bluuebunny/crossref_metadata_2025 huggingface dataset:
Relación entre China y Ecuador: estatizando el modelo neoliberal
Authors: Bautista Peñaloza Andrés Felipe
Published: November 2024

Este artículo tiene como objetivo cuestionar la presencia de la República Popular China en la economía ecuatoriana a partir de considerar que China, lejos de garantizar una mayor autonomía al aparato productivo de Ecuador, reproduce las relaciones económicas y políticas asimétricas en alianza con las élites locales del país sudamericano. El artículo contiene tres partes. En primer lugar, se pretende describir el panorama reciente de las relaciones sino-latinoamericanas. Luego se revisa el papel de América Latina en la política exterior china, en lo que concierne al apoyo al desarrollo destinado desde Beijing. Finalmente, se discute el caso ecuatoriano a partir del financiamiento que el gobierno chino ha realizado en este país, las condiciones bajo las cuales se otorga este financiamiento y los resultados preliminares en el aparato productivo considerando la construcción de la Central Hidroeléctrica Coca-Codo Sinclair como ejemplo de la estatización del modelo neoliberal.
Model of Fuzzy Controlled System with Modified PI-D Controller for Six-phase Electric Drive
Authors: Rinkeviciene Roma
Published: November 2024

The paper deals with an enhanced MATLAB Simulink model of a fuzzy-controlled system for a symmetrical six phase motor supplied with modified PD-I and PD-I fuzzy controllers and analyzes its performance and transients at special form speed references and step response. The Simulink
model of the motor was developed with the actual parameters of a six-phase motor having a displacement of phase windings by 60 electrical degrees.
The model of the six phase motor is based on the d-q mathematical model and indirect field-oriented control. A fuzzy controller with per unit input
and output membership functions and 49 rules is designed. The simulation results are presented and analyzed. The results with modified PD-I and
PI-D fuzzy controllers indicate the robustness of the modelled system with six-phase motor against disturbances and follow the reference input with
great accuracy. The fuzzy controlled system’s response to complex reference input indicates a small speed delay at the beginning of the process and
accurate following of the response input with an error smaller than 0.75 % during the application of load torque disturbance. The step response and
response to complex reference input has no overshoot nor steady-state error.
The impact of green credit policy on corporate carbon emissions
Authors: Sun Ziruo
Published: November 2024

Green credit policy, as an important financial tool, has significant implications for promoting a low-carbon economy and achieving emission reduction targets. This study explored the impact of China's green credit policy on corporate carbon emissions, using the implementation of the Green Credit Guidelines(GCG) as a natural experiment, and designed a difference-in-difference model. Evidence suggests that GCG can reduce the carbon emissions of green credit restricted enterprises by increasing financing constraints. Further research has found that the carbon reduction effect of policies is more significant for smaller enterprises with stricter total factor productivity and regional environmental regulations. These findings contribute to understanding the implementation effectiveness of GCG and provide theoretical and practical support for achieving the carbon peaking and carbon neutrality goals.
On the Coordination of Various Legal Departments in Foreign-Related Rule of Law Education
Authors: Jiao Yang
Published: November 2024

Persisting in the coordinated advancement of domestic rule of law and foreign-related rule of law has become a hot topic in contemporary Chinese legal education. In the current teaching of foreign-related rule of law, there is a lack of coordination among various departments of law, and international law often "does not communicate" with other departments of law. This is specifically manifested as: the awareness of integrating all legal disciplines needs to be strengthened; the relationship between foreign-related rule of law in teaching and international law needs to be clarified; there are differences in the sources of various departments of law, and research methods vary greatly, with the integration of "tools" still pending. There are obvious differences in the topics taught by scholars from different departments of law. Based on the above, teachers need to enhance the international acumen in criminal law teaching; it is necessary to elucidate the common principles of international criminal rule of law; the research community should be broad and gradually establish a community for foreign-related rule of law education.
Influence of soil media on the seedling growth of Terminalia crenulata Hyne ex Roth.
Authors: Devar K.
Published: November 2024

Growth of seedlings of Terminalia crenulata (Syn. T. tomentosa) was found to be best in the soil medium consisting of equal proportion of red soil, sand, FYM and ash as compared to rest of the seven soil media tried. The total dry matter accumulation in the seedlings was 275 per cent more as compared to the soil medium consisting of soil, sand and FYM in the ratio of 2:1:1 used under usual practice in the region.
Enhancing Cybersecurity Attack Detection Using Multiplayer Battle Game Optimizer with Hybridization of Deep Learning Models
Authors:   Research Scholar, Department of Computer Applications, Alagappa University, Karaikudi, 630003, Tamilnadu, India, K. K. ,   Assistant Professor, Department of Computer Science , Government Arts and Science College For Women, Paramakudi, 623707, Tamilnadu, India, Gandhi K. Rajiv
Published: November 2024

