Data Extraction Methods Research Articles

Large Language Model for Automating the Analysis of Cryoprotectants.

The rapid expansion of scientific literature necessitates developing efficient data extraction and analysis methods. This study presents an innovative approach to automating the extraction of cryoprotectant information from scientific publications using a generative pretrained transformer (GPT) model integrated with a Telegram bot interface. Our system processes and analyzes scientific articles to identify and extract relevant data on cryoprotectants and bacteria, significantly reducing the time required for researchers to gather essential information. Our method optimizes the workflow for researchers in cryopreservation and related fields by utilizing modern artificial intelligence technologies, specifically large language models. The Telegram bot, designed to be user-friendly, provides a comfortable and easy platform for quick data access, enhancing scientific research efficiency. The study's methodology involves data preparation, algorithm development, and system validation using a substantial data set of scientific articles. Results demonstrate the model's capability to accurately recognize and extract critical information, although some limitations in term specificity were noted. Our findings suggest that further refinement and training of the model can enhance its accuracy and reliability for specialized scientific applications.

Guidelines and standard frameworks for artificial intelligence in medicine: a systematic review.

The continuous integration of artificial intelligence (AI) into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates the quality of these guideline and summarizes ethical frameworks, best practices, and recommendations. The Appraisal of Guidelines, Research, and Evaluation II tool was used to assess the quality of guidelines based on 6 domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The protocol of this review including the eligibility criteria, the search strategy data extraction sheet and methods, was published prior to the actual review with International Registered Report Identifier of DERR1-10.2196/47105. The initial search resulted in 4975 studies from 2 databases and 7 studies from manual search. Eleven articles were selected for data extraction based on the eligibility criteria. We found that while guidelines generally excel in scope, purpose, and editorial independence, there is significant variability in applicability and the rigor of guideline development. Well-established initiatives such as TRIPOD+AI, DECIDE-AI, SPIRIT-AI, and CONSORT-AI have shown high quality, particularly in terms of stakeholder involvement. However, applicability remains a prominent challenge among the guidelines. The result also showed that the reproducibility, ethical, and environmental aspects of AI in medicine still need attention from both medical and AI communities. Our work highlights the need for working toward the development of integrated and comprehensive reporting guidelines that adhere to the principles of Findability, Accessibility, Interoperability and Reusability. This alignment is essential for fostering a cultural shift toward transparency and open science, which are pivotal milestone for sustainable digital health research. This review evaluates the current reporting guidelines, discussing their advantages as well as challenges and limitations.

IMPLEMENTATION OF NATURAL LANGUAGE PROCESSING (NLP) IN EHR SYSTEMS TO IMPROVE CLINICAL DOCUMENTATION

Background: Electronic health records (EHRs) store valuable clinical data, often in unstructured narrative formats that require manual extraction. This process is time-consuming, costly, and error-prone. Natural language processing (NLP) offers a promising solution for automating data extraction, improving research efficiency, and maintaining accuracy. However, its generalizability and reliability remain areas of active investigation. This study evaluates the performance of an NLP tool for extracting clinical conditions, medications with dosage, and echocardiographic parameters compared to manual retrieval. Objective: To assess the accuracy, sensitivity, and specificity of an NLP tool for extracting clinical data from unstructured EHR narratives, validating its performance against manual data extraction methods. Methods: This prospective study was conducted in three tertiary care hospitals in Punjab, Pakistan, from December 2023 to May 2024. A total of 500 participants were included, stratified by urban (68%, 340) and rural (32%, 160) residency. The NLP tool extracted 5,700 data points across three categories: 3,000 clinical conditions, 1,500 medications with dosage, and 1,200 echocardiographic parameters. Performance metrics, including accuracy, sensitivity, and specificity, were calculated by comparing the tool's results with manual retrieval. Discrepancies were analyzed to identify root causes, including algorithmic and human errors. Results: The NLP tool achieved an accuracy of 98.5%, sensitivity of 96.7%, and specificity of 97.2%, closely aligning with manual retrieval at 99.0%, 97.5%, and 97.8%, respectively. For clinical conditions, the tool retrieved 2,955 of 3,000 data points correctly (98.5%), while manual retrieval achieved 2,970 (99.0%). For medications with dosage, the tool extracted 1,452 of 1,500 data points (96.8%) compared to 1,488 (99.2%) manually. Similarly, 1,178 of 1,200 echocardiographic parameters (98.2%) were correctly retrieved by the tool, compared to 1,185 (98.8%) through manual methods. Urban participants (242 males, 98 females) outnumbered rural participants (106 males, 54 females), with the majority aged 31–70 years (75%). Conclusion: The NLP tool demonstrated high accuracy and near-human precision in extracting structured data from unstructured EHR narratives. Its performance across clinical conditions, medications, and echocardiographic parameters highlights its potential to streamline clinical research while reducing manual workload. Further refinement is required to address context-sensitive errors and enhance generalizability across diverse datasets.

