Complex Analysis Research Articles

Estimating simplet counts via sampling

Simplicial complexes are higher-order combinatorial structures which have been used to represent real-world complex systems. In this paper, we focus on the local patterns in simplicial complexes called simplets, a generalization of graphlets. We study the problem of counting simplets of a given size in a given simplicial complex. For this problem, we extend a sampling algorithm based on color coding, from graphs to simplicial complexes, with essential technical novelty. We theoretically analyze our proposed algorithm named SC3, showing its correctness, unbiasedness, convergence, and time/space complexity. Through extensive experiments on sixteen real-world datasets, we show the superiority of SC3 in terms of accuracy, speed, and scalability, compared to the baseline methods. We use the counts given by SC3 for simplicial complex analysis, especially for characterization, which is further used for simplicial complex clustering, where SC3 shows a strong ability of characterization with domain-based similarity. Additionally, we explore a variant of simplet counting (specifically, estimating the relative counts of simplets) under realistic scenarios where the entire simplicial complex is not provided at once but can only be partially accessed, for instance, through a limited number of API calls. For such scenarios, we propose a random-walk-based sampling algorithm, SCRW, and analyze its theoretical properties. In our experiments, SCRW requires, on average, 16.5× less memory than SC3, while the speed-accuracy trade-offs provided by the two methods are comparable.

Characterization of the Pore Network of a Cohesive Oxisol Through Morphological and Pore Complexity Analyses

Cohesive Oxisols are a type of soil common in the Coastal Plateau in Brazil. These soils represent a challenge for agriculture and their study is fundamental to better land use. There have been a few studies on the porous system of cohesive soils on the micrometer scale. Our study aimed to provide a detailed analysis of the pore complexity of the cohesive horizon of a Brazilian Oxisol using 3D images (volumetric data reconstructed by 2D CT slices) and to correlate these parameters with soil physical–hydric attributes. For this purpose, images with two different resolutions were analyzed from multifractal, lacunarity, and entropy analyses. Additionally, a characterization of hydraulic properties was carried out based on a soil water retention curve (SWRC). No differences were observed between the resolutions for the different physical parameters analyzed. The lacunarity analysis showed a greater homogeneity of the pore system with pores grouped in clusters. The multifractal analysis showed fractal characteristics for the cohesive horizon, suggesting a more homogeneous pore distribution. The main results obtained from the SWRC showed a low available water content due to the predominance of ultramicropores. Overall, the results show a less complex pore system, indicating the presence of pores of small sizes, affecting the water retention and conduction through the soil.

Geometric and topological aspects of the passage from local to global invertibility

As a refinement of the global invertibility problem, we address the issue of estimating the cardinality of a prescribed fiber F-1(q) of a locally invertible map solely in terms of objects that are naturally associated to q itself. The following is a prototypical result. Let F:Rn→Rn be a local diffeomorphism, n≥3, and q∈F(Rn). We show that q is assumed exactly once by F if the pre-image of every 2-plane containing q, when viewed as a geometric surface in Euclidean n-space, is conformally diffeomorphic to R2. The proofs of this and other theorems involve geometric constructions, the Poincaré-Hopf theorem, the Bôcher theorem on positive harmonic functions, condensers on Riemann surfaces, and elliptic estimates. We conclude with a section that is devoted to invertibility problems related to various aspects of dynamics, algebraic and differential geometry, real and complex analysis. The paper is written in a semi-expository style, as an invitation to global injectivity.

A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection

There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods.

GoChem: A Composable Library for Multi-Scale Computational Chemistry Data Analysis.

Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org.

Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors

This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, its implementation presents a range of organizational, technical, and ethical challenges that organizations worldwide must navigate. Change aversion, data security worries, and integration expenses are major roadblocks, but strong digital leadership, company culture, and advancements in NLP and machine learning are key enablers. This paper presents a complex analysis that questions the common perception of AI as only disruptive by delving into the relationship between power dynamics, corporate culture, and technology infrastructures. In this paper, we bring together research from several fields to help scholars and practitioners understand the nuances of AI adoption in HRM, with an emphasis on the importance of inclusive methods and ethical frameworks.

