bluuebunny/crossref_metadata_2025 huggingface dataset:
A longitudinal network analysis of research trends and policy implications: southernmost of Thailand case study
Authors: Rumdon Komgrit , Longha Kamonthip , Kaewsuwan Nawapon , Matcha Wannisa
Published: January 2025

The government funds research projects to address problems, advance knowledge, and support national development. Data repositories are often used to store this research information; however, such information is not optimally used when making the decision. This is particularly important, especially in the areas that require extensive effort and budget allocation to drive development, such as the southernmost provinces of Thailand. This area has been in a violent situation for more than twenty years, leading to poor education, economic challenges, and many more. This study aims to analyze the trends in research topics on these provinces over 30 years (1982–2020) using epistemic network analysis (ENA) on data from the Southern Border Provinces Research Database (SOREDA). Key findings showed a prolonged focus on “education” and “Islamic studies,” reflecting steady government support but raising concerns about its effectiveness. Another important point was that conflict management research arose in response to the surge in violence in 2004 and prolonged existing. The current trending research focused on local–based capital and how it is used to drive society and the economy, such as through tourism. These highlight evolving priorities in addressing the region's challenges and opportunities.
A novel one-dimensional chaotic map with improved sine map dynamics
Authors: Htiti Mohamed , Akharraz Ismail , Ahaitouf Abdelaziz
Published: January 2025

These days, keeping information safe from people who should not have access to it is very important. Chaos maps are a critical component of encryption and security systems. The classical one-dimensional maps, such as logistic, sine, and tent, have many weaknesses. For example, these classical maps may exhibit chaotic behavior within the narrow range of the rate variable between 0 and 1and the small interval's rate variable. In recent years, several researchers have tried to overcome these problems. In this paper, we propose a new one-dimensional chaotic map that improves the sine map. We introduce an additional parameter and modify the mathematical structure to enhance the chaotic behavior and expand the interval's rate variable. We evaluate the effectiveness of our map using specific tests, including fixed points and stability analysis, Lyapunov exponent analysis, diagram bifurcation, sensitivity to initial conditions, the cobweb diagram, sample entropy and the 0-1 test.
Customized dataset-based machine learning approach for black hole attack detection in mobile ad hoc networks
Authors: Moudni Houda , Er-rouidi Mohamed , Lmkaiti Mansour , Mouncif Hicham
Published: January 2025

This article explores the application of machine learning (ML) algorithms to classify the black hole attack in mobile ad hoc networks (MANETs). Black hole attacks threaten MANETs by disrupting communication and data transmission. The primary goal of this study is to develop an intrusion detection system (IDS) to detect and classify this attack. The research process involves feature selection, the creation of a custom dataset tailored to the characteristics of black hole attacks, and the evaluation of four machine learning models: random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and decision tree (DT). The evaluation of these models demonstrates promising results, with significant improvements in accuracy, precision, F1-score, and recall metrics. The findings underscore the potential of machine learning in enhancing the security of MANETs by providing an effective means of attack classification.
Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders
Authors: Korade Nilesh B. , Salunke Mahendra B. , Bhosle Amol , Joshi Dhanashri , Patil Kavita , Sangve Sunil M. , Deshmukh Rushali A. , Patil Aparna S.
Published: January 2025

The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
Radionuclide identification system using convolution neural network for environmental radiation monitoring
Authors: Istofa Istofa , Kusuma Gina , Ningsih Firliyani Rahmatia , Triyanto Joko , Susila I Putu , Prajitno Prawito
Published: January 2025

Radionuclide identification is an important task for nuclear safety and security aspects, especially to environmental radiation monitoring systems. This study aims to build an automatic radionuclide identification system that can be applied in environmental radiation monitoring stations. The gamma energy spectrum was obtained by varying radionuclide types, measurement time and source distance using a scintillation detector. The dataset was collected by converting gamma energy spectrum into images, data pre-processing by removing background noise and normalizing the gamma spectrum. Automatic identification is demonstrated as a development method based on convolutional neural network (CNN) algorithm, where the images come from gamma-ray spectrum in the form of photoelectric peak characteristic. Three CNN architectures are used to train the model, which are VGG-16, AlexNet and Xception. The performance of each model is evaluated using accuracy, precision and recall to find the appropriate architecture. The most optimum results are shown by VGG-16 with an accuracy of 97.72%, a precision of 97.75% and a recall of 97.71%. The models are critically reviewed and it is concluded that the developed models can be further implemented on embedded devices utilizing the tiny machine learning (TinyML) platform in environmental radiation monitoring systems.
A new data imputation technique for efficient used car price forecasting
Authors: Bridge-Nduwimana Charlène Béatrice , Ouaazizi Aziza El , Benyakhlef Majid
Published: January 2025