Cybersecurity is advancing and the rate of cybercrime, which is always rising. Advanced attacks are measured as the novel normal as they are one of the more normal and extensive. Cybersecurity threats have risen promptly in many areas like healthcare, smart homes, energy, automation, agriculture, and industrial processes. An intrusion detection system (IDS) discovers intrusions by analyzing attack designs or mining signatures from system packets. To assess an IDS model, use Machine Learning (ML) and deep learning (DL) approaches for recognizing data traffic into malicious and healthy. ML and DL techniques has earned an extensive interest on countless applications and domains of study, mostly in Cybersecurity. With computing power and hardware becoming more available, ML and DL systems can be employed in order to classify and analyze corrupt actors from a massive group of accessible data. This manuscript presents an Enhancing Detection of Cybersecurity Attack Using Multiplayer Battle Game Optimizer with Hybrid Deep Learning (EDCA-MBGOHDL) technique. The main intention of the EDCA-MBGOHDL technique is to provide a robust framework for cyberattack detection using deep learning integrated with a hyperparameter tuning approach. At first, the feature selection process is implemented by applying improved Harris hawk optimization (IHHO) algorithm for ensuring that only the most relevant features are fed into the model. Furthermore, the hybrid of convolutional neural network, bidirectional long short-term memory and attention mechanism (CNN-BiLSTM-AM) model is employed for the classification of cybersecurity threats. Eventually, the multiplayer battle game optimizer (MBGO) algorithm adjusts the hyperparameter values of the CNN-BiLSTM-AM classifier optimally and outcomes in greater classification performance. The wide range of analysis of the EDCA-MBGOHDL technique takes place using a benchmark dataset. The outcomes pointed out the superior performance of the EDCA-MBGOHDL system across existing models
The Effects of Language Aptitude on Fluency in L2 Writing
Authors: Bai Caiwen
Published: November 2024

Language aptitude is a crucial characteristic of second language learning. This study conducted a literature review analysis to review multiple domestic and international studies on the specific relationship between language aptitude and writing output, with the aim of delving into the impact of linguistic competence on second language writing fluency and the mode it affects writing fluency. The study first outlined the definitions of language aptitude and writing fluency. Then, through specific literature analysis and reference to cognitive psychology and language acquisition theory, it is concluded that language aptitudes undeniably impact writing fluency. This study reveals the following important findings: the study found that language aptitudes, being known as the key aptitudes in second language learning, have a positive impact on writing fluency, emphasizing its indispensable role. Finally, this article explores the specific teaching strategies of educators and how learners can improve their fluency and writing skills during the period of second language writing by effectively enhancing their language aptitudes and providing effective references for second language acquisition. The study further explores the specific relationship between language aptitude and second language writing, delves into their influence, and provides a solid theoretical and practical foundation for more specific factors.
From Tradition to the Future: Exploring Artistic Presentation in Contemporary Interactive Exhibitions
Authors: Xu Yanliang
Published: November 2024

Abstract: With the rise of digital technology and new media, art exhibitions have shifted from static displays to interactive experiences that enhance audience participation and immersion. Interactive exhibitions break the formality and distance of traditional displays, bringing art closer to everyday life. This paper explores the emergence of interactive exhibitions in contemporary art, focusing on how exhibition design and participation mechanisms improve audience engagement. Through field research on Cao Fei: Tidal Flux, this article examines how elements like chairs and sound transform viewers from passive observers to active participants. These designs bridge the gap between art and audience found in traditional exhibitions, integrating art into daily life and promoting art education. By comparing the conventional "white cube" model, such as Andy Warhol exhibitionswith interactive exhibitions, this paper highlights how traditional models often limit audience interaction. In contrast, interactive designs like long benches and open soundscapes in Tidal Flux create an accessible and natural dialogue among visitors.
Cinematic Language under the Female Gaze: A Case Study of Li Yu
Authors: Wu Yue
Published: November 2024

Abstract: This paper aims to discover the concept of the female gaze in the movie Lost in Beijing by Li Yu. The female gaze is a film theory that is in opposition to the male gaze, which is a way of viewing women as sexual beings and not as complex people. This paper posits that Li Yu uses the female gaze as a post-modernist technique to focus on the emotional and psychological aspects of the female characters, thus overturning the established Patriarchal gender norms and bringing to the fore the issues of gender inequality in contemporary Chinese society. Certain filming approaches, including those that include close ups and reflective shots, affect the experience of the film's main subject, who are women. The research also reviews the variance between the 'male gaze' and the 'female gaze' in movies, pointing out that the former is much more forthright and authentic in its portrayal of women. Lastly, the paper considers the further developments of the concept of the female gaze and possibilities to transform the film industry from the perspective of the female representation.
Should Men Pay Bills? -- Financial Disparities in Heterosexual Relationships
Authors: Yao Tianle
Published: November 2024

