Two-level Hierarchical Structure Research Articles

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

AbstractIn response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.

Robust Switching Time Optimization for Networked Switched Systems via Model Predictive Control.

This article presents a model predictive control (MPC) strategy to find the optimal switching time sequences of networked switched systems with uncertainties. First, based on predicted trajectories under exact discretization, a large-scale MPC problem is formulated; second, a two-level hierarchical optimization structure coupled with a local compensation mechanism is established to solve the formulated MPC problem, where the proposed hierarchical optimization structure is actually a recurrent neural network consisting of a coordination unit (CU) at the upper level and a series of local optimization units (LOUs) related to each subsystem at the lower level. Finally, a real-time switching time optimization algorithm is designed to calculate the optimal switching time sequences.

Association of Intensive Care Unit Case Volume With Mortality and Cost in Sepsis Based on a Japanese Nationwide Medical Claims Database Study.

Background The impact of intensive care unit (ICU) case volume on the mortality and medical costs of sepsis has not been fully elucidated. We hypothesized that ICU case volume is associated with mortality and medical costs in patients with sepsis in Japan. Methodology This retrospective nationwide study used the Japanese administrative data from 2010 to 2017. The ICU volume categorization into quartiles was performed according to the annual number of sepsis cases. The primary and secondary outcomes were in-hospital mortality and medical costs, respectively. A mixed-effects logistic model with a two-level hierarchical structure was used to adjust for baseline imbalances. Fractional polynomials were investigated to determine the significance of the association between hospital volume and clinical outcomes. Subgroup and sensitivity analyses were performed for the primary outcome. Results Among 317,365 sepsis patients from 532 hospitals, the crude in-hospital mortality was 26.0% and 21.4% in the lowest and highest quartile of sepsis volume, respectively. After adjustment for confounding factors, in-hospital mortality in the highest quartile was significantly lower than that of the lowest quartile (odds ratio = 0.829; 95% confidence interval = 0.794-0.865; p < 0.001). Investigations with fractional polynomials revealed that sepsis caseload was significantly associated with in-hospital mortality. The highest quartile had higher daily medical costs per person compared to the lowest quartile. Subgroup analyses showed that high-volume ICUs with patients undergoing mechanical ventilation, vasopressor therapy, and renal replacement therapy had a significantly low in-hospital mortality. The sensitivity analysis, excluding patients who were transferred to other hospitals, demonstrated a result consistent with that of the primary test. Conclusions This nationwide study using the medical claims database suggested that a higher ICU case volume is associated with lower in-hospital mortality and higher daily medical costs per person in patients with sepsis.

A Distributed Multiparticle Precise Stopping Control Model Based on the Distributed Model Predictive Control Algorithm for High-Speed Trains

Abstract The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional–integral–derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains.

Coupled development of the urban water-energy-food nexus: A systematic analysis of two megacities in China's Beijing-Tianjin-Hebei area

Promoting the coupled development of water, energy, and food (WEF) subsystems is a critical step to enhance synergies and increase efficiencies in the WEF nexus. However, the evolution and obstacles of coupled development are largely ignored. This study developed a framework dividing links in the nexus to select indicators and integrated models for the degree of coordination and obstacle diagnosis to explore the coupled development of the WEF nexus from 2000 to 2020 in the water-scarce megacities of Beijing and Tianjin. The results show that the average coordination degree of the WEF nexus in Beijing (0.315) and Tianjin (0.317) is at a low level, indicating a limited interaction between the WEF subsystems, while the coordinated development degree of the WEF nexus is increasing. The order degree of the WEF subsystems shows a two-level hierarchical structure, indicating that both similarities and differences are included in place-specific characteristics. Furthermore, obstacle factors are identified from water and energy subsystems, in which environmental water use (W3) and the energy consumption per gross domestic production unit (E4) played the most significant roles and require a higher priority in policy response. The results in this paper complement obstacle factor analysis in WEF nexus practice, and provide operational indicators for nexus governance.

