Selection Of Components Research Articles

Cardiac Critical Care Fellowship Training in the United States and Canada: Pediatric Cardiac Intensive Care Society-Endorsed Subcompetencies to the 2022 Entrustable Professional Activities.

We aimed to define and map subcompetencies required for pediatric cardiac critical care (PCCC) fellowship education and training under the auspices of the Pediatric Cardiac Intensive Care Society (PCICS). We used the 2022 frameworks for PCCC fellowship learning objectives by Tabbutt et al and for entrustable professional activities (EPAs) by Werho et al and integrated new subcompetencies to the EPAs. This complementary update serves to provide a foundation for standardized trainee assessment tools for PCCC. A volunteer panel of ten PCICS members who are fellowship education program directors in cardiac critical care used a modified Delphi method to develop the update and additions to the EPA-based curriculum. In this process, the experts rated information independently, and repetitively after feedback, before reaching consensus. The agreed new EPAs were later reviewed and unanimously accepted by all PCICS program directors in PCCC in the United States and Canada and were endorsed by the PCICS in 2023. The procedure for defining new subcompetencies to the established EPAs comprised six consecutive steps: 1) literature search; 2) selection of key subcompetencies and curricular components; 3) written questionnaire; 4) consensus meeting and critical evaluation; 5) approval by curriculum developers; and 6) PCICS presentation and endorsement. Overall, 110 subcompetencies from six core-competency domains were mapped to nine EPAs with defined levels of entrustment and examples of simple and complex cases. To facilitate clarity and develop a future assessment tool, three EPAs were subcategorized with subcompetencies mapped to the appropriate subcategory. The latter covering common procedures in the cardiac ICU. This represents the 2023 update to the PCCC fellowship education and training EPAs with the defining and mapping of 110 subcompetencies to the nine established 2022 EPAs. This goal of this update is to serve as the next step in the integration of EPAs into a standardized competency-based assessment framework for trainees in PCCC.

Electrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics.

Electrolyte-gated transistors (EGTs) are promising candidates as artificial synapses owing to their precise conductance controllability, quick response times, and especially their low operating voltages resulting from ion-assisted signal transmission. However, it is still vague how ion-related physiochemical elements and working mechanisms impact synaptic performance. Here, to address the unclear correlations, we suggest a methodical approach based on electrochemical analysis using poly(ethylene oxide) EGTs with three alkali ions: Li+, Na+, and K+. Cyclic voltammetry is employed to identify the kind of electrochemical reactions taking place at the channel/electrolyte interface, which determines the nonvolatile memory functionality of the EGTs. Additionally, using electrochemical impedance spectroscopy and qualitative analysis of electrolytes, we confirm that the intrinsic properties of electrolytes (such as crystallinity, solubility, and ion conductivity) and ion dynamics ultimately define the linearity/symmetricity of conductance modulation. Through simple but systematic electrochemical analysis, these results offer useful insights for the selection of components for high-performing artificial synapses.

Heterogeneous Microstructure Provides a Good Combination of Strength and Ductility in Duplex Stainless Steel

SAF2507 duplex stainless steel (DSS) is often used as a structural component in ocean-going vessels and marine petroleum exploitation equipment, which require superior mechanical properties. In this study, we used cold rolling (CR) at room temperature with 55% or 80% deformation amounts and subsequent annealing at 1273 K in 1 min to prepare SAF2507 samples with a heterogeneous structure (HS) that was composed of ferrite and austenite phases with different grain sizes. Compared with the homogeneous structure samples, the yield strength of the HS samples increased, while the ductility did not decrease. The 55%-1273 and 80%-1273 samples exhibited the hetero-zone boundary-affected regions on both sides of the grain boundary, phase boundary, and twin boundary. This resulted in hetero-deformation-induced (HDI) strengthening and strain hardening of samples during tensile deformation, which improved the ultimate tensile strength of the HS samples while maintaining a good uniform elongation. In addition, the heterogeneous structure of DSS had better corrosion resistance than the initial sample of coarse grain (CG) structure; mainly because the HS samples had finer grains and more grain boundaries on the DSS surface than the CG structure, which is conducive to the formation of high-density passivation film on the surface of stainless steel. The current study provides a new method of material selection of some structural components with the demands of high strength and good ductility.

Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties

Abstract The evaluation of the diversity in the cowpea local varieties (CLVs) allows the efficient use of genetic resources for cultivar development. This study aimed to evaluate the diversity of CLVs based on observation of morphological characteristics and to identify the relationship among morphological traits and potential varieties as donor genes to improve yield potential. The CLVs were collected from five provinces in Indonesia during 2017–2018. The evaluation of the performance of CLVs’ morphological characteristics was carried out in the Indonesian Legumes and Tuber Crops Research from March to July 2019. The observed data were 43 agronomic characters which consist of qualitative and quantitative characters. There is a high variation in stem pigmentation and seed color in 214 CLVs. There were nine qualitative characters with no variation. Most quantitative characters showed significant variation among CLVs. The clustering of 214 CLVs results in five clusters in which some CLVs maintain valuable characteristics that could be used in the breeding program. The number of pods per plant, number of racemes per plant, and 100 seed weight were useful for indirect selection in the yield improvement. Based on the genotype by trait biplot, the CLV 112, 191, and 111 could be used as donor parents for seed size improvement and the CLV 169 could be used as a donor parent for the yield potential improvement in the cowpea breeding program. Four cowpea genotypes can be recommended for commercial release or used as potential breeding material in cowpea cultivar development programs. The new findings of this study could provide a basis for the genetic improvement of cowpea.

Design and development of a CanSat for air pollution monitoring with RSSI based position retrieval system

A CanSat is a miniature satellite system designed to be launched and operate in a manner similar to a satellite but on a much smaller scale. The term “CanSat” is derived from the combination of “Can” (referring to the soda can-sized container often used for the prototype) and “Satellite.” These small and cost-effective systems are typically used for educational purposes and scientific experiments. The main objective of this study is to present the design of CanSat, which is intended to track air pollution. The total weight of the CanSat includes the weight of the sensors, communication equipment, and other subsystems, which makes up the CanSat's total weight of 150 g. The data transmission rate can vary depending on the capabilities of the communication equipment used, but typically ranges between 1 and 10 transmissions per second. The Xbee communication protocol is commonly used for telemetry, typically at either 2.4 GHz or 900 MHz, but not both simultaneously. The major components of the CanSat are the power and sensor subsystems. The appropriate sensors are chosen by considering the main design elements, and typically include sensors to measure temperature, humidity and gases that can identify various pollutants to collect data on local air quality. CanSat is also equipped with a location retrieval system to ensure it can be recovered after launch. However, the type of system used can vary depending on the specific design. Power conservation techniques are employed to ensure that the CanSat can operate for as long as possible. Component selection is also carefully considered to make the CanSat more portable, affordable, and able to gather data on air pollution effectively.

Discrimination of vocal folds lesions by multiclass classification using autofluorescence spectroscopy: An ex vivo study.

Autofluorescence spectroscopy is effective for noninvasive detection but underutilized in tissue with various pathology analyses. This study evaluates whether AFS can be used to discriminate between different types of laryngeal lesions in view of assisting in vocal fold surgery and preoperative investigations. A total of 1308 spectra were recorded from 29 vocal fold samples obtained from 23 patients. Multiclass analysis was performed on the spectral data, categorizing lesions into normal, benign, dysplastic, or carcinoma. Through an appropriate selection of spectral components and a cascading classification approach based on artificial neural networks, a classification rate of 97% was achieved for each lesion class, compared to 52% using autofluorescence intensity. The ex vivo study demonstrates the effectiveness of AFS combined with multivariate analysis for accurate classification of vocal fold lesions. Comprehensive analysis of spectral data significantly improves classification accuracy, such as distinguishing malignant from precancerous or benign lesions.

