Standard approaches to historical data, particularly when this data is sparse, inconsistent, and incomplete, can disadvantage marginalized, under-examined, or minority cultures, as they may not be adequately reflected in the conclusions. We present the procedure for adapting the minimum probability flow algorithm and the Inverse Ising model, a physically-grounded workhorse in machine learning, to this demanding task. Dynamical estimation of missing data, combined with cross-validation using regularization, are integral parts of a series of natural extensions that lead to a reliable reconstruction of the underlying constraints. Using a painstakingly selected portion of the Database of Religious History, we illustrate our techniques for analyzing 407 distinct religious groups, from the Bronze Age to the present day. This complex and varied landscape includes sharp, precisely outlined peaks, often the center of state-endorsed religions, and large, spread-out cultural floodplains supporting evangelical faiths, non-state spiritual practices, and mystery cults.

Secure multi-party quantum key distribution protocols find their foundation in the principles of quantum secret sharing, a key area within quantum cryptography. Employing a constrained (t, n) threshold access structure, this paper introduces a quantum secret sharing scheme, with n being the total number of participants and t being the critical number of participants, including the distributor, for recovery of the secret. In a GHZ state, two sets of participants independently execute phase shift operations on their respective particles, enabling subsequent retrieval of a shared key by t-1 participants, facilitated by a distributor, with each participant measuring their assigned particles and deriving the key through collaborative distribution. Security analysis confirms the protocol's ability to defend against direct measurement attacks, interception retransmission attacks, and entanglement measurement attacks. The enhanced security, flexibility, and efficiency of this protocol, relative to similar existing protocols, contribute to a more economical use of quantum resources.

The dynamic nature of cities, overwhelmingly shaped by human activities, necessitates appropriate models for anticipating the transformative trends, a defining aspect of our current epoch. Within the social sciences, encompassing the study of human conduct, a differentiation exists between quantitative and qualitative methodologies, each approach possessing its own set of strengths and weaknesses. Frequently providing descriptions of exemplary processes for a holistic view of phenomena, the latter stands in contrast to mathematically driven modelling, which mainly seeks to make a problem tangible. Both methods delve into the temporal development of informal settlements, a prominent settlement type globally. These areas are portrayed in conceptual work as self-organizing systems, and as Turing systems in mathematical formulations. The social issues in these locations necessitate a deep understanding, which includes both qualitative and quantitative analyses. Drawing upon the insights of C. S. Peirce, a mathematical modeling framework is proposed. This framework synthesizes diverse settlement modeling approaches for a more comprehensive understanding of this phenomenon.

Remote sensing image processing hinges on the crucial role of hyperspectral-image (HSI) restoration. The recent performance of low-rank regularized HSI restoration methods utilizing superpixel segmentation is outstanding. Despite this, the bulk of methods utilize the HSI's first principal component for segmentation, a less-than-ideal solution. This paper presents a robust superpixel segmentation strategy, integrating principal component analysis, for improved division of hyperspectral imagery (HSI) and to further bolster its low-rank representation. To improve the efficiency of removing mixed noise from degraded hyperspectral images, a weighted nuclear norm with three weighting types is designed to effectively exploit the low-rank attribute. Real and simulated hyperspectral image (HSI) datasets served as the basis for testing and confirming the performance of the proposed HSI restoration methodology.

