Rapid Assessment Research Articles

Genome-wide association studies of bundle and single fiber length traits reveal the genetic basis of within-sample variation in upland cotton fiber length

Within-sample variation in cotton fiber len,gth is a major factor influencing the production and quality of yarns. The textile industry has been searching for approaches of improving the long fiber fraction and minimizing the short fiber fraction within a cotton sample to produce superior fiber and yarn quality. USTER® High Volume Instrument (HVI) has been widely used for a rapid assessment of cotton fiber length traits from a fiber bundle. However, its effectiveness for genetic studies has been questioned due to the indirect estimations of the cotton fiber traits that cannot be measured from a fiber bundle. To overcome the limits of the HVI fiber length traits, we utilized the Advanced Fiber Information System (AFIS) measuring fiber length traits directly from individual fibers based on weight or number. Comparative fiber length analyses showed AFIS provided higher sensitivity in detecting the fiber length variations within and among cotton samples than HVI. The weight-based AFIS length traits were strongly correlated with the corresponding HVI lengths, whereas the number-based AFIS mean length showed a relatively weaker correlation with the HVI lengths. Integrations of the weight based-length traits with genome-wide association studies (GWAS) enabled classifying the QTLs specifically associated with long, mean, or short fiber length traits and identified a false positive associated with the indirectly estimated HVI short fiber trait. Unlike the weight based-AFIS length traits, the number-based AFIS length trait did not show a negative correlation with a weight related-HVI property, and identified a single QTL that was not detected by the corresponding HVI trait. These results suggested that integrating the AFIS method with GWAS helped discoveries of the genome loci involved in the within-sample variation in cotton fiber length and characterizations of the fiber length QTLs.

The effect of seasonal temperature difference on estimated wood density in Populus ussuriensis Kom. trees by the resistance drilling method

ABSTRACT Wood density is a main determinant of wood quality assessment for wood end-use and plantation trees. The resistance drilling method (RST) is one of the rapid assessment methods of wood density for standing trees. As the season changes, the water within trees occurs to freeze and is expected to indirectly affect RST results. However, the impact of ambient temperature is unknown. To investigate the effect of seasonal temperature difference on estimated wood density, the RST of Populus ussuriensis Kom. trees was examined across different seasons. A good linear regression model between average wood density and RST was established at normal temperature during summer, with a coefficient of determination of 0.708. The average RST of sapwood and heartwood increased by 6% to 22% from autumn (−5–0°C) to winter (−20–−10°C) compared to normal temperature in summer. Correspondingly, the average wood density estimated by RST increased by 4% to 16% from autumn to winter relative to normal temperature. The quadratic polynomial functions described the relationship between relative RST, relative wood density and seasonal temperature, with coefficients of determination over 0.922. Seasonal temperature had a significant effect on wood density, which was overestimated by RST in winter. This overestimation should be considered in the RST application.

The RARCrec: A rapid riparian vegetation assessment method for use in river systems undergoing vegetative and geomorphic recovery

SummaryMonitoring the condition of returning vegetation over time is essential for effectively managing riparian vegetation. Rapid riparian assessment methods are standard tools for capturing a suite of variables relevant to river health and are accessible to a range of users with different levels of experience. Monitoring efforts are particularly important for rivers undergoing degrading impacts and subsequent management interventions. In coastal catchments of New South Wales, Australia, vegetation is returning to rivers that were historically cleared for agriculture and are now experiencing geomorphic recovery. We present a rapid riparian assessment method for rivers undergoing vegetative and geomorphic recovery in this region. It addresses a gap in available rapid assessments by providing a methodology that can be used at sites with no historical analogue. Our assessment method adapts and extends a widely used method called the Rapid Assessment of Riparian Condition (RARC) to capture the quality of returning riparian vegetation along recovering rivers (rec), including native species diversity and vegetation structure (hence the name RARCrec). The RARCrec incorporates four sub‐indices – vegetation cover, native species richness, proportion of native to exotic species, and features of vegetation health – to produce an overall vegetation condition score that classifies sites as poor, moderate, or good condition. Scores for each sub‐index are assessed for the vegetation strata (groundcover, graminoids, midstorey, vines, and canopy) on the landform units of river bars, benches, and floodplains. The RARCrec has been designed to be sufficiently general for use in the main Vegetation Formations in coastal New South Wales and has been trialled at 36 sites across this region. Assessments can be conducted within half a day by two technically competent users and can be used to monitor vegetation condition over time to inform where to prioritise implementation of management activities. Field methodology and data sheets, including a scoring system, are provided to support the implementation of the RARCrec.

