Implementation Of System Research Articles

Development and Implementation of a Safety Incident Report System for Health Care Discipline Students During Clinical Internships: Observational Study.

Patient safety is a fundamental aspect of health care practice across global health systems. Safe practices, which include incident reporting systems, have proven valuable in preventing the recurrence of safety incidents. However, the accessibility of this tool for health care discipline students is not consistent, limiting their acquisition of competencies. In addition, there is no tools to familiarize students with analyzing safety incidents. Gamification has emerged as an effective strategy in health care education. This study aims to develop an incident reporting system tailored to the specific needs of health care discipline students, named Safety Incident Report System for Students. Secondary objectives included studying the performance of different groups of students in the use of the platform and training them on the correct procedures for reporting. This was an observational study carried out in 3 phases. Phase 1 consisted of the development of the web-based platform and the incident registration form. For this purpose, systems already developed and in use in Spain were taken as a basis. During phase 2, a total of 223 students in medicine and nursing with clinical internships from universities in Argentina, Brazil, Colombia, Ecuador, and Spain received an introductory seminar and were given access to the platform. Phase 3 ran in parallel and involved evaluation and feedback of the reports received as well as the opportunity to submit the students' opinion on the process. Descriptive statistics were obtained to gain information about the incidents, and mean comparisons by groups were performed to analyze the scores obtained. The final form was divided into 9 sections and consisted of 48 questions that allowed for introducing data about the incident, its causes, and proposals for an improvement plan. The platform included a personal dashboard displaying submitted reports, average scores, progression, and score rankings. A total of 105 students participated, submitting 147 reports. Incidents were mainly reported in the hospital setting, with complications of care (87/346, 25.1%) and effects of medication or medical products (82/346, 23.7%) being predominant. The most repeated causes were related confusion, oversight, or distractions (49/147, 33.3%) and absence of process verification (44/147, 29.9%). Statistically significant differences were observed between the mean final scores received by country (P<.001) and sex (P=.006) but not by studies (P=.47). Overall, participants rated the experience of using the Safety Incident Report System for Students positively. This study presents an initial adaptation of reporting systems to suit the needs of students, introducing a guided and inspiring framework that has garnered positive acceptance among students. Through this endeavor, a pathway toward a safety culture within the faculty is established. A long-term follow-up would be desirable to check the real benefits of using the tool during education. Trial Registration: ClinicalTrials.gov NCT05350345; https://clinicaltrials.gov/study/NCT05350345.

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

BackgroundHealthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored.MethodsFollowing the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively.ResultsResults were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals.ConclusionsAccess to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

Enhancing payload capacity to low earth orbit via a relay solution trajectory with laser-powered vehicles

ABSTRACT Low Earth Orbit (LEO) stands as the primary access point for deep space exploration endeavors, encompassing a variety of space missions such as communication satellites, Earth observation platforms, and scientific research missions. In light of the expanding scope of human activities in space, the cost associated with accessing LEO has emerged as a critical focal point for spacecraft designers. Within the spectrum of proposed solutions, laser-powered vehicle has emerged as a viable avenue toward achieving cost-effective launch options. In order to significantly augment the payload capacity of laser-powered launch vehicles destined for LEO, this study introduced a novel Relay Solution Trajectory Flight Scheme. Central to this scheme was the implementation of a relay mirror system affixed to a satellite, which served to stabilize the incident angle of the laser beam directed toward the laser-powered vehicle, thereby ensuring continuous alignment between the laser beam and the vehicle’s axis throughout the duration of the flight. Following the development of comprehensive structural, propulsive, aerodynamic, and trajectory models tailored for the laser-powered vehicle, an optimization of its flight trajectory was conducted, comparing this innovative strategy with two conventional flight strategies. The outcomes of this analysis showcased a notable enhancement of 269.29% in payload capacity compared to conventional strategies that lacked a relay satellite system, within a designated 400 km LEO flight scenario. This endeavor underscored the transformative potential of advanced relay systems in reshaping payload delivery mechanisms and elevating mission feasibility levels, notwithstanding the intricate coordination challenges posed by intricate satellite systems.

Unlocking Trends: Socio-Demographic Insights into Bike Sharing from the 2017 National Household Travel Survey

The implementation of bike-sharing systems represents a novel strategy aimed at enhancing urban mobility by offering shared bicycle services to improve accessibility and user convenience. Nevertheless, there has been a limited exploration into the user demographics of bike-sharing programs on a national scale. This study investigates the socio-demographic characteristics of bike-sharing system users, leveraging the 2017 National Household Travel Survey and negative binomial regression models. It finds that bike-sharing usage is more common among users than non-users, with African American and Hispanic individuals using the system more than other racial groups. Furthermore, low-income individuals (&lt;USD 15,000) and residents of high-density areas (≥25,000 per square mile) show a higher tendency towards bike sharing. The research highlights the lack of national-level studies on bike-sharing user demographics and suggests that future policy and planning should address the needs of specific populations, aiming to enhance urban mobility and accessibility through bike-sharing services.