Particle swarm optimization has proven its worth in successfully applying multiobjective clustering algorithms in several applications. However, the limitation of existing algorithms to operate solely on a single machine impedes their direct parallelization on a cluster, which proves a significant obstacle when processing large-scale data. Data parallelism was a subsequent proposal, arising from advancements in distributed parallel computing frameworks. Paradoxically, the escalating use of parallel processing will, however, introduce a challenge: an imbalanced data distribution, potentially undermining the effectiveness of the clustering algorithm. This work introduces the Spark-MOPSO-Avg parallel multiobjective PSO weighted average clustering algorithm, specifically designed for Apache Spark. Initially, Apache Spark's distributed, parallel, and memory-based computing is employed to divide the complete dataset into multiple partitions, which are then stored in memory. The particle's local fitness is concurrently evaluated, utilizing the partition's data. The calculated result having been obtained, only particle-specific data is transferred, averting the need for a significant amount of data objects to be transmitted between each node. This reduced data flow within the network correspondingly diminishes the algorithm's run time. Further, a weighted average calculation is executed using the local fitness values to alleviate the problem of an imbalanced dataset affecting the final results. In data parallel environments, the Spark-MOPSO-Avg algorithm's performance reveals a lower information loss rate, though the accuracy diminishes by 1% to 9%. However, there's a notable decrease in the algorithm's execution time. selleck compound Within the Spark distributed cluster environment, a notable execution efficiency and parallel computing capability is observed.

Cryptography encompasses many algorithms, each with specific applications. In the realm of these methodologies, Genetic Algorithms are prominently featured in the process of cryptanalyzing block ciphers. Lately, the application of such algorithms and the research surrounding them have experienced a notable increase in interest, with a particular emphasis placed on the analysis and enhancement of their characteristics and properties. The present study concentrates on the fitness functions that are integral components of Genetic Algorithms. The proposed methodology validates that the decimal closeness to the key is implied by fitness functions using decimal distance approaching 1. selleck compound Unlike the preceding, the foundation of a theoretical framework is structured to define these fitness functions and anticipate, in advance, the comparative effectiveness of one approach versus another in applying Genetic Algorithms to break block ciphers.

Quantum key distribution (QKD) provides the means for two remote participants to develop secret keys with information-theoretic guarantees. Many QKD protocols posit a continuous, randomized phase encoding from 0 to 2, a supposition that may not always be validated in experimental contexts. The recently suggested twin-field (TF) QKD methodology is particularly significant due to its capacity to substantially enhance key rates, potentially surpassing certain theoretical rate-loss limitations. In lieu of continuous randomization, a discrete-phase approach might offer a more intuitive solution. selleck compound Despite the presence of discrete-phase randomization, a formal security proof for QKD protocols within the finite-key scenario is currently absent. For security analysis in this particular case, we've developed a method incorporating conjugate measurement and the ability to distinguish quantum states. Our investigation concludes that TF-QKD, with a workable selection of discrete random phases, for example 8 phases covering 0, π/4, π/2, and 7π/4, yields results that meet the required performance standards. Unlike before, finite-size effects become more substantial, demanding that more pulses be emitted. Above all, our method, as the first demonstration of TF-QKD with discrete-phase randomization in the finite-key domain, is also applicable to other quantum key distribution protocols.

Mechanical alloying was employed to process CrCuFeNiTi-Alx type high-entropy alloys (HEAs). In order to understand how aluminum concentration in the alloy affects the microstructure, phase formation, and chemical behavior of the high-entropy alloys, various concentrations were examined. X-ray diffraction studies on the pressureless sintered specimens exposed the presence of face-centered cubic (FCC) and body-centered cubic (BCC) solid solutions. Given the disparate valences of the alloying elements, a nearly stoichiometric compound was produced, consequently boosting the alloy's final entropy. This situation, partly due to the presence of aluminum, was conducive to a transformation of some FCC phase into BCC phase within the sintered bodies. Through X-ray diffraction, the creation of distinct compounds involving the alloy's metals was apparent. The bulk samples' microstructures showcased a variety of phases. The phases and the subsequent chemical analyses demonstrated the alloying element formation. This formation subsequently led to a solid solution and, accordingly, a high entropy. From the corrosion tests, it was determined that the samples featuring a reduced aluminum content were the most resistant to corrosion.

Analyzing the evolutionary trajectories of intricate systems, like human relationships, biological processes, transportation networks, and computer systems, holds significant implications for our everyday lives. Anticipating future relationships between nodes in these dynamic networks has many practical applications. By formulating and resolving the link-prediction problem for temporal networks, this research seeks to advance our understanding of network evolution through the utilization of graph representation learning, an advanced machine learning strategy.