Shrimp phenotypic data extraction and growth abnormality identification method based on instance segmentation

Shrimp phenotypic characteristics encompass externally or partially internally observable traits like body length, width, color, and morphology. These traits are vital for assessing shrimp health, growth, and environmental adaptability. The traditional manual sampling method is labor-intensive and inadequately reflects the overall growth status of cultured shrimp. Therefore, this paper proposes a shrimp phenotypic data extraction and growth abnormality identification method based on instance segmentation. Firstly, overhead images of shrimp are collected to form a dataset. Secondly, an improved YOLOv8 algorithm is utilized for instance segmentation of the shrimp. Then, shrimp phenotypic data is extracted through image processing techniques and reference object transformation. Finally, the Kolmogorov-Smirnov and Shapiro-Wilk tests are used to assess the growth and development status of the shrimp groups, while individual growth abnormalities are identified using the Z-value algorithm and the Interquartile Range method. Additionally, this paper optimizes shrimp farming processes such as grading, selective breeding, and feeding based on phenotypic data. On the self-built dataset, the proposed method achieves an average precision of instance segmentation of 99.5 % and an F1 score of 99.3 %. Comparing the shrimp lengths calculated by our model with manual measurements, the absolute error in shrimp length fitting is 4.63 mm, with a relative error of 4.58 %. The accuracy of detecting abnormal individuals in shrimp is 91 %. The results indicate that the proposed method can effectively evaluate shrimp growth status and identify abnormal growth patterns.

Film Policy and Film Production: The Potential of Baltic Co-Productions and New Film Fund

Abstract The film industry can be considered the most important in creative industries since it possesses the characteristics of industrial organization, a high degree of demand uncertainty, contractual agreements, and business practices. What is more, the film industry strongly influences the other sub-sectors, because of the broad-based advantages that it delivers in terms of employment and economic growth. On the other hand, the economic value of creative industries does not arise from their relative contribution to economic value, but from the co-ordination of new ideas and technologies thereby influencing change in economic and cultural processes. Since European film industries are fragmented, there is a constant need to rethink possible model for their continuous growth. This is especially relevant in the context of smaller film industries in Europe such as in the Baltic States, because their budgets for film production are relatively small compared to bigger film industries. Therefore, in order to foster new industry development, which would also reflect the regional context of the Baltic States and its location strengths, the potential of co-productions - mission-economy approach is proposed as it emphasizes market co-creation, co-shaping, partnerships between business and public sector and other managerial and technological innovations. Research purpose and objectives. The aim of the study is to provide new recommendations for the film centres in Lithuania, Latvia and Estonia regarding film policy based on the mission-economy approach. In order to achieve the aim two objectives were set: 1) to analyse the current financial support model for the national film production and co-productions in the Baltic film industries and 2) to investigate whether the Baltic co-productions can have a new value for the audience. Design / Methodology / Approach. The study used both qualitative and quantitative methods for data extraction and analysis. Firstly, descriptive statistics as a method was used to analyse the financial support for national film production in Lithuanian, Latvian and Estonian film industries during 2018-2023 and the releases of co-produced films between the Baltic States. Furthermore, 6 semi-structured interviews with experts from the Baltic film industries were conducted. Findings. As a result of the research, the authors conclude that there is long-term potential in the Baltic co-production films, but they have to be more oriented towards the audience and reflect the genre as in the case of Nordic films. Moreover, financial support models for co-productions in the Baltic film industries are different in regard to applications and their management. Therefore, a potentially new “Baltic Film and TV Fund” could be established to foster regional co-productions. Originality / Value / Practical implications. As a result of the study, the authors have developed recommendations for the film policy that would reflect the new value of the Baltic co-productions. The results of the research can be used by the Lithuanian Film Centre, the National Film Centre of Latvia, and the Estonian Film Institute to continue establishing the “Baltic Film and TV Fund”. The research results will be partly used for further research within the framework of Audrius Dabrovolskas’ doctoral thesis.