Research on the technical principles and applications of massage robots

Massage robots, as innovative therapeutic devices, have garnered significant attention and application due to technological advancements and society's increasing focus on wellness. However, amidst this progress, gaps persist in fully understanding their capabilities and optimizing their functionalities, highlighting the need for further research in this domain. This paper focuses on exploring the intricate technical principles, mechanical structure, sensing technology, and intelligent control systems of massage robots, with the aim of providing both theoretical foundations and practical insights for researchers and practitioners in this field. Specific inquiries encompass the elucidation of working principles, emulation of human massage techniques, mechanical design considerations, applications of sensor technology, and implementations of intelligent control systems within massage robots. Employing a blend of literature review, comparative analysis, and experimental validation, this paper aims to not only deepen the understanding of massage robot functionality but also contribute to the advancement and refinement of this technology, fostering innovation and growth within the industry.

Comparative carbon emission assessment of vertical and modular ground source heat pump systems

The urgent need to address climate change necessitates a reduction in carbon emissions, particularly within the building sector. To achieve carbon neutrality, innovative technologies such as carbon capture, renewable energy systems, and carbon-neutral materials have been developed. However, there remains a dearth of research quantitatively analyzing carbon emissions through a life cycle assessment while implementing these technologies in real-world building scenarios. Additionally, Ground-source Heat Pumps (GSHPs) demonstrate superior efficiency compared to Air-source Heat Pumps (ASHPs) by leveraging stable ground temperatures, yet their widespread adoption is hindered by high initial investment costs. This study compares and analyzes the carbon emissions of GSHPs with Modular Ground Heat Exchangers (MGHXs), designed to mitigate initial investment barriers, alongside Vertical Ground Heat Exchangers (VGHXs) and ASHPs. The primary objective is to evaluate technology adoption feasibility from a carbon equivalent perspective, focusing on energy demand through building energy simulation. Results indicate that MGHXs exhibit a 6.7 % reduction in carbon emissions compared to VGHXs during production and construction (stage A). However, MGHXs generate 0.57 CO2-eq more per square meter per year during building operation (stage C). The implementation of geothermal energy systems in new buildings across South Korea could potentially achieve a maximum reduction effect of 11.6 % concerning the country's NDC (Nationally Determined Contributions) 2030 carbon reduction target.

Whole-process analysis and implementation of a self-powered wireless health monitoring system for railway bridges: Theory, simulation and experiment

In this work, a self-powered wireless health monitoring system (SP-WHMS) combined with the Internet-of-Things (IoT) is proposed and implemented for a long-term and long-distance health monitoring of railway bridges. This system frees the wireless sensor nodes (WSNs) from their dependence on chemical batteries by using the piezoelectric energy harvester (PEH) to harvest the vibration energy from bridges to power the WSNs. The whole-process of the SP-WHMS including energy conversion, energy storage, energy management and application is analyzed at the first time through theory, simulation and experiment. For the energy conversion, a reported PEH model with high energy conversion efficiency (called D-M PEH) is refabricated, which can achieve a maximum average output power of 0.9 W under unit harmonic acceleration excitation. For the energy storage, a high-precision iteration analysis procedure (IAP) is proposed to predict the charging process of the strong-coupled PEHs and the charging process under non-harmonic excitations. The comparison results show that the IAP’s prediction deviates less than 3% from the experiment result and even less than 1% from the simulation result. For the energy management, an AP64500 chip with two adjustable input voltage thresholds and a stable output voltage ensures the normal operation of the SP-WHMS and makes the SP-WHMS suitable for different types of WSNs. For the application, the synergies between energy conversion, storage and management are considered, and the effectiveness of the SP-WHMS used on railway bridges is tested through an activation experiment. This work provides a technical guidance and framework for the implementation of the self-powered wireless monitoring on railway bridges.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis

BackgroundThe assessment of the effectiveness of teaching interventions in enhancing students’ understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students’ understanding of the PCNE Classification System in pharmaceutical education.MethodsUndergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires.ResultsThe paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire’s validity.ConclusionThe implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students’ comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

A qualitative study of clinicians' impressions regarding the implementation of a clinical and research data collection system