The Impact of the Digital Divide on the Development of Network Markets (Using the Example of the Video Services Market)

Introduction. The problem of the digital divide appeared in the 21st century as a result of the dynamic spread of digital technologies and it very soon became the center of numerous studies and scientific discussions. Today the digital divide is considered to be an urgent socio-economic problem that is widely discussed in the scientific community both in the context of asymmetric access to the social infrastructure (in the context of unequal access to education, healthcare, socially significant information through the use of communication technologies) and in the context of the impact of the digital divide on various significant aspects of a person’s life. The impact of the digital divide is felt in the real and monetary spheres, at the level of individuals and at the level of society, in the markets of goods and services. In this article, we suppose to analyze the impact of the digital divide on the development of network markets (using the example of the video services market).Materials and methods. The methodological basis of the study consists of the following research methods: the method of comparative analysis; the method of synthesis; the method of complex analysis of the socio-economic phenomenon; the method of formal logic; the statistical method. Reseach results. As a result of the analysis, it was revealed that the digital divide affects the development of network markets in several directions: the dynamics of growth through the possibility of access and the content of potential consumers; the speed of the distribution of network externalities through an increase in the number of consumers; the consumption structure and attributes of network goods through product personalization, taking into account the specifics of a particular digital divide.Discussion and conclusions. As a result, the article states the necessity to take into account the factors of the digital divide influence both on a company operating in the video services market and on a country interested in the development of the video services market. Considering these factors will allow them to be used in order to optimize the behavior of players in this market. Among the problems hindering the more dynamic development of the video services market in Russia, in addition to the standard challenges (income level, specifics of the digital divide, price limits), it should be noted the continuing negative experience of illegitimate players’ behavior in the market, representing some sort of an institutional trap.

Bayesian Point Estimation of Linear/Circular Consecutive k‐out‐of‐n : F System Reliability Under Vague Environment

The paper proposes a Bayesian approach to estimate the reliability and mean time to failure (MTTF) of linear and circular consecutive [Formula: see text] systems under vague environments, where uncertainties are represented using vague sets. By assuming model parameters as vague random variables with vague prior distributions, the study employs the Resolution Identity theorem for vague sets to resolve and structure this vagueness within the Bayesian framework. The proposed method uses the squared error loss function to derive vague Bayesian estimates, providing a more realistic and comprehensive analysis of system reliability in the presence of uncertainty. Numerical illustrations are included to demonstrate the applicability and effectiveness of the approach, highlighting its potential for handling vague information in complex system reliability analysis.

Accelerating machine learning queries with linear algebra query processing

The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-making. As two primary components in traditional predictive pipelines, data processing, and model predictions often operate in separate execution environments, leading to redundant engineering and computations. Additionally, the diverging mathematical foundations of data processing and machine learning hinder cross-optimizations by combining these two components, thereby overlooking potential opportunities to expedite predictive pipelines. In this paper, we propose an operator fusion method based on GPU-accelerated linear algebraic evaluation of relational queries. Our method leverages linear algebra computation properties to merge operators in machine learning predictions and data processing, significantly accelerating predictive pipelines by up to 317x. We perform a complexity analysis to deliver quantitative insights into the advantages of operator fusion, considering various data and model dimensions. Furthermore, we extensively evaluate linear algebra query processing and operator fusion utilizing the widely-used Star Schema and TPC-DI benchmarks. Through comprehensive evaluations, we demonstrate the effectiveness and potential of our approach in improving the efficiency of data processing and machine learning workloads on modern hardware.