Abstract: This study investigates the enduring norm and rising debatable question of whether men should bear the financial burden in heterosexual relationships Should men pay bills? analyzing the gender dynamics through the lens of financial disparities. Drawing on a survey of 231 participants across diverse demographics, primarily from China, this study explores how societal norms and economic inequities influence heterosexual relationships and contribute to traditional gender norms. By dissecting factors such as the gender wage gap, the pink tax, and the division of labor in non-reproductive tasks, this paper highlights the economic and cultural forces impacting traditional gender roles. The survey reveals that men are generally perceived and often function as the primary earners, influenced by stereotypical gendered norms and expectations, resulting in women encountering economic disadvantages due to lower earnings and greater financial commitment to personal care. This study also examines societal pressures that dictate spending patterns, especially among women, on aesthetics, which contribute significantly to their financial and emotional strain. The findings suggest a critical need to reassess the traditional financial roles in relationships and push toward more equitable financial practices. The findings prompt a reconsideration of gendered financial responsibilities and advocate for a shift towards more equitable practices.
Innovation Policy Pluralism in Practice: A Critical Review and Reflection on Current Incentive Policies in the AI Industry
Authors: Zhang Yinuo
Published: November 2024

Abstract: The rapid development of artificial intelligence (AI) has prompted the need for effective innovation policies to support and guide the industry's growth. This study aims to critically review and reflect on the current landscape of innovation incentive policies in the AI industry, focusing on the concept of innovation policy pluralism and its implications for balancing incentives, addressing ethical concerns, and fostering international cooperation. The study employs a method of literature review and case analysis, and a comparative analysis of AI innovation policies across countries is conducted, along with case studies of successful and failed policy initiatives. The study also explores emerging trends and best practices in AI innovation policy design. The study underscores the importance of optimizing innovation policy pluralism in the AI industry, leveraging potential synergies between different incentive mechanisms, and developing adaptive and responsive policies. The findings have significant implications for policymakers, researchers, and AI industry stakeholders, emphasizing the need for strategic and collaborative approaches to foster sustainable and equitable AI innovation.
Design Scheme of a Flexible Robot Inspired by an Elephant Trunk with Integrated Infrared and Tactile Sensors
Authors: Hu Yiwei
Published: November 2024

In current robotics research, rigid robots have been widely used on a large scale. However, in some specific working environment situations, the performance of rigid robots is relatively problematic, such as internal maintenance work on large equipment. Therefore, in this context, inspired by the bionic elephant trunk robot, the author proposes a soft robot design scheme. Through modeling, deconstruction and extensive literature research, the existing soft robot structure is optimized. The optimization method mainly combines tactile sensors with infrared visual sensors, and uses the elephant trunk robot to achieve detection and positioning actions in low-light environments. The entire design scheme is divided into three parts, inspired by the flexible robot with the trunk of an elephant, piezoelectric tactile sensors and infrared sensors. The scheme has a high degree of sensor integration, which can realize the linkage and continuous motion of the robot, thus helping the robot to operate in extreme environments such as low light, darkness, and narrow spaces.
A Review on Machine Learning Based on EEG and ECoG Signal for Brain Computer Interface
Authors: Shao Jingyun
Published: November 2024

Abstract. Brain-computer interfaces (BCIs) are devices utilized for the detection and recognition of brain activity. With advancements in machine learning techniques, BCI projects have witnessed an increasing application of these techniques, leading to higher performance. BCI also finds utility in medical applications by providing efficient solutions for individuals with neurological disorders or disabilities. The research focus on convenient and non-invasive electroencephalography (EEG) and high signal-to-noise ratio electrocorticography (ECoG) stems from their respective advantages. This paper presents a comprehensive overview of BCI principles, imaging techniques such as EEG and ECoG along with their advantages and disadvantages, as well as discusses the utilization of various machine learning techniques in EEG and ECoG-based BCI system, including classification and regression models. Furthermore, this paper summarizes the challenges faced by EEG and ECoG-based BCI techniques while also discussing future directions such as artifacting removal technique for EEG-based BCIs and closed-loop feedback technique based on ECoGBCI.
Related Work on Few-shot Method: A Review
Authors: Wang Xihao , Yin Xuewei , Zhang Yingti , Zhang Yanyue
Published: November 2024