Factors influencing emergency medicine worker shift satisfaction: A rapid assessment of wellness in the emergency department.

In emergency medicine (EM), the interplay of wellbeing and burnout impacts not only patient care, but the health, productivity, and job satisfaction of EM healthcare workers. The study objective was to use a rapid assessment tool to identify factors that impact EM worker satisfaction, or "wellness," while on shift in the emergency department (ED) and the association with role and level of satisfaction. This prospective descriptive study utilized a QR-code-based electronic survey instrument that included a 7-point Likert shift satisfaction score. A voluntary response sampling was obtained from EM workers at five EDs. Respondents self-reported role and work site. Association and logistic regression analysis were performed. Of 755 responses, 467 were dissatisfied (score≤5) and 288 were satisfied (score≥6) with their shifts. Physicians reported higher satisfaction on shift than nurses (OR 2.77, 95% CL 2.01-3.81, p<0.01). Factors associated with dissatisfied responses included: admission or transfer process (OR 0.40, CL 0.21-0.77, p<0.01), boarding patients (OR 0.13, CL 0.06-0.27, p<0.01), tools to do my job (OR 0.65, CL 0.46-0.90, p=0.01), and patient flow (OR 0.72, CL 0.53-0.98, p=0.04). Factors linked to a satisfied response included: teaching/learning (OR 2.85, CL 1.86-4.37, p<0.01) and team/coworker interaction (OR 8.92, CL 6.14-12.96, p<0.01). Satisfaction on shift for EM physicians, nurses, and staff differ and are associated with multiple identifiable factors. Focused attention to work environment and operations could help mitigate on-shift dissatisfaction. Endeavors aimed at cultivating and enhancing a supportive teaching and learning environment with an emphasis on team member and coworker interaction could positively impact and improve wellness.

UAVs as a Tool for Optimizing Boat-Supported Flood Evacuation Operations

The frequency and intensity of flood events are increasing year by year as a result of climate change. This poses significant threats to human settlements and adversely affects biodiversity, agriculture, and infrastructure. One of the most prominent and traditional flood evacuation approaches is through the use of boats. Nonetheless, serious challenges exist with respect to determining the optimal deployment locations, routes, and timing. Given research advances in the Unmanned Aerial Vehicles (UAVs) sector—and their ability to offer real-time data and aerial monitoring services—we argue that their applications could help enhance boat-supported flood evacuation operations. In this opinion piece, we explore new opportunities for disaster management and underscore the advantages of integrating UAVs into flood evacuation methodologies, including areas of rapid field assessment, optimal route planning, and improved coordination between rescue boats. Notwithstanding the potential of UAVs, we emphasize several gaps to be explored in terms of large-scale data management/processing, regulatory limitations, and technological know-how. Furthermore, we provide recommendations for bolstering boat deployment protocols, disaster preparedness training programs, policy frameworks, and emergency response systems, which could maximize their efficacy in flood evacuation scenarios.

Rapid remote volcanic ashfall impact assessment for the 2022 eruption of Hunga volcano, Tonga: a bespoke approach and lessons identified

When disasters occur, rapid impact assessments are required to prioritise response actions, support in-country efforts and inform the mobilisation of aid. The 15 January 2022 eruption of Hunga volcano, Tonga, and the resulting atmospheric shockwave, ashfall, underwater mass disturbance and tsunami, caused substantial impacts across the Kingdom of Tonga. Volcanic impacts on the scale observed after the eruption are rare, necessitating a reliance on international advice and assistance. The situation was complicated by the loss of Tonga’s international submarine fibreoptic cable (causing a complete loss of communications for approximately 20 days) along with border closures due to the COVID-19 pandemic. A need emerged for a rapid remote volcanic impact assessment and provision of specialist advice to help inform the response of international partners. Here we present a novel methodology for conducting rapid remote volcanic ashfall impact assessments, conducted over a 10-day period following the eruption. We used three different hazard models for ashfall thickness across the main island of Tongatapu and available asset information and vulnerability functions for buildings, agriculture, electricity networks, water supply and roads, to provide initial estimates of losses due to ashfall from the 15 January eruption. For buildings, we estimated losses both as total losses and as percentages of the total replacement cost of buildings on Tongatapu. For agriculture, we made probabilistic estimates of production losses for three different crop classes. For ashfall clean-up, we estimated ranges of ashfall volumes requiring clean-up from road surfaces and roofs. For water supply, electricity networks and roads, our analysis was limited to assessing the exposure of important assets to ashfall, as we had insufficient information on system configurations to take the analysis further. Key constraints on our analysis were the limited nature of critical infrastructure asset inventories and the lack of volcanic vulnerability models for tropical regions including Pacific Island nations. Key steps towards iteratively improving rapid remote impact assessments will include developing vulnerability functions for tropical environments as well as ground-truthing estimated losses from remote approaches against in-person impact assessment campaigns.

