The establishment of lunar bases introduces unprecedented challenges in energy management, where precise DC power forecasting is pivotal for ensuring uninterrupted power supply and efficient resource utilization in microgrids. With primary reliance on solar energy, supplemented by battery systems, forecasting energy availability becomes critical due to the Moon's unique conditions, such as prolonged night cycles, temperature extremes, and unpredictable dust storms. Conventional forecasting methods fall short in addressing these challenges, paving the way for advanced artificial intelligence (AI) techniques. This paper reviews state-of-the-art AI-driven methods, including machine learning (ML), deep learning (DL), hybrid models, and ensemble approaches. By synthesizing insights from existing literature and highlighting innovative applications, this review provides a comprehensive analysis of AI's transformative role in lunar power systems. Emphasis is placed on hybrid and optimization-based methods, supported by detailed flowcharts illustrating workflows. The paper identifies key research gaps and proposes future directions, including lightweight AI models for computational efficiency, digital twin integration for real-time optimization, and quantum computing for advanced forecasting. Additionally, it explores the integration of AI with other lunar base systems such as thermal regulation and life support, underscoring the interdisciplinary nature of sustainable extraterrestrial operations. The findings underscore AI's potential to revolutionize energy management in lunar microgrids, ensuring sustainable extraterrestrial operations.

Efficiently adapting knowledge representations to dynamic contextual shifts has remained a critical challenge in the evolution of language models. The proposed Contextual Gradient Synthesis framework addresses this challenge through a novel mechanism that aligns model gradient updates with contextual cues, enabling enhanced performance across a variety of linguistic tasks. By introducing a gradient adjustment vector integrated with task-specific loss and context alignment terms, the framework achieves superior adaptability without necessitating extensive retraining cycles. Experimental results demonstrate significant improvements in accuracy, robustness against noisy inputs, and computational efficiency across multiple domains, including question answering, summarization, and dialogue generation. The modular design of the framework facilitates seamless integration into existing architectures while preserving computational scalability, making it suitable for both general-purpose and specialized applications. Context adaptation metrics reveal the framework’s ability to dynamically align outputs with intended objectives, highlighting its potential for tasks requiring high contextual precision. Furthermore, the approach ensures long-term stability during extended training, avoiding loss oscillations and accelerating convergence rates. Robustness analyses under varied input conditions underline its resilience, particularly in scenarios involving incomplete or inconsistent data. Scalability experiments confirm the framework’s capability to handle larger models with reduced training time and memory usage, emphasizing its practicality for real-world deployments. Through rethinking the interaction between gradient optimization and contextual understanding, this research lays a foundation for the development of more adaptive, efficient, and scalable language modeling techniques. The findings highlight not only theoretical advancements but also practical implications, paving the way for future exploration in both linguistic generalization and domain-specific customization.

Image classification models have demonstrated remarkable performance across various applications, yet they remain vulnerable to adversarial attacks, which can significantly impair their accuracy and reliability. This paper presents a novel hybrid defense strategy aimed at enhancing adversarial robustness in image classification. Our approach combines two complementary techniques: adversarial training and input transformations. Adversarial training is implemented using the Projected Gradient Descent (PGD) attack to generate robust features by exposing the model to adversarial examples during training. Concurrently, input transformations, including random resizing, JPEG compression, and noise injection, are applied to disrupt adversarial perturbations and preserve critical image features. The integration of these methods results in a multi-layered defense mechanism that improves the model's resilience to a range of adversarial attacks. We conduct extensive experiments on standard image classification datasets, evaluating the effectiveness of the proposed approach against various attack methods, including FGSM and PGD. The results demonstrate that our hybrid defense strategy significantly enhances robustness while maintaining competitive performance on clean images. This research offers a comprehensive solution for improving the reliability of image classifiers in adversarial settings and provides insights into balancing robustness and accuracy.

Latent feature transformation introduces a novel framework for enhancing linguistic performance through the direct manipulation of latent space dynamics within pre-trained language models. By focusing on recalibrating representational hierarchies, the methodology achieves improved generalization and task-specific adaptability without requiring extensive retraining or architectural redesigns. Experimental results highlight substantial performance improvements across sentiment analysis, machine translation, and summarization tasks, showcasing the model's ability to align with diverse linguistic challenges. The framework leverages differentiable mappings to systematically adjust latent feature distributions, preserving semantic integrity while optimizing task relevance. Quantitative analysis reveals enhanced semantic coherence and error reduction across complex linguistic categories, affirming the robustness of the transformation. Computational efficiency gains, reflected in reduced inference latency and memory usage, demonstrate the practicality of the approach for deployment in resource-constrained environments. Comparative evaluations position latent feature manipulation as a scalable alternative to traditional methods reliant on parameter scaling or extensive fine-tuning. The integration of this framework into pre-trained architectures demonstrates its capacity to enhance interpretability and precision across emergent tasks. Comprehensive experimental validation, supported through rigorous evaluation metrics and diverse datasets, substantiates the technical and operational feasibility of the methodology. Analysis of attention patterns reveals improved alignment with task-critical tokens, further highlighting the framework's potential to refine linguistic outputs. These findings establish latent transformations as a critical avenue for advancing the adaptability and efficiency of large-scale language models in both theoretical and applied domains.

