bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of control algorithms for water quality testing robots
Authors: Luo Yiran
Published: December 2023

The research background of the water quality detection robot lies in effectively monitoring and assessing the quality of water bodies to address the increasingly severe issue of water pollution. This technology, employing advanced sensors and machine learning algorithms, facilitates automated acquisition of water quality data, enabling real-time analysis and reporting of various indicators in the water bodies. Consequently, it provides crucial evidence for safeguarding the environment and human health. This article investigates the problem of posture control in water quality detection robots operating in complex underwater environments. It compares three control algorithms: the traditional PID fuzzy control algorithm, and a BP neural network-based PID algorithm. Through experimental simulation, these algorithms are evaluated based on their differences in system dynamic performance. The results indicate that the utilization of the BP neural network PID algorithm significantly enhances both stability and control precision of the robot when dealing with non-linear environments characterized by uncertainties. This algorithm combines traditional PID control with neural network technology, allowing for adaptive optimization of PID controller parameters through training the neural network. As a result, it overcomes limitations associated with traditional PID and fuzzy control algorithms while improving overall performance.
Program control of obstacle avoidance and bridging cars at different angles
Authors: Jiang Bowen
Published: December 2023

This design aims to solve the problem of being unable to move forward due to obstacles during earthquakes. Through this program, unmanned vehicles can adjust their angles by identifying obstacles. This article uses Arduino software for programming, achieving automatic recognition of programs from different angles. Thus achieving precise angle control and exploring the differences in programs from different perspectives. Compared to traditional manual driving, unmanned obstacle avoidance vehicles can reduce risks and accidents, thereby improving road safety. In the event of an earthquake, there may be obstacles such as building collapse and accumulation on the road, which brings difficulties and risks to the driving of vehicles. Through this design, unmanned vehicles can use sensors such as lasers or cameras to perceive the surrounding environment in real-time, and use algorithms to determine and recognize the position and shape of obstacles. Once an obstacle is found, the unmanned vehicle will automatically adjust its angle to bypass or cross the obstacle, ensuring the safe driving of the vehicle. The design uses Arduino software for programming, which is an open source electronic prototype platform with easy to learn and use features. By programming, unmanned vehicles can be accurately controlled based on different angles and obstacle shapes. This can improve the maneuverability and adaptability of unmanned vehicles, enabling them to flexibly respond to various road conditions and obstacles.
Deep learning-based image classification of MRI brain image
Authors: Xie Xinzhe
Published: December 2023

This article reviews the latest research on MRI brain image classification techniques based on deep learning. Firstly, MRI brain image classification and traditional machine learning methods applied to MRI image classification were briefly introduced. Then, a review was conducted on existing MRI brain image classification methods based on deep learning. The article reviews past and recent related research and provides a detailed introduction to the application of deep learning methods in MRI brain image classification. This includes some traditional machine learning methods and research achievements in deep neural networks (DNN), convolutional neural networks (CNN), and transfer learning. The most commonly used deep learning architecture for image classification is CNN. Research has shown that deep learning methods have high accuracy and performance in MRI brain image classification and can automatically extract image features for effective classification. Therefore, deep learning methods provide doctors with more comprehensive information, help them make more accurate diagnoses and formulate treatment plans, and have broad prospects for application in MRI brain image classification. In the future, the further development of deep learning technology will achieve better auxiliary effects on diagnosis, treatment, and scientific research related to the brain.
Investigation of the causes of adolescent lumbar and shoulder-neck diseases and related mitigation approaches
Authors: Li Jiaying , Yang Zilun , Zhou Enyi
Published: December 2023

With the rapid development of global economy and information technology, peoples lifestyle has undergone great changes, and the prevalence of neck and shoulder pain and low back pain has also increased, and the age of onset is trending younger, which seriously affect their study and life, resulting in greater family burden. The survey shows that in recent years, the incidence of pain has increased year by year due to the high learning pressure of teenagers, playing mobile phones and computers for a long time. Teenagers long-term incorrect sitting position, sleeping position, improper pen use and other bad living habits, mental and psychological factors may eventually induce the pain through affecting biomechanical imbalance. The author has discussed the etiology, intervention and also the prevention of general neck and shoulder pain and low back pain in order to provide more theoretical basis for the management of this pain in the future and better guide the clinic.
Progress in the application of gene editing technology in cancer treatments - Taking CRISPR as an example
Authors: Deng Xiangying
Published: December 2023