AbstractBackgroundSystematically collecting data on clientele databases allows for describing the clientele's needs and addressing various clinical research questions. Conducted in a university psychology clinic, this initiative seeks to improve the overall quality of care provided by integrating evidence‐based practices. This study works to bridge the gap between clinical practice and research.AimsThis study aimed to describe the potential repercussions and clinicians' impressions regarding the implementation of this procedure.Research DesignAn inductive qualitative approach, inspired by Husserl's descriptive phenomenology, was used. To be consistent with this approach, data analysis followed Giorgi's five‐step scientific phenomenological method.Data Collection and AnalysisSemi‐structured individual interviews were conducted with 14 volunteer clinicians using an interview guide. The data were analysed to extract central themes using Giorgi's method, which involves collecting verbal data, reading the data, dividing it into units of meaning, organising the data using the language of the discipline and synthesising the results.ResultsFive central themes emerged: barriers to implementation, potential impacts on therapy, recommendations to enhance participation, perceived utility and an attitude of openness.DiscussionThe pilot project provides valuable insights into the feasibility and acceptability of systematic data collection in a clinical setting. Clinician consultation proves to be essential in the implementation process, highlighting the importance of addressing practical and philosophical obstacles.ConclusionUnderstanding clinicians' experiences can guide future implementations of similar systems and improve clinical practice by supporting the integration of systematic data collection. Enhanced communication and training on the data collection system are suggested.

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

Pilot study of implementing Managing and Adapting Practice in a German psychotherapy master’s program

Despite a significant accumulation of research, there has been little systemic implementation of evidence-based practices (EBP) in youth mental health care. The fragmentation of the evidence base complicates implementation efforts. In light of this challenge, we sought to pilot a system that consolidates and coordinates the entire evidence base in a single direct service model (i.e., Managing and Adapting Practice; MAP) in the context of a legal reform of psychotherapy training in Germany. This pilot study aimed to evaluate the feasibility of the implementation of MAP into the curriculum of the reformed German master's program. Eligible participants were students in the master’s program at Philipps-University Marburg during the winter-term 2022/2023. Students first learned about MAP through introductions and role plays (seminar 1), followed by actively planning and conducting interventions using MAP resources for patients in a case seminar under supervision (seminar 2). A repeated-measures survey was conducted to investigate students’ knowledge gains, perception of MAP and changes in their self-rated confidence to use EBP. Results indicated that students perceive MAP to be manageable to learn. Positive progress was achieved with regard to their knowledge and self-reported confidence to use EBP, although interpretation and generalization of the results are limited by small and homogeneous samples, lack of statistical power and missing comparison groups. The feasibility of the implementation and suitability of measures are discussed. Important implications could be drawn with regard to future investigations.

Transforming the future of ophthalmology: artificial intelligence and robotics’ breakthrough role in surgical and medical retina advances: a mini review

Over the past decade, artificial intelligence (AI) and its subfields, deep learning and machine learning, have become integral parts of ophthalmology, particularly in the field of ophthalmic imaging. A diverse array of algorithms has emerged to facilitate the automated diagnosis of numerous medical and surgical retinal conditions. The development of these algorithms necessitates extensive training using large datasets of retinal images. This approach has demonstrated a promising impact, especially in increasing accuracy of diagnosis for unspecialized clinicians for various diseases and in the area of telemedicine, where access to ophthalmological care is restricted. In parallel, robotic technology has made significant inroads into the medical field, including ophthalmology. The vast majority of research in the field of robotic surgery has been focused on anterior segment and vitreoretinal surgery. These systems offer potential improvements in accuracy and address issues such as hand tremors. However, widespread adoption faces hurdles, including the substantial costs associated with these systems and the steep learning curve for surgeons. These challenges currently constrain the broader implementation of robotic surgical systems in ophthalmology. This mini review discusses the current research and challenges, underscoring the limited yet growing implementation of AI and robotic systems in the field of retinal conditions.

Whistle Blowing System Based on Indonesia Case Study: Qualitatives Research

This research aims to carry out fraud detection efforts through the implementation of a whistleblowing system. In this case, whistleblowing is a system where the public can report various indications of fraudulent practices in both the business and public sectors. This whistleblowing system was designed as the main step for early detection and prevention of fraudulent practices in both the business and public sectors. However, the implementation of whistleblowing still requires the role of whistleblowers who report anonymously. Where the reporter gets guarantees from the recipient of the report for various fraudulent practices (fraud). This article uses a literature study approach (literature review). This article states that whistleblowing channels are very effective in detecting and preventing various fraudulent practices in all public and business sectors.

