability of financial market forecasts. The study provides a comprehensive review of current methodologies, highlighting key innovations and their applications in financial market prediction. Techniques such as neural networks, support vector machines, random forests, and ensemble methods are examined in the context of their ability to analyze vast amounts of financial data, identify patterns, and generate actionable insights. Additionally, the research delves into the integration of alternative data sources, such as social media sentiment, news articles, and macroeconomic indicators, to improve prediction models. Through empirical analysis and case studies, the study demonstrates the effectiveness of advanced machine learning models in predicting stock prices, currency exchange rates, and commodity prices. It also discusses the challenges associated with these techniques, including overfitting, data quality issues, and the need for interpretability in model outputs. Findings suggest that machine learning techniques can significantly enhance the predictive power of financial models, offering more accurate and timely insights for investors and financial analysts. The study concludes with recommendations for future research directions, emphasizing the importance of model transparency, the incorporation of diverse data sets, and the continuous adaptation of machine learning algorithms to evolving market conditions.
Emergency Surgical Management of Acute Bowel Obstruction Due to Colonic Malignancy at a District General Hospital in Greece. A 10-Year Retrospective Study (2012 – 2022)
Authors: Kountouri Ismini , Dimasis Periklis , Katsarelas Ioannis , Kotarela Afroditi , Bakalis Nikolaos , Chandolias Miltiadis , Galani Manolakou Maria Martha , Gkiatas Nikolaos , Manolakaki Dimitra
Published: July 2024

In this 10-year retrospective study we used the data from patients who were operated upon due to acute bowel obstruction, at the Surgical Department of General Hospital of Katerini in Greece, from 2012 to 2022. Patients were classified based on the date of operation, their sex, their age group (ranging from 26 to 92 years) and the etiology of the bowel obstruction. A sample of 197 patients was studied. Out of these, it was found that in 73 (37%) the cause of the obstruction was colorectal cancer. In 46 of those patients (63%) the tumor was resected, while in the remaining 27 (37%), some kind of diverting stoma was installed. In 17(43,5%) of the 39 total patients, who suffered from some kind of rectosigmoid cancer, Hartmann's operation was chosen. Our results showcase that, at district hospitals, the surgeon is sometimes obligated to a damage control approach regarding obstructive colon cancer and thus the majority of the patients will eventually end up with a stoma.
Clinical Characteristics of Distinct Subgroups of Patients with Primary Sjögren’s Syndrome, Classified according to Serological Profiles and the Presence of Cutaneous Vasculitis; a Comparative Study
Authors: Bodakçi Erdal
Published: July 2024

This study aimed to determine the specific characteristics of extraglandular manifestations especially cutaneous involvement in patients with primary sjögren’s syndrome(pSS), focusing on the different clinical and histologic types of SS-associated vasculitis. A retrospective study was conducted and 402 patients (378 women and 24 men) with pSS were analyzed. Patients were categorized into three subgroups based on serological tests. These; 1) quadruple seropositive (positive for anti-Sjögren’s-syndrome-related antigen A antibodies (anti-SSA; anti-Ro) and anti-Sjögren’s-syndrome-related antigen B antibodies (anti-SSB; anti-La), rheumatoid factor (RF), and antinuclear antibody (ANA)), 2) double seropositive (positive for ANA and anti-SSA/Ro antibodies), 3) quadruple seronegative (negative for ANA, RF, anti-SSA/Ro and anti-SSB/La antibodies). The number of quadruple seropositive patients was 72 (18.6%), double seropositive 174 (43.2%), and quadruple seronegative was 85 (21.1%). The diagnosis age of quadruple seropositive pSS was 42.4±10.8, significantly younger than patients with double seropositive and quadruple seronegative pSS (p = 0.021, p=0.112). In terms of organ involvement, salivary gland enlargement, arthralgias, arthritis, Raynaud's phenomenon, lymphadenopathy, vasculitis-purpura, interstitial lung disease, neurological involvement, autoimmune thyroiditis, renal interstitial disease, anemia, leukopenia, hypergammaglobulinaemia and hypocomplementaemia were more common in quadruple seropositive pSS compared to quadruple seronegative (p
Transforming Neural Networks on Manifold and Renormalization
Authors: Garayev Guman
Published: July 2024

This paper presents an enhanced Fully Connected Neural Network (FCNN) framework that incorporates manifold learning and Renormalization Group (RG) methods. By integrating Christoffel symbols, the model adapts to the curvature and topology of the data manifold, improving learning from geometrically complex data. Additionally, RG methods enable effective multi-scale data processing. We demonstrate the enhanced model’s performance on synthetic and real-world datasets, showing improved accuracy compared to standard FCNNs, particularly in complex systems simulation.
Recent Advances and Future Perspectives in Vascular Organoids and Vessel-on-Chip
Authors: Cheruku Gowtham Reddy , Wilson Chloe Veronica , Raviendran Suriya , Xiao Qingzhong
Published: July 2024