Abstract. As Natural Language Processing (NLP) technologies continue to expand into novel domains, few-shot learning emerges as a pivotal approach to addressing the challenge of data scarcity. Traditional neural networks, due to their strong reliance on abundant data, have posed limitations in their applicability to new domains. Consequently, there is a pressing need to introduce fresh research perspectives and solutions within this realm, aiming to propel its development towards greater practicality and efficiency. Firstly, solving the zero or few-shot Dialogue State Tracking problem has become necessary as the demand for deploying such systems in new domains continues to grow. This article explores the performance of D-REPTILE, a meta learner for Destination Address (DST) problems, in unknown domains. Then PET is extensively studied. Real-world tests on RAFT show cue-based learning works in low-sample settings, reinforcing the importance of instruction-based learning for human-like few-shot capabilities. Thirdly, to address the overfitting problem, this paper also explores the LA- UCL model, as well as its application, development, and challenges, which enables the LA-UCL model to enhance the Large Language Model data expansion effect through two modules. Finally, CausalCollab is introduced, which uses Incremental Stylistic Effects (ISE) as a guiding estimator for assessing the effectiveness of LM-human cooperation tactics through time.
A Review and Outlook of the Latest Results on Document-level Information Extraction
Authors: Li Huanyuan , Wei Lai , Wang Ziheng
Published: November 2024

Abstracts. Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.
Application of PID Control Technology in Unmanned Aerial Vehicles
Authors: Shi Mingrui
Published: November 2024

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.
Short-Term Photovoltaic Power Prediction Based on Deep Learning
Authors: Zhang Siyuan
Published: November 2024

Abstract. In order to cope with the surge in global demand for clean energy and the challenges under the goals of "carbon peaking" and "carbon neutrality", this paper discusses the short-term prediction of photovoltaic (PV) power based on deep learning. This paper first summarizes the theoretical basis of deep learning and its application in PV power prediction, then expounds the key steps such as data preprocessing, data augmentation, and feature construction, and introduces the specific applications of deep learning models such as convolutional neural network, recurrent neural network and its variants long short-term memory network and gated recurrent unit in PV power prediction. Finally, this paper summarizes the progress of the current technology and looks forward to the future development direction of photovoltaic power prediction including multi-factor fusion prediction and optimization of deep learning models.
Research on the Algorithmic Structures in Artificial Intelligence
Authors: Sui Yining
Published: November 2024

Abstract. In recent years, artificial intelligence (AI) has experienced remarkable growth, largely driven by significant advancements in algorithm structures. This paper provides a comprehensive review of the key algorithmic frameworks employed in AI, with a primary focus on traditional algorithms and their evolution in response to modern deep learning techniques. Traditional algorithms, such as decision trees, support vector machines, and genetic algorithms, have long served as foundational pillars in AI research. However, the advent of deep learning has introduced new paradigms that significantly enhance these algorithms in terms of performance, scalability, and adaptability. By analyzing the classification, characteristics, and limitations of traditional algorithms, this study compares them with deep learning models, highlighting both their strengths and shortcomings. Furthermore, this paper examines how deep learning improves traditional algorithms through case studies that showcase enhanced performance, broader application domains, and evolving design principles. This study is based on an analysis of publicly available datasets and a comprehensive review of the current literature. The findings suggest that while traditional algorithms offer a solid foundation, deep learning has revolutionized algorithmic design, paving the way for new applications and innovations in AI. Ultimately, this review underscores the critical role of integrating deep learning into traditional algorithmic frameworks for the future of AI.
Review of Self-Learning Systems Based on End-to-End Deep Learning in Bionic Robots
Authors: Zhao Kaisa
Published: November 2024

Abstract. This paper examines the integration of end-to-end artificial intelligence neural networks with self-learning systems in bionic robots, highlighting its critical role and practical implications. It explores the advantages of biomimetic design in enhancing robot adaptability, functionality, and intelligence. Key challenges such as energy efficiency, material innovation, and sensory development are discussed. The study further demonstrates how self-learning systems enhance deep learning capabilities, enabling robots to independently acquire and refine skills. Case studies illustrate successful applications, such as balance control and adaptive learning in robotics. Looking ahead, autonomous and adaptable bionic robots are poised to revolutionize sectors like search and rescue, environmental monitoring, and underwater exploration. This work underscores the transformative synergy between deep learning, self-learning, and neural networks in robotics.
The Mindset of AGI Unexplainability in Human-computer Interaction Scenarios from Turing's "Computing Machinery and Intelligence"
Authors: Chen Heyang
Published: November 2024