ХМАРНІ РІШЕННЯ РЕАЛІЗАЦІЇ ГІБРИДНОЇ IT-ІНФРАСТРУКТУРИ ІНФОРМАЦІЙНО-ОСВІТНЬОГО СЕРЕДОВИЩА ВИЩОГО ВІЙСЬКОВОГО НАВЧАЛЬНОГО ЗАКЛАДУ

According to the cloud-based project, the information and education environment (IE) of a higher military educational institution is being modernized. Hybrid IT infrastructure and cloud solutions for its implementation are analyzed. As a result, a comprehensive model of dialogically close service solutions is presented as a prototype of an open, coordinated and standardized cloud system. The hierarchical two-level structure of the complex model is represented by: at the lower (physical) level, hardware for providing cloud services; at the upper (abstract) level, physically deployed software. The article presents a combined scheme of corporate IT infrastructure maintenance with priority given to the IaaS services segment, the classic scenario of hybrid IT infrastructure deployment, and a ready-made management software solution. These requirements have been transformed into a cloud-based hybrid system architecture that combines the external infrastructure of service providers with the internal local infrastructure of a military educational institution by integrating corporate resources and services into the information and educational environment. The latter are represented by ready-made solutions from Microsoft Azure, Office 365, Imagine Academy, Virtual Academy, and Google Workspace, which Microsoft and Google provide to educational institutions free of charge. The basic version of the freely distributed cloud platform Apache CloudStack v.4 was chosen as a tool for managing the resources of the hybrid IT infrastructure. In this context, the selection criteria and the overall topological ordering of the cloud platform are defined. The organizational emphasis of the platform implementation as a three-level hierarchical investment of structural elements is set. We propose a technical structure of the implementation consisting of two computers, one of which performs the functions of the CloudStack MS Management Server and the Primary Storage, and the other is responsible for the hypervisor and contains the Secondary Storage. Keywords: information and educational environment, hybrid IT infrastructure, virtualization, service solutions, cloud platform, virtual machine, deployment model.

Hierarchical energy management system for multi-microgrid coordination with demand-side management

This paper presents an energy management system (EMS) with demand-side management (DSM) capabilities to optimally coordinate multiple microgrids connected to the same main grid. The proposed EMS comprises a two-level hierarchical control structure, with local controllers at microgrid level, and a supervisory control at the main-grid level. The main contribution of the proposed EMS is that it uses model-based predictive controllers (MPC) and DSM at both levels of the hierarchy. The main-grid level minimizes the imported power from the main grid and the shift from the expected demand for each microgrid. Moreover, it computes optimal aggregated power and demand side management references for each microgrid. The microgrid level manages the energy resources of each microgrid based on the reference signals sent by the main-grid level. Thus, the microgrid level tracks the main-grid level references, while minimizing both the power variation in the battery energy storage system and the variation in shifting factor to manage the demand. A simulation of a multi-microgrid system with three microgrids is implemented to validate the performance of the proposed two-level hierarchical MPC-based EMS with DSM. The proposed control scheme uses fuzzy Takagi–Sugeno models to predict the generation and consumption of the microgrids at both control levels. Furthermore, the proposed configuration is compared with two conventional EMS from the literature. The proposed architecture outperforms previously proposed controllers since, in the considered simulations, it (i) reduces to at least 1/4 the loss-of-power-supply probability for each individual microgrid, (ii) halfs the cycling of the individual storage devices in each microgrid, and (iii) reduces the overall system cost in ∼1.5%.

