Analytic Hierarchy Approach Research Articles

Individuals with a fragility fracture and a prescription for bone active medication have a positive perception of the medication but do not associate it with fracture risk reduction.

Our purpose was to examine perceptions about bone active medication from individuals with a fragility fracture and a prescription for bone active medication. In this qualitative description study, eligible participants were those who attended an Osteoporosis Canada education session, and reported sustaining a previous fragility fracture and receiving a prescription for bone active medication. We conducted one-on-one interviews and analyzed the data using the analytic hierarchy approach. We interviewed 32 female participants (age range 58-89years). Based on our analysis, two themes were developed: (1) most participants spoke positively about bone active medication, indicating they were willing to start, or continue to take, their medication. Positive perceptions were held by participants who sustained a fracture while taking bone active medication, participants whose healthcare provider had stopped the prescription, and participants who reported side effects from the medication; (2) most participants did not discuss bone activemedication in relation to their fracture and did not appear to connect the medication to the concept of fracture risk. Instead, participants talked about themedication in relation to bone health in general, or to bone density. Participants appeared to have positive perceptions of bone active medication, despite sustaining a fracture while taking the medication, reporting medication side effects, or having a healthcare provider stop the prescription. Participants did not connect bone active medication to the concept of fracture risk, illustrating the need for healthcare providers to emphasize the connection between fracture risk and bone active medication.

Transformation trajectory of wetland and suitability of migratory water bird habitat in the moribund Ganges delta.

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.

A simple predictive three-step chemical model for gaseous stoichiometric hydrogen–oxygen detonation quenching

Two types of three-step chain-branching kinetics for gaseous hydrogen–oxygen detonation quenching are proposed. The reaction rates dependency on density and pressure, as well as the change in molecular weight are included and fitted to results of detailed chemistry not only along the Hugoniot curve but also behind transverse waves. The trend of induction and reaction times and reduced activation energy of zero-dimensional constant pressure combustion process as well as the induction, reaction lengths, and peak thermicity of the ZND model are focused on in the calibration procedure. One model (3SM-I) is designed to model the ideal detonation whereas the other (3SM-N) focuses on the Dn−κ curve during calibration, i.e. the detonation velocity-mean front curvature curves, aiming to capture the non-idealities. The predictive ability using the proposed models is evaluated in a detonation transmission configuration, a layer of ▪ mixture being confined by N2, in determining the critical height, reactive layer height at which detonation transmission is not possible. Both approaches get closer to the critical height from detailed chemistry. With the present set of model parameters, 3SM-I is better than 3SM-N for prediction of critical height. Moreover, borrowing analytical ignition criteria and wave hierarchy approach, the comparison of various chemical models that are able to recover the critical height suggests that the slope of the Dn−κ curve, linked with the chemical response to losses, is the first key parameter for the critical height determination. In addition, the dynamics of the extinction near criticality such as appearance of transverse detonation and quenching distance from 3SM-I are in very good agreement with that of detailed chemistry due to better estimation of the chemical features along the Hugoniot curve and behind transverse waves. The present study provides a guideline to get simplified chemical models from detailed chemistry for predictive simulations.Novelty and significance statementThe novelty of research is two-fold. The first is to provide general guideline to get simplified models from detailed chemistry for the simulation of detonation extinction with improved predictive ability. The tested configuration was that of a stoichiometric hydrogen–oxygen mixture semi-confined by nitrogen. The evolution of the induction, reaction times, and the maximum thermicity were used as fitting targets for our 3-step model. Furthermore, they were evaluated not only at the post-shock states along the Hugoniot curve, but also behind the transverse waves, keystones of the cellular structure. Second, borrowing analytical ignition criteria and wave hierarchy approach, the comparison of different chemical models that were able to recover the critical height suggested that the slope of the normal velocity–curvature curve was the first key parameter for the critical height determination. Moreover, the dynamics of extinction near criticality, and quenching distance depended on the reaction rate dependency on density and pressure.