The background of CRISPRs development lies in the long-standing need for precise gene editing tools, which culminated in the revolutionary CRISPR-Cas9 system in 2012. This technology has had profound impacts on biology, medicine, and biotechnology, offering versatile applications in gene therapy, agriculture, and research across eukaryotes, prokaryotes, and archaea. In the context of cancer research, CRISPR has shown promise in understanding cancer biology, identifying therapeutic targets, enhancing immunotherapy, and potentially enabling early cancer diagnosis and personalized treatment. However, challenges remain, including the need to enhance precision, address ethical concerns, and conduct rigorous clinical trials. This article examines CRISPRs advancements in cancer treatment and concludes that, when compared to other gene editing methods, it offers benefits like robust targeting, easy operation, and efficient editing. Based on these benefits, the application prospect of CRISPR in enhancing personalized medicine, drug development, and cancer treatment is also discussed in this article. This article highlights the need for addressing precision and safety issues and the importance of interdisciplinary collaboration and stringent regulation in realizing the full potential of CRISPR in cancer treatment. In conclusion, CRISPR offers promising avenues for improving cancer treatment by enabling precise gene editing and personalized therapies. Despite the challenges, continuous advancements in technology and research, along with stricter regulation, will ensure the safe and effective use of CRISPR in cancer, providing hope for cancer patients and shaping the future of oncology research.
Potential application of Saccharina japonica and its extracts in cosmetology
Authors: Xi Weng
Published: December 2023

Saccharina japonica (S. japonica), a type of brown algae widely consumed in east Asia due to its enormous health benefits, has now been greeted with great expectation in the field of cosmetics. Although many cosmetological functions of S. japonica extracts have been reported, there is still a lack of comprehensive review on the potential application of S. japonica and its extract in cosmetology. This article aims to sum up the cosmetological functions of S. japonica extracts, including anti-inflammation, skin whitening, anti-oxidation, and anti-bacterial activities. Moreover, the production of bioactive chemicals from S. japonica is explored, hoping to provide new insights into cosmetic manufacturing. Study exploration was conducted using online databases (PubMed) using the keywords Saccharina japonica, Laminaria japonica, kelp, cosmetics, anti-inflammation, skin whitening, anti-oxidation, anti-bacterial, etc., focusing on established pre-clinical trials proving relative bioactivities.
Applications in gene editing in cell totipotency
Authors: Dai Lihui , Wang Yuxin
Published: December 2023

Cell totipotency is that in multicellular organisms, each of cells contains all sets of genetic materials. Therefore, cells have the potential of developing into new individuals. According to tissue culture in plants, this has inspired biologists and they started conducting extensively in the field of cell totipotency for a long time. Moreover, scientists have made significant achievements in terms of the field in cell totipotency from embryonic cell division technology, nuclear transfer, induced pluripotent stem cells and so on. However, based on limitations in the development of technology, mechanisms of differentiation and dedifferentiation in animal cells are still unknown. The emergence of CRISPR technology in the twenty-first century has revolutionized an efficient method to research structures and functions in genes. Because of its durability and efficacy in genome editing, the CRISPR/Cas9 system has recently become one of the trendiest issues. Whats more, this kind of technology also had a huge breakthrough in many fields, and it has typically been used to genetically modify pluripotent stem cells, which are then differentiated into specific cell types and used for functional study and even clinical transplantation. This review aims to introduce the general applications of CRISPR and expound its potential breakthroughs and mechanisms in animal cell totipotency, which may guide future research efforts in this area.
Immunotherapy and combined treatment in gastric cancer
Authors: Ji Ruqian
Published: December 2023

Gastric Carcinoma (GC) ranks as the fifth inducing cause for cancer incidence, and its the fourth inducing reason for cancer-related mortality. Traditional treatments, which involve a combination of chemotherapy and surgical resection, often yield unfavorable prognoses for gastric cancer patients, especially for those with advanced stages or metastases. Immunotherapy has recently emerged as the significant focus in the treatment of advanced gastric cancer, particularly for patients with MSI-H/dMMR. Medications targeting PD-1, such as nivolumab and Keytruda, have gained marketing approval in recent years. Additionally, some biomarkers like HER2, FGFR, and VEGF have shown positive efficacy in clinical research. However, resistance to immunotherapy remains a significant challenge, and the effectiveness of these pharmaceuticals often lasts only about a year for patients suffering advanced gastric cancer. Consequently, theres a need to explore new immune checkpoint inhibitors and discover novel combination therapy approaches. This review is aimed at introducing several latest immune checkpoint inhibitors currently in clinical phases or in pre-clinical stages, alongside innovative combination strategies for treating gastric cancer.
Progress of related drugs and new drugs for immunotherapy of triple-negative breast cancer
Authors: Yang Chengyuan , Bao Jiaming
Published: December 2023

