Dynamic searchable symmetric encryption (DSSE) schemes empower data owners to outsource their encrypted data to clouds while retaining the ability to update or search on it. Despite a lot of efforts devoted in recent years, there are still several challenges that have not been well addressed. First, the confidentiality of data might be compromised if forward privacy and backward privacy cannot be ensured. Second, only the traditional single keyword-file search has attracted tremendous attention, while other popular queries like Boolean queries and range queries are not fully investigated. Lastly, how to solve these problems on untrusted servers that may deviate from pre-defined protocols is also challenging. In this article, aiming to tackle the above problems, we propose a novel DSSE scheme named${\sf HeX}$based on Trusted Execution Environment (TEE) that supports rich queries on untrusted servers while guaranteeing forward and backward privacy. We achieve strong forward and backward security by designing a deferred obfuscating read-write technique atop the bitmap index. We further extend the basic scheme to realize Boolean queries and range queries by reducing them to basic keyword queries. Strict theoretical analysis is conducted to prove the security of${\sf HeX}$, and extensive evaluations illustrate its efficiency and practicality.

202511 bchy

The automatic detection of defects in manufacturing catheter production assumes a crucial role in ensuring safety within the downstream healthcare industry. However, existing deep-learning-based industrial defect detection methods commonly rely on large-scale datasets, making them unsuitable for catheter products with limited defective samples. Aiming at filling this gap, this work proposes a few-shot learning-based detection method to address three key challenges encountered in the quality inspection of catheter products: limited data, scale variance, and intraclass variation. First, a fine-tuning-based few-shot learning scheme is introduced to gain knowledge from the abundant base dataset in advance. Subsequently, an enlarged scale feature pyramid network is designed to cover the variant size of catheter defects. Finally, a contrastive proposal memory bank is put forward to alleviate the intraclass variation problem caused by different viewpoints and efficiently utilize similar features. Experimental results on the collected catheter defect dataset demonstrate the superior performance of our proposed method compared to other existing prevalent methods.

This article discusses the problem of how to efficiently build aircraft routes that better withstand potential disruptions, such as bad weather, technical problems, and passenger delays. This optimization problem is called robust aircraft maintenance routing problem (RAMRP). There are three approaches in the literature to deal with the RAMRP, such as the buffer time allocation approach (BT), the departure retiming approach (DR), and the scenario-based stochastic programming approach (SSP). Most of the previous approaches have some shortcomings in terms of fleet productivity and delay absorption. In addition, the majority of the RAMRP models overlook maintenance regulations, which result in the generation of infeasible routes. In this article, RAMRP is investigated with two main objectives. First, a novel robustness approach, called the turn-around time reduction approach (TRTR), that avoids the shortcomings of the existing approaches, is incorporated into RAMRP. The second objective is to develop an RAMRP model that simultaneously considers all maintenance regulations. The effectiveness of the proposed RAMRP model along with the TRTR is demonstrated using real data from a major Middle Eastern airline. The results reveal an improved performance of the TRTR over the BT by about 3.43%-12.20% and 2.5%-13.58%, while handling the expected propagated delay costs and fleet productivity, respectively. In addition, the results show that the TRTR is better than the SSP by about 2.07%-18.82%, while minimizing the propagated delay costs. Therefore, the TRTR has a great potential to be implemented in the actual industry.

Air-cargo transportation has become increasingly important for global logistics systems nowadays. However, due to the intensive market competition and diverse uncertainties arising from demand and operating costs, the pricing decisions for air-cargo carriers are extremely challenging but underexplored. Besides, many airlines are holding risk-averse attitudes in decision making in order to survive in the highly volatile and competitive market. Therefore, in this paper, we apply the mean-variance theory to characterize the risk-averse behaviors of decision makers, and analytically derive the equilibrium prices for two competing risk-averse air-cargo carriers under demand and cost uncertainties. Then, we uncover how the crucial factors, like risk sensitivity coefficients, market competition, market share, demand uncertainty, and cost uncertainty, affect the carriers' optimal prices. In addition, important cost thresholds and relative risk-averse attitude thresholds are identified. Our analytical results demonstrate the symmetry in the optimal prices and critical thresholds for the two carriers. Besides, we reveal the importance to consider both carrier's own and the competitor's risk attitudes and operating characteristics in decision making when market competition exists. Moreover, we reveal the direct and indirect impacts of risk attitudes on the optimal prices, thus highlighting the importance to integrate risk considerations into the optimal pricing decision framework. Finally, we show that market situations play a critical role in characterizing the effects of diverse parameters on the equilibrium prices, which should be carefully evaluated by decision makers in air-cargo carriers.

The existing economic production quantity (EPQ) models in imperfect production systems normally assume either deteriorating production processes or deteriorating inventory. In this paper, the two aspects are combined together in a single approach to solve single machine, single product EPQ problem. There are assumed to be two states: 1) in-control and 2) out-of-control state. At the beginning of each production cycle, the production process is in control with a low defective rate. After a certain time period, the production process switches from an in-control to an out-of-control state. As a result, the defect rate increases to another level and more defective items are produced. Defective products are reworked after the normal production process all together but before that they are stored in an inventory, so a corresponding inventory holding cost is generated. Backordering is also considered in this model and products are first used to satisfy the backlog demand in each production run. Three different cases are considered and discussed in which the switch of state occurs in the backlog period, the inventory surplus period, and after the normal production period, respectively. By finding the optimal total production and backlog quantity, the expected total cost per unit product is minimized.

In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms.