Solar energy is an abundant and renewable source of energy that can help to mitigate challenges such as air pollution and global climate change. Several models have been developed to estimate solar radiation from geostationary satellite images, however each of these models are instrument specific and require modifications to be used with images from other instruments. A large portion of Iran is geographically located in an arid region with a great potential for solar energy applications. Previous studies to investigate the spatial and temporal variation of solar radiation over Iran has relied on measurements from radiometric stations. This study leverages the satellite measurements to evaluate the spatial and seasonal variation of solar radiation over the county. The study uses the Heliosat-II method to retrieve solar radiation from Meteosat-5 images. The mean annual bias and RMSE of the retrievals when compared with measurements ranges between 1.85%-8.3% and 9.9%-14.0%, respectively. The country can be divided into six zones based on mean annual incoming radiation: i) the coastal regions of the Caspian Sea and the mountainous areas of the Northwest, with incoming radiation ranging from 3200 to 4520 Wh•m −2 ; ii) the Northwest region, with incoming radiation between 4520 and 4960 Wh•m −2 ; iii) the region extending from the Southwest and West to the capital, Tehran, and towards the Northeast, with incoming radiation between 4960 and 5400 Wh•m −2 ; iv) the central region, with incoming radiation ranging from 5400 to 5800 Wh•m −2 ; v) the eastern part of the country and some segmented regions in the central part, with incoming radiation between 5800 and 6000 Wh•m −2 ; and vi) the Southeast, with incoming radiation ranging from 6000 to 6800 Wh•m −2 .

In the face of current modular robots that rely on manual coding to generate deformation instructions and are limited by binary instructions, this research proposes a Cognitive Modular Control (CMC) architecture inspired by ACT-R theory. Build simulation tool ReoForm to conduct experiments through simulation-based experimental design. The data results show that each module of the architecture demonstrates feasibility and universality in terms of time-to-completion, energy consumption, manipulability index and robustness indicators.

Recursive Model Refinement introduces a novel mechanism for iteratively enhancing latent knowledge representation and task-specific adaptability within transformer-based architectures. The framework operates through a structured feedback-driven methodology, enabling the recalibration of internal embeddings to better align with evolving contextual demands. Experiments reveal significant reductions in error rates across complex linguistic tasks, including translation and summarization, through improved contextual coherence and token-level alignment. A mathematical formalization underpins the process, ensuring scalability and computational efficiency without compromising foundational pre-trained capacities. Comparative analyses highlight consistent performance gains relative to baseline methods, with observable benefits in semantic drift mitigation and vocabulary utilization rates across domainspecific benchmarks. The integration of recursive mechanisms into pre-trained models effectively balances generalization and specificity, a critical requirement for diverse natural language applications. Results demonstrate improved stability in latent space organization, reflected in clustering metrics and reduced representational discrepancies. Error analysis provides further evidence of the framework's capacity to systematically address inherent limitations in static representations. Through the use of scalable implementations and well-defined loss convergence patterns, the methodology establishes itself as a robust addition to the field of computational linguistics. The proposed approach holds promise for refining the adaptability of language models through iterative knowledge augmentation. Empirical evidence suggests that the refinements foster not only performance consistency but also enhanced domain alignment. The contributions of this work provide a foundation for advancing the operational and representational dimensions of large-scale language models.

We design, implement, and demonstrate a detect and avoid (DAA) system for unmanned aerial vehicles (UAVs) using broadcast Remote ID (RID). An on-board RID receiver detects UAVs and takes avoidance action if a user-defined perimeter is breached (in xy position and z altitude). On breach, the UAV will land, return to launch (RTL), move horizontally, or move vertically. A video alarm is displayed on the on-screen display (OSD), as well as on ground control stations (GCS) connected via wireless telemetry. All of the software and designs are open source, freely available to the drone community, and can be implemented with off-the-shelf parts. This paper demonstrates one possible detect and avoid technology option for collision avoidance between UAVs.