Acupoint selection patterns for epilepsy in ancient texts based on visual network analysis

To analyze the disease patterns and acupoint selection characteristics of acupuncture for epilepsy in ancient acupuncture texts, providing references and ideas for clinical acupuncture treatment of epilepsy. Texts from the Chinese Medical Classics (5th edition) regarding acupuncture for epilepsy are reviewed. The frequency of acupoints, meridian association, distribution, specific points, corresponding epilepsy subtypes, and needling techniques are statistically analyzed. The Apriori algorithm is used for association rule analysis, and a complex network analysis is conducted for high-frequency acupoints and their corresponding subtypes and treatments. A total of 205 acupuncture prescriptions are identified. Ancient texts favored differentiation-based treatments for epilepsy, primarily classified into epilepsy, wind epilepsy, and five epilepsy. Commonly used acupoints include Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), with a focus on the acupoints of the governor vessel, the bladder meridian, and the conception vessel. The acupoints on the head, face are combined with the acupoints on the limbs, with skillful use of the five-shu points and intersection acupoints. The most frequent combinations are Shenmen (HT7)-Baihui (GV20), Shenting (GV24)-Baihui (GV20), and Xinshu (BL15)-Shenmen (HT7). Visual network analysis revealed that Baihui (GV20)-Shenting (GV24), Baihui (GV20)-Shenmen (HT7), and Baihui (GV20)-Zhaohai (KI6) are core acupoint combinations. Treatment mainly involved moxibustion or combined acupuncture and moxibustion. The acupoint selection for epilepsy treatment in ancient texts is precise, frequently using Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), etc., with emphasis on calming epilepsy, awakening the spirit, relaxing tendons, and nourishing the heart.

Analysis of the spiking dynamics of a diode laser with dual optical feedback

In a complex dynamical system, noise, feedback, and external forces shape behavior that can range from regularity to high-dimensional chaos. Multiple feedback sources can significantly alter its dynamics, potentially even suppressing the system’s output. This study investigates the impact of competing feedback sources on a stochastic complex dynamical system using a photonic neuron—a diode laser with external optical feedback. By varying the feedback intensities from two external reflectors, we explore how dual feedback influences the system’s behavior. Using ordinal analysis and advanced measures of complexity, we quantify the system’s dynamics and uncover underlying symmetries. Our findings reveal that the interaction between the two feedback sources induces a more intense deterministic behavior, distinct from the dynamics produced by each feedback source individually. Additionally, clear temporal symmetries emerge across all dynamical regimes. By employing a novel entropy-vector representation, we are able to identify a unique signature that characterizes the system’s dynamics.

From Alt-text to Real Context: Revolutionizing image captioning using the potential of LLM

The "From Alt-Text to Real Context" project harnesses the transformative capability of Meta Llama-3.2-11B-Vision-Instruct, thus unleashing an unprecedented image captioning system with advanced multimodal reasoning and attention mechanisms. These descriptions are richly contextual and domain-adaptive, moving them beyond the boundaries of traditional models. The platform has been constructed very carefully, keeping a heavy robust backend in Python (Flask/FastAPI) and an interactive frontend created in React.js for easier human interaction. With strong, advanced tools that include Hugging Face Transformers, OpenCV, NumPy, and Pandas, it shines for fine-tuning models, image preprocessing, and manipulation for particular, domain-specific customizations suited for a wide variety of applications from accessibility to e-commerce and education. The system generates alt-text and can perform complex image analysis and identify key features, providing more comprehensive scene descriptions. It is equipped with sentiment analysis to decipher emotive signals; it can provide critical metrics to quantify positive sentiment. This sophisticated AI system is built on the power of LLM outshines with zero-shot and multilingual capabilities and can also be utilized in tasks like image-based captioning and storytelling. Combining state-of-the-art AI, high adaptability, and great user-friendliness, this project raises new standards for contextual understanding, applicability in real-life scenarios, and image captioning.

Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis

Automated tools for quantification of idiopathic pulmonary fibrosis (IPF) can aid in ensuring reproducibility, however their complexity and costs can differ substantially. In this retrospective study, two automated tools were compared in 45 patients with biopsy proven (12/45) and imaging-based (33/45) IPF diagnosis (mean age 74 ± 9 years, 37 male) for quantification of pulmonary fibrosis in CT. First, a tool that identifies multiple characteristic lung texture features was applied to measure multi-texture fibrotic lung (MTFL) by combining the amount of ground glass, reticulation, and honeycombing. Opacity-based fibrotic lung (OFL) was measured by a second tool that performs a simpler binary classification of tissue into either normal or opacified lung and was originally developed for quantifying pneumonia. Differences in quantification of MTFL and OFL were assessed by Mann-Whitney U-test and Pearson correlation (r). Also, correlation with spirometry parameters (percent predicted total lung capacity (TLC), percent predicted vital capacity (VC), percent predicted forced expiratory volume in 1 s (FEV1), diffusing capacity of the lungs for carbon monoxide (DLCO), partial pressure of oxygen (PO2) and carbon dioxide (PCO2)) were assessed by r. The prognostic values for 3-year patient survival of OFL, LSS and MTFL were investigated by multivariable Cox-proportional-hazards (CPH) models including sex, age and TLC and including sex, age and VC. Also, Kaplan-Meier analysis with log rank test between subgroups separated by median OFL and MTFL were conducted. No significant difference between OFL and MTFL was observed (median and interquartile range: OFL = 29% [20-38%], MTFL = 31% [19-45%]; P = 0.44). For OFL significant correlation was observed to MTFL (r = 0.93, P < 0.01) and VC (r=-0.50, P = 0.03). For MTFL no significant correlation to spirometry parameters was found. The total time for one analysis was lower for the automated MTFL (MTFL: 313 ± 25s vs. OFL: 612 ± 61s, P < 0.001). Both analyses were significant predictors in the multivariable CPH analysis including TLC (hazard-ratios: MTFL 1.03 [1.01–1.06], P = 0.02; OFL 1.03 [1.00-1.06], P = 0.03). No parameter was a significant predictor in the CPH models including VC (hazard-ratios: MTFL 1.01 [0.98–1.04], P = 1; OFL 1.01 [0.97–1.05], P = 1). OFL showed significance in Kaplan-Meier analysis (MTFL: P = 0.17; OFL: P = 0.03). Using a simple opacity-based quantification of pulmonary fibrosis in IPF patients displayed similar results and prognostic value compared to a more complex multi-texture based analysis.

Proficiency, Clarity, and Objectivity of Large Language Models Versus Specialists' Knowledge on COVID-19 Impacts in Pregnancy: A Cross-Sectional Pilot Study.

The COVID-19 pandemic has significantly strained healthcare systems globally, leading to an overwhelming influx of patients and exacerbating resource limitations. Concurrently, an "infodemic" of misinformation, particularly prevalent in women's health, has emerged. This challenge has been pivotal for healthcare providers, especially gynecologists and obstetricians, in managing pregnant women's health. The pandemic heightened risks for pregnant women from COVID-19, necessitating balanced advice from specialists on vaccine safety versus known risks. Additionally, the advent of generative Artificial Intelligence (AI), such as large language models (LLMs), offers promising support in healthcare. However, they necessitate rigorous testing. To assess LLMs' proficiency, clarity, and objectivity regarding COVID-19 impacts in pregnancy. This study evaluates four major AI prototypes (ChatGPT-3.5, ChatGPT-4, Microsoft Copilot, and Google Bard) using zero-shot prompts in a questionnaire validated among 159 Israeli gynecologists and obstetricians. The questionnaire assesses proficiency in providing accurate information on COVID-19 in relation to pregnancy. Text-mining, sentiment analysis, and readability (Flesch-Kincaid grade level and Flesch Reading Ease Score) were also conducted. In terms of LLMs' knowledge, ChatGPT-4 and Microsoft Copilot each scored 97% (n=32/33), Google Bard 94% (n=31/33), and ChatGPT-3.5 82% (n=27/33). ChatGPT-4 incorrectly stated an increased risk of miscarriage due to COVID-19. Google Bard and Microsoft Copilot had minor inaccuracies concerning COVID-19 transmission and complications. At the sentiment analysis, Microsoft Copilot achieved the least negative score (-4), followed by ChatGPT-4 (-6) and Google Bard ( -7), while ChatGPT-3.5 obtained the most negative score (-12). Finally, concerning the readability analysis, Flesch-Kincaid Grade Level and Flesch Reading Ease Score showed that Microsoft Copilot was the most accessible at 9.9 and 49, followed by ChatGPT-4 at 12.4 and 37.1, while ChatGPT-3.5 (12.9 and 35.6) and Google Bard (12.9 and 35.8) generated particularly complex responses. The study highlights varying knowledge levels of LLMs in relation to COVID-19 and pregnancy. ChatGPT-3.5 showed the least knowledge and alignment with scientific evidence. Readability and complexity analyses suggest that each AI's approach was tailored to specific audiences, with ChatGPT versions being more suitable for specialized readers and Microsoft Copilot for the general public. Sentiment analysis revealed notable variations in the way LLMs communicated critical information, underscoring the essential role of neutral and objective healthcare communication in ensuring that pregnant women, particularly vulnerable during the COVID-19 pandemic, receive accurate and reassuring guidance. Overall, ChatGPT-4, Microsoft Copilot, and Google Bard generally provided accurate, updated information on COVID-19 and vaccines in maternal and fetal health, aligning with health guidelines. The study demonstrated the potential role of AI in supplementing healthcare knowledge, with a need for continuous updating and verification of AI knowledge bases. The choice of AI tool should consider the target audience and required information detail level.