Abstract. The emergence of AI-generated content (AIGC) can be traced back to as early as 1950, when Alan Turing introduced the famous "imitation game" in his paper Computing Machinery and Intelligence, which proposed a method to determine whether a machine possesses "intelligence." Since the introduction of the GAN model by Goodfellow et al. in 2014, the issue of autonomy in AIGC has not seen any breakthroughs. However, due to the challenges posed by robustness and the lack of explainability, society is already beginning to anticipate the social issues and anxieties that might arise with the advent of autonomous artificial general intelligence (AGI). The increasing influence of AI technology on society has further driven concerns about the ethical implications of both AIGC and AGI. Specifically, the relationship between human-computer interaction (HCI) and AI ethicsparticularly the role of explainable AIhas become increasingly crucial. Merely understanding the issue of non-explainability from a technical standpoint is no longer sufficient to form a principled basis for AI ethics. In fact, Turing to some extent foresaw the possibility that AI's future development would face such issues. This paper seeks to offer a direction that moves beyond the traditional AI ethics research framework by reinterpreting Turing's original question and analyzing some of the objections he raised. The goal is to provide a new mindset for exploring the necessary modes of thinking for human-computer interaction in the era of AGI.
Research on Confrontation Sample Attack for License Plate Recognition
Authors: Ding Ruitao
Published: November 2024

Abstract. The deep learning technology is becoming widely used in the sectors of safety monitoring and intelligent transportation management, and this has made license plate recognition systems crucial. However, these systems also face security challenges from antisample attacks. This study examines the literature on antisample assaults on license plate recognition software, with a focus on recent advancements and methodologies. First, this paper analyzes several major adversarial sample generation methods, especially gradient-based methods, which can significantly reduce the accuracy of license plate recognition systems without being detected by the naked eye. Then, this paper introduces the basic working principle of the license plate recognition system and its importance in practical applications and finally summarizes the research progress and related methods in recent years. Through a comprehensive analysis of the existing literature, this paper aims to provide researchers with a comprehensive overview of license plate recognition systems against sample attacks and their defense strategies for further development in the field.
Applications of Denoising Diffusion Probability Model in Image Processing
Authors: He Renke , Jiang Ruiqi , Miao Zhilin
Published: November 2024

Abstract. In 2020, Jonathan Ho et al. from the University of Berkeley proposed the Denoising Diffusion Probabilistic Models (DDPM), which improved on the shortcomings of the previous-generation Deformable Part Models (DPM). At the same time, they also surpassed previous generative models in image synthesis effects by using noise prediction, such as Generative Adversarial Networks (GAN), Variational Auto-Encoders (VAE), flow-based models and energy-based models, etc. After that, the denoising diffusion probabilistic models gradually received more discussions and research. In 2022, OpenAI launched the DALL-E 2, realizing Text-to-Image generation combined with the denoising diffusion probabilistic model. Two years later, with the release of Sora, the first Text-to-Video large models based on DALL-E, the denoising diffusion probabilistic models have received unprecedented attention in the multi-modal field. This article first provides the background and development process of the denoising diffusion probabilistic models. Secondly, it introduces the algorithmic principles of the diffusion models. Following that, it lists the applications of the models in the area of images. In the last section, it expounds on the advantages, disadvantages and future trends of the denoising diffusion probabilistic models.
Reviewer Acknowledgements for Accounting and Finance Research, Vol. 13, No. 4, 2024
Authors: Chen Sarah
Published: November 2024

Reviewer Acknowledgements for Accounting and Finance Research, Vol. 13, No. 4, 2024
Pengelelolaan Fasilitas Laboratorium Komputer di SMA Islam Al Amalul Khair Palembang
Authors: Marlina Leny , Akbar Ahmad Fauzi , Ibrahim Ibrahim , Febriyanti Febriyanti
Published: November 2024

This study aims to analyze the implementation of computer lab facility procurement, the factors influencing it, and the solutions for the procurement process at SMA Islam Al Amal Khair Palembang. The study was conducted at SMA Islam Al Amalul Khair Palembang using a descriptive qualitative research method. The informants in this study were the school principal and the vice principal in charge of facilities and infrastructure. Data were collected through observations, interviews, and documentation. The data analysis techniques used include data reduction, data presentation, and conclusion drawing, while data validity was ensured through source triangulation and technique triangulation. The research findings indicate that the implementation of computer lab facility procurement at SMA Islam Al Amalul Khair Palembang has been successfully carried out. However, there are several aspects that need further improvement, particularly in the procurement process, to enhance the school's quality and competitiveness with other institutions
Caudal versus regional block with the use of ultrasound in pediatric analgesia: a systematic review of the main clinical results
Authors: Carrilho Nathalye Stefanny Resende , Moreira Neto Mozart , Morais Gabriela Campos
Published: November 2024

