Dynamic Integration Research Articles

Artificial intelligence in academic writing: composing a for-and-against essay

Over the past years, Artificial Intelligence (AI) and Chat Generative Pre-Trained Transformer (ChatGPT) as its type have been extensively investigated and proved to be effectually employed within various spheres including education, offering promising opportunities for enhancing teaching and learning processes. However, the realization of these opportunities within the realm of Academic Writing (AW) instruction have not yet been thoroughly studied. Therefore, this article addresses this matter and exposes the results of a conducted analysis of the potential for integrating ChatGPT into AW instruction within the University curriculum. Specifically, it focuses on the issue of teaching master students how to create a successful for-and-against academic essay. Besides, the aim of this article is to develop an overarching framework for the incremental incorporation of ChatGPT into the AW course. To achieve this aim, the article resorts to such methods as theoretical positioning, comparative analysis, and qualitative research. Accordingly, the presented study expounds on the phenomenon of ChatGPT, its attributes, merits, and shortcomings, while also outlining the notion, features, key elements, and stages of producing for-and-against academic essays. In addition, it delineates the possibilities of ChatGPT’s application at various stages of essay writing, which may appear challenging for students. Furthermore, it recognizes potential drawbacks of ChatGPT, such as the risk of its overutilization and overreliance, which may result in obliterating students’ cognitive capabilities and AW skills. The presented study also underscores the necessity for additional immersion in the matter under discussion to address the issue related to long-term effects of integrating ChatGPT into AW instruction. Ultimately, the article sheds light on the AI’s dynamic integration in modern educational settings and provides implications for further research in this field.

Molecular dynamics-informed material point method for hypervelocity impact analysis

This paper introduces a framework specifically designed to simulate hypervelocity impact scenarios precisely. The framework utilizes the multiscale shock technique (MSST) from molecular dynamics (MD) to accurately model material states under extreme impact loading conditions, focusing on calculating the equation of state (EOS). A vital aspect of this work is the acquisition and application of the Mie-Grüneisen EOS, which is highly relevant in impact analysis research. The framework employs the material point method (MPM) to conduct analyses of hypervelocity impacts using the derived EOS. This method offers a detailed insight into the dynamic responses of materials subjected to hypervelocity impacts, underscoring the integration of molecular dynamics with the MPM.

Image manipulation localization via dynamic cross-modality fusion and progressive integration

Image manipulation localization aims at distinguishing forged regions from the whole test image. Though many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: (1) how to fuse diverse types of features with forgery clues; (2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.

A method to integrate hydraulic structure models into 3D terrain models for irrigation infrastructure visualization

Seamless integration of three-dimensional (3D) terrain models and hydraulic structure models is a technical challenge in the construction of 3D virtual scenes for irrigation areas. This study proposes a level-of-detail (LOD)-based dynamic classification integration method for hydraulic structure models and 3D terrain models, called CM-D-LOD. Hydraulic structures are classified according to their point, line, and surface morphologies, as well as their dependence on or independence of the terrain into four categories: point-like hydraulic structures independent of terrain, line-like hydraulic structures dependent on terrain, surface-like hydraulic structures dependent on terrain, and surface-like hydraulic structures independent of terrain. By utilizing the proposed model classification integration method, a visualization management platform for virtual geographical environments of irrigation areas is developed, and experiments are conducted in the Zhuluo Ba Irrigation Area within the large economic zone along China’s eastern coast. Results demonstrate that the integration accuracy can be controlled between 0.2 and 0.7 m and that the 3D virtual scene of the irrigation area can be updated in real time. The proposed classification integration method transforms the traditional global model integration approach into a more efficient one, significantly improving the efficiency of constructing virtual geographical scenes for irrigation areas.