We plan seek triplets corresponding y3-plet within n-DJC groups n D. A n D has three constituents, its DJC, removable conflict matrix L and rotation matrix L. In this paper, we will discuss the calculation of removable conflict matrix L which is given by crossed union of JEDS in different DJC of n D. Moreover, the configurations with crossed conflicts and unreasonable conflict can be determined to be 3-uncolorable. And the calculation of L of 4 D is given synchronously.

In light of the revolutionary requirements of the sixth generation (6G) and beyond wireless networks, reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) have emerged as pivotal technologies due to their potential for improving spectral efficiency, user fairness, and interference management. This survey explores the theoretical foundations, architectural frameworks, and design strategies of RIS-assisted RSMA, emphasizing the combined adaptability of RIS's wireless propagation control and RSMA's multiuser flexibility for dynamic spectrum management. The article first discusses the fundamental concepts of RSMA and RIS technologies. Then, we investigate various enabling technologies for RIS-RSMA networks, highlighting key advancements in interference mitigation, energy efficiency, and security for future networks. Subsequently, some optimization techniques crucial for enhancing RIS-RSMA network performance are presented. Additionally, we examine advanced machine learning (ML) approaches that enable RIS configurations to dynamically adapt to changing network requirements. Techniques such as deep reinforcement learning support real-time adjustments, creating more scalable and resilient RIS-RSMA architectures. Finally, we discuss open research directions for advancing RIS-assisted RSMA in emerging 6G applications. We also consider the potential of advanced ML techniques, including quantum-based ML and large language models, to handle the complexities of large-scale network optimization. This comprehensive survey addresses critical challenges and current advancements. It offers a roadmap for future research in RIS-assisted RSMA networks, paving the way for robust, intelligent, and adaptive 6G wireless communication systems.

The increasing prevalence of digital learning platforms and collaborative open-source software development has resulted in abundant data capturing learning behaviors. This paper investigates the intersection of process mining and learning analytics to understand how students and contributors engage in Massive Open Online Courses (MOOCs) and Free/Libre Open-Source Software (FLOSS) communities. Building on a diverse set of studies, we explore methods to extract, analyze, and interpret learning patterns from event logs, with a focus on bridging methodologies across these domains. We also incorporate perspectives from broader fields of data analytics and educational research to propose an integrated framework for optimizing engagement and retention in diverse online learning environments. By leveraging advanced process discovery, conformance checking, and machine learning techniques, this study offers novel insights into educational data mining and collaborative learning practices. Our findings have implications for the design of adaptive learning systems and the future of digital education.

As big data applications become increasingly complex, their architectures-comprising various middleware and processing components-demand continuous refinement to maintain optimal performance. This paper presents OSTIA (On-the-fly Static Topology Inference Analysis), a tool designed to support the continuous architecting of dataintensive applications (DIAs) by addressing three key areas: detecting common anti-patterns, performing algorithmic manipulations, and facilitating formal verification of architectural models. OSTIA operates by reverse-engineering the topologies of big data applications, such as those built on Apache Storm and Apache Hadoop, to ensure compliance with framework-specific constraints and identify potential design flaws. Through case studies involving both industrial and open-source applications, I demonstrate OSTIA's effectiveness in improving the reliability and performance of streaming and batch-processing systems. This paper also highlights OSTIA's recent enhancements, including formal verification capabilities and additional heuristics, and discusses its impact on the iterative process of continuous architecting.

SRAM devices are becoming one of the most promising alternatives for the implementation of embedded physical unclonable functions as the start-up value of each bitcell depends largely on the variability related with the manufacturing process. Not all bit-cells experience the same degree of variability, so it is possible that some cells randomly modify their logical starting value, which forces to use some kind of post-processing to assure high reliability in PUF response. Unfortunately, unreliable cells are difficult to be detected in advance. This work proposes a method to estimate the ratio of useful cells in an SRAM implemented with a commercial CMOS technology by characterizing the robustness of the value of the start-up logic state of a cell against external disturbances and the mismatch between the devices of their internal latch.

