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This study presents a modular and adaptable approach for the automated extraction of morphological features from meibography images, focusing on Meibomian gland (MG) analysis. The proposed method leverages piecewise linear modeling to derive clinically interpretable metrics that capture key structural characteristics of MGs. The workflow consists of three main stages: (1) semi-automated region of interest (ROI) selection, (2) MG identification and segmentation, and (3) extraction of gland- and image-level metrics. The approach was validated using 616 meibography images from two different imaging systems, demonstrating robustness, adaptability, and high classification accuracy for Meiboscale grading. Key metrics such as the shortening ratio and dropout area proved effective in distinguishing different stages of Meibomian gland dysfunction (MGD). By balancing automation, interpretability, and computational efficiency, this method provides a practical and scalable tool for the objective assessment of MG morphology, with potential applications in clinical practice and large-scale ophthalmic research.

Hepatic encephalopathy (HE) is a neurocognitive dysfunction based on metabolic disorders caused by severe liver disease, which has a high one-year mortality. Mild hepatic encephalopathy (MHE) has a high risk of converting to overt HE, and thus the accurate identification of MHE from cirrhosis with no HE (noHE) is of great significance in reducing mortality. Previously, most studies focused on studying abnormality in the static brain networks of MHE to find biomarkers. In this study, we aimed to use multi-layer modular algorithm to study abnormality in dynamic graph properties of brain network in MHE patients and construct a machine learning model to identify individual MHE from noHE. Here, a time length of 500-second resting-state functional MRI data were collected from 41 healthy subjects, 32 noHE patients and 30 MHE patients. Multi-layer modular algorithm was performed on dynamic brain functional connectivity graph. The connection-stability score was used to characterize the loyalty in each brain network module. Nodal flexibility, cohesion and disjointness were calculated to describe how the node changes the network affiliation across time. Results show that significant differences between MHE and noHE were found merely in nodal disjointness in higher cognitive network modules (ventral attention, fronto-parietal, default mode networks) and these abnormalities were associated with the decline in patients’ attention and visual memory function evaluated by Digit Symbol Test. Finally, feature extraction from node disjointness with the support vector machine classifier showed an accuracy of 88.71% in discrimination of MHE from noHE, which was verified by different window sizes, modular partition parameters and machine learning parameters. All these results show that abnormal nodal disjointness in higher cognitive networks during brain network evolution can be seemed as a biomarker for identification of MHE, which help us understand the disease mechanism of MHE at a fine scale.

In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if β percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

In the Quantum Key Distribution (QKD) network, authentication protocols play a critical role in safeguarding data interactions among users. To keep pace with the rapid advancement of QKD technology, authentication protocols must be capable of processing data at faster speeds. The Secure Hash Algorithm (SHA), which functions as a cryptographic hash function, is a key technology in digital authentication. Irreducible polynomials can serve as characteristic functions of the Linear Feedback Shift Register (LFSR) to rapidly generate pseudo-random sequences, which in turn form the foundation of the hash algorithm. Currently, the most prevalent approach to hardware implementation involves performing block computations and pipeline data processing of the Toeplitz matrix in the Field-Programmable Gate Array (FPGA) to reach a maximum computing rate of 1 Gbps. However, this approach employs a fixed irreducible polynomial as the characteristic polynomial of the LFSR, which results in computational inefficiency as the highest bit of the polynomial restricts the width of parallel processing. Moreover, an attacker could deduce the irreducible polynomials utilized by an algorithm based on the output results, creating a serious concealed security risk. This paper proposes a method to use FPGA to implement variational irreducible polynomials based on a hashing algorithm. Our method achieves an operational rate of 6.8 Gbps by computing equivalent polynomials and updating the Toeplitz matrix with pipeline operations in real-time, which accelerates the authentication protocol while also significantly enhancing its security. Moreover, the optimization of this algorithm can be extended to quantum randomness extraction, leading to a considerable increase in the generation rate of random numbers.