Establishing a link between the chemical composition and biological activities of Gladiolus italicus Mill. from the Turkish flora utilizing in vitro, in silico and network pharmacological methodologies

Objectives Five solvent extracts (n-hexane, ethyl acetate, ethanol, ethanol/water (70%), and water) of Gladiolus italicus Mill. from Turkey were evaluated for chemical and biological properties. Methods Antioxidant activities, inhibitory properties against key enzymes involved in the etiology of chronic diseases were tested, as well as cytotoxic effects on different cell lines. Chemical characterization was also carried out to determine the most abundant compounds of each extract. Results The highest total phenolic content (TPC) was observed in the water extract while highest TFC in ethanol/water extract. The most abundant compounds in the extracts were hyperoside (69041.06 mg kg−1), isoquercitrin (46239.49 mg kg−1), delphindin-3,5-diglucoside (42043.81 mg kg−1), myricetin (21486.61 mg kg−1), and kaempferol-3-glucoside (21199.76 mg kg−1). Molecular dynamic (MD) simulations confirmed the structural stability and dynamic conformational integrity of these complexes over a period of 100 ns. In network pharmacology, A total of 657 unique target genes were screened: 52 associated with programmed cell death-1 (PD-1), 85 with vascular endothelial growth factor receptor-2 (VEGFR2), and 130 with fibroblast growth factor receptor-2 (FGFR2), identifying crucial gene interactions for these proteins. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted, revealing significant interactions and pathways such as the advanced glycation end products (AGE) and their receptors (RAGE) signaling pathway in diabetic complications and T- helper 17 (Th17) cell differentiation, among others. This elucidation of complex networks involving key genes like AKT Serine/Threonine Kinase 1 (AKT1), MYC proto-oncogene (MYC), tumor protein 53 (TP53), Interleukin 6 (IL6), and tumor necrosis factor (TNF) provides a promising foundation for the development of targeted therapies in the treatment of non-communicable diseases. Conclusion These results show that G. italicus could be a natural source of potent antioxidants and enzyme inhibitors which need to be further explored for the development of biopharmaceuticals.

Functional segregation and dynamic integration of the corticotectal descending signal in rat

The superior colliculus (SC) receives inputs from various brain regions in a layer- and radial subregion-specific manner, but whether the SC exhibits subregion-specific dynamics remains unclear. To address this issue, we recorded the spiking activity of single SC neurons while photoactivating cortical areas in awake head-fixed Thy1-ChR2 rats. We classified 309 neurons that responded significantly into 8 clusters according to the response dynamics. Among them, neurons with monophasic excitatory responses (7–12 ms latency) that returned to baseline within 20 ms were commonly observed in the optic and intermediate gray layers of centromedial and centrolateral SC. In contrast, neurons with complex polyphasic responses were commonly observed in the deep layers of the anterolateral SC. Cross-correlation analysis suggested that the complex pattern could be only partly explained by an internal circuit of the deep gray layer. Our results indicate that medial to centrolateral SC neurons simply relay cortical activity, whereas neurons in the deep layers of the anterolateral SC dynamically integrate inputs from the cortex, SNr, CN, and local circuits. These findings suggest a spatial gradient in SC integration, with a division of labor between simple relay circuits and those integrating complex dynamics.

Sustainable Development of Digital Cultural Heritage: A Hybrid Analysis of Crowdsourcing Projects Using fsQCA and System Dynamics