In this paper, we introduce the concept of "Decentralized Distributed Massive MIMO" (multiple-input multipleoutput), which decentralizes the baseband processing to the network edge and distributes the Massive MIMO (also known as cell-free massive MIMO). In Decentralized Distributed Massive MIMO (DD-mMIMO), the signal processing is performed at edge processing units (EPUs) located closer to the access points (APs) rather than in a single remote central processing unit (CPU), significantly reducing the latency. This avoids cluster edge effects that may arise in cell-free massive MIMO. To avoid this, DD-mMIMO defines coordination regions which may overlap, which implies the APs in the overlap area may be coordinated by more than one EPU. To analyse the performance of the system, we derive an expression of the achievable uplink spectral efficiency (SE) within multiple-antenna APs for various data detection algorithms. The numerical results demonstrate that DD-mMIMO has the potential to fulfill the ultra-dense low-latency requirement in the next generation mobile network and beyond.

Accurate loss estimation is fundamental to the design and maintenance of photovoltaic (PV) plants. Among the various types of losses affecting PV performance, this study focuses on mismatch losses (MsL). We introduce novel stochastic models to quantify MsL in PV strings and at the combiner box. Specifically, we leverage the statistics of the minimum of N Gaussian random variables to model the stochastic behavior of MsL under realistic PV generation scenarios. To this end, we derive both exact and approximate expressions for the expected value of this minimum. Furthermore, by adapting the proposed stochastic model for MsL in strings, we develop a new formulation to estimate MsL due to PV module degradation over time. The proposed models are numerically evaluated under different parameter configurations and compared with the deterministic approach. Additionally, the stochastic MsL model for strings is validated against the Monte Carlo (MC) method employed in PVsyst, while the MsL model for degraded PV modules is validated against real field measurements, demonstrating the applicability of the proposed approach. Notably, the results obtained from the stochastic models reveal that MsL at the string and combiner box levels depends on the number of PV modules and strings, respectively.

With the ability to create seamless connectivity that can be automated, the Internet of Things (IoT) has transformed the way industries work, as well as how we live everyday. Yet the proliferation of IoTs has exposed glaring security flaws in their ecosystems, which implicate the use, the enterprise, and the policymaker comprehensively. Then this review will look at the landscape of IoT security in terms of emerging threats, vulnerabilities within the system and existing defense mechanisms. The study, examining key topics such as cybersecurity challenges, data breach prevention, and improvement of resilience for IoT, is a result of a thorough analysis of high impact research. It further points out the contributions of emerging technologies, such as blockchain, machine learning and edge computing in solving those problems. Moreover, since current research lacks metrics for securing resource constrained devices, ensuring interoperability and providing scalable defenses for heterogeneous environments, there are gaps identified in this review. This study proposes a strategic framework for future research and innovation to improve security of the IoT ecosystem and to foster trust and resilience in a world of increasing connectivity.

The cylindrical screen has bee proposed and utilized in studying the double slit/grating experiments. In this article we derived the math formulas for describing the patterns on the cylindrical screen.

Generic elbow prostheses often fail to meet the specific anatomical needs of patients, leading to high misalignment rates in Total Elbow Arthroplasty (TEA). This misalignment negatively impacts surgical efficiency, patient satisfaction, and critical functional outcomes, including range of motion and load-bearing capacity. Although personalized prostheses could address these challenges, current practices rely on ad-hoc intraoperative adjustments that frequently fall short of meeting individual anatomical requirements. A key obstacle to achieving truly personalized designs is the absence of a quantitative method for evaluating alignment. To tackle this issue, we developed a 3D computational model to simulate TEA and introduced the Alignment Improvement Metric (AIM), a novel quantitative tool for assessing prosthesis alignment. Using AIM, we designed three personalized prosthesis approaches optimized for individual, group, and cohort scenarios. Computational validation on 120 clinical participants demonstrated an average alignment improvement of up to 30% with just 3 prostheses, highlighting the potential of this AI-driven solution to enhance TEA outcomes and patient satisfaction.