Modeling and Control Simulation of Power Converters in Automotive Applications

This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1ϕ (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference.

Application of multicriteria decision analysis to assess the greenness of molecularly imprinted polymers synthesis components

The study applies multicriteria decision analysis (MCDA) to rank the components of molecularly imprinted polymers (MIPs) synthesis according to their greenness. The components are taken from papers that are describing synthesis of MIPs for persistent organic pollutants (POPs) sorbents. Functional monomers (n = 14), target/surrogate molecules (n = 10), porogens (n = 8), cross-linking agents (n = 8) and initiators (n = 4) are described with 10 criteria referring to their safety of application, toxicity, environmental persistence and bioaccumulability. The rankings are performed according to three scenarios, with the application of different weights. The ranking results give a specific guide on the selection of green synthesis components for polybrominated biphenyl ethers (PBDEs) as representatives of POPs, by identification of problematic and recommended chemicals. The results are useful in designing MIP synthesis protocols.

Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor

Fuel cell distributed drive electric tractors (FCDET) are one of the necessary means to achieve truly green agriculture. However, low traction efficiency, poor control coordination, and excessive energy consumption are the main reasons hindering the industrialization of FCDET. This paper proposed a parametric collaborative optimization design method and innovative verification system that considers the drive system and energy system. Based on the tractor plowing operating conditions, a 7-DOF coupled dynamics model of the distributed drive system and a tire-soil interaction model were established, and the key component selection and parameter matching were completed. On the drive system optimization level, the front and rear wheelside transmission ratios are optimized based on a multi-island genetic algorithm (MIGA). On the energy system optimization level, the globally optimal power distribution ratio is found based on the dynamic programming algorithm (DP). A complete experimental prototype verification system is constructed based on the MATLAB/Simulink-NI PXI joint simulation platform, physical prototype test bench, and real vehicle multi-index test platform. The results show that the average efficiencies of the optimized drive and energy system are increased by 0.38 % and 3.82 %, the total energy consumption (total hydrogen consumption) is reduced by 25.40 % and 15.39 %, respectively. The maximum fault-free operating time is 5.5h, the average traction power is 21.07 kW, and the average traction force is 10610 N in the all-wheel drive mode. The experimental results fully demonstrate that the electric tractor with the fuel cell distributed drive system as the core can meet the verification of multiple indicators. This study can provide a new theoretical basis and technical verification method for the optimal design and system control of fuel cell distributed drive electric tractors.

Iatrogenic Alzheimer’s disease in recipients of cadaveric pituitary-derived growth hormone

Alzheimer’s disease (AD) is characterized pathologically by amyloid-beta (Aβ) deposition in brain parenchyma and blood vessels (as cerebral amyloid angiopathy (CAA)) and by neurofibrillary tangles of hyperphosphorylated tau. Compelling genetic and biomarker evidence supports Aβ as the root cause of AD. We previously reported human transmission of Aβ pathology and CAA in relatively young adults who had died of iatrogenic Creutzfeldt–Jakob disease (iCJD) after childhood treatment with cadaver-derived pituitary growth hormone (c-hGH) contaminated with both CJD prions and Aβ seeds. This raised the possibility that c-hGH recipients who did not die from iCJD may eventually develop AD. Here we describe recipients who developed dementia and biomarker changes within the phenotypic spectrum of AD, suggesting that AD, like CJD, has environmentally acquired (iatrogenic) forms as well as late-onset sporadic and early-onset inherited forms. Although iatrogenic AD may be rare, and there is no suggestion that Aβ can be transmitted between individuals in activities of daily life, its recognition emphasizes the need to review measures to prevent accidental transmissions via other medical and surgical procedures. As propagating Aβ assemblies may exhibit structural diversity akin to conventional prions, it is possible that therapeutic strategies targeting disease-related assemblies may lead to selection of minor components and development of resistance.