Cultural heritage crowdsourcing has emerged as a promising approach to address the challenges of digitizing and preserving cultural heritage, contributing to the sustainable development goals of cultural preservation and digital inclusivity. However, the long-term sustainability of these projects faces numerous obstacles. This study explores the key configurational determinants and dynamic evolutionary mechanisms driving the sustainable development of cultural heritage crowdsourcing projects, aiming to enhance their longevity and impact. An innovative integration of fuzzy-set qualitative comparative analysis (fsQCA) and system dynamics (SD) is employed, drawing upon a “resource coordination–stakeholder interaction–value co-creation” analytical framework. Through a multi-case comparison of 18 cultural heritage crowdsourcing projects, we identify necessary conditions for project sustainability, including platform support, data resources, knowledge capital, and digitalization performance. The study reveals multiple sufficient pathways to sustainability through configurational combinations of participant motivation, innovation drive, social capital, and social impact. Our system dynamics analysis demonstrates that crowdsourcing project sustainability exhibits significant nonlinear dynamic characteristics, influenced by the interaction and emergent effects of the resource–participation–performance chain. This research offers both theoretical insights and practical guidance for optimizing crowdsourcing mechanisms and sustainable project operations, contributing to the broader goals of sustainable cultural heritage preservation and digital humanities development. The findings provide a roadmap for policymakers and project managers to design and implement more sustainable and impactful cultural heritage crowdsourcing initiatives, aligning with global sustainability objectives in the digital age.

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

Blockchain-Based Dynamic Cloud Data Integrity Auditing via Non-Leaf Node Sampling of Rank-Based Merkle Hash Tree

ESTUDIO DE DIFERENTES ALTERNATIVAS DE FUNCIONAMIENTO DE LOS GRUPOS EN LAS CENTRALES ELÉCTRICAS DE CANARIAS

Island electricity systems aim to adapt to the environmental expectations established by the European framework. For this reason, it is necessary to increase efforts to increase the penetration of renewables and optimize energy production. An extensive preliminary research study has been carried out on the current production system, its variables and its conditioning factors, with which a series of operating alternatives have been obtained that will optimize the energy production system through combustion technology (non-renewables) and combine them with the production of energy through renewable sources. renewables, complying both in dynamic response, security, scaling and integration with such systems, in terms of efficiency and power. The reduction of greenhouse gas emissions and the penetration of these renewable energies in the island's electricity systems will be increased. As has been proven in the research background, the usually established combination of power producing equipment is not the best to reduce pollution, and there are other possible combinations with better results. The study methodology followed leads to take measures based on: a) changing the type of fuel in the equipment that allows it, b) using the combination of less pollutant producing equipment with response capacity and c) considering the integration and incorporation of the pumped hydroelectric electric storage plant (PHES) "Chira-Soria" into Gran Canaria's electrical system. The different measures contribute to the improvement in the reduction of greenhouse gases (GHG) in a convincing way. Keywords: Isolated power system, Generation expansion planning, Energy transition, Insular electrical systems (IES), Energy policy, Canary Islands, Renewable energy, Pumped hydroelectric storage plant (PHES).

Improvement of the energy resolution and non-proportional light response of CsI:Tl to gamma radiation using dynamic integration times

The analysis of the light response of thallium-doped cesium iodide to gamma radiation was performed on single scintillation waveforms recorded using a digital oscilloscope. As an alternative to classical pulse processing procedures, the use of dynamic integration times instead of fixed shaping/integration times was demonstrated. The dynamic integration time, changeable with the amplitudes of the CsI:Tl scintillation pulses, allowed to optimise the light collection with simultaneous minimization of a noise contribution. It was shown that for low-energy γ-rays (22 keV–60 keV), the scintillation pulses should be integrated using shorter integration times (approximately 7 μs–14 μs), whereas for higher energies (662 keV–835 keV), the best energy resolution was achieved with integration times of about 28 μs–30 μs. The approach also allowed for the registration of more proportional CsI:Tl light responses to deposited X and gamma radiation in the energy range of 22 keV–835 keV.