Recent advancements in vascular organoid (VO) and vessel-on-chip (VoC) technologies have revolutionized our approach to studying cardiovascular diseases (CVDs), offering unprecedented insights through more physiologically relevant models. VOs generated from human pluripotent stem cells exhibit remarkable self-organization capabilities, forming complex three-dimensional structures that closely mimic human blood vessel architecture and function, while VoCs engineered with groundbreaking microfluidic systems that meticulously recreate the physical and functional attributes of blood vessels. These innovative constructs serve as powerful tools for investigating vascular development, disease progression, and therapeutic efficacy. By enabling the creation of patient-specific VOs and VoCs, they pave the way for personalized medicine approaches, allowing researchers to delve into genetic variations, intricate cellular interactions, and dynamic processes with exceptional resolution. The synergy between VOs and VoCs with cutting-edge technologies such as single-cell sequencing and high-resolution imaging has further amplified their potential, unveiling novel mechanisms underlying CVDs and identifying promising therapeutic targets. Herein, we summarize different types of VOs and VoCs and present an extensive overview on the generation and applications of VOs and VoCs in CVDs. We will also highlight clinical and translational challenges and future perspectives around VOs and VoCs.
Stigma and Discrimination among Adolescents Living with Perinatal HIV in Thailand: Reports from Their Caregivers
Authors: Geoffroy Audrey , Sirirungsi Wasna , Jongpaijitsakul Parinya , Chamjamrat Wanna , Ruklao Chutima , Kongka Manoosin , Sukhaphan Usa , Sathan Somporn , Thina Sriphan , Khayanchoomnoom Tassawan , Le Cœur Sophie
Published: July 2024

In Thailand, thousands of children living with perinatal HIV have been exposed to HIV-related stigma/discrimination but its frequency and expressions have been little studied. Our objective was to assess stigma/discrimination prevalence, associated risks factors and its expressions among adolescents. All caregivers of adolescents aged 12-19 years living with perinatal HIV and receiving antiretrovirals in 20 hospitals throughout Thailand were invited to complete a face-to-face questionnaire on their adolescent’s life and medical history. Of the 572 adolescents living in family settings, 464 had their HIV-status known in the community. The overall stigma/discrimination prevalence was 46%. Factors associated included living in the north-east region (OR: 2.80, 95%CI: 1.19-4.96), having a low intellectual ability (OR:2.68, 95%CI: 1.45-5.16), a lower Body Mass Index (BMI) (OR BMI > 18.5: 0.61, 95%CI: 0.37-0.98), conflicts with caregivers (OR: 1.86, 95%CI: 1.26-2.76) and caregivers being members of a support group (OR: 2.34, 95%CI: 1.58-3.48). Expressions of stigma/discrimination included bullying, social isolation, behavioral discrimination and public disclosure. The prevalence of stigma/discrimination among adolescents living with perinatal HIV is high. Despite existing policies, stigma eradication remains necessary to normalize their life as they grow into adulthood.
AI-Based Financial Transaction Monitoring and Fraud Prevention with Behaviour Prediction
Authors: Xu Jiahao , Yang Tianyi , Zhuang Shikai , Li Huixiang , Lu Wenran
Published: July 2024

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.
Predicting 3D Rigid Body Dynamics with Deep Residual Network
Authors: Oketunji Abiodun Finbarrs
Published: July 2024

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a dataset of 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.
Pharmacophore-Assisted Covalent Docking Identifies a Potential Covalent Inhibitor for Drug-Resistant Genotype 3 Variants of Hepatitis C Viral NS3/4A Serine Protease
Authors: Iman Kanzal , Mirza Muhammad Usman , Sadia Fazila , Froeyen Matheus , Trant John F. , Chaudhary Safee Ullah
Published: July 2024

The emergence of drug resistance inducing mutations in Hepatitis C virus (HCV) coupled with genotypic heterogeneity has made targeting NS3/4A serine protease difficult. In this work, we investigated the mutagenic variations in the binding pocket of Genotype 3 (G3) HCV NS3/4A and evaluated ligands for efficacious inhibition. We report mutations at 14 positions within the ligand-binding residues of HCV NS3/4A including H57R and S139P within the catalytic triad. We then modeled each mutational variant for pharmacophore-based virtual screening (PBVS) followed by covalent docking towards identifying a potential covalent inhibitor, i.e. cpd-217. The binding stability of cpd-217 was then supported by molecular dynamic simulation followed by MM/GBSA binding free energy calculation. The free energy decomposition analysis indicated that the resistant mutants alter the HCV NS3/4A-ligand interaction, resulting in an unbalanced energy distribution within the binding site leading to drug resistance. cpd-217 was identified to interact with all NS3/4A G3 variants with significant covalent docking scores. In conclusion, cpd-217 emerges as a potential inhibitor of HCV NS3/4A G3 variants that warrants further in vitro and in vivo studies. The study will pave the way for drug design and development of HCV G3 NS3/4A.
Facile Preparation of Smart Sponge Based on Zeolitic Imidazolate Framework for the Efficient Separation of Oily Wastewater
Authors: Zhang Yu-Ping , Chen Xin-Xin , Yuan Pei , Chen Hai-E , Li Song-Wei
Published: July 2024

