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This review (with 187 refs.) summarizes the progress that has been made in the design of lateral flow biosensors (LFBs) based on the use of micro- and nano-materials. Following a short introduction into the field, a first section covers features related to the design of LFBs, with subsections on strip-based, cotton thread-based and vertical flow- and syringe-based LFBs. The next chapter summarizes methods for sample pretreatment, from simple method to membrane-based methods, pretreatment by magnetic methods to device-integrated sample preparation. Advances in flow control are treated next, with subsections on cross-flow strategies, delayed and controlled release and various other strategies. Detection conditionst and mathematical modelling are briefly introduced in the following chapter. A further chapter covers methods for reliability improvement, for example by adding other validation lines or adopting different detection methods. Signal readouts are summarized next, with subsections on color-based, luminescent, smartphone-based and SERS-based methods. A concluding section summarizes the current status and addresses challenges in future perspectives. Graphical abstract Recent development and breakthrough points of lateral flow biosensors.

The keys factor in making wind power one of the main power sources to meet the world's growing energy demands is the reliability improvement of wind turbines (WTs). However, the eventuality of fault occurrence on WT components cannot be avoided, especially for doubly-fed induction generator (DFIG) based WTs, which are operating in severe environments. The maintenance need increases due to unexpected faults, which in turn leads to higher operating cost and poor reliability. Extensive investigation into DFIG internal fault detection techniques has been carried out in the last decade. This paper presents a detailed review of these techniques. It discusses the methods that can be used to detect internal electrical faults in a DFIG stator, rotor, or both. A novel sorting technique is presented which takes into consideration different parameters such as fault location, detection technique, and DFIG modelling. The main mathematical representation used to detect these faults is presented to allow an easier and faster understanding of each method. In addition, a comparison is carried out in every section to illustrate the main differences, advantages, and disadvantages of every method and/or model. Some real monitoring systems available in the market are presented. Finally, recommendations for the challenges, future work, and main gaps in the field of internal faults in a DFIG are presented. This review is organized in a tutorial manner, to be an effective guide for future research for enhancing the reliability of DFIG-based WTs.

Nowadays, utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply. Different methodologies exist for utilities to improve the reliability of network. In this paper, demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization (PSO) algorithm. Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction, urban air pollution and greenhouse gas emissions. Additionally, the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks. The reliability analysis is performed in terms of loss of load expectation (LOLE) and expected energy not served (EENS) indexes, where the characteristics contain load profile, load peak, voltage profile and energy loss. Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.

The mountainous power grids exhibit significantly lower reliability compared to conventional urban grids due to inherent structural weaknesses, dispersed load distribution, and higher failure probabilities of power supply equipment. With the ongoing construction and commissioning of electrified railways in western China, it is crucial to analyze the impact of strong shocks and random fluctuations in traction loads on mountainous power grids, and to study the reliability enhancement of these grids considering electrified railways access, to ensure their safety and the reliable, continuous power supply for the railways. Therefore, this paper proposes a method to enhance the reliability of mountainous power grids considering electrified railways access. First, stochastic fluctuation characteristics of traction loads are simulated through train traction calculations. Subsequently, the reliability level of mountainous grids is quantitatively evaluated, with a novel line vulnerability index established to identify weak grid sections. Finally, two complementary enhancement strategies are proposed: dynamic line capacity expansion and optimized backup capacity allocation. Case studies demonstrate the effectiveness of the method through comparative analysis of reliability indices before and after implementation, confirming both technical validity and practical feasibility.

As extreme weather events become more frequent and severe, energy systems face growing challenges from increasing demand and equipment failures. This study presents a climate-resilient design framework through a case study of Ningbo, China—a coastal city facing typhoon risks. Using real energy demand data and operational parameters from a Ningbo energy center, a multi- scenario analytical model is developed to evaluate combined effects between extreme weather patterns, equipment vulnerability, and demand fluctuations. Three adaptation strategies are explored: equipment addition, energy storage integration, and active load optimization. The methodology employs NSGA-II multi-objective optimization and robust analysis techniques to systematically evaluate solutions under extreme conditions. Results show that equipment addition consistently achieves the best cost-reliability trade-off, while energy storage becomes more valuable under severe typhoon conditions. The active load strategy offers moderate improvements but is limited under extreme events. This work integrates real-world data and coordinates multi-strategy evaluation, providing quantitative insights into the resilience benefits of each approach. The proposed framework can be adapted to other regions and extreme weather types, offering practical guidance for designing robust energy systems in the face of climate change.

In this paper, a cost-benefit analysis based optimal planning model of battery energy storage system (BESS) in active distribution system (ADS) is established considering a new BESS operation strategy. Reliability improvement benefit of BESS is considered and a numerical calculation method based on expectation is proposed for simple and convenient calculation of system reliability improvement with BESS in planning phase. Decision variables include both configuration variables and operation strategy control variables. In order to prevent the interaction between two types of variables and enhance global search ability, intelligent single particle optimizer (ISPO) is adopted to optimize this model. Case studies on a modified IEEE benchmark system verified the performance of the proposed operation strategy and optimal planning model of BESS.