Towards a Deep Learning-Powered Herbarium Image Analysis Platform

Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity. ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023, Sahraoui et al. 2023). Within the e-Col+project (ANR-21-ESRE-0053), we are developing multiple deep learning models aimed at identifying plant morphological traits. We have developed pipelines and models for cleaning, analyzing, and transforming herbarium images, including models for: i) detecting non-vegetal elements, such as barcodes, envelopes, labels, etc.; ii) detecting plant organs, including leaves, flowers, fruits, etc.; and iii) segmenting to recognize plant parts for image cleaning. We are also developing models for classification tasks related to various morphological traits. To validate these models, improve their generalization, and make them easily usable by end-users, deploying them within a generic platform is crucial. The generic platform called PlantAI, currently under development by the e-Col+ project, should enable easy deployment during development for testing and allow users to load annotations for new traits in order to train a model and add it to the existing catalog. The platform is based on a microservice architecture, allowing users to upload images, create custom datasets, and access various AI models for image analysis. The platform is composed of four main modules, as illustrated in Fig. 1. The first module is the collaborative workspace manager, which allows users to create projects and image datasets and invite other users to collaborate on a project. The second module is the navigation interface and dashboards. This module integrates a search engine using metadata and AI annotations, a navigation interface between projects, datasets, and specimens, as well as dashboards for analysis across datasets, specimens, and AI models. The third module is the dataset manager, which handles metadata and annotations associated with the specimens. These annotations can be produced either by expert users or by AI models. The fourth module is the AI models management module, so that models can be used to generate AI annotations of specimen. During the development lifecycle of an AI model, users can create datasets and annotate them with AI models. These annotations can be in two possible states: validated by experts and non-validated. Users collaborating on a project can indicate errors in the model predictions and leave comments to explain their evaluations. These corrections made by experts can be used to retrain the models and thus improve their performance. This platform, will be highly beneficial for botanists, enhancing the efficiency and effectiveness of biodiversity analyses from herbarium scans. We aim to provide users with a catalog of AI models through this platform and allow them to import their own datasets with their own annotations regarding traits of their choice. Users will be able to select a model from the AI model catalog and train it using their dataset. Ultimately, the model obtained from this training will be automatically deployed to be available for AI annotation. The annotations produced by this model will be automatically available in the filtering and navigation interface, thus allowing for dynamic and automatic integration of the AI annotations into the navigation interface.

‘From the Ivory Tower’? Memory Workers and Mnemonic Practices in Communities

AbstractMemory work involves mnemonic practices such as remembering, forgetting, and enactment of the past to address past wrongdoing, foster future action, and contribute to a sense of belonging. Working with diversity and plurality of memories, however, necessitates confronting the underlying politico‐ethical considerations and struggles when memory work transcends organizational bounds. This study focuses on the viewpoints of memory workers in communities in order to theorize the various possibilities and limitations of memory work as such. In this work, the politico‐ethical tensions are evident between the requirements of practicing an occupation and those of the communities, who in turn exhibit hierarchies, conflicts, and diversity within and between themselves. I suggest that taking a community‐centric approach to memory work can serve the dynamic integrity of memories, and foster community engagement and empowerment. Memory workers, then, can account for the politico‐ethical struggles over memories by orchestrating interpretive, open, and embodied mnemonic practices to remain in tune with the diverse, disputed, polyvocal, and ever‐unfolding memories. The contributions of this paper carry implications for a more pluralistic and dynamic approach to memory work, suited to our times marked by increased historical consciousness, rival memories, and fierce debates over what and how must be remembered.

A Gradient Dynamics-Based Singularity Avoidance Method for Backstepping Control of Underactuated TORA Systems.

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods.

Circadian and environmental signal integration in a natural population of Arabidopsis

Plants sense and respond to environmental cues during 24 h fluctuations in their environment. This requires the integration of internal cues such as circadian timing with environmental cues such as light and temperature to elicit cellular responses through signal transduction. However, the integration and transduction of circadian and environmental signals by plants growing in natural environments remains poorly understood. To gain insights into 24 h dynamics of environmental signaling in nature, we performed a field study of signal transduction from the nucleus to chloroplasts in a natural population of Arabidopsis halleri. Using several modeling approaches to interpret the data, we identified that the circadian clock and temperature are key regulators of this pathway under natural conditions. We identified potential time-delay steps between pathway components, and diel fluctuations in the response of the pathway to temperature cues that are reminiscent of the process of circadian gating. We found that our modeling framework can be extended to other signaling pathways that undergo diel oscillations and respond to environmental cues. This approach of combining studies of gene expression in the field with modeling allowed us to identify the dynamic integration and transduction of environmental cues, in plant cells, under naturally fluctuating diel cycles.