This research presents an innovative methodology for addressing missing data challenges, specifically applied to predicting the resale value of used vehicles. The study integrates a tailored feature selection algorithm with a sophisticated imputation strategy utilizing the HistGradientBoostingRegressor to enhance efficiency and accuracy while maintaining data fidelity. The approach effectively resolves data preprocessing and missing value imputation issues in complex datasets. A comprehensive flowchart delineates the process from initial data acquisition and integration to ultimate preprocessing steps, encompassing feature engineering, data partitioning, model training, and imputation procedures. The results demonstrate the superiority of the HistGradientBoostingRegressor for imputation over conventional methods, with boosted models eXtreme gradient boosting (XGBoost) regressor and gradient boosting regressor exhibiting exceptional performance in price forecasting. While the study’s potential limitations include generalizability across diverse datasets, its applications include enhancing pricing models in the automotive sector and improving data quality in large-scale market analyses.
Revolutionizing nonrigid demons registration with the whale optimization algorithm
Authors: Roy Abhisek , Roy Pranab Kanti , Mitra Anirban , Daw Swarnali , Basu Dipannita , Chakraborty Sayan
Published: January 2025

Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.
Improving water quality parameter prediction with multi-level linear regression model and hybrid feature selection
Authors: Khurshid Aleefia , Korke Samruddhi , Kothari Yudhir , Alone Shruti , Bais Khushali
Published: January 2025

Predicting and modeling the quality of water is essential to guarantee that the water is safe to drink. The chlorine content in water needs to be monitored in real-time to provide a consistent supply of drinkable water. Additionally, potassium and chlorine have a major impact on how appealing the water is, as they are important components that influence taste and odor. Therefore, to evaluate the levels of chlorine and potassium, this work presents a multivariable linear regression approach backed by a hybrid feature extraction method. To bridge the gap between the filter and wrapper approaches, a hybrid approach is used to remove unnecessary information and reduce processing time and complexity. Here the quantitative parameters, in conjunction with categorical parameters, are instrumental in enabling accurate prediction of two water quality parameters. The two developed multi-level regression (MLR) models for the prediction of potassium and chloride are useful when factors affecting water parameters fluctuate at the site level as well as over larger spatial or temporal scales giving consumers a visual representation of how each parameter influences prediction. The converged model outperforms in comparison with other machine learning algorithms with an MAE of 7.42e-15 for potassium and 3.72e-14 for chloride.
Photoplethysmograph-based time-frequency and machine learning applications on biomedical signal analysis for medical diagnosis
Authors: Jana Soumyadip , Pal Partha Sarathi
Published: January 2025

Machine learning (ML) integration in biomedical signal processing and medical diagnosis has the potential to revolutionize healthcare by improving diagnostic accuracy. This paper focuses on the applications of different ML algorithms for analyzing real-time physiological data collected from Photoplethysmography (PPG) sensors. Heart rate variability (HRV) analysis using electrocardiography (ECG) signals makes the process longer and bulky. Therefore, this paper demonstrates the real-time generation of HRV signals using a simple, low-cost, and non-invasive PPG sensor which is further processed using the Arduino ATMEGA328P microcontroller and then interfaced to a PC for display to investigate the usefulness of HRV feature analysis. HRV features have been computed using time domain analysis (TA), and frequency domain analysis (FA). At last, these TA and FA indices have been given to different ML models that could predict the gender, age group, and physiological conditions of a human being. Prediction of the physiological conditions using TA, FA, and ML models simultaneously makes the proposed approach more novel than the other existing methods. Comparative analysis of different ML approaches using ROC curves and confusion matrices has been shown to find the effectiveness and precision of different proposed models. It shows random forest ML approach has achieved 91% accuracy in identifying the physiological conditions. This simple yet accurate real-time PPG-based time-frequency ML system might be useful in medical assessment with faster response.
Modeling non-linear communication systems using neural networks
Authors: Fateh Rachid , Ougraz Hassan , Belaadel Youness , Pouliquen Mathieu , Frikel Miloud , Safi Said
Published: January 2025