The widespread use of social media platforms like Twitter and Facebook has enabled people of all ages to share their thoughts and experiences, leading to an immense accumulation of user-generated content. However, alongside the benefits, these platforms also face the challenge of managing hate speech and offensive content, which can undermine rational discourse and threaten democratic values. As a result, there is a growing need for automated methods to detect and mitigate such content, especially given the complexity of conversations that may require contextual analysis across multiple languages, including code-mixed languages like Hinglish, German-English, and Bangla. We participated in the English task where we have to classify English tweets into two categories namely Hate and Offensive and Non Hate-Offensive. In this work, we experiment with state-of-the-art large language models like GPT-3.5 Turbo via prompting to classify tweets into Hate and Offensive or Non Hate-Offensive. In this study, we evaluate the performance of a classification model using Macro-F1 scores across three distinct runs. The Macro-F1 score, which balances precision and recall across all classes, is used as the primary metric for model evaluation. The scores obtained are 0.756 for run 1, 0.751 for run 2, and 0.754 for run 3, indicating a high level of performance with minimal variance among the runs. The results suggest that the model consistently performs well in terms of precision and recall, with run 1 showing the highest performance. These findings highlight the robustness and reliability of the model across different runs.

The evolution of language models has achieved remarkable breakthroughs in understanding and generating human-like text, yet a gap persists in managing recursive semantic dependencies that require dynamically layered contextual interpretation. Addressing this limitation, the proposed Dynamic Recursive Framework (DRF) introduces an innovative recursive processing mechanism that adapts context dynamically across layered dependencies, enhancing language models' abilities to handle complex, recursive structures in text. Unlike conventional architectures that rely on static attention mechanisms, the DRF utilizes recursive feedback loops to achieve complex semantic alignment, particularly valuable for tasks requiring the interpretation of long-range dependencies, polysemous disambiguation, and layered meanings. Experimental evaluations reveal that the DRF improves semantic alignment accuracy and contextual consistency, while only minimally increasing computational demands, showing its potential as a significant advancement for tasks demanding recursive contextual comprehension. Implemented on an open-source language model, the DRF's contributions to both performance and interpretive depth illustrate its transformative impact on the structural capabilities of language models, paving the way for models that can process the complexity of human language with enhanced precision and adaptability.

Cognitive bias is an objective existence in cognitive electronic warfare. In this paper, the cognitive bias caused by the measurement error of the electronic reconnaissance system and the cognitive bias of the commander and operator are regarded as quantum superposition states, and the mathematical model of the cognitive bias is established by using quantum cognitive theory, i.e., based on the quantum-like Bayesian network, a framework for inferencing the influence of cognitive bias on situation decision is established, wherein the quantum entanglement factor is defined for describing the relationship of human-machine cognitive biases. By taken the time domain parameter measurement of radar pulse as an example, the cognitive bias is quantitatively analyzed. The experimental results verify the effectiveness of the proposed method. The literature search shows that the proposed method belongs to the first in the field of cognitive electronic warfare, and has a distinct methodological innovation.

Consumer energy forecasting is essential for managing energy consumption and planning, directly influencing operational efficiency, cost reduction, personalized energy management, and sustainability efforts. In recent years, deep learning techniques, especially LSTMs and transformers, have been greatly successful in the field of energy consumption forecasting. Nevertheless, these techniques have difficulties in capturing complex and sudden variations, and, moreover, they are commonly examined only on a specific type of consumer (e.g., only offices, only schools). Consequently, this paper proposes HyperEnergy, a consumer energy forecasting strategy that leverages hypernetworks for improved modeling of complex patterns applicable across a diversity of consumers. Hypernetwork is responsible for predicting the parameters of the primary prediction network, in our case LSTM. A learnable adaptable kernel, comprised of polynomial and radial basis function kernels, is incorporated to enhance performance. The proposed HyperEnergy was evaluated on diverse consumers including, student residences, detached homes, a home with electric vehicle charging, and a townhouse. Across all consumer types, HyperEnergy consistently outperformed 10 other techniques, including state-of-the-art models such as LSTM, AttentionLSTM, and transformer.