Landslide susceptibility mapping of a hilly region through a semi-quantitative technique

ABSTRACT Characterization of landslide susceptibility carries great importance in hilly regions as it is one of the frequently occurring natural hazards which comes with huge destruction of life as well as property. Identification of such landslide susceptible zones is usually decided based on the necessity and available resources. It is considered a valuable input for planning developmental activities by policymakers. The current study focuses on Pabbar Catchment, located in the state of Himachal Pradesh in India and prepares a Landslide Susceptibility Map (LSM) at a catchment scale using Analytical Hierarchy Approach (AHP). Eleven geo-morphological attributes of the catchment called causative factors were used in thematic form while creating the LSM. Being a semi-quantitative method, AHP performed satisfactorily to produce a realistic distribution of landslide susceptibility with a prediction accuracy of 0.768 under the Area Under Curve (AUC) technique. Approximately 72% of the geographical area from the catchment falls under the ‘high’ landslide susceptible zone. Unavailability of LSM at this scale is addressed through this study that may be useful to regional planners for future land use planning.

ASSESSMENT OF THE SPATIAL APPROPRIATENESS OF THE FUTURE URBANIZATION OF THE CITY OF (AIN BEIDA, ALGERIA) USING GEOGRAPHIC INFORMATION SYSTEMS, THE ANALYTICAL HIERARCHY PROCESS, AND THE DELPHI METHOD

Spatial adequacy evaluation is a valuable tool for planning land use strategies. It provides planners with data and outcomes that can help them make informed decisions to ensure effective and secure utilization of land in future urbanization processes. The objective of this study is to analyze the urbanization patterns in the city of Ain El Beida to determine the most appropriate areas for urban development. The aim is to create self-sustaining growth centers that effectively support the city, while also helping decision-makers address current expansions and protect agricultural lands. The study utilized spatial analysis methodology to evaluate and map the suitability of expanding the city's future space. It proposed an integration strategy that combined modern methods and techniques of the Delphi methodology to establish criteria for appropriate land use. The influence of these criteria will be analyzed spatially by administering a questionnaire to a sample of specialists in urban planning and city geography, followed by the application of spatial analyst techniques. The user's text is straightforward and precise. within the GIS framework, integrated via the analytic hierarchy approach. The user's text is straightforward and precise. To evaluate the suitability of the spatial location, to determine the significance of the factors influencing the expansion process based on their relative importance, to ensure that the desired goals are achieved in a way that reflects the priority of the decision-making criteria, and to identify the most favorable options for urban development in the study area. The findings indicated that the optimal locations for the urban growth of Ein El-Baida were determined to be 239.57 hectares, which accounts for 26.31 per cent of the entire land area under investigation. These areas are situated on the northern side of the city along National Road 80 (Ain Beida Presh) and on the western side along National Road 10 (Oum el-Bouaghi Ain Beida). These locations have been identified as the most suitable areas for spatial expansion due to their numerous incentives for urban attraction. Firstly, there is a considerable distance from the fertile and generously compensated agricultural areas, which offer unoccupied and level terrain, as well as its proximity to the central area of the pristine city of Ein. The study also determined that integrating GIS with hierarchy is crucial for constructing spatial models that accurately represent physical reality through mathematical means. This integration aids planners in selecting the most appropriate alternatives for urbanization and achieving comprehensive development of the city.

A Multi-Criteria Decision-Making Framework to Evaluate the Impact of Industry 5.0 Technologies: Case Study, Lessons Learned, Challenges and Future Directions

AbstractSmart technologies have demonstrated striking outcomes regarding the early diagnosis of diseases and the delivery of the necessary healthcare in the last decade. However, by emphasizing the core fundamentals of social justice and sustainability, together with digitalization and smart technologies that predicate raising productivity and flexibility, Industry 5.0 has proven to achieve more efficient results. Industry 5.0 technologies provide more intelligent ways for human employees and higher efficiency development while also improving safety and performance in many applications. In this research, the contribution is focused on the healthcare and how Industry 5.0 technologies demonstrate several advantages for the healthcare sector, starting with automated and precise disease prediction, moving on to aiding medical personnel in continual surveillance and monitoring and concluding with successful digital automation of smart equipment. The objective of this study is to apply a hybrid multi-criteria decision-making approach under a neutrosophic environment to evaluate the advantages of industry 5.0 technologies in the healthcare sector. Industry 5.0 primary value is to reach human-centric, sustainable, and resilient industries. While Industry 5.0 technologies sub-values regarding the healthcare sector are determined and distinguished according to the 3-main values mentioned previously based on literature. The methodologies applied in this study are: The Analytical Hierarchy approach (AHP) evaluates the main values and sub-values. Subsequently, the effectiveness of industry 5.0 technologies according to their values to the healthcare sector are ranked by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach is constructed under uncertainty based on a neutrosophic environment to achieve accuracy in the evaluation process. The results show that the most influential technology in healthcare are AI and cloud computing, while nano-technology, drone technology, and robots are at the end of the ranking. While validating the suggested technique, outcome comparisons were carried out to demonstrate the benefits of the methodologies. A sensitivity study indicates that adjusting the weightings of the sub-values has no significant effect on the ranking of technologies.