Recent Advances in Intelligent Data Analysis and Its Applications, 2nd Edition

The swift growth of cloud computing, the Internet of Things, and the industrial Internet has brought about a surge in complex data analysis tasks that are deeply intertwined with societal and economic progress [...]

Structure-Stability Relationships in Pt-Alloy Nanoparticles Using Identical-Location Four-Dimensional Scanning Transmission Electron Microscopy and Unsupervised Machine Learning.

Nanoparticulate electrocatalysts for the oxygen reduction reaction are structurally diverse materials. Scanning transmission electron microscopy (STEM) has long been the go-to tool to obtain high-quality information about their nanoscale structure. More recently, its four-dimensional modality has emerged as a tool for a comprehensive crystal structure analysis using large data sets of diffraction patterns. In this study, we track the alternations of the crystal structure of individual carbon-supported PtCu3 nanoparticles before and after fuel cell-relevant activation treatment, consisting of a mild acid-washing protocol and potential cycling, essential for forming an active catalyst. To take full advantage of the rich, identical location 4D-STEM capabilities, unsupervised algorithms were used for the complex data analysis, starting with k-means clustering followed by non-negative matrix factorization, to find commonly occurring signals within specific nanoparticle data. The study revealed domains with (partially) ordered alloy structures, twin boundaries, and local amorphization. After activation, specific nanoparticle surface sites exhibited a loss of crystallinity which can be correlated to the simultaneous local scarcity of the ordered alloy phase, confirming the enhanced stability of the ordered alloy during potential cycling activation conditions. With the capabilities of our in-house developed identical-location 4D-STEM approach to track changes in individual nanoparticles, combined with advanced data analysis, we determine how activation treatment affects the electrocatalysts' local crystal structure. Such an approach provides considerably richer insights and is much more sensitive to minor changes than traditional STEM imaging. This workflow requires little manual input, has a reasonable computational complexity, and is transferrable to other functional nanomaterials.

Top-Down or Bottom-Up? Complexity Analyses of Synchronous Multiparty Session Types

Multiparty session types (MPST) provide a type discipline for ensuring communication safety, deadlock-freedom and liveness for multiple concurrently running participants. The original formulation of MPST takes the top-down approach, where a global type specifies a bird’s eye view of the intended interactions between participants, and each distributed process is locally type-checked against its end-point projection. A more recent one takes the bottom-up approach, where a desired property of a set of participants is ensured if the same property is true for an ensemble of end-point types (a typing context) inferred from each participant. This paper compares these two main procedures of MPST, giving their detailed complexity analyses. To this aim, we build several new algorithms missing from the bottom-up or top-down workflows by using graph representation of session types ( type graphs ). We first propose a subtyping system based on type graphs, offering more efficient (quadratic) subtype-checking than the existing (exponential) inductive algorithm by Ghilezan et al. Next for the top-down, we measure complexity of the four end-point projections in the literature. For the coinductive projection with full merging, we build a new sound and complete PSPACE-algorithm using type graphs. For bottom-up, we develop a novel type inference system from MPST processes which generates minimum type graphs , succinctly capturing covariance of internal choice and contravariance of external choice. For property-checking of typing contexts, we achieve PSPACE-hardness by reducing it from the quantified Boolean formula (QBF) problem, and build nondeterministic algorithms that search for counterexamples to prove membership in PSPACE. We also present deterministic analogues of these algorithms that run in exponential time. Finally, we calculate the total complexity of the top-down and the bottom-up approaches. Our analyses reveal that the top-down based on global types is more efficient than the bottom-up in many realistic cases; liveness checking for typing contexts in the bottom-up has the highest complexity; and the type inference costs exponential against the size of a process, which impacts the total complexity.