Failure mode and effects analysis based on rough cloud model and MULTIMOORA method: Application to single-point mooring system

The failure mode and effects analysis (FMEA) is an important tool for risk assessment, which can identify and eliminate potential failure modes in a system. However, due to the inherent drawbacks of the traditional FMEA method, the risk assessment results may be inaccurate. In this study, an FMEA approach based on the rough cloud model and MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form) method is developed to evaluate the risk of the single-point mooring system. The main contributions of this study are as follows. First, an interval asymmetric rough cloud (IARC) model is proposed by combining cloud model and rough set theory, which can deal with the randomness of expert individual evaluation and the uncertainty of expert group evaluation. Second, an IARC power weighted average operator is proposed to eliminate the influence of extreme expert opinions when aggregating expert opinions. Third, according to the feature of single-point mooring system failure, a two-level risk factor hierarchical structure is established, and the subjective and objective weights are integrated to determine the risk factor weights. Fourth, an extended MULTIMOORA method based on the rough cloud model is developed to determine the risk priority of failure modes. To illustrate the implementation process of the approach, we have applied it to a single-point mooring system of an FPSO (Floating Production Storage and Offloading) in the South China Sea. The comparison analysis with the existing methods is also presented to validate the feasibility and effectiveness of this approach.

Hospital context in surgical site infection following colorectal surgery: a multi-level logistic regression analysis

Surgical site infections (SSI) are associated with poor health outcomes. Their incidence is highest after colorectal surgery, with little improvement in recent years. The role of hospital characteristics is undetermined. Thus, our aim was to investigate whether SSI incidence after colorectal surgery varies between hospitals, and whether such variance may be explained by hospital characteristics. Data were retrieved from the electronic platform of the Directorate General of Health, from 2015 to 2019. Hospital characteristics were retrieved from publicly available data on the Portuguese public administration. Analysis considered a two-level hierarchical data structure, with individuals clustered in hospitals. To avoid overfitting, no models were built with more than one hospital characteristic. Cluster level associations are presented through median odds ratio (MOR) and intraclass cluster coefficient (ICC). Beta coefficients were used to assess the contextual effects. A total of 11 219 procedures from 18 hospitals were included. The incidence of SSI was 16.8%. The ICC for the null model was 0.09. Procedural variables explained 25% of the variance, and hospital dimension explained another 17%. Over 50% of SSI variance remains unaccounted for. After adjustment, heterogeneity between hospitals (MOR: 1.51, ICC: 0.05) was still found. No hospital characteristic was significantly associated with SSI. Procedural variables and hospital dimension explain almost half of SSI variance and should be taken into account when implementing prevention strategies. Future research should focus on compliance with preventive bundles and other process indicators in hospitals with significantly less SSI in colorectal surgery.

Fractional order-based hierarchical controller design and evaluation with Bürger-Diehl settler model in a total nitrogen removal wastewater treatment process.

The primary objective of this study was to establish two-level structured control techniques based on the globally known benchmark simulation model no. 1 (BSM1) and the Bürger-Diehl settler model in order to improve effluent quality. The latter was based on the activated sludge model no. 1 (ASM1), while the classic Takacs model was superseded by the more recent Bürger-Diehl settler model with enhanced predictive potential. A two-level hierarchical control structure was considered to maintain the dissolved oxygen concentration basing on the ammonia levels and also a nitrate controller was considered to improve nitrogen removal efficiency. Fractional order PI controllers were considered at the secondary level and advanced control techniques, namely, MPC and fuzzy, were implemented at the primary level. Two advance control schemes, being, FPI-MPC and FPI-fuzzy were designed in the present work. The controllers were designed based on the plant model which was identified using prediction-error minimization method. It was observed that the implemented control strategies in consideration with the plant modifications showed a profound impact in improving the plant performance in terms of the effluent quality. FPI-fuzzy resulted in noticeable 60% and 53% reduction in total nitrogen violations for dry and storm climatic conditions, respectively.

A Composite Indicator to Assess Sustainability of Agriculture in European Union Countries

Few studies have been conducted to assess agricultural sustainability in the European Union (EU), and all of them fail to provide a holistic view of sustainability in a relevant temporal horizon that could effectively support the design of policies. In this paper, a composite indicator is constructed based on the geometric aggregation of 12 basic indicators measured yearly in the period 2004–2020 (17 years) on all EU countries plus United Kingdom, with weights determined endogenously according to the Benefit of Doubt (BoD) approach. Our composite indicator has a two-level hierarchical structure accounting for the contributions of the economic, social and environmental dimensions of sustainability. In our results, Bulgaria, Croatia, Lithuania and Poland are the countries with the strongest growth rate of sustainability, while countries reaching the 90th percentile of the score in sustainability include Austria, Czechia, Estonia, France, Germany, Hungary, Latvia, Lithuania, Slovakia and Sweden. In overall, the social and the environmental dimensions have similar levels, while the level of the economic dimension is definitely higher. Interestingly, several countries with a high level of sustainability are characterized by a decline of the economic dimension, including Austria, Finland, Italy, Latvia and Slovakia. The reliability of our composite indicator is supported by the substantial agreement of sustainability scores with subsidies attributed by the Common Agricultural Policy (CAP). Therefore, our proposal represents a valuable resource not only to monitor the progress of EU member countries towards sustainability objectives, but also to refine the scheme for the attribution of CAP subsidies in order to stimulate specific sustainable dimensions.

Design of interfaces to promote the bonding strength between dissimilar materials

A fundamental challenge during the manufacturing of hybrid polymeric structures is to promote the bonding strength between dissimilar materials. While there are existing studies to achieve tough interfaces by developing new chemistry or using adhesive layers, they are performed in an ad-hoc manner and are challenging to be incorporated into automatic advanced manufacturing processes (e.g., multi-material additive manufacturing). In this study, instead of relying on the new chemistry or adhesive layers, we investigate the influences of interfacial geometry on the bonding strength between soft and stiff polymers with a chemical dissimilarity. The interfacial geometry under consideration involves two-level hierarchical structures with different patterns (Strip, Buzzsaw, or Zigzag) in each level. Uniaxial tensile tests are conducted to characterize the enhancements in the interface compared to a flat interface. Finite element analysis is then applied to capture the experimental results and assist to understand the mechanism of interfacial properties' enhancement. Parametric studies based on the computational model also reveal design considerations to promote the mechanical strength of fabricated components. The understanding and computational tools developed in this study would provide valuable design guidelines in terms of interfacial geometry and material selections for optimal mechanical performances of hybrid multifunctional polymeric products.

A hierarchical recommendation system for E-commerce using online user reviews

• We propose a hierarchical recommendation system for e-commerce domain that uses online user reviews. • Our recommendation system uses not only item title and description, but also item’s reviews when generating item embedding vectors. • We provide an analysis of using all item reviews and randomly selected item reviews when making a recommendation. • We provide a transparent recommendation system that capable of explaining all recommendations. Recommendation systems are considered as one of the important components of e-commerce platforms due to their direct impact on profitability. In this study, we propose a hierarchical recommendation system to increase the performance of the e-commerce recommendation system. Our DeepIDRS approach has a two-level hierarchical structure: (1) The first level uses bidirectional encoder representations to represent textual information of an item (title, description, and a subset of item reviews), efficiently and accurately; (2) The second level is an attention-based sequential recommendation model that uses item embeddings derived from the first level of the hierarchical structure. Furthermore, we compare our approach DeepIDRS with various approaches from different perspectives. Our results in the real-world dataset show that DeepIDRS provides at least 10% better HR@10 and NCCG@10 performance than other review-based models. With this study, for e-commerce, we clearly show that a hierarchical, explainable recommendation system that accurately represents the item title, description, and a subset of item reviews, improves performance.