Introduction: Regional anesthesia has been gaining prominence in recent decades. A variety of peripheral and central nerve blocks have been developed to ensure that perioperative pain can be effectively controlled. Caudal block is one of the most widely administered techniques of regional anesthesia, and the use of ultrasound has enabled the optimization and precision of the techniques. Objective: This study aimed to analyze the main clinical considerations and outcomes of ultrasound-guided caudal and regional block for pediatric analgesia. Methods: The systematic review rules of the PRISMA Platform were followed. The search was carried out from May to July 2024 in the Web of Science, Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 120 articles were found. 75 articles were evaluated and 27 were included in this systematic review. Considering the Cochrane tool for risk of bias, the overall assessment resulted in 5 studies with a high risk of bias and 21 studies that did not meet GRADE. Most studies showed homogeneity in their results, with I2 = 18.7% < 50%. All these techniques protect children from perioperative pain and, combined with periprocedural sedation, allow surgical interventions with spontaneous breathing in groups of patients with maximum risk of difficult airways. We consider this specific point the main advantage of regional anesthesia in pediatric patients. Ultrasound-guided caudal epidural, performed under sedation with a non-instrumented airway, is an effective technique in daily clinical routine; however, higher body weight and surgical procedures in the mid-abdomen are risk factors for pain-related block failure. Patients who, regardless of chronological age, were born as extremely premature infants have a higher risk of respiratory events. For bilateral inguinal hernia surgeries, ultrasound-guided caudal epidural block and transversus abdominis plane block showed similar analgesic efficacy in the first six hours postoperatively. Furthermore, ultrasound-guided lower limb peripheral nerve block is a simple and safe method to provide adequate and longer-lasting analgesia compared with ultrasound-guided caudal block for pediatric lower limb surgeries. Ultrasound-guided erector spinae block was safe and effective in pediatric patients undergoing unilateral lower abdominal surgery, as it provided longer-lasting analgesia and fewer analgesic requirements than caudal block and fewer side effects. In children undergoing regional anesthesia, the incidence of infection, hematoma, and local anesthetic toxicity is low.
Slot fed circularly polarized magneto-electric dipole antenna array fed by optimized printed microstrip gap waveguide network
Authors: Hassan Abdelmoniem T. , Kishk Ahmed A.
Published: November 2024

A compact, circularly polarized 8 × 8 antenna array is designed for the 60 GHz band. The array comprises circularly polarized magneto-electric dipoles (CP-ME-Dipole) excited by narrow slots. The slots are fed by a printed gap waveguide (PGWG) cooperative network optimized based on the termination of the effective impedance of the array elements. Thus, it accounts for the space mutual coupling of the antenna elements. A procedure based on the full-wave analysis of a 4 ´ 4 array is used to estimate each element’s 8 × 8 array effective port impedance. The cooperative feeding network is designed based on the known effective impedances. The array is divided into two half subarrays out of phase from each other, and a rectangular waveguide feeds both sides. The commonly measured bandwidth of 18.3% achieves return loss better than 10 dB and an axial ratio below 3 dB (AR) of less than 3 dB. A maximum gain of 26.2 dBic with a high radiation efficiency of 82% radiation efficiency.
Pharmaceutical Active Compounds Contamination and Seasonal-Based Ecotoxicity Assessment of the Yamuna River for the Delhi stretch
Authors: Alsubih Majed , Khan Roohul Abad
Published: November 2024

Yamuna River is the only river in Delhi. This results in heavy reliance on Yamuna to meet Delhi Water demands. This has prompted several research works covering physicochemical, biological, heavy metal concentration, emerging pollutant occurrence, and their risk assessment. This study investigated occurrence, seasonal variation (pre-monsoon, monsoon, and post-monsoon), and risk assessment posed by 7 PhACs at five sampling locations along a 22 km stretch of Yamuna River in Delhi. The samples were collected and analysed in pre-monsoon, monsoon, and post-monsoon seasons. The seven PhACs, comprised of 2 antibiotics (Ciprofloxacin; CIP, sulfamethoxazole; SMZ), 2 NSAIDs (Paracetamol; PCM, Ketoprofen; KPF), 1 anxiety control (Lorazepam, LOR), 1 anticonvulsant (Carbamazepine; CBZ) and 1 statin (Fluvastatin; FUT). The PhACs range of occurrence across three seasons was PCM 75-589 ng L-1, KPF 31-238 ng L-1, CBZ 11-192 ng L-1, LOR 62-462 ng L-1, CIP 48-192 ng L-1, SMZ 192-1534 ng L-1, and FUT 0-421 ng L-1. The seasonal occurrence was in the order of post-monsoon> pre-monsoon> monsoon. PCM, CBZ FUT posed a negligible ecotoxicological risk, LOR posed a low-medium risk, and ketoprofen, ciprofloxacin, and sulfamethoxazole posed high risks. Based on the risk index, all seasons have high ecological risk at every sample point. Discharges of untreated sewage and insufficient and inefficient treated wastewater are the primary contributors of PhACs in the Yamuna River. This study concludes that existing WWTPs need drastic upgrades. Policies and measures should also be developed to prevent untreated wastewater from reaching the Yamuna River. This necessitates further studies to investigate processes suitable for installing and treating wastewater along the longitudinal section of the drain and assess their technical feasibility.
LINHA DE CUIDADO DA GRAVIDEZ E O CONTEXTO CONTEMPORÂNEO DA SAÚDE PÚBLICA EM MANAUS: UMA ANÁLISE DESCRITIVA
Authors: Cavalcanti Rafaella Bandeira de Melo Souza , Pinto Vitor Emanuel Nunes , Leão Gabriel Machado Pinheiro , Geromel Vitor Augusto Pereira , Praciano Beatrice Alves Rebouças Tomé , Santos Junior Iran Higino Costa dos , Gomes Felipe Alves , Oliveira Cássio Gabriel Barbosa de , Oliveira Suzane Lima de , Evaristo Jucyette da Silva Gomes , Souza Celsa da Silva Moura
Published: November 2024