Fabrication of durable materials with excellent oil adsorption capacity and separation performance for the treatment of oily wastewater is meaningful based on smart responsiveness. Herein, a solvent-responsive melamine sponge (MS) was developed via silanization followed by the in-situ growth of zeolitic imidazolate framework-8 (ZIF-8). Detailed characterization of the resultant composite MS was conducted using scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The multiscale hierarchical MS substrate exhibited highly hydrophobic property in the pH range of 1-11, along with a satisfactory adsorption capacity in the range of 65.4-134.2 g/g for different oils. The modified surface transformed from superhydrophobicity/superlipophilicity to superhydrophilicity/underwater superoleophobicity upon ethanol wetting, reverting to its original superhydrophobic state upon drying. The separation flux of the MS substrate was above 1.5×104L/m2h for both oil and water removal, along with the separation efficiency more than 98.7%. No obvious changes of the separation performance after 50 successive immiscible oil/water separation indicated the excellent durability and robustness of the anchored ZIF-8 nanoparticles on the surface of modified MS substrate. More importantly, oil-in-water emulsion separation was successfully carried out via the ZIF-8 MS composite with a high separation efficiency over 99.1%. The developed smart sponge with high oil absorption capacity, excellent chemical stability and fire resistance exhibited a wide range of potential practical applications in the convenient treatment of oily wastewater.
Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models
Authors: Zhang Xu
Published: July 2024

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.
Moderate Overweight of Male Rats Chronically Fed with a Hyperlipidic Diet Developed Early Silent Ang-(3–4)-Sensitive Myocardial Molecular Lesions and Preserved Echographic Parameters
Authors: Crisóstomo Thuany , Luzes Rafael , Gonçalves Matheus Leonardo Lima , Pardal Marco Antônio Estrela , Muzi-Filho Humberto , Costa-Sarmento Glória , Mello Debora B. , Vieyra Adalberto
Published: July 2024

Overweight/obesity and cardiovascular diseases are associated with high rates of mobility and mortality. We investigated inflammatory lesions and ionic transporters in the left ventricle of male rats chronically fed a high-fat diet (HL). They developed moderate overweight with early expansion of visceral adiposity. The inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-), Na+-transporting ATPases, sarco-endoplasmic reticulum Ca2+-ATPase (SERCA2a), and the abundance of Angiotensin II receptors were studied together with lipid and glycemic profiles, and the left ventricle echocardiographic parameters fractional shortening (FS) and ejection fraction (EF). HL rats doubled IL-6 and TNF-, which returned to normal levels when signaling coupled to Angiotensin II type 2 receptors (AT2R) was supraregulated by Ang-(3–4) administration. The decreased glucose tolerance was accompanied by significantly reduced plasma triglycerides due to intense hepatic steatosis (Crisóstomo et al. Metabolism Open. 2022, 14, 100176). AT2R (and AT1R) abundance did not change. (Na++K+)ATPase and ouabain-resistant Na+-ATPase were downregulated and upregulated, respectively, normalized with Ang-(3–4). SERCA2a lost its most critical regulatory property, inhibition by excess substrate. The ultrasound showed no changes in FS and EF. We conclude that very moderate overweight can cause silent initial molecular tissue damage in heart, which can evolve to cause systolic dysfunction.
Postoperative Organ Dysfunction Risk Stratification Using Extracellular Vesicles-derived circRNAs in Pediatric Congenital Heart Surgery
Authors: Alhamdan Fahd , Yuki Koichi
Published: July 2024

Breakthroughs in surgical and medical techniques have significantly improved outcomes for children with congenital heart disease (CHD), but research continues to address the ongoing challenge of organ dysfunction after surgery. Our study explored circular RNAs (circRNAs) within plasma-derived extracellular vesicles (EVs) in children undergoing CHD surgery. Post-surgery EV circRNAs showed dramatic expression changes between organ dysfunction (OD) and control groups. Tissue injury-related pathways were consistent across pre- and post-surgery in OD. The top two significant predicted tissue sources of these circRNAs originated from the respiratory system, aligning with the major organ dysfunction type (respiratory failure). Five of these circRNAs, namely circ-CELSR1, circ-PLXNA1, circ-OBSL1, circ-DAB2IP, circ-KANK1, significantly correlated between PELOD (Pediatric Logistic Organ Dysfunction), supporting the potential of circRNAs as prognostic markers. These findings pave the way for EV circRNAs as promising tools for managing post-surgical organ dysfunction and potentially guiding therapeutic strategies in children with CHD.