Triple-negative breast cancer (TNBC) stands as the most aggressive kind of breast cancer, characterized by a terrible prognosis. TNBC is more prevalent in patients under 40 years old and in those with BRCA1 mutations. Treatment for TNBC typically involves chemotherapy, radiation therapy, targeted therapy, immunotherapy, and other approaches. However, the discovery of new therapeutic interventions is hampered because of the absence of precisely defined targets, making immunotherapy a prospective option for giving greater benefits to TNBC patients. Based on numerous clinical studies, we explore the research development of various immune checkpoint inhibitors in the therapies of TNBC in this review. Programmed death-1 (PD-1) inhibitors, programmed death-ligand 1 (PD-L1) inhibitors, cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) inhibitors, antibody-drug conjugates, and PARP inhibitors are among these inhibitors. Additionally, we briefly introduce novel immunotherapies and combination therapies.
The influence of acetaminophen on human health
Authors: Liu Yutong
Published: December 2023

In pain management, acetaminophen, a widely used analgesic and antipyretic, holds a central position. The metabolic pathways can lead to acetaminophen-induced hepatotoxicity, elucidating from glucuronidation to the cytochrome P450 system. It meticulously details the complexities of acetaminophen metabolism, culminating in the formation of the toxic metabolite, n-acetyl-p-benzoquinone imine (NAPQI). Providing a holistic understanding guiding clinical practice, it explores risk factors, clinical implications, and management strategies. Transitioning to respiratory risks, potential associations between acetaminophen use and asthma, rhinoconjunctivitis, and eczema in adolescents is investigated. The study offers nuanced insights, urging caution in acetaminophen use during adolescence. In addition, the role of drug metabolism in acetaminophen-induced hepatic necrosis is also analyzed. Despite its temporal origins, the studys identification of cytochrome P450 as a catalyst for NAPQI formation remains foundational, contributing mechanistic insights to drug-induced liver injury knowledge. Synthesizing these studies reveals a delicate balance between acetaminophen metabolism and detoxification, crucial in determining its hepatotoxic potential. This nuanced understanding informs clinical practice and beckons researchers to bridge gaps, embarking on further explorations. In essence, this synthesis becomes a compass guiding the scientific community toward a profound comprehension of acetaminophen, epitomizing the standards expected in high-quality, peer-reviewed literature.
Unravelling the biochemical mysteries of mycotoxins: Nature's fungal toxins
Authors: Qian Runjin
Published: December 2023

Understanding the biochemical intricacies of mycotoxin toxicity is essential for effectively addressing these challenges. This comprehensive research delves into the multifaceted world of mycotoxins, beginning with an exploration of their origins, discussing the fungal species responsible for their production, the environmental conditions conducive to their development, and their ecological roles and adaptive significance. The research proceeds to elucidate the biochemical mechanisms underpinning mycotoxin toxicity, describing how these toxins interact with essential biomolecules, disrupt cellular processes, and manifest specific biochemical effects. The research delves into key topics of mycotoxin metabolism and biotransformation in vivo, discussing detoxification processes in the liver, the role of enzymes, and genetic variants that influence susceptibility. The acute and chronic health effects of mycotoxins and the role of biochemical biomarkers in assessing exposure are explored in depth. Synergies and co-exposures are discussed, and the biochemical mechanisms behind interactions with other toxic substances and their implications for risk assessment and public health are discussed. This research underscores the necessity of continuous research in unravelling the biochemical complexities of mycotoxins, emphasizing their impact on agriculture, food safety, and human health, and the urgency of vigilance in addressing these challenges.
Intranasal alpha-adrenergic receptor agonists and corticosteroids as medical treatments for nasal congestion
Authors: Dai Yifan
Published: December 2023

Nasal congestion has been a longstanding issue which exerts burdens on both individual patients and society as a whole. It arises from various underlying illnesses like nasal polyposis, rhinosinusitis, allergic rhinitis and rhinitis medicamentosa, but the fundamental mechanism of induced nasal congestion is nasal mucosas swelling as a result of inflammation in all of these cases. Among all the therapies currently available, intranasal sprays of alpha-adrenergic receptor agonists or corticosteroids appeared to be prominent options. After reviewing research on treatments extensively, limited studies focused on the comparison of -adrenergic agonists and corticosteroids. This research explores the mechanisms, efficacy, and safety of these two medical approaches. Intranasal alpha-adrenergic receptor agonist sprays target the smooth muscles for vasoconstriction, acting rapidly to give way to airflow. They are effective as a treatment for short-term nasal congestion, otherwise, continued use may lead to rhinitis medicamentosa. Corticosteroid sprays, on the other hand, aim to reduce the severity of nasal inflammation via suppression of pro-inflammatory molecules. They are a promising treatment barely with concerns about the occurrence of side effects, authenticated by their excellent pharmaceutical performance over years.
The current status and prospects of research on orphan G protein-coupled receptor
Authors: Cai Kaifan , Guo Zhining , Huang Yanlong
Published: December 2023