Nonlinear systems present significant modeling challenges due to their complex dynamics and often unpredictable behavior. Traditional mathematical approaches can struggle to represent such systems accurately. In recent years, neural networks have emerged as promising tools to address this challenge. This article explores the use of neural networks to model nonlinear systems, focusing specifically on the application of the Hammerstein system. We examine network architecture and training methodologies suited to the complexity of nonlinear dynamics. Additionally, we explore strategies to improve the interpretability of neural network models in this context, enabling a better understanding of the underlying behavior of the system. Through a case study and empirical evaluations, we demonstrate the effectiveness of neural network-based approaches for estimating the behavior of nonlinear systems. Our work highlights the potential of neural networks as a versatile and powerful tool for modeling complex nonlinear phenomena.
Simulation of ray behavior in biconvex converging lenses using machine learning algorithms
Authors: Carlos-Chullo Juan Deyby , Vilca-Quispe Marielena , Fernandez-Granda Whinders Joel , Castro-Gutierrez Eveling
Published: January 2025

This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
Exploring diverse prediction models in intelligent traffic control
Authors: Vilakkumadathil Sahira , Thiyagarajan Velumani
Published: January 2025

Traffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance.
Performance analysis of different BERT implementation for event burst detection from social media text
Authors: Mangal Dharmendra , Makwana Hemant
Published: January 2025

The language models play very important role in natural language processing (NLP) tasks. To understand natural languages, the learning models are required to be trained on large corpus. This requires a lot of time and computing resources. The detection of information like events, and locations from text is an important NLP task. As events detection is to be done in real-time so that immediate actions can be taken, hence we need efficient decision-making models. The pertained models like bi-directional encoders representation from transformers (BERT) gaining popularity to solve NLP problems. As BERT based models are pre-trained on large language corpus it requires very less time to adapt for domain specific NLP task. Different implementations of BERT have been proposed to enhance efficiency and applicability of the base model. The selection of right implementation is essential for overall performance of NLP based system. This work presents the comparative insights of five widely used BERT implementations named as BERT-base, BERT-large, Distill BERT, Robust BERT approach (RoBERTa-base) and RoBERT-large for event detection from the text extracted from social media streams. The results show that Distill-BERT model outperforms on basis of performance metric like precision, recall, and F1-score while the fastest to train also.
Diabetes detection and prediction through a multimodal artificial intelligence framework
Authors: Kulkarni Gururaj N. , Kelapati Kelapati
Published: January 2025

Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.
A hybrid combination of improved mayfly optimization based modified perturb and observe for solar based water pumping system
Authors: Sawant Dattatray Surykant , Srinivasa Rao Yerramreddy , Sawant Rajendra Ramchandra
Published: January 2025

In recent years, solar water pumping systems (WPS) have been fuel-free and environmentally beneficial because they have gained a lot of attention in the agricultural and industrial sectors. Traditional water pumps consume higher amount of energy which make it as frequently unreliable, low efficiency and needs high maintenance. For WPS applications, Brushless DC (BLDC) motors are far superior options than other induction motors because of their high efficiency, high dependability, and low maintenance needs. Thus, in this research, the major goal is to develop a more efficient, reliable, and maintenance-free solar WPS solution. This paper describes a sensorless control strategy that reduces the need for hall sensors and increases system’s overall reliability. Solar system power is typically impacted by partial shadowing and cannot reach the maximum available power because the traditional perturbed and observe (P&O) algorithm fails. This paper integrates the modified P&O (MP&O) algorithm with an improved mayfly optimization (IMO) name called IMO-MP&O to address these issues by efficiently extracts the maximum power from solar. From the results, it clearly shows that IMO-MP&O achieved higher efficiency of 99.58% than the existing P&O MPPT which is analyzed the MATLAB sim-power-system toolboxes.
Tree-based models and hyperparameter optimization for assessing employee performance
Authors: Gustriansyah Rendra , Puspasari Shinta , Sanmorino Ahmad , Suhandi Nazori , Sartika Dewi
Published: January 2025