Synthetic datasets for open software development in rare disease research

BackgroundGlobally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity and uniqueness of rare disease data. This creates a dilemma, as there is a lack of both methods and necessary data to create appropriate approaches initially. This work contributes to bridging this gap by providing synthetic datasets that can form the foundation for such developments.MethodsUsing a hierarchical data generation approach parameterised with publicly available statistics, we generated datasets reflecting a random sample of rare disease patients from the United States (US) population. General demographics were obtained from the US Census Bureau, while information on disease prevalence, initial diagnosis, survival rates as well as race and sex ratios were obtained from the information provided by the US Centers for Disease Control and Prevention as well as the scientific literature. The software, which we have named SynthMD, was implemented in Python as open source using libraries such as Faker for generating individual data points.ResultsWe generated three datasets focusing on three specific rare diseases with broad impact on US citizens, as well as differences in affected genders and racial groups: Sickle Cell Disease, Cystic Fibrosis, and Duchenne Muscular Dystrophy. We present the statistics used to generate the datasets and study the statistical properties of output data. The datasets, as well as the code used to generate them, are available as Open Data and Open Source Software.ConclusionThe results of our work can serve as a starting point for researchers and developers working on methods and platforms that aim to improve the availability of rare disease data. Potential applications include using the datasets for testing purposes during the implementation of information systems or tailored privacy-enhancing technologies.

Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification.

Dosimetric consequences of flap dose due to rapid beam off control for a high intensity carbon ion radiation therapy synchrotron.

In carbon ion radiation therapy (CIRT) the predominant method of irradiation is raster scanning, called dose driven continuous scanning (DDCS) by Hitachi, allowing for continuous synchrotron extraction. The reduction in irradiation time is highly beneficial in minimizing the impact of patient and target movement on dose distribution. The RF knock out (RFKO) slow-extraction method is commonly used for beam on/off control. When the Hitachi synchrotron receives a beam off signal the control system stops the RFKO and after some delay time (t-delay) during which the beam intensity declines, a high-speed steering magnet (HSST) is used to sweep the remaining beam from isocenter to a beam dump for safety reasons. Mayo Clinic Florida (MCF) will use a very short delay of the HSST operation from the RFKO beam OFF signal to minimize the delay time and delayed dose. MCF clinical beam intensity, a tenfold increase over HIMAK, is still less than 100 mMU/ms (approximately 4.9×109 pps for 430 MeV/u). The rapid beam off control (RBOC) proposed for MCF is associated with the occurrence of flap dose (FD), which refers to the asymmetric shoulder of the spot dose profile formed from the beam bent by HSST deviating from its planned spot position on the isocenter plane. In this study, we quantitatively assessed FD, proposed a treatment planning system (TPS) implementation using a flap spot (FS) and evaluated its impact on dose distribution. The experiments were conducted at the Osaka Heavy Ion Therapy Center (HIMAK) varying the t-delay from 0.01 to 1ms in a research environment to simulate the MCF RBOC. We studied the dependence of FD position on beam transport and its dependence on energy and beam intensity. FD was generated by delivering 10000 continuous spots on the central axis that are occasionally triggered by an external 10Hz gate signal. Measurements were conducted using an oscilloscope, and the nozzle's spot position monitor (SPM) and dose monitor (DM). All spot profile data were corrected for the gain of the SPM's beam intensity dependence. FD was determined by fitting the (SPM) Profile data to a double Gaussian. The position of the FS was found to be transport path dependent, with FS occurring on the opposite sides of the scanning x-direction for vertical and horizontal ports, respectively, as predicted by transport calculations. It was observed that the FD increases with beam intensity and did not exhibit a significant dependence on energy. The effect of FD on treatment planning is shown to have no significant dose impact on the organs at risk (OARs) near the target for clinical beam intensities and a modest increase for very high intensities. Using HIMAK in research mode the implications are that the FD has no clinical impact on the clinical CIRT beam intensities for MCF and maybe planned for higher intensities by incorporating FS into the TPS to predict the modest increased dose to OARs. A method for commissioning and quality assurance of FD has been proposed.

Artificial Intelligence Based Essay Grading System

There are quite a number of challenges faced by teaching staff which if ameliorated can help reduce the quality of time spent on monotonous task associated with students essays grading More so, these days in Nigeria and other related parts of the world institutions deal with a large number of students. These however make grading time consuming and costly, therefore an automated system that can handle the tasks is highly desirable. The developed automated essay grading system in this research is restricted to grading of short and structured essay responses. Java programming language was used for the implementation and MySQL relational database system with WAMPSERVER as the back end. The results showed the user-friendly modules which enable users to navigate through various interfaces easily and work as required. The developed system has an Exact Agreement rate of 0.4 and a Pearson Correlation of 0.93 with human graders.