Navigating Mediated Spaces: Screens and Connectivity in Ikebukuro Chinatown’s Chinese Diaspora

This research explores the profound impact of digital media on the lives of Chinese immigrants in Ikebukuro Chinatown. It illustrates how the Internet and digital platforms have transformed their economic, social, and identity landscapes. Employing qualitative methods such as surveys and interviews, this study underscores the pivotal role of social media in creating expansive networks. These networks facilitate ethnic entrepreneurship and enhance cultural visibility, contributing to the deterritorialization of traditional community confines. This empowerment enables the Chinese diaspora in Ikebukuro to cultivate a rich, interconnected social tapestry that extends beyond geographic limitations. The findings underscore the central role of digital media mediatization processes in redefining immigrant experiences. Additionally, they promote deeper, more dynamic integration within the host society’s multicultural environment. This transformative shift emphasizes the emergence of a more fluid, networked form of community and identity among immigrants. It challenges conventional enclave models and offers new perspectives on diaspora engagement in the digital age.

Effect of crystallographic orientation on the deformation and mechanical behavior of CoCrFeNi in Berkovich nanoindentation

The hardness and plastic deformation behavior of single-phase concentrated solid solution alloy CoCrFeNi were studied for selected crystallographic orientations (⟨001⟩, ⟨011⟩ and ⟨111⟩) using Berkovich nanoindentation. The integration of transmission electron microscopy (TEM) and molecular dynamics (MD) simulations was carried out to elucidate comprehensively the distinction in the deformation mechanisms, the evolution of dislocation structures, and the pile-up of differently oriented CoCrFeNi alloys subjected to indentation by a Berkovich indenter. In MDs, a Berkovich indenter, meticulously crafted to replicate its real geometry, was effectively employed in investigating the nanoindentation. The ⟨001⟩-oriented CoCrFeNi exhibited the highest hardness and the lowest magnitude of pop-in events, followed by the ⟨011⟩ and then the ⟨111⟩ orientations. The pile-up behavior was strongly dependent on the crystallographic orientation and occurred by three sequential processes. The deformation twinning was activated only in the ⟨001⟩ orientation and inclined toward the center direction of the indent due to the force inclining toward the radial direction of the indent. The clear manifestation of the evolution of dislocations in the three orientations during indentation was elucidated. More SFTs or Stair-rods were observed in the ⟨001⟩ orientation, attributable to the intensified reaction of Shockley partials stemming from its higher density of Shockley partials, followed by the ⟨011⟩ and then the ⟨111⟩ orientations. The anisotropic nanoindentation-hardness was correlated with the microstructures. The highest hardness and lowest magnitude of pop-in events of the ⟨001⟩ orientation could be ascribed to its intense dislocation interactions, high-density SFTs and the formation of twins. In the ⟨111⟩ orientation, the three-fold symmetry emission of prismatic dislocation loops along the three slip directions not parallel to the surface leaded to a weak interaction between dislocations, and then resulted in the lowest hardness. This research may be helpful to enhance our comprehension of the intricate mechanical behaviors exhibited by different oriented FCC high entropy alloys under Berkovich indentation.

Evaluating Taiwan’s Geothermal Sites: A Bounded Rationality Data Envelopment Analysis Approach

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan’s transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan’s strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research.