O estudo propõe uma análise descritiva da rede de assistência à gestante em Manaus, abordando os desafios, mudanças e fragilidades no atendimento. A Rede de Atenção Materno-Infantil - Rede Cegonha visa assegurar às mulheres o direito ao planejamento familiar, atenção humanizada ao parto e ao puerpério e um desenvolvimento saudável, bem como o acompanhamento em gestações com complicações. A gravidez de alto risco envolve fatores clínicos, obstétricos e sociais que comprometem a saúde materna e fetal. Patologias como hipertensão, pré-eclâmpsia, diabetes gestacional e Síndrome de Hellp aumentam o risco de morbidade e mortalidade, tornando o pré-natal adequado, com pelo menos seis consultas, fundamental para prevenir complicações e melhorar os desfechos maternos e infantis. O estudo foi realizado através  de visitas a USFs, maternidades e distritos de saúde, além de palestras com profissionais da SEMSA e UFAM. Assim, observou-se a estrutura e funcionamento dos serviços de pré-natal, parto e puerpério, identificando desafios como a falta de acessibilidade, sobrecarga de profissionais e demora na realização de exames. O estudo também abordou a mortalidade materna, a adesão ao pré-natal e a execução das políticas assistenciais. Dessa forma, evidenciou-se uma disparidade entre a prática vigente e as ações premeditadas pela política de assistência à saúde materno-infantil, revelando uma lacuna na garantia de bem-estar e cuidados adequados a gestantes de Manaus.
CONTRIBUIÇÕES DA METODOLOGIA ONE-MINUTE PRECEPTOR NA FORMAÇÃO DE ESTUDANTES DE GRADUAÇÃO EM MEDICINA E DE PROFISSIONAIS RESIDENTES MÉDICOS
Authors: Souza Maria Cristina Almeida de , Toassi Ramona Fernanda Ceriotti
Published: November 2024

Introdução: Preceptores são profissionais responsáveis pela supervisão/orientação de estudantes/profissionais residentes em contextos de atenção à saúde. Para que exerçam a prática da preceptoria, a instrumentalização pedagógica é essencial para a formação qualificada e produção de cuidado integral e resolutivo em saúde. Objetivo: Identificar evidências científicas sobre as contribuições da utilização da metodologia Preceptoria em um Minuto/One-Minute Preceptor (OMP) por preceptores na formação de estudantes de graduação em Medicina e de profissionais residentes médicos. Metodologia: Revisão integrativa da literatura (RIL) realizada na base de dados da Biblioteca Virtual em Saúde. Foi utilizado o descritor controlado – Preceptoria/Preceptorship – e o descritor não controlado – Preceptor Minuto/One Minute Preceptor. Tais descritores foram combinados utilizando-se o operador booleano AND. Foram incluídos artigos de pesquisa original, revisão de literatura, relatos de experiência, Trabalhos de Conclusão de Curso (TCC)/Dissertações/Teses e manuais sobre a utilização da OMP na formação de estudantes/profissionais residentes da Medicina, publicados entre 2020-2023, em português/inglês. As publicações selecionadas foram analisadas pela análise temática. Resultados: Foram analisadas 10 publicações (seis artigos, dois manuais e dois TCC). OMP foi considerada uma metodologia de fácil aplicabilidade, realizada em curto espaço de tempo, permitindo discussões ágeis e focadas entre estudantes-residentes-preceptores, aprimorando o desenvolvimento de habilidades clínicas e de raciocínio crítico-reflexivo. OMP estimulou estudantes/profissionais residentes a pensarem criticamente sobre as situações/casos, formulando perguntas e propondo resoluções, possibilitando a definição de um melhor plano de tratamento. OMP também contribuiu para um maior número de diagnósticos diferenciais pelos estudantes e qualificou tanto a comunicação estudante-profissional residente-preceptor, quanto o feedback do preceptor ao educando. Conclusão: Esta RIL identificou contribuições da OMP na formação de estudantes/profissionais residentes da Medicina relacionadas ao desenvolvimento de habilidades clínicas, de raciocínio crítico-reflexivo, de elaboração de maior número de diagnósticos diferenciais, de discussão mais aprofundada de casos e na comunicação com o preceptor, facilitando o feedback sobre seu aprendizado.
Концептуальні візії державного регулювання боргової політики
Authors: Уракін М. Г.
Published: November 2024