This review provides an overview of the current status and prospects of research on orphan G protein-coupled receptors (oGPCRs). It begins by discussing the classification and structure of GPCRs and then delves into the methods used to identify and characterize ligands for oGPCRs, including ligand screening strategies, correlation analysis, functional analysis techniques, and interaction evaluations. The review further examines the functionality and de-orphanization research of specific oGPCRs, such as GPR35, GPR55, GPR75, and GPR88. It discusses their basic information, signalling pathways, research on endogenous agonists, and their associations with diseases and physiological activities. Finally, the review concludes with a discussion of future directions and prospects for research on oGPCRs, emphasizing the importance of understanding receptor functionality and signalling pathways for drug development.
The influence of dietary sugar intake on colorectal cancer
Authors: Jing Yi
Published: December 2023

Research Question: This study aims to investigate the consequences of daily sugar intake on human health, particularly focusing on its role in the development of colorectal cancer. The research question explores the intricate mechanisms linking elevated sugar consumption to colorectal cancer and aims to understand the diverse dietary regimens influence on this association. Methods: A comprehensive analysis was conducted to examine the impact of persistent, elevated sugar consumption on the integrity of the intestinal tract and its correlation with colorectal cancer. Various dietary regimens, characterized by different sugar types and quantities, were studied to understand their role in exacerbating the likelihood of developing colorectal cancer. Detailed investigations were carried out to unravel the underlying mechanisms, including insulin resistance, systemic cellular inflammatory responses, and the shift in colonic biological milieu. Inflammatory mediators and specific pro-inflammatory cytokines were analysed in different tissue compartments to comprehend the complex interplay leading to compromised intestinal barrier integrity and increased susceptibility to colorectal cancer. Conclusion: Our study reveals compelling evidence linking high-sugar diets to the development of colorectal cancer. Elevated sugar consumption disrupts the integrity of the intestinal tract, primarily through insulin resistance and systemic cellular inflammatory responses. This disruption leads to an upsurge in inflammatory mediators and specific pro-inflammatory cytokines within various tissue compartments. Consequently, the compromised intestinal barrier loses its ability to prevent the infiltration of pathogenic microorganisms, significantly elevating the risk of colorectal cancer. The diverse range of dietary regimens further complicates this association, highlighting the need for comprehensive research in understanding the intricate role of dietary factors in colorectal cancer.
Neural interface technology for human-computer interaction
Authors: You Terry
Published: December 2023

Brain-Computer Interfaces (BCI) offer a direct communication channel between the brain and external devices, marking a pivotal convergence of neuroscience and technology. Neural activities, essentially electrical impulses, can be captured either invasively, with methods such as Electrocorticography (ECoG) which require surgical implantation, or non-invasively using techniques like Electroencephalography (EEG) that operate externally. Once acquired, raw neural data undergoes processing; external and physiological noises are filtered out, and meaningful patterns or neural fingerprints are extracted. Modern BCIs then employ machine learning, specifically deep learning, to translate these cleaned neural patterns into discernible commands, with continuous feedback loops enhancing system adaptability. These decoded signals can control varied devices, from medical-grade robotic limbs to cursors on screens. BCIs have transformative applications across sectors: theyre pivotal in neurorehabilitation after brain injuries, providing feedback where traditional methods might fail; theyre integrated with virtual reality for immersive feedback; they revolutionize cognitive training and meditation practices; and theyre finding a foothold in high-risk sectors like deep-sea exploration and military operations. Additionally, research on tactile Event-Related Potential (ERP)-based BCIs emphasizes the importance of congruent Control-Display Mapping for efficient user experience. However, with these advancements come ethical concerns, such as the potential invasion of the privacy of ones thoughts, challenges to human identity and autonomy, societal disparities in access, and health implications.
Current status of power system for unmanned aerial vehicle fuel cell under different scenarios
Authors: Xu Junpeng
Published: December 2023

With the increasingly wide application of the unmanned aerial vehicle, a new power resource is in urgent demand in the market. Domestic and foreign researchers have also done much research on how to improve the power system. Considering the energy density, gravimetric specific power, economic benefits and environmental protection, fuel cell becomes the best choice to solve the problem of unmanned aerial vehicle endurance. The gravimetric specific power of fuel cells is low, and it is difficult to cope with the large range fluctuation of load power when the UAV performs complex flight tasks. After comparing the performance and products of various types of fuel cells, the cathode open air-cooled proton exchange membrane fuel cell is chosen as the most suitable one for UAVs. This paper will illustrate how fuel cell and battery hybrid power system works and introduce their structure. At the end of the paper, some conclusions and prospects for the development of UAV power systems are also made.
FDM 3D printer temperature control system based on PID control
Authors: Long Kai
Published: December 2023