The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Analysis of VFDPC for three-level neutral point clamped AC-DC converters with capacitor balancing s
Authors: Razali Azziddin Mohamad , Mohd Yusoff Nor Azizah , Ab Shukor Syahar Azalia , Mohamed Hariri Muhammad Hafeez , Jidin Auzani , Sutikno Tole
Published: January 2025

This paper presents an analysis of the dynamic performance of a three-level neutral point clamped (NPC) AC-DC converter utilizing the advanced control technique of virtual flux direct power control (VFDPC). VFDPC estimates the three-phase grid voltage and instantaneous active and reactive power components, eliminating the need for an AC input voltage sensor used in conventional direct power control (DPC). This reduction in sensors decreases system complexity and cost while mitigating high-frequency noise and interference. Integrating VFDPC into 3L NPC AC-DC converters significantly enhances overall performance, leading to more efficient and robust power conversion systems. However, a significant challenge in the three-level NPC topology is the voltage imbalance in the neutral point of the DC-link capacitor, which can cause excessive voltage stress on switching devices and degrade system performance. To address this, a novel lookup table has been developed, incorporating strategies to balance the capacitor voltage. The results of this study demonstrate that VFDPC generates nearly sinusoidal line currents with reduced current total harmonic distortion (THD). Additionally, VFDPC ensures unity, lagging, and leading power factor operation, while providing flexibility to adjust the DC-link output voltage and accommodate load variations. These capabilities highlight VFDPC effectiveness in managing power quality and system stability, even under varying load conditions.
SmartSentry: a comprehensive framework for automated vulnerability discovery in Ethereum smart contracts
Authors: Zaazaa Oualid , El Bakkali Hanan
Published: January 2025

In the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security.
Editorial V19 N3
Authors: Ornstein Sheila  Walbe , Cuperschmid Ana Regina Mizrahy , Fabricio Márcio Minto
Published: January 2025

Editorial
Interoperability level for bridge structural analysis from the IFC data interpretation
Authors: Teixeira Guilherme Palla , Ribeiro José Carlos Lopes , César Júnior Kléos Magalhães Lenz , Nunes Lucas Andrade , Carvalho José Maria Franco , Oliveira Diôgo Silva , Nalon Gustavo Henrique
Published: January 2025

Importing IFC (Industry Foundation Classes) data from bridge structural models into structural analysis software is common in the BIM (Building Information Modeling) workflow. However, some software tools still do not import IFC data in the extended version that covers bridge models. In addition, there is a lack of a holistic framework for determining indicators that effectively represent the interoperability level for bridge structural analysis. To prevent data loss in bridge models, this work proposes a tool for interpreting IFC data related to the semantics of bridge elements, geometry, and material properties. A new methodology was developed to quantitatively evaluate an interoperability level for bridges structural analysis, considering the relevance of the imported information by defining numerical weight values. Then, a new framework was proposed for determining each data flow’s Interoperability Level for Bridge Structural Analysis (ILBSA). The interoperability level results showed that commercial bridge structural analysis software requires significant advances in interpreting IFC data.
Formação em jornalismo: Implicações Educativas e reverberações psicossociais
Authors: Mallmann Andréia Denise , Sidoruk Fabiana , Hamann Cristiano
Published: January 2025

O presente estudo procura problematizar um projeto de extensão desenvolvido em um curso de graduação em Jornalismo, com vistas a discutir o processo de formação profissional na área. O projeto contemplou a cobertura da desinstitucionalização de um hospital psiquiátrico, percurso que demonstrou como a construção da cobertura, e concomitantemente de formação universitária, convocou questões de ordem ética e política. Por meio de uma abordagem etnográfica, discute-se a escolha e a definição de fontes, o percurso desenvolvido e as implicações do jogo de visibilidade/invisibilidade na produção do projeto de extensão, indicando implicações para o campo da formação em jornalismo.
Entre memórias e reflexões sociológicas: experiências de extensão universitária na escola Zumbi dos Palmares
Authors: Hoffmann-Horochovski Marisete Teresinha , Machado Valéria Floriano , Santos Camilla de Sousa dos , Pereira Rafaela Berger
Published: January 2025