Стаття присвячена дослідженню концептуальних підходів до державного регулювання боргової політики, особливо актуальних для країн із перехідною економікою. Автор висвітлює основні проблеми, пов’язані з управлінням державним боргом, включаючи надмірне боргове навантаження, валютні ризики, низьку ефективність використання боргових інструментів та загрози дефолту. Особливу увагу приділено важливості врахування впливу глобальних викликів, таких як пандемія COVID-19, військові дії в Україні, інфляційні процеси та політична нестабільність, що суттєво ускладнюють процес розробки ефективної боргової стратегії. У статті представлено огляд теоретичних підходів до управління борговою політикою, включаючи монетаристський та кейнсіанський концепти. Зазначено, що монетаристський підхід акцентує увагу на обмеженні державних запозичень, щоб уникнути інфляційних ризиків та забезпечити довгострокову фінансову стабільність. Натомість акцентовано, що кейнсіанський підхід наголошує на можливості використання боргових інструментів для стимулювання економічного розвитку та підтримки внутрішнього попиту, особливо в періоди економічної рецесії. Проведено порівняльний аналіз цих основних підходів. Крім того, проаналізовано погляди провідних міжнародних експертів, таких як Дж. Стігліц і П. Кругман, які пропонують практичні варіанти уникнення боргових пасток і реструктуризації зобов’язань. У дослідження систематизовано принципи управління борговою політикою та наголошено на важливості їх врахування при регулюванні боргом. Автор розглядає ключові принципи управління державним боргом, зокрема стабільність, прозорість, ефективність використання коштів та диверсифікацію джерел фінансування. Дослідження пропонує нові підходи до оптимізації структури боргу, щоб уникнути ризиків ліквідності та підвищити економічну стійкість. Метою статті є формулювання концептуальних засад для вдосконалення боргової політики країн із перехідною економікою. У дослідженні структуровані основні ризики регулювання боргової політики. На основі проведеного аналізу запропоновано рекомендації щодо адаптації підходів до регулювання державного боргу, які спрямовані на підвищення ефективності його управління, зниження боргових ризиків та забезпечення фінансової стабільності в умовах глобальних викликів.
Управління дебіторською заборгованістю для підвищення рівня фінансово-економічної безпеки підприємства
Authors: Юрченко С. В. , Мачак Т. О. , Овдін В. С.
Published: November 2024

Зміст статті охоплює концепцію управління дебіторською заборгованістю, що полягає в необхідності забезпечення фінансово-економічної безпеки підприємства. Дослідження ґрунтується на всебічному підході, який поєднує теоретичне вивчення поняття дебіторська заборгованість та практичні засоби її управлінського процесу. В статті послідовно висвітлено роль ефективного управління дебіторською заборгованістю, як невід’ємного активу підприємства у підвищенні рівня його фінансово-економічної безпеки, а також розроблено рекомендації щодо оптимізації процесів управління нею. В процесі дослідження систематизовано основні методи та інструменти управлінського аналізу, проектування та моделювання, що сприяє покращенню управлінських рішень щодо дебіторської заборгованості. Особливої уваги приділено основним чинникам виникнення дебіторської заборгованості, серед яких важливими, на нашу думку, є такі, що пов’язані з умовами кредитування, платоспроможністю контрагентів, ефективністю розрахункових процедур. З’ясовано, що дебіторська заборгованість має як позитивний, так і негативний вплив на підприємство. З одного боку наявність такої заборгованості може виступати джерелом короткострокового фінансування для підприємства, що дозволяє йому управляти грошовими потоками раціонально. З іншого боку, високий рівень заборгованості може призвести до збільшення ризиків неплатежів, що негативно вплине на фінансову стабільність і загрозу ліквідності підприємства. Це дослідження дозволило сформувати необхідний комплекс заходів щодо зниження фінансових ризиків, пов’язаних з дебіторською заборгованістю, що сприятиме ефективному управлінню дебіторською заборгованістю дозволяючи мінімізувати фінансові ризики та підвищити рівень фінансово-економічної безпеки підприємства.  Основною гіпотезою є те, що ефективне управління дебіторською заборгованістю дозволяє мінімізувати фінансові ризики та забезпечити високий рівень фінансово-економічної безпеки підприємства. Теоретичне значення роботи полягає в удосконаленні теоретичних засад управління дебіторською заборгованістю. Практичне значення – в розробці рекомендацій щодо оптимізації управлінських практик для підвищення рівня фінансово-економічної безпеки підприємства. Перспективи подальших досліджень пов’язані з удосконаленням механізмів управління дебіторською заборгованістю в умовах сьогодення.