With the continuous development of manufacturing technology today, the emerging technology of 3D printing is becoming more mature, and the surface accuracy and quality of printed products are receiving increasing attention from users. The stability and appropriateness of the nozzle temperature have a significant impact on the quality of the prints. This project focuses on the fused deposition modeling (FDM) 3D printer as the research subject, introduces the structure types of FDM 3D printers, and addresses the temperature control issue of FDM printers by using PID control for improvement. By applying SIMULINK to model and simulate the nozzle temperature control system, the simulation results of PID control are analyzed and compared. The results show that PID control shortens the stabilization time, improving the stability and control accuracy of the control system. PID control can enhance the temperature control accuracy of FDM printers, thus improving the precision and surface quality of the printed products.
Principles and examples of drag reduction in civil airliners
Authors: He Ziang , Lu Zhiyuan , Sun Yitong
Published: December 2023

Civil aviation has grown rapidly over the past hundred years as demand from air travellers has increased. Since the mid to late 20th century, there have been recurring global energy crises due to political and economic upheavals. In this international context, airlines have tended to operate less draggy, more fuel efficient aircraft in order to maximise profits through fuel cost savings . To this end, aircraft manufacturers have historically tried a variety of methods to address the issue of drag reduction.This paper highlights the importance of three drag reduction methods utilized on modern subsonic airliners by introducing their basic principle and evaluating their effectiveness based on previous research and typical experiments. This paper summarizes and analyzes different approaches to reduce drag in civil aviation. It first investigates the principle behind frictional, induced, and profile drag in theoretical aspects. It then discusses in detail the biomimicry microstructure based on shark skin and its uniqueness on aircraft fuselages surface to reduce frictional drag, the split scimitar winglet and its ideal performance to reduce induced drag when compared with other wingtip devices with different cant angles. The newly introduced adaptive lifting surface changes the wings geometric configuration momentarily, and its ability to reduce profile drag at different stages of flight. This paper also comprehensively compares both the benefits and potential compromises of these drag reduction methods.
Sequential recommendation based on graph transformer
Authors: Sun Zixuan
Published: December 2023

Sequential Recommendation (SR) is an important scenario in recommendation tasks. Sequential recommendations model the sequential pattern between item-item or user-item based on a user's recent activity in a time series to predict their next preference. However, existing methods are based only on the conventional Graph Neural Networks (GNN) as a model architecture for adaptive fine-tuning of specific SR tasks. To get better recommendation results, more advanced GNNs can be used as the network architecture of the SR method. This paper introduces graph transformer, a combination of GNN and a good sequential task processing model. Then a cross-sectional comparison is made with the current SR method model and its suitability for application in SR tasks is discussed. The comparison shows that the graph transformer is similar in principle and structure to the current SR models, and requires the addition of some adaptive components to be applied in SR tasks. The superior performance after application can be demonstrated from the results data of the Benchmarking-GNNs and Long-Range Graph Benchmark on the models.
Application of SLAM technology combined with AR in minimally invasive surgery
Authors: Yang Jingyu , Zhou Taiqi
Published: December 2023

Minimally invasive surgery (MIS) is revolutionizing the medical field, offering patients faster recovery times and reduced risk of complications. However, MIS demands precise navigation and visualization of the surgical site, which can be challenging due to the limited field of view and constrained working space. In this study, we investigate the integration of simultaneous localization and mapping (SLAM) technology with augmented reality (AR) to enhance the capabilities of MIS and improve surgical outcomes. SLAM technology enables real-time 3D mapping and tracking of the surgical environment, while AR overlays crucial information, such as anatomical structures and surgical guidance, onto the surgeon's view. The proposed system combines these technologies to create a comprehensive, interactive representation of the patient's anatomy, providing surgeons with a more intuitive and accurate understanding of the surgical site. We assess the performance of the integrated SLAM-AR system based on a review of relevant literature, focusing on its accuracy, usability, and impact on surgical efficiency. The findings suggest that the SLAM-AR system has the potential to significantly improve spatial awareness and navigation during MIS, potentially reducing procedure times and complication rates. Furthermore, the system shows promise for enhancing surgeon training and promoting the adoption of MIS techniques in various surgical fields.
Pravne in upravne dimenzije arhivskega gradiva
Authors: Melik Jelka
Published: December 2023