A multifunction superhydrophobic surface with excellent mechanical/chemical/physical robustness

Superhydrophobic surfaces have many advantages in wetting fields. However, the lack of easyily production methods and limited mechanical/chemical/physical robustness remain the two most pertinent barriers to the scalability, large-area production, and widespread use of superhydrophobic materials. In this work, a mechanical/chemical/physical robustness superhydrophobic coating sample consisting of a regular array of square Cu mesh with secondary nano structures of polytetrafluoroethylene (PTFE) and SiO2 powders on the sidewalls is produced through a scalable and easyily production technique of the combination of hot-press sintering and subsquent spray-coating. The Cu mesh is used as a template to fabricate the grooves and bulges as the first-level roughness, and the SiO2 powder is maintained on the surface of the coatings to form two-level hierarchical structures, with both micro and nano structures firmly affixed by low-surface energy polymer binder (PTFE). For comparison, Cu mesh and Cu mesh/Cu sample are also produced. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and time-dependent apparent contact angle are used to examine the as-prepared materials. The corrosive solution, high temperature, UV irradiation, ultrasonic treatment, water drop hitting, sand drop impacting, multi-cycled sandpaper abrasion, tape-peeling test, and self-cleaning test was conducted to simulate the real-world damages. All of the armored superhydrophobic coating samples have an apparent water contact angle of more than 150°, as well as outstanding mechanical and chemical resilience. The salt spray test and mechanical behavior of the as-prepared samples revealed that the armored superhydrophobic coating sample has the lowest corrosion rate, the highest tensile strength, and the greatest elongation among these threee as-prepared samples, implying that it has the best anti-corrosion ability and mechanical property. This strategy might give guidance for the development of composite materials that need to retain mechanical/chemical/physical robustness in severe working conditions.

A New Tag Index Scheme Enables Fast Peptide Retrieval for Protein Identification

Sequence tag index in the field of computational proteomics can be used to facilitate faster open-search-based identification of modified peptides and in-depth analysis of mass spectrometry data. In protein-identification search engines, sequence tag index are playing a prominent role in recent ten years due to fast searching speed. However, in pursuit of less index space consumption, some protein search engines design excessively concise index schemes which lead to higher computational burden. We proposed a new tag index scheme named TIIP with a better balance between space and time complexity. TIIP has a unique two-level hierarchical index structure which allows rapid retrieval of all peptide sequences and their corresponding masses. Theoretically, the index space consumption of TIIP is not much higher compared to the typical tag index schemes, but the time complexity of sequence retrieval can be reduced to O(1), and practically, TIIP has about one million fold improvement in searching speed compared with brute force approach.

An Integrated Multi Criteria Decision Making Model for Evaluating Park-and-Ride Facility Location Issue: A Case Study for Cuenca City in Ecuador

A park-and-ride (P&amp;R) system is a set of facilities where private vehicle users can transfer to public transport to continue their journey. The main advantage of the system is decreasing the congestion in the central business district. This paper aims to analyze the most significant factors related to a Park-and-Ride facility location by adopting a combined model of Analytic Hierarchy Process (AHP) and Best Worst Method (BWM). The integrated model is applicable for complex problems, which can be structured as a hierarchy with at least one 5 × 5 pairwise comparison matrix (PCM) (or bigger). Applying AHP for at least 5 × 5 PCM may generate inconsistent matrices, which may cause a loss of reliable information. As a solution for this gap, we conducted BWM, which generates more consistent comparisons compared to the AHP approach. Moreover, the model requires fewer comparisons compared to the classic AHP approach. That is the main reason of adopting the AHP-BWM model to evaluate Park-and-Ride facility location factors for a designed two-level hierarchical structure. As a case study, a real-world complex decision-making process was selected to evaluate the Park-and-Ride facility location problem in Cuenca city, Ecuador. The result shows that the application of multi-criteria methods becomes a planning tool for experts when designing a P&amp;R system.