PurposeThe study aims to provide a quick-and-robust multifactorial screening technique for early detection of statistically significant effects that could influence a product's life-time performance.Design/methodology/approachThe proposed method takes advantage of saturated fractional factorial designs for organizing the lifetime dataset collection process. Small censored lifetime data are fitted to the Kaplan–Meier model. Low-percentile lifetime behavior that is derived from the fitted model is used to screen for strong effects. A robust surrogate profiler is employed to furnish the predictions.FindingsThe methodology is tested on a difficult published case study that involves the eleven-factor screening of an industrial-grade thermostat. The tested thermostat units are use-rate accelerated to expedite the information collection process. The solution that is provided by this new method suggests as many as two active effects at the first decile of the data which improves over a solution provided from more classical methods.Research limitations/implicationsTo benchmark the predicted solution with other competing approaches, the results showcase the critical first decile part of the dataset. Moreover, prediction capability is demonstrated for the use-rate acceleration condition.Practical implicationsThe technique might be applicable to projects where the early reliability improvement is studied for complex industrial products.Originality/valueThe proposed methodology offers a range of features that aim to make the product reliability profiling process faster and more robust while managing to be less susceptible to assumptions often encountered in classical multi-parameter treatments.

Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system is attempting to address this by investing in energy storage within the context of standalone microgrids (SMGs), which can operate in an island mode and off-grid. While renewable-rich SMGs can facilitate a higher level of renewable energy penetration, they also have more reliability issues compared to conventional power systems due to the intermittency of renewables. When an SMG system needs to be upgraded for reliability improvement, the cost of that reliability improvement should be divided among diverse customer sectors. In this research, we present four distinct approaches along with comprehensive simulation outcomes to address the problem of allocating reliability costs. The central issue in this study revolves around determining whether all consumers should bear an equal share of the reliability improvement costs or if these expenses should be distributed among them differently. When an SMG system requires an upgrade to enhance its reliability, it becomes imperative to allocate the associated costs among various customer sectors as equitably as possible. In our investigation, we model an SMG through a simulation experiment, involving nine distinct customer sectors, and utilize their hourly demand profiles for an entire year. We explore how to distribute the total investment cost of reliability improvement to each customer sector using four distinct methods. The first two methods consider the annual and seasonal peak demands in each industry. The third approach involves an analysis of Loss of Load (LOL) events and determining the hourly load requirements for each sector during these events. In the fourth approach, we employ the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. The annual peak demand approach resulted in the educational sector bearing the highest proportion of the reliability improvement cost, accounting for 21.90% of the total burden. Similarly, the seasonal peak demand approach identified the educational sector as the most significant contributor, though with a reduced share of 15.44%. The normalized average demand during Loss of Load (LOL) events also indicated the same sector as the highest contributor, with 12.34% of the total cost. Lastly, the TOPSIS-based approach assigned a 15.24% reliability cost burden to the educational sector. Although all four approaches consistently identify the educational sector as the most critical in terms of its impact on system reliability, they yield different cost allocations due to variations in the methodology and weighting of demand characteristics. The underlying reasons for these differences, along with the practical implications and applicability of each method, are comprehensively discussed in this research paper. Based on our case study findings, we conclude that the education sector, which contributes more to LOL events, should bear the highest amount of the Cost of Reliability Improvement (CRI), while the hotel and catering sector’s share should be the lowest percentage. This highlights the necessity for varying reliability improvement costs for different consumer sectors.

In this study, we investigate a decentralized assembly system in which the suppliers are unreliable and have uncertain production capacities. We focus on the case in which a manufacturer deals with two suppliers that provide complementary products. We assume that the suppliers have the opportunity to improve their reliabilities through investments. The manufacturer determines her order quantities and the suppliers decide on their investment amounts in their capacities. The timing of the decisions has a substantial effect on the optimal behaviors of the players. We investigate the problem under four different settings based on the sequence of events: (1) simultaneous ordering and investment in which decisions are made concurrently, (2) ordering after observation of capacities in which the manufacturer orders after observing the suppliers’ capacities, (3) ordering before realization of capacities in which the manufacturer orders after the suppliers’ investment decisions, and (4) ordering before investments in which the suppliers invest after the manufacturer’s ordering decision. We demonstrate the existence of a Pareto optimal equilibrium in the first two scenarios. In addition, we show that in the fourth scenario, there exists a unique Nash equilibrium in the suppliers’ game. Based on the realized capacities of the suppliers, it may be beneficial for them to share their production information with the manufacturer. In addition, we indicate that using a sequential decision strategy can enhance the performances of the supply chain and the members. When the suppliers are the leaders, they implement investment inflation strategies to stimulate the manufacturer to place larger orders. When the manufacturer is the leader, she uses an order inflation strategy to increase the investment of the suppliers. Our numerical analysis revealed that in different situations, the players may prefer ordering before realization of capacities scenario or ordering before investments. Finally, we extended our results to a multiple-suppliers case in which the suppliers are identical.

This paper presents a novel, domain-independent method for enhancing system reliability based on reverse engineering of algebraic inequalities. Although system reliability has been extensively studied, existing approaches have not addressed the challenge of improving reliability without knowing the reliability of individual components. This work fills this gap by demonstrating that the reliability of both parallel and series–parallel systems can be improved without any information about component reliability values. Specifically, this study establishes that in parallel systems, a symmetric arrangement of interchangeable components of the same type across parallel branches consistently yields higher system reliability than an asymmetric arrangement—regardless of the individual component reliabilities. This finding is derived through the reverse engineering of a new general algebraic inequality, proposed and proved for the first time. Furthermore, applying the same approach to series–parallel systems reveals that asymmetric arrangements of interchangeable redundancies offer superior system reliability compared with symmetric configurations.