This experience report describes a university extension developed in the Metropolitan Region of Curitiba that aimed to rescue and record the fighting memories of the Zumbi dos Palmares community based on youth experiences. Paulo Freire, bell hooks, Maurice Halbwachs, Norbert Elias, and Georg Simmel theoretically guided this discussion. Methodologically, two activities were carried out with students in their first high school year in the space of their school and village, respectively: the construction of reliquaries for memory work and a lambe-lambe workshop. Results highlight the relation between university and school and the centrality of interaction in producing and recovering collective memory.
Bacurau: A arte de entrelaçar imagens, sons e memórias no processo de criação cinematográfica
Authors: Ferraraz Rogério , Magno Maria Ignes Carlos
Published: January 2025

Bacurau – pássaro comum no Brasil, de plumagem muito macia, voo silencioso, olhos muito brilhantes e só encontrado à noite – deu nome ao filme de Kleber Mendonça Filho e Juliano Dornelles. O filme conta a história de um povoado do sertão brasileiro que misteriosamente desaparece do mapa e é invadido por drones e estranhos turistas que começam a atacar as pessoas do lugar. A comunidade precisa identificar o inimigo e criar coletivamente um meio para se defender. Na ficção, Bacurau sumiu do mapa e se tornou apenas uma lembrança sem referência geográfica, mas na história contada Bacurau está repleto de referências culturais, alusões cinematográficas e lembranças fílmicas, nacionais e internacionais, e pede que comecemos a querer ligar “os fios da meada no rio”   criativo do filme. Essa é a nossa proposta de resenha: refletir sobre esse processo de criação do fazer cinema.
Atividades em Sala de Aula
Authors: Itacarambi Ruth Ribas
Published: January 2025

A Educomunicação tem referências e sustentação no projeto de decolonialidade, por meio da transposição dialógica como medium, que inclui a urgência de uma educação midiática e tecnológica com as realidades e contextos; propõe a (re)construção e o respeito à inclusão das vozes de grupos identitários, secularmente silenciados pela lógica colonialista, como as populações afrodiaspóricas, africanas e indígenas. De metodologia participante e de viés hermenêutico, este artigo analisa sua inserção como uma práxis de intervenção, gênese e imbricamento com os movimentos sociais, culturais e educacionais do campo em emergência.
A casa-museu acolhe obras in situ (ou site specific)
Authors: Barbosa Paulo Eduardo , Lara Regina
Published: January 2025

El objetivo es reflexionar sobre la presencia de obras de arte site-specific en las casas-museo, considerando la fricción que proponen como lenguaje del arte. Los museos están vacíos en el sentido de que pueden albergar cualquier cuerpo, sin estar atados a lo que exponen, dependiendo de las relaciones de temporalidad y espacialidad. En el caso del arte site-specific, lo que importa no es la obra en sí, ni el lugar, sino el vínculo que se establece entre ambos. La escala, como herramienta del lenguaje artístico, invita a vivir experiencias sensoriales, enfatiza el roce y la fricción, sugiriendo otras realidades.
O IPPMG em revista, entre o analógico e o digital
Authors: Herbst Hélio
Published: January 2025

O presente artigo tem como objeto de estudo os artigos publicados sobre o Instituto de Puericultura e Pediatria Martagão Gesteira (IPPMG), implantado na Cidade Universitária da Ilha do Fundão, Rio de Janeiro, em revistas especializadas em Arquitetura e Urbanismo brasileiras, impressas e digitais. Objetiva-se examinar o impacto das soluções de projeto do Instituto com base nos aportes formulados por Hans Robert Jauss (1994 [1967]) e Roger Chartier (2002 [1988]). Pretende-se investigar o significado dos proje tos arquitetônico e paisagístico, elaborados entre 1949 e 1953 por Jorge Machado Moreira e Roberto Burle Marx, respectivamente, problematizando-se especificidades relacionadas aos meios de produção e difusão de conhecimentos, em diferentes tempo
Checklist of forensically significant Rove beetles (Coleoptera: Staphylinidae: Staphylininae: Staphylinini) from India
Authors: Bharti Meenakshi , Sharma Shweta
Published: January 2025