Rapid health technology assessment to inform policy: a case study with respiratory syncytial virus

Abstract Background Respiratory syncytial virus (RSV) can cause severe acute respiratory tract infection in infants and older adults. However, until recently, no immunisation interventions were available to reduce the burden of RSV disease in the general population. New health technologies have now been authorised in Europe for infants (a long-acting monoclonal antibody (nirsevimab) and a maternal vaccine (RSVpreF)), and two vaccines for adults aged ≥60 years (RSVPreF3 and RSVpreF). A rapid health technology assessment (HTA) was requested by the Irish Department of Health to support an interim decision on RSV immunisation. Methods The rapid HTA commenced in January 2024, and comprised description of technology, a summary of international practice, national and international epidemiology and burden of disease, and a costing analysis. The summary of international practice (including underpinning evidence) was undertaken rather than a systematic review of effectiveness and safety due to the rapidly changing evidence base and short timeline within which information was required. Results While new recommendations regarding immunisation against RSV are emerging internationally in response to recently authorised technologies, implementation and funding strategies are not currently widespread. Compared with a full HTA, a rapid HTA enabled an efficient and flexible approach to producing a high quality evidence synthesis to inform a temporary policy decision, taking approximately 6 months to complete rather than 12 months or longer. A full HTA is planned to inform long-term decision-making on this topic, but would not have been feasible to inform an interim decision. Conclusions This rapid HTA will inform a temporary decision on immunisation policy for a single RSV season in Ireland. Conducting a rapid HTA prior to a full HTA has enabled timely decision-making, with implications for Ireland’s national healthcare system and people at risk of severe RSV-related disease. Key messages • Innovations in immunisation against RSV are leading to changes in policy across Europe. • Rapid HTA can support timely, evidence-based public health decision-making in a fast-evolving field.

Methodological framework for early detection of dengue outbreaks in Tanzania

Abstract Background Dengue fever is a major public health problem because it is a widespread and rapidly transmitted mosquito-borne viral disease. We developed a framework for early detection of dengue outbreaks in Tanzania, starting with detection of suspected and/or confirmed cases, followed by site-specific risk characterization, both useful for planning and prioritizing outbreak interventions or epidemic preparedness. Methods Our initial focus was on the identification and categorization of indicators that are specifically tailored to the early detection and classification of outbreak events. The framework was then evaluated using (i) available cases with syndromic and laboratory-confirmed disease information from ProMED emails using decision tree analysis, and (ii) available historical dengue epidemic data at the local level from 2019 with 6,795 confirmed cases using negative binomial regression analysis adjusted for month and area. Results The laboratory-confirmed diagnosis (dengue yes or no) was consistent with the results of the suspected case classification algorithm for clinically defined syndromic cases. There was strong evidence of an increase in dengue cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])) when regressing confirmed dengue cases in 2019 as the dependent variable and site-specific risk as the independent variable. Conclusions The rapid dengue outbreak risk assessment developed may be useful in controlling the epidemic in Tanzania. The suspected case classification algorithm can be a very useful tool to better assess whether a potential dengue case is present and thus requires laboratory confirmation. The framework can be used to rapidly predict the risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. Key messages • Framework for a novel rapid risk assessment for early detection of dengue outbreaks in Tanzania. • Framework relevant to epidemic preparedness.

Improving Workplace Safety and Health Through a Rapid Ergonomic Risk Assessment Methodology Enhanced by an Artificial Intelligence System

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort.

Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (&gt;10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.