Publicly available continuously updated topic specific databases of randomised clinical trials: A scoping review.

Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.

Understanding the Relationships Between Physical Activity and Climate Change: An Umbrella Review.

Physical activity is crucial for maintaining health and preventing chronic diseases. However, climate conditions can impact physical activity patterns, and conversely, physical activity can impact climate change. This umbrella review aims to summarize the existing evidence regarding the relationships between physical activity and climate change, as revealed in previous reviews. Systematic reviews and meta-analyses were analyzed. Searches were conducted across GreenFILE, PubMed, and Web of Science databases for peer-reviewed articles published in English. A 2-stage independent screening process was conducted, and methodological quality was assessed using the JBI framework. Standardized data extraction methods were then applied. Out of 1292 articles retrieved, 7 met all eligibility criteria and were included in the review. Three articles examined physical activity as a primary exposure, 3 focused on the impacts of climate change, and one considered both physical activity and climate change as primary exposures. Findings reveal an interplay between climate change and physical activity. While rising temperatures may prompt outdoor activities up to a certain temperature threshold, air pollution and extreme heat can have harmful effects. Active transportation and improved logistics around large-scale sporting events can mitigate climate change by reducing greenhouse gas emissions, but transportation systems linked to sports venues may increase emissions. Research on the reciprocal relationship between climate change and physical activity domains, and integrating the community into climate discussions, is crucial for equity, especially for marginalized communities. Incorporating climate adaptation and mitigation strategies in physical activity infrastructure to minimize environmental impacts is critical.

Film Policy and Film Distribution: The Case of the Baltic Film Industries

Abstract The aim of the study is to analyse the development of the Baltic film distribution markets and to provide new recommendations for its potential growth based on mission economy approach and bigger role of the states in co-creating and co-shaping the markets especially for the Baltic co-production of films and their distribution. The study used both qualitative and quantitative methods for data extraction and analysis. Firstly, descriptive statistics as a method was used to analyse the market shares of the main film distribution companies in Lithuanian, Latvian and Estonian film industries. Furthermore, 5 semi-structured interviews with experts from the Baltic film industries were conducted. As a result of the research, the author concludes that there is a potential in family genre films that are produced in the Baltic States, but in general the Baltic co-production films lack quality, storytelling that would arouse the interest of the audience and potentially new regional film fund “Baltic Film and TV Fund” would have to focus not only on increased financing for co-productions, but also on other measures that would help to produce better quality genre films including co-productions and their distribution.

Data-driven fault identification method of RV reducer used in industrial robot

The RV reducers are complex and sealed mechanical systems that are difficult to conduct fault diagnosis in advance. The previous research worked on the fault identification of RV reducer were mainly carried out on the test platforms instead of real complex working conditions. Most of faults were intentionally created in laboratory instead of real malfunction caused by factory daily operation. In the present paper, the actual failure mode of RV reducer for the industrial robots in factory is taken as the goal of fault diagnosis. The constant speed segment data extraction method is designed to overcome the difficulty of frequency domain analysis caused by non-uniform rotation in working conditions and ensure the quality and effectiveness of features extraction. Several machine learning classification models are selected regarding their inherent features. The proper DNN binary classification model shows the best performance that can meet the requirements of fault identification in industrial environment.