Targeted and untargeted lipidomics with integration of liver dynamics and microbiome after dietary reversal of obesogenic diet targeting inflammation-resolution signaling in aging mice

Obesity, a global epidemic linked to around 4 million deaths yearly, arises from lifestyle imbalances impacting inflammation-related conditions like non-alcoholic fatty liver disease and gut dysbiosis. But the long-term effects of inflammation caused by lifestyle-related dietary changes remain unexplained. In this study, we used young male C57Bl/6 mice which were fed either an obesogenic diet (OBD) or a control diet (CON) for six months. Later, a group of mice from the OBD group were intervened to the CON diet (OBD-R) for four months, while another OBD group remained on the OBD diet. The OBD induced distinct changes in gut microbial, notably elevating Firmicutes and Actinobacteria, while reducing Bacteroidetes and Tenericutes. OBD-R restored microbial abundance like CON. Analyzing liver, plasma, and fecal samples revealed OBD-induced alterations in various structural and bioactive lipids, which were normalized to CON in the OBD-R, showcasing lipid metabolism flexibility and adaptability to dietary shifts. OBD increased omega 6 fatty acid, Arachidonic Acid (AA) and decreased omega 3-derived lipid mediators in the OBD mimicking non-alcoholic fatty liver disease thus impacting inflammation-resolution pathways. OBD also induced hepatic inflammation via increasing alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels and proinflammatory markers CCR2, TNF-α, and IL-1β in liver. Transitioning from OBD to CON mitigated inflammatory gene expression and restored lipid and cholesterol networks. This study underscores the intricate interplay between lifestyle-driven dietary changes, gut microbiota, lipid metabolism, and liver health. Notably, it suggests that shift from an OBD (omega-6 enriched) to CON partially alleviates signs of chronic inflammation during aging. Understanding these microbial, lipidomic, and hepatic inflammatory dynamics reveals potential therapeutic avenues for metabolic disorders induced by diet, emphasizing the pivotal role of diet in sustaining metabolic health.

Understanding cultural dynamics shaping clinical reasoning skills: A dialogical exploration.

Our study examined the influence of national cultural predispositions on training medical professionals and doctor-patient dynamics using a dialogical approach, guided by Hofstede's framework. This framework provided valuable insights into how cultural tendencies shape the learning and application of clinical reasoning skills in different cultural contexts. We found that dimensions such as power distance and individualism versus collectivism significantly influenced clinical reasoning, while other dimensions had more nuanced effects. Junior doctors in Southern nations, despite initially lagging behind, developed advanced clinical reasoning skills with experience, eventually matching their Northern counterparts. The study highlighted the link between cultural norms and educational practices, variations in family involvement during reasoning, adherence to clinical guidelines and doctors' emotional engagement in clinical care between Southern and Northern contexts. Additionally, we recognised that effective clinical reasoning extends beyond technical knowledge, involving an understanding and integration of cultural dynamics into patient care. This highlights the pressing need to prioritise this topic.

Free Vibration Response Studies on Bridges

Abstract Bridges are essential components of the transportation network. These are unique constructions that serve as a transportation link between regions separated by physical barrier. Because of their critical role in ensuring passenger safety, these structures require constant maintenance. When vehicle cross a bridge, the structural components vibrate, which over time may cause damage to these components. The preferred method of maintaining structural health is currently seeing a rise in the use of Structural Health Monitoring (SHM) systems on infrastructure, especially bridges. In order to overcome the drawbacks of visual inspection methods, it is crucial to continuously monitor the integrity and analyse the dynamic properties of bridges. This work provides a comprehensive analysis of bridge health monitoring, emphasizing three key elements: vibration-based structural health monitoring (SHM), vehicle-bridge interaction analysis, and machine learning integration. The work improves monitoring, maintenance, and safety protocols by expanding our knowledge of structural integrity assessment for bridges by a detailed examination of the convergence of these domains. Notably, the work uses a condensed 2D model to forecast bridge natural frequencies Understanding the dynamic behaviour and structural integrity of the vital infrastructures is greatly aided by free vibration response research on bridges. The results, insights, and new directions in the subject of bridge free vibration analysis are summarized in this review paper. The uses and drawbacks of several analytical, numerical, and experimental techniques are covered. The research on the free vibration response of bridges is also reviewed in this review paper, with an emphasis on new developments incorporating machine learning principles.