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The reliability of inverse-mode silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) under dc stress and its potential impact on the performance of basic analog amplifiers are investigated. In order to properly reflect the stress effects in various circuit applications, the degradations under three different configurations (active bias, diode connection, and off state) were experimentally characterized with the stress voltages applied up to 3000 s for each case. Based on the changes in the Gummel response, the degradations in device parameters such as current gain (β), transconductance (gm), and base-to-emitter resistance (rπ) were extracted and compared with the forward-mode counterpart. In addition, with the use of a small-signal equivalent model of a SiGe HBT, simple single-stage analog amplifiers were simulated as representative examples and their circuit-level performance metrics including gain and bandwidth were studied to estimate degradation characteristics with accumulated stress. It was found that transimpedance gain decreases and operation bandwidth increases to different levels due to device degradation, whereas a voltage amplifier exhibited much less changes.

We present a novel approach for assessing the reliability of error-prone systems and networks using an interpretable prototype-based classification framework. Specifically, reliability levels of consecutive k -out-of- n success systems, failure networks, and domination networks are classified using Generalized Matrix Learning Vector Quantization (GMLVQ). Beyond achieving accurate classification, the proposed method provides informative insights into the influence of input probabilities on the resulting reliability levels. To further enhance the interpretability of the learned relevance matrix in GMLVQ, we introduce two graph-based visualization strategies that reveal structural patterns and relevant feature interactions that are critical for reliability-level classification. The proposed approach is generally applicable to arbitrary coherent systems and can be further adapted to estimate the probability of a union of any finite family of events based on their individual and pairwise intersection probabilities.

Probabilistic design of wind turbines requires definition of the structural elements to be included in the probabilistic basis: e.g., blades, tower, foundation; identification of important failure modes; careful stochastic modeling of the uncertain parameters; recommendations for target reliability levels and recommendation for consideration of system aspects. The uncertainties are characterized as aleatoric (physical uncertainty) or epistemic (statistical, measurement and model uncertainties). Methods for uncertainty modeling consistent with methods for estimating the reliability are described. It is described how uncertainties in wind turbine design related to computational models, statistical data from test specimens, results from a few full-scale tests and from prototype wind turbines can be accounted for using the Maximum Likelihood Method and a Bayesian approach. Assessment of the optimal reliability level by cost-benefit optimization is illustrated by an offshore wind turbine example. Uncertainty modeling is illustrated by an example where physical, statistical and model uncertainties are estimated.

PurposeThis study aims to investigate a reliability-level demand-oriented pharmaceutical supply chain design with maximal anticipated demand coverage. Different hospitals with the particular reliability value associated with the various pharmaceutical items (PIs) are considered. An inter-connected multi-period supply chain comprising manufacturers, distribution centers, hospitals and patients is assumed for the smooth flow of health-care items, enhancing supply chain reliability. A reliability index for PIs is depicted to highlight product preference and facilitate hospitals’ service levels for patients.Design/methodology/approachA mixed-integer multi-objective programming problem that maximizes maximal demand coverage minimizes the total economic costs and pharmaceutical delivery time is depicted under intuitionistic fuzzy uncertainty. Further, a novel interactive neutrosophic programming approach is developed to solve the proposed pharmaceutical supply chain management (PSCM) model. Each objective’s marginal evaluation is elicited by various sorts of membership functions such as linear, exponential and hyperbolic types of membership functions and depicted the truth, indeterminacy and falsity membership degrees under a neutrosophic environment.FindingsThe proposed PSCM model is implemented on a real case study and solved using an interactive neutrosophic programming approach that reveals the proposed methods’ validity and applicability. An ample opportunity to generate the compromise solution is suggested by tuning various parameters. The outcomes are evaluated with practical managerial implications based on the significant findings. Finally, conclusions and future research scope are addressed based on the proposed work.Research limitations/implicationsThe propounded study has some limitations that can be addressed in future research. The discussed PSCM model can be merged with and extended by considering environmental factors such as the health-care waste management system, which is not included in this study. Uncertainty among parameters due to randomness can be incorporated and can be tackled with historical data. Besides, proposed interactive neutrosophic programming approach (INPA), various metaheuristic approaches may be applied to solve the proposed PSCM model as a future research scope.Practical implicationsThe strategy advised is to provide an opportunity to create supply chains and manufacturing within India by helping existing manufacturers to expand, identifying new manufacturers, hand-holding and facilitating, teams of officers, engineers and scientists deployed and import only if necessary to meet timelines. Thus, any pharmaceutical company or organization can adopt the production and distribution management initiatives amongst hospitals to strengthen and enable the pharmaceutical company while fighting fatal diseases during emergencies. Finally, managers or policy-makers can take advantage of the current study and extract fruitful pieces of information and knowledge regarding the optimal production and distribution strategies while making decisions.Originality/valueThis research work manifests the demand-oriented extension of the integrated PSCM design with maximum expected coverage, where different hospitals with pre-determined reliability values for various PIs are taken into consideration. The practical managerial implications are explored that immensely support the managers or practitioners to adopt the production and distribution policies for the PIs to ensure the sustainability in supply chain design.

Sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. Facility selection and reliable transportation network play an important role in emergency rescue. In this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. Based on this, a multi-objective optimization model is proposed. The first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. The original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. The model is applied to a case, and the results are analyzed to verify the effectiveness of the model.

ABSTRACT Objective: Education is the cornerstone of diabetes management, and numerous educational studies used Diabetes Knowledge Level Tests to determine the effectiveness of education. Our study was planned to adopt the revised Diabetes Knowledge Test (DKT2) of the Michigan Diabetes Research and Training Center for the Turkish population. Material and Methods: A total of 296 diabetic subjects using insulin were included in the study. After the determination of the validity of the language and content of the test, it was applied to the patients. The reliability of the study was assessed using Cronbach’s alpha coefficient. The results of the DKT2 demographic values, and laboratory tests of the patients were noted. Results: Cronbach’s alpha values were 0.60, 0.59, and 0.70 for the first part, second part, and complete test, respectively. The test-retest reliability values were 0.76 and 0.87 (p<0.001), respectively. The correct response rate to the first part was 32.68±2.47% in patients with Type 1 diabetes and 32.16±2.66% in patients with Type 2 diabetes using insulin. The correct response rate to the second part was 19.68±2.05% and 19.55±2.96%, respectively. Discussion: The Turkish adapted version of DKT2 is a reliable tool to measure patients’ level of diabetes knowledge. However, in order to increase the level of knowledge of the patients, education of diabetes should be improved.

Decision on repairs or strengthening of existing structures with special focus on heritage structures or structures with areas where people may congregate has to be based on detail technical survey according to ISO 13822 and ISO 2394. The extent of structural intervention has to be always well planned taking into account economic, societal and environmental aspects. It is necessary to consider whether instead of significant structural intervention which could lead to unneeded economical loss of existing structures or also to the loss of heritage value of historic monuments, it is not better to make optimisation of structural intervention and accept the procedure for effective risk management.

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems.

Based on the value function of the prospect theory, this paper constructs a security function, which is used to describe the victims’ feelings about the distance in emergency evacuation. Since different paths between the demand points and the emergency shelters are generally of different importance degrees, they are divided into main paths and auxiliary paths. The security function values and the reliability levels of main paths and auxiliary paths are given different weights. The weighted sum of the security function values and the weighted sum of the reliability level function values of all demand points are maximized to determine the location and the number of the emergency shelters, the transfer paths, the reinforced edges and the incremental reliability level of the selected edge. In order to solve the model, a two-stage simulated annealing-particle swarm optimization algorithm is proposed. In this algorithm, the particle swarm optimization (PSO) algorithm is embedded into the simulated annealing (SA) algorithm. The cumulative probability operator and the cost probability operator are formed to determine the evolution of the particles. Considering the budget constraint, the algorithm eliminates the shelter combinations that do not meet the constraint, which greatly saves the calculation time and improves the efficiency. The proposed algorithm is applied to a case, which verifies its feasibility and stability. The model and the algorithm of this paper provide a basis for emergency management departments to make the earthquake emergency planning.

With the development of the service-oriented computing (SOC), web service has an important and popular solution for the design of the application system to various enterprises. Nowadays, the numerous web services are provided by the service providers on the network, it becomes difficult for users to select the best reliable one from a large number of services with the same function. So it is necessary to design feasible selection strategies to provide users with the reliable services. Most existing methods attempt to select services according to accurate predictions for the quality of service (QoS) values. However, because the network and user needs are dynamic, it is almost impossible to accurately predict the QoS values. Furthermore, accurate prediction is generally timeconsuming. This paper proposes a service decision tree based post-pruning prediction approach. This paper first defines the five reliability levels for measuring the reliability of services. By analyzing the quality data of service from the network, the proposed method can generate the training set and convert them into the service decision tree model. Using the generated model and the given predicted services, the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service. Moreover, this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting. Experimental results show that the proposed method is effective in predicting the service reliability.