Beetles play a substantial role in calculating the postmortem interval (PMI) during the later stages of decomposition of a carcass. The dominant families are Silphidae, Dermestidae, Histeridae, Tenebrionidae, Trogidae, and Staphylinidae. For accuracy in PMI calculations based on insect fauna, it becomes imperative to catalogue the insect diversity of an area. Herein, we provide checklist of forensically important rove beetles from India belonging to subfamily Staphylininae of family Staphylinidae.
Phenology of Rhododendron wattii Cowan (Ericales: Ericaceae) - a threatened plant of Nagaland, India
Authors: Jing Imtilila , S.K. Chaturvedi
Published: January 2025

The paper deals with the flowering phenology of Rhododendron wattii Cowan (Ericaceae family), a threatened and endemic plant from Nagaland, northeastern India. The study was conducted at Dzukou Valley, Kohima District of Nagaland, on a single tree of R. wattii growing at an elevation of 2,600 m with no other tree of the same species in the vicinity. Flowering occurs from the end of February to April, and fruiting is observed from April to December. The flowers present in trusses of 18–25 flowers are pink with darker flecks and purplish basal blotches. They are foraged and pollinated by the Fire-tailed Sunbird Aethopyga ignicauda and bumble bees (Bombus spp.). The only attractant for the foragers is the nectar secreted in the five nectaries at the base of the corolla tube. Catastrophes like frequent forest fires and anthropogenic activities are responsible for the disappearance of this species.
Accumulation of advanced oxidative protein products exacerbate satellite glial cells activation and neuropathic pain
Authors: Tu Chen , Wang Shi-Cheng , Dai Meng-Xuan , Lai Si-Qi , Huang Zhi-Wei , Yu Yong-Peng , Chen Yun-Biao , Zeng Ji-Huan , Wang Liang , Zhong Zhao-Ming
Published: January 2025

Background

Neuropathic pain (NP) is a debilitating condition caused by lesion or dysfunction in the somatosensory nervous system. Accumulation of advanced oxidation protein products (AOPPs) is implicated in mechanical hyperalgesia. However, the effects of AOPPs on NP remain unclear.

Methods

A rat model of NP was established by chronic constriction injury (CCI) and employed to evaluate the changes of mechanical withdrawal threshold, thermal and cold withdrawal latency, as well as AOPPs levels. The effects of AOPPs on the activation of satellite glial cells (SGCs) in the dorsal root ganglion (DRG), receptor for advanced glycation end-products (RAGE) expression, and NF-κB signaling pathway activation were also investigated using western blotting, immunofluorescence, and the Fluo4-AM fluorescence probe for calcium signaling. Additionally, oxidative stress levels and inflammatory cytokine production in SGCs, triggered by AOPPs exposure, were measured through the DCFH-DA probe for ROS detection and ELISA kits for cytokine quantification.

Results

CCI significantly elevated the AOPPs levels in the plasma and sciatic nerve and caused AOPPs accumulation in the DRG. Exogenous AOPPs activated SGCs, increased reactive oxygen species and inflammatory response, upregulated the RAGE, and activated NF-κB signaling. The RAGE inhibitor FPS-ZM1 effectively inhibited AOPPs-induced SGC activation. Additionally, AOPPs intervention worsened CCI-induced hyperalgesia and neuroinflammation in vivo.

Conclusion

These results indicate that AOPPs exacerbate the SGC activation and NP following nerve injury, and AOPPs accumulation might play an important role in the pathogenesis of NP.
Analysis of logistics measures of CEP service providers for the last-mile delivery in small- and medium-sized cities: A case study for the Aachen city region
Authors: Ghazal Abdulrahmon , Narayanan Santhanakrishnan , Adeniran Ibraheem Oluwatosin , Kehrt Carina , Antoniou Constantinos
Published: January 2025

The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.
Future perspectives for PDE5 inhibitors bridging the gap between cardiovascular health and psychological status
Authors: Sansone Andrea , Guida Eugenia , Dolci Susanna , Frangione Valeria , Asso Amanda , Bellia Gilberto , Jannini Emmanuele A.
Published: January 2025