Assessment of the Seasonal Potential of Macroalgae and Grass in the Sea of Azov for Methanogenesis and Optimization of the Digestate’s Carbon/Nitrogen Ratio

Large amounts of macroalgae and grass are dumped on the shores of the Sea of Azov in different seasons. Aquatic plant biomass management could contribute to sustainable development. By mixing them with co-substrates in an anaerobic bioreactor, not only can biogas be extracted, but suitable fertilizers can also be obtained. This study discusses the possibility of using methanogenesis waste from Azov Sea algae and sea grass as a fertilizer for agriculture. The main criterion is the presence of carbon (C) and nitrogen (N) in the waste products of methanogenesis. The influence of climatic and seasonal factors in the Azov region on the quality and quantity of storm emissions, on the productivity of methanogenesis, and changes in the ratio of carbon (C) and nitrogen (N) during methanogenesis and in the fermented substrate has been established. The influence of the ratio of the components of the mixture in various proportions, before methanogenesis, on the productivity of methanogenesis and the change in the ratio of carbon (C) and nitrogen (N) during the process of methanogenesis, and in the fermented substrate were studied. The biomass of the Sea of Azov, cattle manure and wastewater waste in various proportions, were used as components of the mixture. Recommendations are given for the selection of mixture components for methanogenesis, with predicted indicators of the ratio of carbon (C) and nitrogen (N) in the fermented substrate.

Development of a Compact Incremental Forming Machine

Since the beginning of the 21st century, incremental sheet-metal-forming processes, such as single-point incremental forming (SPIF), have been the subject of extensive research. The SPIF process is highlighted as an efficient and cost-effective solution for producing complex parts with different materials and scales, surpassing conventional methods and being ideal for small series and customized products. Various machines can be used to implement SPIF, such as adapted milling machines, serial robots, and dedicated machines, each with its own advantages. However, although it requires a higher initial investment, a dedicated machine offers superior performance. The objective of this project was the creation of a compact and portable dedicated machine, which included the design of suitable kinematics, a mechanical project, and numerical control. The structural design led to the optimization of the dimensions of the robot arms. Direct and indirect kinematics were analyzed. Finally, the careful selection and adaptation of components were carried out, bearing in mind the support system of the forming punch, including the selection and sizing of motors, reducers, and linear actuators. A functional early prototype was successfully built and tested.

Triangle-Shaped Cerium Tungstate Nanoparticles Used to Modify Carbon Paste Electrode for Sensitive Hydroquinone Detection in Water Samples.

In this study, we propose an eco-friendly method for synthesizing cerium tungstate nanoparticles using hydrothermal techniques. We used scanning, transmission electron microscopy, and X-ray diffraction to analyze the morphology of the synthesized nanoparticles. The results showed that the synthesized nanoparticles were uniform and highly crystalline, with a particle size of about 50 nm. The electrocatalytic properties of the nanoparticles were then investigated using cyclic voltammetry and electrochemical impedance spectroscopy. We further used the synthesized nanoparticles to develop an electrochemical sensor based on a carbon paste electrode that can detect hydroquinone. By optimizing the differential pulse voltammetric method, a wide linearity range of 0.4 to 45 µM and a low detection limit of 0.06 µM were obtained. The developed sensor also expressed excellent repeatability (RSD up to 3.8%) and reproducibility (RSD below 5%). Interferences had an insignificant impact on the determination of analytes, making it possible to use this method for monitoring hydroquinone concentrations in tap water. This study introduces a new approach to the chemistry of materials and the environment and demonstrates that a careful selection of components can lead to new horizons in analytical chemistry.