ChineseCVD: a web-based Chinese-specifi c cardiovascular risk calculator incorporating long COVID, COVID-19vaccination, SGLT2i and PCSK9i treatment effects

Abstract Background Web-based risk prediction tools for cardiovascular diseases are crucial for providing rapid risk estimates for busy clinicians, but there is none available specifically for Chinese subjects. This study developed ChineseCVD, a risk calculator for cardiovascular disease. Methods Adult patients attending government-funded family medicine clinics in Hong Kong between 1st January 2000 and 31st December 2003 were included. The primary outcome was major adverse cardiovascular events (MACE) defined as a composite of myocardial infarction, heart failure, transient ischaemic attacks/ischaemic strokes, and cardiovascular mortality. Results A total of 155,066 patients were used as the derivation cohort. Over a median follow-up of 16.1 (11.6-17.8) years, 31,061 (20.44%) had MACE. Cox regression identified male gender, age, comorbidities, cardiovascular medications, systolic and diastolic blood pressure, and laboratory test results (neutrophil-lymphocyte ratio, creatinine, ALP, AST, ALT, HbA1c, fasting glucose, triglyceride, LDL and HDL) as significant predictors of the above outcomes. ChineseCVD further incorporates the impact of smoking status, COVID-19 infection, number of COVID-19 vaccination doses, and modifier effects of newest medication classes of PCSK9i and SGLT2i. The calculator enables clinicians to demonstrate to patients how risks vary with different medications. Conclusions The ChineseCVD risk calculator enables rapid web-based risk assessment for adverse cardiovascular outcomes, thereby facilitating clinical decision-making at the bedside or in the clinic.

A Methodological Framework for High-Resolution Surface Urban Heat Island Mapping: Integration of UAS Remote Sensing, GIS, and the Local Climate Zoning Concept

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria’s capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024.

Framework for approaching federated causal inference based on real-world observational data sources

Abstract Context Causal inference methods, generally requiring detailed individual information, allow us to evaluate public health interventions in real-world observational settings. When comparing interventions across borders and for this purpose leveraging distributed sensitive individual-level data, a federated approach can be aided. However, reusing such distributed data while maintaining privacy and interoperability, is a challenging endeavor. Methodological framework In the context of the BY-COVID project, a methodological framework to approach federated causal inference was published in October 2023. It is presented as a set of guidelines in the form several consecutive steps, starting with defining a research question, to building a causal model, translating the causal model into data requirements captured within a Common Data Model (CDM), generating synthetic data complying with the requirements captured in the CDM, and developing interoperable analysis scripts which can be distributed for deployment in different federated nodes. The proposed framework was built through implementing and combining existing methodologies and taking into account the principles of legal, organizational, semantic and technical interoperability. Objectives This presentation will provide an overview of the proposed framework, and its demonstration by performing an international comparison of the real-world effectiveness of SARS-CoV-2 primary vaccination campaigns in preventing infections at different federated sites. Challenges encountered (e.g. difficulties in complying with data requirements by linking heterogeneous local data sources) and lessons learned in the course of the demonstrator will be highlighted. Implications We aim to contribute to a public health research field where the rapid assessment of emerging population-health research questions (potentially feeding into evidence-based policy-making) is achievable, in that way contributing to European pandemic preparedness.

The primary healthcare workforce crisis: a comparative assessment of the European region

Abstract Background The healthcare workforce crisis strongly affects the primary healthcare sector, worsening the work conditions of healthcare workers and hampering service delivery and health system resilience. This study introduces a health system and governance approach to identify transformative capacities in health system contexts. Methods A qualitative comparative methodology was employed, and a rapid assessment of the primary healthcare workforce was conducted across nine countries: Denmark, Germany, Kazakhstan, Netherlands, Portugal, Romania, Serbia, Switzerland, and the United Kingdom/ England. Results Our findings reveal both convergence and pronounced diversity across the healthcare systems, with none fully aligning with the ideal attributes of primary healthcare suggested by WHO. However, across all categories, Denmark and the Netherlands, and to a lesser extent also Kazakhstan, depict closer alignment to this model than the other countries. Workforce composition and skill-mix vary strongly, while disparities persist in education and data availability, particularly within Social Health Insurance systems. Policy responses and interventions span governance, organisational, and professional realms, although with weaknesses in the implementation of policies and a systematic lack of data and evaluation. The WHO primary healthcare model only marginally informs policy decisions, but it may receive some more attention in Kazakhstan. Conclusions We conclude that aligning primary healthcare and workforce considerations within the broader health system context may help move the debate forward and build governance capacities to improve resilience in both areas. Speakers/Panelists Tiago Correia NOVA IHMT, Lisbon, Portugal Viola Burau Aarhus University, Aarhus C, Denmark Marius-Ionuț Ungureanu Babeș-Bolyai University, Cluj-Napoca, Romania Anna Sagan WHO European Centre for Primary Health Care, Almaty, Kazakhstan