A review of the harvesting methods for offshore renewable energy – advances and challenges

The present work offers general information about present-day and developments in offshore renewable energy, specially of wind, wave and the solar energy harnessing structures. In the introduction, the authors raise awareness of the despite more untapped potentials that exist in the oceans and the offshore Wind Energy Market that is experiencing a steep rise in the recent past due to technological advancement and the great policies that support this energy source. This mainly focuses on the very huge additionality of offshore wind power and the improvements in floating solar PV systems. Section three outlines an assortment of studies that were done by different writers on the strengths and weaknesses of offshore renewable energy technologies through a meta-analysis of the literature. The method of data extraction revolved around several factors, namely technological admissibility, resource identification, location and environmental and economic factors. The sources used comprised of scholarly articles published within the peer reviewed journals in the period of 2013 to 2024, to ensure that the study offered a comprehensive analysis on the technological advancements. The results and discussion section gives an analysis of the results achieved and demonstrates the developments in Offshore Wind Energy, Wave Energy Conversion, and Floating Solar PV Systems. Thus, offshore wind energy is described as the rapidly evolving segment and particular focus is made on the floating wind farms and their advancements in the aspects of power density and structural stability. Wave energy converters are discussed regarding their applicability to complement hybrid electricity production despite limited data on the devices’ performance. Another key feature is that the FFPV systems are reported to be more efficient and cheaper than the land mounted systems. Thus, the article emphasizes the need to produce the further literature on offshore renewable energy, political support, and cooperation between different sectors. It concludes that an offshore renewable energy has the potential for the future development due to advancement in the technology as well as due to environmental factors.

Systemic reviews and meta-analysis of the use of midodrine in hepatorenal syndrome

Aims. This study analyzes the effectiveness of midodrine in reducing or managing Hepatorenal Syndrome (HRS). Design. A systematic review and meta-analysis were conducted to gather and assess relevant literature. The review included evaluating study aims, methodologies, outcomes, and implications, with data presented in a tabular format for clarity. The protocol specified the search strategy, selection criteria, and data extraction methods for transparency. Data sources. Articles were sourced from Google Scholar and PubMed using inclusion and exclusion criteria. Quality assessment data were extracted, synthesized, analyzed, and reported. Review methods. The systematic review focused on peer-reviewed journals, considering factors such as time, language, and full-length article availability. Studies were excluded if they were duplicates, abstracts only, or non-English. Results. Midodrine was found to improve arterial pressure and renal function in HRS patients, suggesting a link between circulatory and renal functions. Sample sizes varied across studies, with some involving small patient groups. Terlipressin appeared more effective than midodrine and octreotide in improving renal function due to its impact on mean arterial pressure (MAP). Specific findings included significant increases in arterial pressure and renal function improvement in both treatment and control groups. Conclusion. Midodrine shows potential in managing HRS, decreasing nitrite and nitrate levels in ascites patients, and improving renal function. However, significant, well-designed clinical trials are needed to determine optimal dosage, duration, and pharmacological side effects. The meta-analysis highlights opportunities for further research on midodrine in combination treatments and cost-effectiveness evaluations. Impact. This study opens avenues for future research on midodrine's role in HRS treatment, particularly in combination therapies and cost-effectiveness studies.

Using the Termux mobile application for forensic expertise of Android devices

Mobile device logical data extraction methods extract the data present on the device by interacting with the operating system and accessing the file system. These methods are important because they provide valuable data, work on most devices, and are easy to use. Again the concept of rooting comes into play when extracting data. Boolean methods don't actually require root access to extract data. However, having root access on the device allows you to access all the files present on the device. This means that some data can be extracted on a non-rooted device, while root access will unlock the device and give access to all files present on the device. Therefore, having a rooted device will greatly affect the amount and type of data that can be retrieved using the logical methods. The adb utility is often used to logistically retrieve data from the device. It facilitates various actions with the device, such as installing and debugging programs, copying data, creating system backups. adb provides access to the phone shell, which can be used to execute various commands on the device. But if to work with this and other necessary utilities from a mobile phone, using the Termux terminal, in some cases it significantly saves time and effort. Termux allows to read, process, analyze data from a target device using just phone, providing a highly mobile way of working with data.