TP53 gene mutations are key drivers in over 50% of human cancers, contributing to uncontrolled cell growth and loss of tumor-suppressing functions. There is a critical need for analyzing the mutations as well as their implications in diagnoses. This project addresses the requirement for a comprehensive computational approach to understand the impact of TP53 mutations across diverse cancer types. The research integrates various techniques, including statistical modeling, survival analysis, supervised classification, deep learning, K-Mer analysis, and rule-based counting. Genetic sequences from the TP53 database are used to identify mutated nucleotides through K-Mer analysis and the Random Forest algorithm while overexpressed TP53 levels are analyzed through a one-sided T-test and p-values on sequences. Transcriptional activity of the protein is forecasted using advanced classifiers on TP53 biophysical simulation data, tumor properties are predicted with germline data, and tumor types are determined through a Sequential Neural Network on TP53 polymorphism data. Furthermore, rule-based counting techniques unravel amino acid category distributions in TP53 DNA. The results demonstrated the prevalence of TP53 mutations among cancer patients, a high accuracy of 97% in identifying tumor types, a strong performance in predicting mutated nucleotides, and the impact of different classifiers on TP53 transcriptional activity. The survival analysis demonstrated TP53's influence on patient outcomes and showed a ~0.1 survival probability at 60 months for individuals with the TP53 gene. This research advances the understanding of TP53 mutations and provides a comprehensive framework for healthcare professionals to utilize for treatment strategies in precision oncology.

In this article, we have studied the device modeling and optimization of perovskite solar cell with Formamidinium Bismuth Iodide (FA)3Bi2I9 as the absorber layer, PEDOT: PSS (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate) as the hole transport material and C60 (Fullerene) as the electron transport material. Rigorous theoretical investigation has been performed for the device parameter optimization especially the ETL, HTL and active layers. Different aspects of design strategies like temperature, carrier density, series and shunt resistance have been analyzed. The optimized device structure shows maximum ETA of 30.81% with the FF of 83.53% for (FA)3Bi2I9 based perovskite solar cell.

The usage of machine learning technology in numerous situations helps to avoid straightforward crimes or even find the offenders in real-time. The way we look sets humans apart from many other species and is a distinctive feature. However, its function extends beyond simple classification; it is also crucial in ascertaining whether an individual belongs to the same species as humans. There is currently a prevalent issue with this significant functionality. Because of the low resolution, the camera is unable to distinguish and identify a person's face when taking an image. However, because of the camera's poor image quality, it can be challenging to identify an incident that takes place after a security camera is placed. However, all that is needed to make it work and identify faces is a strong algorithm; hardware will not be as expensive, nor will it be as involved in that field. Facial recognition can be used to accurately control widgets and apps that rely on it. Facial recognition, an AI-based computing technology, helps locate and identify human faces in digital images and videos. Faces are one of the most important biometrics for identifying individuals in real-time for information processing. Various methods have been proposed to extract and recognize facial features, but they remain challenges for real-time applications. Even with the temporal backdrop, issues and further limits are brought about by factors like occlusion, face identification, and recognition. The purpose of this article is to discuss various face object detection and face recognition algorithms.

This paper introduces a novel method for applying sequence learning with Long Short-Term Memory (LSTM) networks to long-term condition monitoring of rotating machinery. Coolant intrusion into an industrial spindle was simulated on a test bed, and two channels of vibration data were measured over approximately ten months of operation. A one-dimensional signal (1D) Convolutional Neural Network (CNN)-LSTM framework was developed, and its architecture was optimized. Two key hyper-parameters, input signal lengths and the number of neurons in the LSTM layers, were optimized to achieve the best accuracy over the entire duration of the operation. The long-term classification trends for coolant intrusion varied depending on the model architectures. The optimized architecture of the supervised model was used to create an Autoencoder (AE) encoder for unsupervised clustering. Variational Autoencoders (VAEs) achieved the best silhouette scores among the AE architectures tested. Trajectories of Averaged Cluster Centers (ACCs) of the latent space variables of the VAE are good indicators of the failure progression and the remaining useful life (RUL) of the spindle.