Namen prispevka je opredeliti upravno dimenzijo dokumentarnega in arhivskega gradiva ter pokazati razhajanja med tujim in slovenskim dojemanjem upravne vrednosti arhivskega gradiva.
Na podlagi primerjalne metode smo ocenjevali slovenski pristop k problemu in z analitično metodo ugotavljali, kje v slovenski pravni ureditvi so napake, ki povzročajo zmedo v terminologiji in škodo na področju varovanja dokumentarnega in arhivskega gradiva javne uprave.
S primerjavo različnih uveljavljenih praks na področju varovanja dokumentarnega in arhivskega gradiva ugotavljamo, da so potrebne spremembe v slovenskem razumevanju upravne dimenzije arhivskega gradiva ter posledično dopolnitev prakse in pravnih predpisov, ki zadevajo dejavnost na področju varovanja dokumentarnega in arhivskega gradiva.
The Coherent Multi-disciplinary Knowledge Case of Prehistorical Insight: Information Science at the Edge of Structured Data Comprehension
Authors: Rückemann Claus-Peter
Published: December 2023

Up to these days, we are experiencing an omnipresent lack of a general approach for cognitive addressing of knowledge structures. This article presents new results and component reference implementations based on frameworks of coherent conceptual knowledge. Coherent conceptual knowledge provides valuable instruments for multi-disciplinary contextualisation, e.g., for contexts in prehistory and protohistory. This research addresses scientific methodologies, valorisation and intelligent re-valorisation of any scientific insight, cognostic addressing of structures, also known as nucleal cognstructures. The resulting component reference implementations enable productive, fertile environments, and learning-improvement-cycles. Central goal of this research is a consistent coherent conceptual integration of knowledge. Prehistory and prehistoric archaeology and their contexts and contextualisation provide a plethora of instructive multi-disciplinary scientific scenarios of high complexity. Thus, component reference implementations for these scenarios are implementation blueprints for informational modeling, industrial learning, and improvement cycles. The results of this long-term research provide solutions based on practical information science, beneficial for prehistory, prehistoric archaeology, and their multi-disciplinary contexts as well as for providing approaches to general solutions.
A Proposed Model to Measure of the Performance of ATM Machine
Authors: Amer Abeer , Ebied Mohamed , Labib Nevine
Published: December 2023

Data Mining (DM) plays an important role in banking sector to help improve customer experience especially when using automated teller machine (ATM). The role of ATM has been maximized recently due to the national trend of digital transformation and the emergence of COVID-19 pandemic since the ATM provides banks’ customers with financial transaction method in public places without any human interaction. which led to the difficulty of financial institutions in measuring the performance of automated teller machines and counting data Whether it is data with a direct or indirect impact on the machines This paper presents a model that helps measuring the efficiency of the ATM by building data warehouse (DW) (Schedule server of ATM, Location, transaction and customer relationship management (CRM)and compares between two DM algorithms (decision tree and naive baye). The decision tree (C5) has been proved to be more convenient method for measuring an ATM's efficiency.
Improving the Efficiency and Controllability of the Global Development of Graphical Interfaces on WPF
Authors: Pashystyi Vladyslav
Published: December 2023

The issue of the correct distribution of work among team members has been an important part of software development for many years. Often the work must be divided even within one task. However, this can even degrade the speed and efficiency of execution. It is also likely to make the code unstable. This is especially true of the development of interface components. The solution lies on the surface - this is a good old organization of the process. Interested developers with affordable and clear development conditions are capable of fantastic efficiency.
How Deeply are Emotions Encoded in Language Communication and is This Detectable in Text
Authors: Slavova Velina , Andonov Filip
Published: December 2023

The study aims at exploring the relatedness between the emotional valence (positive-negative emotion) conveyed by conventional texts and their phonemic content. In the proposed approach, the content of a text is considered as composed of layers, assuming that the sub-lexical phonological elements involved contribute to the transmission of an emotional tone. The sub-lexical layer investigated here is composed of “biphones” - pairings of a vowel and a consonant in either order. A previous analysis of metadata derived from a corpus of sentences evaluated for emotion found that the frequencies of the biphones are strongly correlated with emotional valence. The same method of representing the sub-lexical layer was applied to real-world texts. The validity of the relationships derived from the corpus was tested on arbitrarily downloaded texts on diverse topics published by several news agencies. The statistical analysis shows that the derived relationship between emotional valence and the sub-lexical content of biphones is present in structured real-world, ordinary texts. In conclusion: language communication incorporates phonemic patterns that indicate emotional valence. The overall body of a text carries sub-lexical information about its emotional tone. This approach and result are useful for the fields of sentiment analysis and text emotion recognition. The overall result raises the hypothesis that the cognitive mechanisms that shape the phonemic forms used to mentally build a structured language expression reflect emotional valence. The phenomenon needs further investigation whether it depends on the native language of the reader and how, or if at all biphones per se inspire positive or negative emotion in the listener.
Approach Allowing Design Adaptive View Models
Authors: Chebanyuk Olena , Pashystyi Vladyslav
Published: December 2023