Examining medical urgent and patient precedence within the telemedicine landscape with its infrastructure and policies: A comprehensive review

Medical facilities are confronted with grave issues, including a population that is aging and a physician shortage. In an effort to address these issues, telehealth and remote health monitoring systems (RHMS) aim to reduce hospital visits by a small amount. RHMS lessens the workload for primary care patients and enhances inter-unit communication, which lessens the strain on emergency rooms. Due to significant advancements in mobile information transfer and processing of signal technologies, some healthcare researchers have made efforts to employ RHMS in place of hospital visits to provide triage and prioritization for individuals. Prioritization is a strategy for giving people urgent medical care in an attempt to save their lives, while clinical examination determines the severity of a sickness or damage. To emphasize the disadvantages of the present patient screening and prioritization system over the telehealth environment, a crucial inquiry is needed. Based on two crucial axes, an in-depth examination of patient prioritization and medical crisis assessment in a videoconferencing setting was provided in this research. First, a collection, analysis, and classification of earlier research on triage of patients and prioritization in this kind of setting was done. Second, a variety of priorities and standards, as well as various approaches and procedures for prioritization, were provided and examined. The subsequent outcomes were attained: The shortcomings and issues with the current patient prioritization and assessment methods were highlighted. The assessment and priority for individuals who have cardiac conditions were not presented. In the years to come, a structure based on the theory of evidence, as well as the incorporation of a multiple-layer analytical hierarchy approach and method for ranking preference due to resemblance to perfect solutions, can be used to prioritize the care of several patients with ongoing cardiovascular disease by triaging them based on various emergencies dimensions.

An analytical hierarchy approach for studying the impact of human error, environmental factors, and equipment failure on mine accidents: a case study in India

An analytical hierarchy approach for studying the impact of human error, environmental factors, and equipment failure on mine accidents: a case study in India

Exploring the Indian MSME perspective on integrating AI-enabled solutions in the recruitment process: an analytical hierarchy (AHP) approach

Exploring the Indian MSME perspective on integrating AI-enabled solutions in the recruitment process: an analytical hierarchy (AHP) approach

Exploring the Indian MSME perspective on integrating AI-enabled solutions in the recruitment process: an analytical hierarchy (AHP) approach

Exploring the Indian MSME perspective on integrating AI-enabled solutions in the recruitment process: an analytical hierarchy (AHP) approach

Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach

Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach

Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach

Estimating regulatory governance gaps for adoption of augmented reality in automobile sector: the application of analytical hierarchy approach

Building models to evaluate internal comprehensive quality of apples and predict storage time

Apple quality and customer satisfaction are significantly impacted by variations in apple quality throughout storage. In this study, the evaluation of apples' internal overall quality and the estimation of storage time were investigated. By using Pearson correlation analysis and an analytical hierarchy approach, an internal comprehensive quality index was created. A calibration model and a high-order kinetic model were created for the internal comprehensive quality index using the competitive adaptive reweighted sampling (CARS) algorithm in conjunction with partial least squares regression (PLSR) and in accordance with the results of fitting chemical kinetic reactions to variations in internal comprehensive quality with storage time. The calibration model and the high-order kinetic model were combined to create a prediction model for the storage time of apples. Results revealed that the determination coefficient of the prediction (Rp2) and root mean square error (RMSE) of the calibration model were 0.9419 and 0.0023 respectively, and a residual predictive deviation (RPD) of 5.77; the correlation coefficient (R) and RMSE of the higher-order kinetic model were 0.9620 and 0.0038 respectively; the Rp2 of the prediction model was determined as 0.8957, with a root mean square error of 4.63 d. Results show that the proposed calibration model and higher-order kinetic model are capable of evaluating the internal comprehensive quality of apples, and that the determined prediction model is capable of projecting the storage time of apples with an acceptable margin of error while still meeting the real requirements.