Driven by the global growth in AI services, the demand for efficient hardware is at an all-time high in the race for continued performance scaling of hyperscale data centers. Integrated photonics devices have emerged as a promising alternative for reducing energy consumption. We report an optical modulator design that incorporates chalcogenide and graphene materials to achieve the minimal power consumption per bit during high-speed operation. The device design and its energy consumption performance are presented. Our results pave the way for ultra-efficient modulators revealing operations at high-speed modulation bandwidth up to 104 GHz whilst reducing energy consumption to 52.3 fJ/bit.

This paper presents an integrated approach for modelling and predicting gas turbine performance by combining thermodynamic modelling techniques and machine learning-based regression analysis. Traditional thermodynamic modelling, using the Brayton cycle and component performance maps, is a fundamental tool for analyzing gas turbine performance, but often faces limitations due to operational variabilities and nonlinearities. Machine learning techniques, such as linear regression, decision trees, and ensemble methods, are utilized to complement the thermodynamic model, providing more accurate predictions of key performance parameters like discharge pressure, temperature, and efficiency, particularly during off-design conditions. The proposed hybrid approach leverages thermodynamic models to simulate the physical processes of compression, combustion, and expansion, while machine learning refines these predictions using historical operational data. By integrating the physical laws governing gas turbines with adaptive machine learning algorithms, this paper demonstrates improved prediction accuracy, robustness in transient operations, and enhanced efficiency in gas turbine monitoring and predictive maintenance. The results indicate a 15% reduction in prediction error when compared to using thermodynamic or machine learning models alone. This integration aims to bridge the gap between physics-based modeling and data-driven techniques, offering a comprehensive solution for optimizing gas turbine operations. The hybrid approach not only ensures real-time adaptability but also facilitates predictive maintenance, minimizing operational risks and extending component life cycles

The Web-based E-Waste Facility Locator alleviates the burden of getting rid of e-waste and helps conduct e-waste disposal in a more responsible manner. The information offered permits to find the nearest e-waste facilities and available drop-off options. Regular people and non-specialized engineering enterprises are trained even after the fact. The review of such huge levers as the dynamics of the global research on e-waste prompts to focus on the strategies for e-waste management, the impact on human health of such activity, the modern state of the art of waste recycling and recycling, and, finally, the future of e-waste and the philosophy of sustainable development and the principle of circular economy. It therefore embodies a perspective of alternative in reducing the effects of e-waste.

The development of advanced detection frameworks has become critical in addressing the escalating sophistication of cyber threats targeting data security and operational stability. Hierarchical Threat Signal Profiling (HTSP) introduces a novel approach that integrates autonomous profiling mechanisms with hierarchical modeling techniques to enhance threat detection capabilities. The framework achieves precise identification of malicious activities through layered analysis of signal behaviors, effectively addressing limitations in traditional signature-based and heuristic-based methods. Detection performance was rigorously evaluated across multiple ransomware families, demonstrating high accuracy and low false positive rates in diverse testing scenarios. Behavioral profiling within the HTSP framework leverages multi-dimensional analysis to distinguish unique ransomware patterns, offering actionable insights into malicious operations. Modular system architecture ensures scalability and compatibility with existing cybersecurity infrastructures, while maintaining efficiency in high-throughput environments. Resource monitoring revealed the potential of CPU utilization and encryption speeds as auxiliary indicators for real-time detection. The framework also excels in identifying anomalies in network traffic and file access patterns, further strengthening its operational reliability. Advanced techniques, such as dynamic profiling and predictive analytics, enhance the framework's adaptability to emerging attack strategies, ensuring comprehensive protection against evolving threats. Experimental results affirm its capacity to handle diverse challenges, positioning the HTSP framework as a groundbreaking solution for securing critical digital assets. The research demonstrates the importance of autonomous systems in modern cybersecurity paradigms and sets a strong foundation for continued innovation in proactive threat mitigation.

Field Programmable Gate Arrays find extensive use across various domains, ranging from telecommunications to Machine Learning tasks. In the context of space applications, they emerge as a highly favourable choice, owing to their high degree of flexibility and their accessibility in different silicongrade technologies. However, due to the high associated costs of radiation-hardened devices, the prevalence of radiation-tolerant devices is more common in practical deployment scenarios. In this sense, robustness-enhancing techniques can still be applied, thus allowing the employment of these devices in long-duration tasks without the need for a radiation-hardened counterpart. In this paper, we propose a modelling approach for estimating the reliability of a digital design system in space before carrying out fault injection tests and radiation campaigns. For this purpose, we employ a high-level tool called Möbius to obtain a first reliability estimation against Single Event Upsets by means of Fault Tree Models. We also propose a novel methodology for defining which fault mitigation techniques should be applied to each design sub-module, considering the most commonly used in the space environment. In particular, we focus on a specific use case, namely a Soft Graphic Processing Unit IP, implemented on the Xilinx RT XQRKU060 FPGA. We analyze the criticality, power and area impact of the most important design parts of the GPU when implemented in hardware, introducing a classification approach for associating an appropriate fault mitigation technique with each of them. Finally, in the last section of the paper, we hint at future development and conclude the work.