The problem of setting interface settings for global software development processes is actual now. Successful solving of this problem can be source many benefits in view models, other words interface representation designing. Thus, in turn, allow to increase amount of web-site visitors and web applications users. Other advantage is to allow different developers to understand interface well and design application using n-tired architectural style. One of the important task is to archive dynamic localization for switching interface languages. Paper represents an approach of view models designing in context of dynamic localization for desktop application using WPF technology. Paper contains the review of existing frameworks for flexible interface designing supporting localization process. Approach touches some mathematical foundation of interface components representation, recommendation for framework designing, touching design patterns. Paper contains some practical examples of preparation interface using different languages.
Multi-Datasets Evaluation of GB-RAS Network Based Stegdetectors Robustness to Domain Adaptation Problem
Authors: Progonov Dmytro
Published: December 2023

Protection of state and commercial critical infrastructures is topical task today. Among known threats, special interest is taken to early detection of hidden (steganographic) channels usage by attackers for unauthorized transmission of sensitive data. These channels are created by modification of the innocuous files, such as digital images, for message hiding and further transmission of modified files. Detection of formed stego files is non-trivial task due to the wide usage by attackers of adaptive embedding methods that preserve low level of cover image distortion during embedding. Modern solutions for stego images detection are based on utilization the novel convolution neural networks for revealing weak alterations of cover’s features during message hiding. Training of such networks is performed on fixed set of standard image databases, such as BOSS, ALASKA etc. Despite high detection accuracy of pre-trained stegdetectors, theirs performance “in the wild” on natural images remains an open question. The work is devoted to performance analysis of advanced GB-Ras convolutional network based stegdetector for adaptive embedding methods detection on natural images presented in various datasets. Obtained results proved the effectiveness of neural network applying for mitigation with domain adaptation problem for modern stegdetectors. The GB-Ras model allows improving detection accuracy of stego images for standard ALASKA (up to 6% for for S-UNIWARD embedding methods) and VISION (up to 13% for S-UNIWARD and to 9% for MG embedding methods) datasets in comparison with cover rich models based stegdetectors. Nevertheless, the performance of considered GB-Ras based stegdetector for high quality images from MIRFlickr dataset is conceded to maxSRMd2 rich model based stegdetector (up to 11% for S-UNIWARD and 12% for MG embedding methods). Therefore, considered GB-Ras network may be used as a promising candidate for further design of novel stegdetectors that are robust to domain adaptation problem.
A Hybrid Approach for Assessing Problem Solving Skills under Fuzzy Conditions
Authors: Voskoglou Michael
Published: December 2023

Volumes of research have been written about problem solving, which is one of the most important components of the human cognition affecting the progress of the human society for ages. In this work a hybrid method is applied for the assessment of a student group’s problem solving skills with qualitative grades (i.e. under fuzzy conditions). Namely, soft sets are used as tools for a parametric assessment of the group’s performance, the calculation of the GPA index and the Rectangular Fuzzy Assessment Model are applied for evaluating the group’s qualitative performance, grey numbers are used as tools for assessing the group’s mean performance and neutrosophic sets are utilized when the teacher is not sure about the individual grades obtained by some (or all) students of the group
Semantization of The Web-Oriented Advisory Software: Models, Methods And Technologies
Authors: Rogushina Julia , Gladun Anatoly , Pryima Serhii , Kholodniak Yuliia
Published: December 2023

We consider actual trends in use of semantic technologies for development of the Web-oriented advisory software. Requirements and expressiveness of knowledge representation in this domain cause use of ontological knowledge model, and the Semantic Web standards and tools provide the technological foundation for creation of intelligent advisory applications that use external ontologies as sources of domain knowledge. Personification of advisory services needs in analysis of learning outcomes of informal and non-formal learning, and therefore we propose method of their formalization with use of background knowledge about with classifications of professions and qualifications in agricultural domain. This method is based on matching of semantic properties of information objects with different structure represented by atomic competencies. Services provided by advisory system are considered from the point of view of semantic technologies. Proposed approach is used for development of applied advisory system AdvisOnt aimed to combine the market of educational services with the labor market. AdvisOnt characteristics satisfy requirements to distributed semantic application.
Comparison of Gambier Extract (Uncaria Gambier Robx) and Angiotensin Receptor Blocker on Proteinuria Reduction and Antioxidants - Enhancement in Nephrotic Rat Models
Authors: Siregar Rosmayanti , Ramayani Oke , Haris Delyuzar , Lelo Aznan , Jusuf Nelva , Eyanoer Putri , Rusda Muhammad , Amin Mustafa
Published: December 2023