Multifractal Applications in Hydro-Climatology: A Comprehensive Review of Modern Methods

Complexity evaluation of hydro-climatic datasets is a challenging but essential pre-requisite for accurate modeling and subsequent planning. Changes in climate and anthropogenic interventions amplify the complexity of hydro-climatic time-series. Understanding persistence and fractal features may help us to develop new and robust modeling frameworks which can work well under non-stationary and non-linear environments. Classical fractal hydrology, rooted in statistical physics, has been developed since the 1980s and the modern alternatives based on de-trending, complex network, and time–frequency principles have been developed since 2002. More specifically, this review presents the procedures of Multifractal Detrended Fluctuation Analysis (MFDFA) and Arbitrary Order Hilbert Spectral Analysis (AOHSA), along with their applications in the field of hydro-climatology. Moreover, this study proposes a complex network-based fractal analysis (CNFA) framework for the multifractal analysis of daily streamflows as an alternative. The case study proves the efficacy of CNMFA and shows that it has the flexibility to be applied in visibility and inverted visibility schemes, which is effective in complex datasets comprising both high- and low-amplitude fluctuations. The comprehensive review showed that more than 75% of the literature focuses on characteristic analysis of the time-series using MFDFA rather than modeling. Among the variables, about 70% of studies focused on analyzing fine-resolution streamflow and rainfall datasets. This study recommends the use of CNMF in hydro-climatology and advocates the necessity of knowledge integration from multiple fields to enhance the multifractal modeling applications. This study further asserts that transforming the characterization into operational hydrology is highly warranted.

EEMD-GA-LSTM: An innovative approach for daily solar radiation estimation in southern of Algeria

In a solar-rich area, like the Saharan climate in southern Algeria, to optimize all diversions of use of this energy to the maximum, it is necessary to accurately evaluate the radiation received from the sun. To improve prediction accuracy of daily solar radiation, we present in this paper a new synergistic model that combines three powerful techniques LSTM (Long Short-Term Memory Network), GA (Genetic Algorithm) and EEMD (Ensemble Empirical Mode Decomposition). EEMD is a technique that breaks down complex and highly non-linear solar radiation data into smaller and more manageable components to identify hidden trends, patterns. The GA is used to optimize hyperparameters of LSTM network so that time relevance can be well captured in the solar radiation data. The EEMD-GA-LSTM model was tested in several southern Algerian regions (Biskra, Tamanrasset, Adrar and Tindouf) with different climates. As compared to other existing models including ANN-GA, ANN-PSO, ANFIS-GA and ANFIS-PSO our method performed markedly good R² values and lower RMSE values (RMSE from 3.0125% to 1.554%). The findings underline the model's robustness and reliability in solar radiation prediction, providing valuable information for renewable energy assessment in arid and semi-arid regions. The current study shows the benefit of hybrid models combining metaheuristic optimization and deep learning in a complex environmental data set analysis, paving pathways for future work in both solar energy fields and climate prediction.

On Two Novel Generalized Versions of Diffie-Hellman Key Exchange Algorithm Based on Neutrosophic and Split-Complex Integers and their Complexity Analysis

On Two Novel Generalized Versions of Diffie-Hellman Key Exchange Algorithm Based on Neutrosophic and Split-Complex Integers and their Complexity Analysis