Mental health support for healthcare workers: what is it about governance and policy implementation

Abstract Background We adopt a health system and governance perspective to address the mental health needs of healthcare workers, considering the nature of interventions and the levels and actors involved in governance. The aim is to move the debate forward by identifying governance gaps hampering the implementation of health workforce policies and exploring strategies to effectively increase mental health support. Methods A qualitative comparative methodology is applied based on a case study design utilising a multi-level intersectoral governance matrix. The assessment matrix comprises four key categories: health policy and data, social policy and psycho-social conditions, organisation of work, and job and professional conditions. We conducted a rapid assessment of healthcare workforce developments in the European context, drawing on secondary sources and country experts. The country sample comprises Germany, Portugal, Romania, Switzerland and the United Kingdom. Results Awareness of mental health threats among healthcare workers increased, but policy discourse is driven by service delivery and labour market demands. The attention to healthcare workers’ needs is stronger on the international level especially supported by WHO action, and weakest at national/regional levels. Although organisations and professions demonstrate varying degrees of activity, their efforts are scattered and lack sustainability. Similar challenges were identified across healthcare systems, including limited action, disconnected actors, missing coordination, and a lack of attention to governance gaps and system weaknesses. Conclusions There are similar problems across countries. Mental health policy is driven by labour market needs and ignores research evidence and the individual needs. Successful mental health policy implementation needs multi-level governance and coherent coordination mechanisms, and generally greater attention to the ‘human side’ of the healthcare workforce. Speakers/Panelists Sara Alidina Central Administration of the Health System, Lisbon, Portugal Tomas Zapata WHO/Europe, Copenhagen, Denmark

Development and Validation of a New Approach to Assess the Seismic Vulnerability of Masonry Structures Based on the CARTIS Form

ABSTRACT Rapid methodologies for seismic vulnerability assessment are crucial for implementing effective seismic risk mitigation strategies over large areas. This paper addresses data accuracy and specificity uncertainties in vulnerability indices by introducing a novel rapid methodology built upon the CARTIS form. To transition from typology identification to vulnerability analysis, the study proposes a new Informed Vulnerability Index for Structures (In.V.I.S.) calibrated upon a hybrid vulnerability index formulation based on the GNDT II level approach, and specifically tailored to fit the main CARTIS form fields. Collaboration with local experts, as required by the CARTIS form, aids in identifying representative building typologies within urban centres, informed by historical data and expert knowledge. The In.V.I.S. is computed based on the distinctive characteristics of these building typologies to accurately reflect vulnerability levels. The feasibility of this approach is proved by its application to six municipalities in the Garfagnana area (Lucca, Italy). Finally, vulnerability and fragility curves are proposed for the main building typologies, as a demonstration of the potential applications of the proposed index. Overall, this research underscores the importance of integrating local expertise, historical context and rapid vulnerability assessment methodologies to enhance the accuracy and effectiveness of seismic vulnerability assessments.

Risk Analysis Of Musculoskeletal Disorders (MSDs) Of The Material Handling Process In Waste Processing Operators Using The Rapid Whole Body Assessment Method

: Businesses should never stop trying to include every employee in their operations and never skimp on safety precautions. One of the safety methods in the worker-centered approach implementation is the significance of ergonomics. Typically, material handling phases in production processes include physical labor that demands a lot of operator effort. Handling objects by hand raises the possibility of workplace mishaps, which can lead to musculoskeletal injuries. This study focuses on the different labor postures used for gathering, transferring, and positioning waste as well as the manual handling of waste items up to the shredding area. This study evaluates work postures that may result in symptoms of musculoskeletal diseases using the Rapid Entire Body Assessment (REBA) technique. There are three steps in this process. Prior to lowering the waste bucket to the designated place, gather the trash first, then hoist and carry it till it reaches the mechanical area. Three grades 9, 2, and 6 were obtained from the analysis utilizing the REBA (Rapid Entire Body Assessment) approach. Based on these findings, the first stage has to be improved right away. Thus, in order to meet the improvement objective, we created a telescopic reach that enables employees to change the height a device that aids in bettering workers' posture while picking up trash.