Remote Exercise Engagement Among Individuals With Cardiovascular Disease

Purpose: To systematically review the literature regarding barriers and facilitators to remote exercise among individuals with cardiovascular diseases (CVD). Review Methods: Six databases were searched (inception—December 2023). Studies including barriers and facilitators for remote exercise reported by individuals living with CVD were considered. Quality was rated using the Mixed Methods Assessment Tool (MMAT). Results were synthesized following a thematic analysis approach. Characteristics of interventions were reported using the Template for Intervention Description and Replication (TIDieR). Summary: From an initial pool of 8543 records, 18 studies met the inclusion criteria. Among these, 13 were qualitative studies, with individual interviews being the most frequent data extraction method. Nine studies received 5/5 MMAT scores (ie, high quality). Twelve studies reported remote exercise interventions, mostly delivered within cardiac rehabilitation, by physiotherapists, nurses, and dietitians, predominantly via telephone calls or videoconferences, with only one study incorporating text messages. Five key themes influencing remote exercise interventions were identified: technology (encompassing access and literacy, self-monitoring/motivation, and security concerns), individual health considerations (health/personal aspects and coronavirus disease-2019), social factors (support from health care team and family/social support), environment (facilities), and logistical factors (time and displacement). Several themes served as both facilitators and barriers. Key insights for remote exercise interventions included mobile accessibility, interactive chat sessions, minimal click interactions, short sessions, personalized feedback, and flexible timing. Results emphasize the importance of promoting social interaction and support between participants and health care teams to mitigate barriers and enhance facilitators in remote exercise interventions for individuals with CVD.

A method for accurate extraction of three-dimensional point cloud feature data in loading test of container ships

The accuracy of container slot dimensions directly affects the efficiency and safety of container loading and unloading. However, traditional methods of container loading tests need to be improved for their long cycles, high costs, and low production efficiency. With the application of three-dimensional laser scanning devices in ship manufacturing, the use of simulated container loading test methods based on point cloud becomes a breakthrough in key technology for container ship construction. This paper presents a multi-objective segmentation method of cargo hold feature point cloud for container ship simulated loading tests. Data preprocessing algorithms are applied to remove noise and pseudo-image, and then a Random Sample Consensus algorithm is used to remove bulkhead and cargo bottom point clouds. Based on the density-based spatial clustering of applications with noise algorithm and the region growing algorithm, an algorithm for multiple segmentation of guide rail and bottom cone point clouds is devised, and a four-plane segmentation of guide rail point clouds is realized. Finally, a method for three-dimensional point cloud data flatness assessment and a Hausdorff distance based method for calculating the guide rail lateral and longitudinal spacing are proposed. These methods are validated through the loading test of a 16,000 TEU container ship.

Automating Government Report Generation: A Generative AI Approach for Efficient Data Extraction, Analysis, and Visualization

This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

Study on ultrasonic lamb wave testing technology of honeycomb sandwich structures

Abstract Honeycomb sandwich composite panels have complex structures, large sizes, many types of defects, and significant sound attenuation, which lead to low efficiency and poor sensitivity of ultrasonic non-destructive testing. In the study, a non-linear ultrasonic lamb wave testing technique for honeycomb sandwich composite panels is proposed. Firstly, the anisotropic propagation characteristics and modal structure of lamb waves in honeycomb sandwich composite panels are analyzed, and the lamb wave modes with non-linear ultrasonic accumulation are excited. Secondly, the data extraction method and imaging algorithm of non-linear ultrasonic lamb wave imaging testing are proposed. Finally, an enhanced imaging algorithm is proposed to improve image quality based on the sliding window algorithm. Based on the comparative analysis of air-coupled ultrasonic testing images and metallographic tests, it can be seen that non-linear ultrasonic lamb wave testing signals can be used to characterize the damage distribution characteristics of honeycomb sandwich composite panels, display debonding and weak debonding defects, and have the advantages of being able to perform on the same side testing and high efficiency, which can be used for non-destructive testing of honeycomb sandwich composite panels.

Optimizing Data Extraction using Preprocessing for Enhanced Efficiency

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web. To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications. In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs. Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.