Security and privacy concerns pose significant obstacles to the widespread adoption of IoT technology. One potential solution to address these concerns is the implementation of cryptographic protocols on resource-constrained IoT edge nodes. However, the limited resources available on these nodes make it challenging to effectively deploy such protocols. In cryptographic systems, finite-field multiplication plays a pivotal role, with its efficiency directly impacting overall performance. To tackle these challenges, we propose an innovative and compact bit-serial systolic layout specifically designed for Montgomery multiplication in the binary-extended field. This novel multiplier structure boasts regular cell architectures and localized communication connections, making it particularly well suited for VLSI implementation. Through a comprehensive complexity analysis, our suggested design demonstrates significant improvements in both area and area–time complexities when compared to existing competitive bit-serial multiplier structures. This makes it an ideal choice for cryptographic systems operating under strict area utilization constraints, such as resource-constrained IoT nodes and tiny embedded devices.

A new approach is proposed in order to interpret spontaneous potential (self-potential) anomalies related to simple geometric-shaped models such as sphere, horizontal cylinder, and vertical cylinder. This approach is mainly based on using neural network inversion of SP anomalies, particularly modular algorithm, for estimating the parameters of different simple geometrical bodies. However, Hilbert transforms are involved to determine the origin location in order to reduce the parameters which minimize the ambiguity in the inverted models. The inversion has been tested first on synthetic data from different models, using only one well-trained network. The results of inversion show that the parameter values derived by the inversion are identical to the true values of parameters. Noise analysis has been also examined, where the results of the inversion produce acceptable results up to 10% of white Gaussian noise. The validity of the neural network inversion is demonstrated through published real field SP taken from southern Bavarian Woods, Germany. A comparable and acceptable agreement is shown between the results of inversion derived by the neural network and those from the real field data.

Simple power analysis (SPA) attacks are widely used against several cryptosystems, principally against those based on modular exponentiation. Many types of SPA have been reported in the literature in the recent years. There is a real necessity to eliminate the vulnerabilities of cryptosystems, such as CRT-RSA or the Elliptic Curve Cryptosystem, that make them susceptible to these attacks. There are many modular exponentiation algorithms that try to reinforce the security of these systems, of which one was proposed by Da-Zhi et al. Da-zhi’s algorithm was presented as a secure and efficient countermeasure against side channel attacks; however, recently it was shown that its security can be defeated. In this paper, a means of protecting the algorithm is presented. The proposed technique can be applied in any algorithm that computes dummy operations through its execution.

In this study, the authors give a generalisation of special moduli for faster interleaved Montgomery modular multiplication algorithm with simplified pre-computational phase for GF(pn), where p ≥ 2 is a prime number and n is a positive integer. The authors propose different sets of moduli that can be used in elliptic curve crytographic applications and pairing-based cryptography. Moreover, this method also leads to efficient implementations for the elliptic curve parameters given in standards. It is shown that one can obtain efficient Montgomery modular multiplication architecture in view of the number of AND gates and XOR gates by choosing proposed sets of moduli. The authors eliminate final substraction step with proposed sets of moduli. These methods are easy to implement for hardware.

This paper presents an efficient modular algorithm for the dynamic simulation of systems of multiple flexible robots with multiple concurrent constraints. This research represents an important extension of previous work in the modular dynamic simulation of complex rigid-body systems. In addition to a summary of the algorithm, the treatment of potentially critical nonlinear strain and kinematic effects is also discussed. The algorithm is validated through several examples, including both series and parallel robot configurations. [PUBLICATION ABSTRACT]

We show that the combination of dynamic programming with partial-order decomposition algorithms enables us to solve sequencing problems in polynomial time for substantially larger classes of precedence constraints than previously realized. The algorithm's efficiency depends on the maximum number of jobs that are not related by the precedence constraints in certain subsets of the jobs. We also demonstrate how to modify this general algorithm lo take advantage of special problem characteristics.