Hydrocarbon-Rich Bio-Oil Production from Ex Situ Catalytic Microwave Co-Pyrolysis of Peanut Shells and Low-Density Polyethylene over Zn-Modified Hierarchical Zeolite

Peanut shells, a major economic and oil crop in China, boast an abundant availability and remarkably high lignin content compared to other agricultural residues. Previous work indicated that the modified hierarchical zeolite (Zn-ZSM-5/MCM41) was effective in promoting the conversion of intermediate macromolecules during the lignin pyrolysis reaction and enhancing the yield and selectivity of liquid products. Thereby, this study aims to improve the quality of liquid products in the ex situ catalytic microwave co-pyrolysis of peanut shells and LDPE by utilizing Zn-ZSM-5/MCM41. Employing a compound center experimental design, we optimized reaction conditions through response surface analysis. The impact of microwave pyrolysis temperature and the catalyst-to-feedstock ratio on yield distribution and liquid product selectivity was explored. Results indicated a marginal increase in liquid product yield with rising pyrolysis temperatures. Moreover, an initial increase followed by a subsequent decrease in liquid product yield was observed with an increase in the catalyst-to-feedstock ratio. Optimal conditions of 450 °C and a catalyst-to-peanut hull ratio of 2.34% yielded the highest bio-oil yield at 34.25%. GC/MS analysis of the bio-oil revealed a peak in hydrocarbon content at 68.36% under conditions of 450 °C and a catalyst-to-feedstock ratio of 13.66%. Additionally, the quadratic model effectively predicted bio-oil yield and the selectivity for major chemical components. This study underscores the potential of Zn-ZSM-5/MCM41 in optimizing liquid product quality during catalytic co-pyrolysis, offering insights into bio-oil production and its chemical composition.

Mapping Solar Flux Distribution in Parabolic Trough Collectors to Assess Optical Performance

This paper describes the development and testing of a novel device capable of mapping the flux distribution in parabolic trough solar collectors (PTCs). Accurate knowledge about the flux distribution is essential in any concentrated solar power (CSP) application, in particular PTCs equipped with concentrator photovoltaic (CPV) cells, since their efficiency highly depends on the collector’s light focusing properties. However, the assessment of CSP collectors’ optical performance requires sophisticated measurement technology, as the error budget of any sun-tracking optical device comprises a variety of factors ranging from physical properties such as mirror reflectivity to mechanical aspects like gravity sag and play in gears. The presented approach features a scanner consisting of a CPV cell mounted on a pair of linear actuators. A thorough state of the art analysis was conducted and numerous technological approaches were assessed. Design aspects such as component selection, cooling, and data acquisition are discussed and finally exemplary measurement results are presented.

An Electromagnetic Load Identification Method Based on the Polynomial Structure Selection Technique

Electromagnetic loads can effectively monitor motor health and improve motor design. Considering the weak correlation of the modal shape and Chebyshev orthogonal polynomial in the space-time independent electromagnetic load identification method, a proposed method combining the polynomial structure selection technique together with limited measured displacement responses is presented, in which an error reduction ratio is used to pick out the significant mode shape matrix and the Chebyshev orthogonal polynomial. The time-history function of the electromagnetic load is reconstructed by combining the significant mode shape matrix and the identified concentrated load through modal transformation, and the corresponding spatial distribution function is fitted by the significant Chebyshev orthogonal polynomial. Eventually, a comparative numerical study considering the selection of significant components and measurement noise is carried out to prove the effectiveness of the presented method.

Investigating the use of Deep Learning, in Materials Research for Predicting Material Properties, Identifying new Materials, and Optimizing Material Selection for Mechanical Components

The rapid advancements in deep learning techniques have spurred a paradigm shift in materials research, revolutionizing the way we predict material properties, identify novel materials, and optimize material selection for mechanical components. This paper explores the integration of deep learning methodologies into materials science, presenting a comprehensive investigation into their efficacy and potential applications. The paper explores the development of deep learning models for predicting material properties.[1] Leveraging vast datasets containing information on diverse materials and their corresponding properties, we delve into the application of neural networks to establish robust predictive models. By extracting complex relationships within the data, deep learning facilitates the accurate estimation of material characteristics, enabling researchers and engineers to streamline the materials discovery process. In addition to property prediction, the study explores the role of deep learning in the identification of new materials with superior or tailored attributes. By training models on extensive databases encompassing known materials and their functionalities, we investigate the ability of deep learning algorithms to suggest novel materials with specific desired properties. This capability holds immense promise for accelerating the discovery of innovative materials, especially in fields where tailored material performance is critical. Furthermore, the paper examines the utilization of deep learning in optimizing material selection for mechanical components. By considering a holistic approach that factors in mechanical, thermal, and other relevant properties, we explore how neural networks can assist in selecting the most suitable materials for specific applications. This not only enhances the efficiency of the design process but also contributes to the development of more durable, efficient, and sustainable mechanical components. Through a systematic exploration of the integration of deep learning in materials research, this paper provides valuable insights into the transformative potential of these techniques. The findings contribute to the ongoing discourse on the intersection of artificial intelligence and materials science, paving the way for accelerated advancements in materials discovery, design, and application.