A simple method to automatically remove artificial terrain from airborne LiDAR DTMs in plain areas

Alluvial plains are highly vulnerable to floods and ground disasters. Recent rapid urbanization and climate change have heightened disaster risks in urban areas. Geomorphological maps, crucial for estimating disaster risks, delineate landform boundaries based on patterns of concave breaks of slope and micro-landforms. However, enhancing the accuracy of such maps requires data extraction methods capable of capturing these features with greater precision. Traditional topographic measurements derived from adjacent elevation points in airborne light detection and ranging (LiDAR) digital terrain models (DTMs) fail to accurately represent slope variations on low ground surfaces due to the inclusion of numerous noise-like artificial terrain features. Consequently, analyzing natural terrain becomes challenging. To address this issue, our study devised a method to automatically identify and eliminate noise-like artificial terrain from LiDAR DTMs. We achieved this by removing major artificial terrain features from land-use vector data, creating an edge-preserving smooth DTM, and selectively removing and interpolating only those areas where were large differences between the smooth DTM and the LiDAR DTM. This method minimizes the interpolation of artificial terrain and quotes the LiDAR DTM for other areas, thereby minimizing the data quality loss. It is possible to identify and demarcate topographic boundaries in plains with a longitudinal gradient of approximately 1% or less at a high resolution, which can be used to investigate the relationship between flood and ground characteristics and landform volume in the plains. This method can be easily processed using only QGIS and free open data. This approach enhances the precision of disaster risk estimation and facilitates more effective urban planning in vulnerable areas.

Exploring the Impact of Artificial Intelligence and Machine Learning on Predicting Outcomes in Regional Development Conventions: A Case Study of the Tanger-Tetouan-Al Hoceïma Region, Morocco

Objectives: For over six years, Morocco has been engaged in an initiative aimed at promoting inclusive and sustainable territorial development, characterized by the implementation of an advanced regionalization experiment. However, the main challenge facing government leaders today is managing a growing number of regional development conventions. Methods: To achieve these objectives, we developed a predictive model using artificial intelligence and machine learning aimed at anticipating the outcomes of regional development conventions. Through this method, we were able to identify the risks associated with their potential failure and forecast their impact. Simultaneously, various data extraction methods and classification algorithms were implemented. We collected and analyzed information from 310 past and current regional development conventions, spanning from 2016 to 2020, considering various factors influencing their success or failure. We conducted extensive experiments to assess the effectiveness of various predictive models. Results: The results we obtained indicate that the classifiers Ada Boost, Random Forest, and Bernoulli NB are the three most effective algorithms for predicting the outcomes of regional development conventions. Conclusion: This study contributes to shaping a broader discourse on the practical application of artificial intelligence in public policies and regional development, highlighting its potential to enhance resource allocation and alleviate the burden of repetitive administrative tasks for organizations with limited resources.

Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic Review.

Clinical sign algorithms are a key strategy to identify young infants at risk of mortality. Synthesize the evidence on the accuracy of clinical sign algorithms to predict all-cause mortality in young infants 0-59 days. MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. Studies evaluating the accuracy of infant clinical sign algorithms to predict mortality. We used Cochrane methods for study screening, data extraction, and risk of bias assessment. We determined certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. We included 11 studies examining 26 algorithms. Three studies from non-hospital/community settings examined sign-based checklists (n = 13). Eight hospital-based studies validated regression models (n = 13), which were administered as weighted scores (n = 8), regression formulas (n = 4), and a nomogram (n = 1). One checklist from India had a sensitivity of 98% (95% CI: 88%-100%) and specificity of 94% (93%-95%) for predicting sepsis-related deaths. However, external validation in Bangladesh showed very low sensitivity of 3% (0%-10%) with specificity of 99% (99%-99%) for all-cause mortality (ages 0-9 days). For hospital-based prediction models, area under the curve (AUC) ranged from 0.76-0.93 (n = 13). The Score for Essential Neonatal Symptoms and Signs had an AUC of 0.89 (0.84-0.93) in the derivation cohort for mortality, and external validation showed an AUC of 0.83 (0.83-0.84). Heterogeneity of algorithms and lack of external validation limited the evidence. Clinical sign algorithms may help identify at-risk young infants, particularly in hospital settings; however, overall certainty of evidence is low with limited external validation.