Evaluating functional ability in older adults’ object retrieval behavior from kitchen furniture using OpenPose and REBA

The purpose of this study is to evaluate, through analysis, the ability of older persons to retrieve items from kitchen cabinets. To achieve this goal, data were collected from 128 valid questionnaires and supplemented with field research and user interviews. The study revealed that the elderly’s behavior in retrieving items from kitchen spaces is characterized by both high frequency and difficulty. For this experiment, a total of 42 participants, comprising 21 males and 21 females from the self-care elderly population in the Yangtze River Delta region, were recruited. Two different experimental settings were arranged: one with kitchen utensils arranged in a straight line and another with a purpose-made chest of drawers with varying heights. Video recordings using the Logitech C930C were utilized to capture the gestures and behaviors of the elderly while retrieving objects from the kitchen cupboards (cabinets). By employing a combination of the OpenPose model and the Rapid Entire Body Assessment (REBA) method, which involves calculating human posture angles, REBA scores, and determining the risk level of Work-Related Musculoskeletal Disorders (WMSDs), a risk assessment framework for manual operations associated with WMSDs was developed. Using the angle data acquired from the user operation experiment as parameters, a gradient model of the elderly user’s operational capability was established. The findings indicated a significant impact of neck, trunk, and knee movements on the subjects (P < 0.001). The participants were able to distinguish between different levels of exertion, categorizing movements as ‘easy’, ‘moderate,’ or ‘strenuous.’ These results form the basis for a comfort gradient model for leaning over and retrieving items. Given the prevalent conditions of bone and joint degeneration and osteoporosis among the elderly population, it is evident that they face challenges when accessing items in the kitchen. Therefore, investigating the elderly’s execution abilities during the retrieval process becomes crucial. Understanding how different cabinet heights impact the joint angles of the elderly can be instrumental in optimizing cabinet designs for elderly users, thereby reducing their physical exertion in the kitchen and enhancing their comfort levels. This research holds significant value in improving the quality of life for the elderly population at home and fostering the advancement of elderly-friendly design principles.

Analisis Postur Kerja Pada Bagian Packing Menggunakan Metode ManTRA Dan REBA

PT. Indonesia Plafon Semesta is a company engaged in manufacturing, this company produces various kinds of PVC ceilings. Problems faced by PT. Indonesia Plafon Semesta is the activity of making PVC ceilings in the packing section, where the activity conditions or workers' posture is not good. When packing by squatting and bending, the position is less ergonomic, resulting in excessive force exertion and the risk of injury. This research aims to determine the level of complaints felt by workers in body parts by identifying them using the Nordic Body Map (NBM), Manual Task Risk Assessment (ManTRA) to determine the level of risk experienced by workers and knowing how many angles are formed. Rapid Entire Body Assessment (REBA) is used to calculate worker posture and provide a score of the worker's proposed posture using Anthropometry. Data processing was carried out using NBM calculations for workers who had the highest score of 51 and the lowest 44. Knowing the level of risk experienced by workers with Mantra protection with the back, forearms, neck and wrists &gt; 15 and the angle formed in the REBA calculation using score 10. Improvement of the proposed posture with Anthropomerti with a TPO calculation of 32.5 cm and JTD cm of 69 by paying attention to the object being worked on with REBA calculation results having a score of 7.

Backtesting the iconic Polyfytos balanced-cantilever bridge to enhance condition assessment

This paper introduces a comprehensive framework for reliable damage assessment and decision-making for bridge intervention planning. By utilizing historical data and 3D-UAV photogrammetry, a detailed 3D numerical model is created, incorporating advanced modeling and scenario-based approaches. The methodology is applied to the Polyfytos bridge in Greece, which faces significant vertical deck deflections that disrupt traffic and impact local communities. This approach offers a reliable, cost-effective, and efficient process, allowing stakeholders to make informed decisions with minimal physical interaction with the bridge. The integration of advanced modeling with in-situ measurements and expert judgment is emphasized. For the Polyfytos bridge, the framework helps prioritize retrofits and assess the deck’s remaining strength under various loads, providing estimates of its overstrength. This aids in evaluating the bridge’s ability to handle additional vertical loads through pushdown analysis. Overall, the framework is valuable for rapid infrastructure assessment, identifying damage patterns, severity, and the urgency of interventions related to structural strength.