Rapid Decision-Making Tool for Electric Powertrain Sizing for Motorcycles during New Product Development

As part of the intergovernmental and public interventions to reduce carbon dioxide emissions, there are no existing regulations to ban the sale of petrol motorcycles (PM), but it is expected that motorcycle regulations will follow car regulations with several years of delay. There is an emerging trend in motorcycle uptake, which will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfies design challenges specific to electric motorcycles (EM) and electric powertrains. There is significant importance in motorcycle design to quantify the vehicle-level performance indicators and specifications, which are not limited to total vehicle mass, range, acceleration performance, and top speed. Those performance indicators should be quantified for different powertrain configurations and component selections to identify the most suitable configuration for the specific motorcycle development. In this paper, an innovative powertrain sizing approach is proposed to provide solutions for EMs against the design challenges specific to electric motorcycles. The innovative approach is to apply the practice of design space exploration (DSE) in resilient system design (RSD) to EM development. As a proof of concept, a case study of battery sizing is presented, in which a powertrain sizing tool is used to identify battery pack sizing requirements using requirement-based design (RBD), sensitivity analysis and DSE. The case study shows that the RBD approach allows EM product developers to identify a single solution, while DSE clearly demonstrates the trade-off between different configurations, taking multiple design variables into account. The tool prioritises high accessibility and high confidence with limited information at the early phases of electric motorcycle powertrain component sizing and selection.

Structural and mechanical roles of wood polymer assemblies in softwood revealed by gradual removal of polysaccharides or lignin

A deep understanding of the inherent roles of wood polymers such as cellulose, hemicelluloses, and lignin in the hierarchical structure of wood is of key importance for advancing functional wood-based materials but is currently lacking. To address this gap, we clarified the underexplored contributions of wood polymer assemblies to the structural support and compressive properties of wood by chemically removing polysaccharides or lignin from wood blocks of a conifer Cryptomeria japonica. Compositional and structural evaluations revealed that cellulose, hemicelluloses, and lignin contributed to the dimensional stability of wood, especially that the polysaccharide network at cell corners sustained the honeycomb cell structure. Wood polymer assemblies featuring the anatomical structure of wood were also evaluated in terms of compressive properties. The modulus and strength reflected the density and anisotropy, whereas fracture behavior was well characterized by each wood polymer assembly through the classification of stress–strain curves based on principal component analysis. The difference in fracture behaviors indicated that the rigid lignin and flexible cellulose assemblies, possibly mediated by hemicelluloses, complementarily determine the unique compressive response of wood. These findings enable the adjustment of wood functionality and the selection of composite components for wood modification while inspiring the development of novel wood applications.

Evaluating resilience of coastal communities upon integrating PRISMA protocol, composite resilience index and analytical hierarchy process

This research intends to investigate the resilience of coastal communities to natural hazards upon adopting: (i) Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol as a novel approach for the selection of resilience indicators and major components; (ii) Analytical Hierarchy Process to generate a weight for each indicator and major component of resilience; and (iii) indexing method to calculate the extent of resilience for each community. The results highlight that 25.81 % of the communities in the study area are registered in the very low to low resilience class, whereas 58.07 % are classified as very high to high. Additionally, this research identifies 11 critical action areas for building resilience in Khulna City, which is crucial for major stakeholders in setting up development strategies and plans for disaster risk reduction and sustainability.