The serendipitous discovery that inhibiting type 5 phosphodiesterase (PDE5) using sildenafil, a potent PDE5 inhibitor (PDE5i) initially developed for cardioprotection, introduced the possibility of orally managing erectile dysfunction (ED) led to an increase in research data, which are currently considered groundbreaking for the new discipline of sexual medicine. Findings from a number of laboratories and clinics around the world unanimously demonstrated the following: (i) the major cause of ED is directly or indirectly related to cardiovascular disease (CVD); (ii) ED and CVDs share the same risk factors, which are related mainly to lifestyle choices; (iii) the first therapeutic approach to both ED and CVDs is to transform harmful lifestyles into virtuous lifestyles; and (iv) PDE5is in general, particularly sildenafil, are very safe, if not protective, for use in CVD patients. However, the use of PDE5is has faced several challenges. Many patients and some healthcare providers (HCPs) often share the misconception that using these drugs can increase the risk of CVD. Some patients might desire to fulfill the unmet need for privacy linked to the stigma of being treated for ED or might be enticed by the idea of buying drugs online, either because of shame or cheaper prices, without knowing the risks associated with counterfeit drugs. The aim of this narrative revision of the current literature is to demonstrate that (i) the orodispersible film of sildenafil is safe from a CV perspective; (ii) it is a discreet formulation that respects the need for privacy; and (iii) it is virtually the unique PDE5i formulation too expensive to produce outside the correct channels, making it impossible to be counterfeit.
Factors influencing role preferences in decision-making of healthy women with BRCA1/2 pathogenic variants: subanalysis from a randomised controlled decision coaching trial
Authors: Kautz-Freimuth Sibylle , Shukri Arim , Stracke Claudia , Isselhard Anna , Berger-Höger Birte , Steckelberg Anke , Vitinius Frank , Dikow Nicola , Kiechle Marion , Meisel Cornelia , Wöckel Achim , von Mackelenbergh Marion Tina , Schmutzler Rita , Rhiem Kerstin , Stock Stephanie
Published: January 2025

Background

Patients who actively engage in their medical decision-making processes can experience better health outcomes. This exploratory study aimed to identify predictors of preferred and actual roles in decision-making in healthy women with BRCA1/2 pathogenic variants (PVs).

Methods

Women with BRCA1/2 PVs without a history of breast and/or ovarian cancer were recruited in six centres across Germany. Those returning the baseline questionnaires (T1) were randomly assigned to the intervention or control group (IG, CG). The IG completed a decision-coaching (DC) programme, the CG received standard care. A second survey (T2) followed after 12 weeks. Ordinal regression analyses were performed. Sociodemographic and outcome-related baseline variables were used to identify predictors of (i) desired role at T1 in the total group and (ii) actual role at T2 in the CG and the IG. Role preferences were measured with the Control Preferences Scale.

Results

389 women completed the baseline questionnaires, 191 were randomised to the CG and 198 to the IG. At T1, high decisional conflict (OR 1.016, 95% CI 1.001–1.023, p = 0.038) and a negative self-concept (OR 1.030, 95% CI 1.008–1.054, p = 0.009) were significant predictors for preferring a more passive role. At T2, high baseline decisional conflict significantly predicted taking a more passive role in the CG, whereas in the IG, baseline decisional conflict showed no influence. Furthermore, in the IG, younger age (OR 1.049, 95% CI 1.001–1.098, p = 0.044) and a non-academic education (OR 0.46, 95% CI 0.213–0.775, p = 0.006) were identified as significant predictors for taking a more active role.

Conclusions

High initial decisional conflict was identified as an important predictor for preferring and taking a passive role in decision-making among women with BRCA1/2 PVs. Participating in the DC programme can counteract passivating effects of an initially high decisional conflict and particularly support younger PV carriers and those with lower educational status to take an active role. With this profile, the DC programme expands the existing counselling and care concept to include a measure that can also specifically cover the support needs of younger women and those with a lower education level.