Background: Proteinuria is a significant clinical manifestation that causes edema in several diseases, including Nephrotic Syndrome (NS). Untreated proteinuria is strongly linked to the progression of kidney failure. One of the adjuvant therapies could be used to reduce proteinuria such as Angiotensin Receptor Blocker (ARB) including Losartan®. Gambier is a traditional medicinal plant widely known for its antioxidant effects. Catechin, a compound contained in Gambier Extract (GE), has been used to reduce microalbuminuria in diabetics. However, its application in NS has not been widely studied. Objective: This study compared the effects of GE and ARB in reducing proteinuria and increasing antioxidant activity levels, as well as reported histopathological findings in the nephrotic Wistar rat model. Methods: An experimental design study with a control group and a posttest was conducted. The experimental animals were divided into four groups: the control group (K1), the group with puromycin aminonucleoside (PAN) injection (K2), the group with PAN injection + GE (K3), and the group with PAN injection + Losartan® (K4). The standard GE used was Sarie Uncariae® by Toyo Brothers, PT while the ARB (Losartan®) was obtained from Novell, PT. Protein urine, the activity level of total superoxide dismutase (T-SOD), and malondialdehyde (MDA) were assessed using the colorimetric method. Renal histopathology was assessed based on Rollerman’s criteria. Results: Gambier extract significantly reduced proteinuria, as depicted by a decrease in protein/volume urine (p = 0.009), increased antioxidant activity, as illustrated by an elevation in T-SOD activity levels (p = 0.007), and tended to decrease MDA levels compared to Losartan®. Based on histopathological findings, GE tended to reduce the percentage of kidney damage in rats induced by puromycin. Conclusion: Gambier extract has been shown a higher antioxidant effect by increasing T-SOD activity levels, reducing proteinuria and also exhibiting a tendency to diminish kidney damage.
Deciphering the Role of TGF-β1 in Altering Collagen I and Collagen III in the New Zealand Rabbit’s (Oryctolagus cuniculus) Urethral Wall in Urethral Stricture Development
Authors: Satyagraha Paksi , Purnomo Athaya , Alluza Hamid , Indradiputra I , Nurhadi Pradana , Anita Kenty , Yueniwati Yuyun , Permatasari Happy , Purnomo Basuki
Published: December 2023

Background: Presently, there's a lack of standardization in animal models used for studying urethral stricture. Transforming Growth Factor Beta 1 (TGF-β1) is known to regulate the deposition of extracellular matrix in both normal and pathological conditions. This factor holds promise as a potential model for simulating urethral stricture. Objective: This study aims to investigate the impact of Transforming Growth Factor Beta 1 (TGF-β1) on Collagen I and Collagen III within the urethral wall of New Zealand Rabbits (Oryctolagus cuniculus) in the context of developing urethral stricture in animal models. Methods: We conducted genuine laboratory experiments using Male New Zealand rabbits (Oryctolagus cuniculus), which were categorized into five groups: control, placebo, and three treatment groups (TGF-β1 injections of 1 µg, 2 µg, 4 µg). After a duration of 6 weeks, we conducted urethrography, histopathological analysis, and assessed the formation of collagen I and collagen III within the urethral wall. Results: Elevating the dosage of TGF-β1 led to a reduction in the average urethral lumen diameter of rabbits (29.3% in the 2µg group and 34% in the 4µg group) compared to the control group. In fact, three rabbits experienced a decrease of ≤ 50% in their urethral lumen diameter. As the doses of TGF-β1 increased, we observed significant increases in the density of collagen I, and collagen III in both the periluminal and peripheral regions of the urethral spongiosum. Additionally, there was a tendency for the collagen I/collagen III ratio to decrease in the periluminal region, with collagen III density surpassing that of collagen I. In the peripheral spongiosa area, notable mean differences were observed between the control group, 1T, and 2T groups, with collagen I density tending to be higher than that of collagen III. Furthermore, the percentage of urethral lumen diameter exhibited a robust negative correlation with periluminal collagen I density (r = -0.672, p = 0.001), peripheral spongiosa collagen I density (r = -0.603, p = 0.005), periluminal collagen III density (r = -0.717, p = 0.001), and an exceptionally strong negative correlation with collagen III density of peripheral spongiosa (r = -0.804, p = 0.000). Conclusion: TGF-β1 exerts an influence on altering the composition of collagen I and collagen III within the urethral wall of rabbits, leading to a reduction in the diameter of the urethral lumen. Further research is warranted to determine the optimal dose of TGF-β1 required to induce urethral stricture effectively.