Automatic building segmentation from high resolution remote sensing images is currently a research hotspot. In recent years, deep learning, particularly CNNs (convolutional neural networks), has shown promising results. However, the state-of-the-art models currently available still struggle with boundaries, sharp edges, and complex geometry shapes. In order to address these challenges, we propose Boundary and Edge Aware U-Net (BEAU-Net), a boundary and edge attention network that is based on UNet. BAM focuses on the features with rich boundary information. It enhances the models' capability to differentiate between buildings and their surroundings, specially useful in urban regions where buildings are concentrated in proximity. EAM emphasizes refined features such as sharper edges and complex geometric shapes. It enables the model to capture complex geometrical building architectures and detect abrupt changes in edge details. We have conducted extensive experiments on the Massachusetts Building, WHU Building, and INRIA Aerial Image Labeling Dataset to validate the effectiveness of our framework. The results demonstrate significant improvement in the IoU and F1 scores, especially where narrow boundaries and complex shapes are present.

The integration of Neural Pathway Embedding within large language models has led to significant advancements in language generation quality. By incorporating hierarchical interchange networks, the model effectively captures complex linguistic patterns and semantic relationships, resulting in more coherent and contextually appropriate text. The observed enhancements in BLEU scores, reduced syntax error rates, and increased lexical diversity demonstrate the efficacy of the proposed approach. While certain limitations, such as increased computational requirements and the need for further evaluation across diverse languages, have been identified, the overall findings validate the potential of Neural Pathway Embedding to elevate the performance of large language models in various natural language processing tasks. Future research directions could involve optimizing the model architecture to balance performance improvements with computational efficiency. Investigating the model's adaptability to multilingual contexts would provide insights into its versatility and potential for global applications. Additionally, exploring the integration of Neural Pathway Embedding with other advanced techniques, such as reinforcement learning or transfer learning, may further enhance the model's capabilities. Continuous refinement and comprehensive evaluation across diverse linguistic tasks and datasets will be pivotal in advancing the field of large language models.

Precise temperature profile estimation is indispensable for optimizing combustion efficiency, minimizing pollutant emissions, and enhancing overall system performance. However, existing methods such as Thermocouples 1 suffer from limitations, including slow response times and reduced accuracy, while infrared pyrometry [2] and rapid thermometry [3] offer improved precision but are often hampered by limited spatial resolution. To address these challenges, this study presents a novel approach that integrates Laser Absorption Spectroscopy (LAS) [4] with Multi-Output Gaussian Process Regression (MOGPR), combining the benefits of both techniques to accurately estimate non-uniform temperature profiles in combustion systems. The proposed method leverages a single transition of molecular absorption spectra to infer the parameters of a Boltzmann distribution, thereby representing the temperature profile, and is rigorously evaluated using both simulated and experimental data [1], demonstrating its potential for reliable and efficient temperature estimation in combustion systems.

In biomedical data analysis, the advancement of deep learning techniques faces challenges stemming from the scarcity of ethically sourced datasets and the prohibitive costs associated with data acquisition. In response to these obstacles, we introduce a novel hybrid training model that operates on a blended dataset comprising both real and synthetic data concurrently. Our approach, HybridMorph, is a pairwise medical image registration model built upon the foundations of VoxelMorph [1]. It leverages two distinct training strategies: an unsupervised setting geared towards optimizing standard image matching and a supervised setting incorporating auxiliary segmentation to enhance accuracy. Additionally, we draw inspiration from SynthMorph [2], a synthetic image-based training model capable of accommodating various image types and intensities. Our research showcases the efficacy of transferring knowledge gleaned from Synthmorph models to fine-tune weights for specific tasks, culminating in successful trials of few-shot learning and rapid adaptation capabilities. These methodologies illustrate how hybrid approaches can streamline training processes, mitigate computational resource demands, and alleviate the substantial data prerequisites inherent in contemporary deep learning models. Furthermore, our exploration into transfer learning underscores the potency of such techniques, paving the way for examining meta-learning, multi-task learning, and other avenues to mitigate data dependencies and complexity. Moreover, we are pleased to offer open access to our code repository at https://github.com/CaffineAddic/HybridMorph.

With the onset of longer life expectancies throughout the world comes the result of discovering more age-related diseases. One of these being dementia: a condition known for causing gradual decline in mental processes, an example being memories. This paper will address the current cost-effectiveness of many interventions regarding dementia and propose future directions with this factor kept in mind.  The condition is set to triple by 2050, an alarming rise.