Spatial optimization of site selection for development tourism in hawraman area using Analytical Hierarchy Approach:

In this study, the analysis of the suitable places for the development of tourism in Hawraman region is done using the model (AHP). Hawraman region has most of the potential in terms of altitude, climate, water resources, vegetation cover, animals, wild birds, population and historical and religious sites, archaeological sites, caves, language and culture, gardening, construction, food and clothing ... etc, Hawraman has more than (25) tourism areas, some of which are currently visited by tourists from different parts of Kurdistan, Iraq and abroad. The statistics of the last four years of tourists to Hawraman are (86,050) thousand foreign tourists and (172,102) thousand domestic tourists. Therefore, the aim of the study is to develop a comprehensive development of the areas that have not been invested so far according to various scientific tools , such as according to this scientific study, (18) different natural and human criteria were used to produce a four-level map to determine the suitable location for tourism development in Hawraman, Which are the levels of (very suitable, moderately suitable, low and limited suitable, unsuitable) which accounted for (11.9%, 63.64%, 8.44% and 16.02%) of the total area of the study area.

Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments.

A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.

Key Elements for E-Learning Success in the Context of the Coronavirus Crisis

Using the Analytical Multi-Criteria Hierarchy (AHP) approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this article seeks to discover crucial success criteria for e-learning during COVID-19 in order to enhance process education. Based on established assessment criteria and multi-channel e-learning methodologies, data was gathered by interviewing 69 e-learning leaders in educational institutions during COVID-19. We discovered that managing technology, assisting management, educating students about the usage of the e-learning platform, and requiring a high level of information technology exchange from the lecturer are all important. Universities, staff, and students have had the biggest influence on online learning during COVID-19. The most useful of the five learning models is blended learning. These findings show that e-learning preparation plays a significant impact in boosting the educational process during the COVID-19 pandemic, regardless of the specific technology used by an educational institution.

Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

SWOT Analysis of Dairy Marketing Chain of JMF using Analytical Hierarchy Process

Background: Jharkhand State Cooperative Milk Producers' Federation (JMF) plays a vital role in the processing of raw milk purchased from Jharkhand State member-producers and contributes to the development of the dairy sector. The current study explores the essential components in JMF's dairy supply chain, focusing on the marketing unit, and provides a preliminary framework for making decisions regarding its ramifications. Methods: To compile a SWOT analysis of the milk marketing system in JMF, data were collected from dairy producer members, including dairy farmers, marketing unit executives, and key informants. This includes establishing the objectives of the dairy sector and the internal and external critical factors (CFs). Because SWOT analysis does not give an investigative basis for evaluating the priorities of CFs, the analytic hierarchy approach is utilised to determine the priorities of detected CFs. The CFs have also been ranked according to their importance. Results: This study's findings indicate that the SWOT-based methodology gives crucial sensitivity when evaluating marketing chain strategy for dairy cooperatives like JMF. The findings of this study show that wide range of quality dairy products, absence of strong sales and marketing experience, emerging new markets offering greater marketing potential, seasonal slump are the major strength, weakness, opportunity and threat of JMF, respectively.

A Surgical Complexity Factor for Clefts of Primary and Secondary Palates.

The surgical treatment of patients with cleft lip and palate depends on the characteristics of the affected anatomical structures (palate, lip and nose). The objective of this work was to develop a quantified classification for these clefts, to represent their surgical complexity. This work was developed with the team of surgeons of the SUMA Cleft Leadership Center (CLC) Smile Train Mexico. The method of Multiple-Criteria Decision Analysis was applied using the Analytic Hierarchy Approach. A surgical complexity factor associated with each cleft was defined and it was validated in a sample of fifty patients treated at the SUMA-CLC.Clinical Relevance- A quantitative classification that represents the surgical complexity of clefts provides an objective unified criteria for planning the surgical treatment of patients, as well as having standardized procedures for the surgical treatment of patients.