A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

Solving a task of coordinated control over a ship automated electric power system under a changing load

This paper reports the synthesis of the main processes for the basic control over a complex technical vessel system. The issue related to the semantics of the description and the method for decomposing management tasks has been proposed to resolve in the context of the synthesis of the program to coordinate control over vessel technical systems and complexes with a sophisticated structure. An example of a five-unit ship’s automated electric power system (SAEPS) for the predefined level of generated power, taking into consideration the efficiency criteria, was used to synthesize the algorithms that execute transitions from one level to another while taking into account the pre-emergency and emergency states of SAEPS. The organization of the sequential process of enabling/disabling generator units (GUs) implies developing a program for managing the coordinator's supervisor as part of a distributed two-level hierarchical structure of SAEPS control when the load changes. The sequence of operations to launch, synchronize, transfer the loading, and stop GU is based on the formation of GU optimal composition, the distribution of loads among GUs running in parallel, and the implementation of the program for optimizing the primary engine of the power plant. The reported principles for constructing GU composition control procedures based on the principle of "rigid and flexible" thresholds have made it possible to build a diagram of adjustment of the time delay in enabling GU dependent on the demanded power. It has been proven that the proposed technique improves the reliability of SAEPS operation as it eliminates possible emergency modes when false control combinations are assigned. Databases on the quantity of GUs, their technical condition, loading, fuel consumption, and environmental parameters have been built. The synthesis of control over a five-unit SAEPS has made it possible to determine the algorithmization procedure based on using an extended data array and simplify the functioning algorithm involved in the operations of choosing the structure for a five-unit SAEPS

Evaluating Sustainable Conceptual Designs Using an AHP-Based ELECTRE I Method

Evaluating sustainable conceptual designs is a crucial part of the development of new products in various sectors because it contributes to the survival and prosperity of companies. Such designs must be compatible with a product’s functional requirements and be sustainable in economic, environmental, and social terms. Various criteria, some qualitative and others quantitative, are considered when evaluating sustainable conceptual designs; some criteria may also have sub-criteria, and these criteria and sub-criteria may differ in importance. How to effectively evaluate various criteria and sub-criteria and weight them are the major research problem. This study developed an analytic hierarchy process (AHP)-based ELECTRE I method to resolve the evaluation problem; the method entails a two-level hierarchical structure to depict the relationships between the major criteria and sub-criteria and their corresponding weights, with weights obtained through an AHP. Furthermore, an extension based on the addition of net concordance value and net adjusted discordance value is proposed for ranking alternative designs; this extension yields more distinguishable results than the method of [Nijkamp and Van Delft Multi-Criteria Analysis and Regional Decision-Making, Vol. 8 (Springer Science &amp; Business Media, 1977)], and the proposed extension in the ELECTRE I method captures more information in the modified summation matrix when aggregating values in the concordance matrix and the modified discordance matrix, representing an advantage over the method of [Zhang et al. Virtual network embedding using node multiple metrics based on simplified ELECTRE method, IEEE Access 6 (2018) 37314–37327]. In addition, to demonstrate the feasibility of the proposed method, the example of the evaluation and selection of sustainable conceptual designs in the furniture manufacturing industry is presented. Finally, a numerical comparison is made to show the advantages of the proposed method.

Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul.

Environment and social life are open to hazards, because of the distribution, diffusion, and conversion processes of chemicals contained in hazardous materials. These chemicals are very dangerous. Various precautions should be taken into consideration during the displacement of hazardous materials. Therefore, it is important to identify and minimize the risks in the transportation of hazardous material. This work investigates to identify the critical risk factors and their weights for hazardous material transportation operations. The literature is reviewed, critical risk factors for hazardous material transportation are defined, and data from different experts is collected. A two-level hierarchical structure is established to evaluate risk factors. Then, the experts' evaluations of main and sub-risk factors are consolidated using the modified Delphi method. Weights of main and sub-risk factors are obtained using the Pythagorean fuzzy analytic hierarchy process method. To show the robustness of the proposed decision-making methodology, a sensitivity analysis is conducted.