Natural Disaster Mitigation Strategies: A Comprehensive Review

The world is often rocked by the devastating effects of natural disasters, presenting a formidable challenge to communities everywhere. In order to address these challenges and minimize their impact, it becomes imperative for proactive measures to be taken. This comprehensive review delves into the wide array of strategies put in place to mitigate the vulnerability of both populations and infrastructure when faced with these hazardous occurrences. Taking into account an extensive range of literature, case studies, and expert opinions, this review strives to synthesize crucial findings that shed light on effective approaches towards mitigation. It commences by classifying natural disasters according to their distinct characteristics and geographical spread, bringing attention to their individual traits. Furthermore, it embarks on an analysis aimed at uncovering the root causes and driving factors behind these calamities - underlining how essential it is for us all to comprehend their dynamics in order to craft tailored mitigation plans. The significance of pre-disaster preparedness is emphasized through an exploration of risk assessment procedures, early warning systems implementation practices as well as land-use planning strategies. The critique emphasizes the importance of proactive measures, such as fortifying infrastructure and incorporating resilience principles into city planning. It also discusses the vital role of community engagement, education, and capacity building in bolstering disaster preparedness at a local level. The review delves into innovative methods for promoting community resilience, including participatory decision-making processes and initiatives focused on knowledge exchange. Furthermore, it evaluates the impact of technological advancements like remote sensing, geographic information systems (GIS), and predictive modeling on enhancing early warning systems, risk assessment protocols, and disaster response efforts. The analysis suggests various opportunities for utilizing technology to strengthen mitigation strategies effectively Post-disaster Response and Recovery: Analyzing the challenges and opportunities in postdisaster response and recovery efforts, emphasizing the importance of rapid and coordinated interventions to restore essential services, infrastructure, and livelihoods. The review explores strategies for promoting resilient reconstruction and long-term recovery. Policy Frameworks and International Cooperation: Evaluating the role of policy frameworks, international cooperation, and funding mechanisms in supporting effective disaster mitigation efforts. The review identifies policy gaps and barriers to implementation, along with recommendations for enhancing policy coherence and coordination at the national and international levels. In conclusion, this comprehensive review underscores the multidimensional nature of natural disaster mitigation and the importance of adopting integrated approaches that combine technical expertise, community engagement, and policy support. By synthesizing existing knowledge and best practices, this review provides valuable insights for policymakers, practitioners, and researchers striving to build more resilient and sustainable communities in the face of natural hazards. By synthesizing existing knowledge and best practices, this review provides valuable insights for policymakers, practitioners, and researchers striving to build more resilient and sustainable communities in the face of natural hazards.

National Database for Emergency Management (GIS based)

This study looks at the advantages of using geographic information systems for emergency management from two angles: the data management mode and the pre-warning signal emergency management requirements. This study discusses the need for geographic information systems in disaster management. This study explores the manifestation of the function graphics with one mining map, which comprises of the graphics data and characteristics data, the layer stack, and theme maps presenting, through the analysis of geographic information systems functions appropriate for pre-warning signals. It communicates with the conventional pre-warning signal system in order to present the pre-warning signal's outcome based on geographic information systems in the interim. Based on previous research, this study proposes a standard approach for pre- warning using geographic information systems. The project aims to create dynamic, multidimensional technical instruments for emergency management system implementation, as well as useful concepts for the development of pre-warning systems that are intimately related to actual places. Keywords: pre-warning, geographic information system, emergency management, and theme map

Estimation of wind dynamic load linear transfer matrix using proper orthogonal decomposition method and genetic algorithm

ABSTRACT Due to the advantages of Laplace domain in both time and frequency domains, the dynamic analysis and control tools in engineering software like MATLAB and Python have been designed based on it. Dynamic analysis of structures requires determination of the overall transfer matrix, which is obtained by multiplying the structure's transfer matrix (TM) with the load TM. With the input vector of unit intensity white-noise processes and the overall TM, the structural responses can be obtained. Furthermore, having the load TM is crucial for implementation of active control systems. In this article, the TM of along-wind loads was estimated for a tall building using the proper orthogonal decomposition (POD) method and the genetic algorithm (GA). To evaluate the effectiveness of the proposed approach, an example of a tall building was provided. The wind load cross-power spectral matrix (XPSD) was decomposed using POD technique, followed by the truncation of higher loading modes. Subsequently, the loading TM was derived by estimating the target non-linear functions with linear transfer functions through GA technique. Then, the responses of the structure were computed using the loading TM. The results demonstrated the efficacy of the proposed method in acquiring the loading TM and predicting wind-induced responses.