Trial registration

DRKS-ID: DRKS00015527. Registered 30/10/2019.
Risk factors for disease progression and clinical outcomes in patients with COVID-19 in Taiwan: a nationwide population-based cohort study
Authors: Kuo Raymond N. , Chen Wanchi , Shau Wen-Yi
Published: January 2025

Background

Since 2021, COVID-19 has had a substantial impact on global health and continues to contribute to serious health outcomes. In Taiwan, most research has focused on hospitalized patients or mortality cases, leaving important gaps in understanding the broader effects of the disease and identifying individuals at high risk. This study aims to investigate the risk factors for disease progression through a nationwide population-based cohort study on COVID-19 in Taiwan.

Methods

This study included 15,056 patients diagnosed with COVID-19 between January 1, 2021, and December 31, 2021, using the Taiwan National Health Insurance Research Database. Baseline and clinical characteristics were collected to verify the association with progression to severity outcomes, including hospital admission, intensive care unit (ICU) admission, invasive ventilatory support, fatal outcome, and the composite outcome of these four events. Patients were observed for 30 days for disease progression. Multivariable logistic regression models were used to calculate odd ratios and 95% confidence intervals (CIs) for each outcome, adjusting for age, sex, region, risk factors, and vaccination status.

Results

Overall, 8,169 patients diagnosed during outpatient visits and 6,887 patients diagnosed during hospitalization were analyzed. Adjusting for age, sex, region, risk factors, and vaccination status, elderly patients had higher risks of hospital admission, ICU admission, invasive ventilatory support, fatal outcome, and composite outcome. Specifically, the risk of the fatal outcome was significantly higher for patients aged 75–84 (odds ratio: 6.11, 95% CI: 4.75–7.87) and those aged 85 years and older (12.70, 9.48–17.02). Patients with cardiovascular disease exhibited higher risks of hospital admission (1.60, 1.31–1.96), ICU admission (1.52, 1.31–1.78), invasive ventilatory support (1.57, 1.26–1.96), and fatal outcomes (1.26, 1.03–1.54) and the composite outcome (1.66, 1.20–1.54). Diabetes mellitus was identified as a significant risk factor for all clinical outcomes (hospital admission: 1.89, 1.53–2.35; ICU admission: 1.53, 1.30–1.79; invasive ventilatory support: 1.27, 1.01–1.60; the composite outcome: 1.45, 1.28–1.66), except for the fatal outcome.

Conclusions

This study indicated the impact of sex, age, and risk factors on the clinical outcomes of COVID-19 patients in Taiwan. Elderly patients and those with cardiovascular disease or diabetes mellitus had higher risks for severe outcomes, including hospitalization, ICU admission, invasive ventilatory support, and mortality. These findings can provide evidence for a better understanding of risk factors for disease progression and inform targeted intervention.
Implicit bias assessment by career stage in medical education training: a narrative review
Authors: Crump Alisha , Al-Jorani May Saad , Ahmed Sunya , Abrol Ekas , Jain Shikha
Published: January 2025

Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person based on individual characteristics. Early evaluation of implicit bias in medical training can prevent long-term adverse health outcomes related to racial bias. However, to our knowledge, no present studies examine the sequential assessment of implicit bias through the different stages of medical training. The objective of this narrative review is to examine the breadth of existing publications that assess implicit bias at the current levels of medical training, pre-medical, graduate, and postgraduate. Protocol for this study was drafted using the Scale for the Assessment of Narrative Reviews (SANRA). Keyword literature search on peer-reviewed databases Google Scholar, PubMed, Ebsco, ScienceDirect, and MedEd Portal from January 1, 2017, to March 1, 2022, was used to identify applicable research articles. The online database search identified 1,512 articles. Full screening resulted in 75 papers meeting the inclusion criteria. Over 50% of extracted papers (74%) were published between 2019 and 2021 and investigated implicit bias at the post-graduate level (43%), followed by the graduate level (34%), and pre-medical level (9.4%). Fourteen percent were classified as mixed. Studies at the medical and medical graduate level identified an implicit preference towards white, male, non-LGBTQIA+, thin, patients. Study findings highlight notable gaps within the sequential assessment of implicit bias, specifically at the pre-medical training level. Longitudinal epidemiological research is needed to examine the long-term effect of implicit biases on existing healthcare disparities.