In order to forecast the seasonal water quality in Nepal, this study proposes a hybrid deep learning model that makes use of a sizable dataset of water quality variables. Utilizing both temporal and spatial patterns in the input, the model combines recurrent neural networks (RNN) with convolutional neural networks (CNN). The outcomes show a considerable increase in forecast accuracy over traditional methods, offering a trustworthy instrument for preventive water quality management.

A large-scale IT outage on July 19th, 2024, primarily affecting Windows systems with CrowdStrike software, caused significant disruptions in airlines, finances, and government services. The culprit was a faulty CrowdStrike update, triggering system crashes. We analyze this incident through the lens of the "release first, fix later" approach discussed in the literature, emphasizing technology managers' need for alternative mitigation strategies, including patch management solutions and disaster recovery plans. Additionally, policymakers may explore measures to incentivize thorough testing, while consumers require robust business continuity plans. Vigilance from all parties is crucial in the ever-evolving software landscape.

In this letter, we propose an extremely high scanning-rate slot array antenna utilizing uniplanar-coupled waveguide cavities, allowing for easy fabrication and a lowprofile planar implementation. In contrast to current techniques employing alternating electric and magnetic coupling topologies, our novel approach relies on a pure magnetic coupling topology, serving as the key for uniplanar implementation. We design and fabricate both metallic waveguide and substrate-integrated waveguide implementations to showcase the effectiveness of the proposed technique. Experimental results demonstrate that the two uniplanar-coupled waveguide antennas can scan angle ranges of 65 • and 70 • , respectively, within a 40 MHz frequency band centered at 4 GHz (equivalent to a 1% bandwidth). Compared to state-of-the-art methods, these proposed frequency-scanning antennas exhibit exceptionally high scanning rates, ease of fabrication, and a low-profile implementation.

Time-coding digital metasurface (TCDM) has attracted extensive attention in radar deception jamming due to its ability to adjust the reflected electromagnetic waves flexibly. However, wideband imaging radar can obtain the complex structural features of targets through the high-resolution range profile (HRRP), thus identifying simple scattering point deception jamming. In this paper, a method of target HRRP deception imitation is proposed based on TCDM. The non-periodic time modulation model of TCDM is derived and it is theoretically proved that the complex distribution of scattering points in HRRP can be accurately imitated by a single TCDM with a specific nonperiodic sequence. Comparisons with existing schemes reveal that the proposed solution combines the advantages of flexibility, realism, operability, and resource efficiency. Both simulation and experiment results show that the cosine similarity between real and imitated HRRP peaks can exceed 90% in various scenarios, which verifies the robustness and practicality of the proposed method.

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an optimization framework to address the challenge of dynamic channel conditions and device mobility in end-to-end communication systems. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors, state transition, and dynamic elements throughout the transmission process. Additionally, we enhance the multi-level feature transmission policy by introducing an additional 5th-level edge-assisted semantic communication, maximizing recognition performance by leveraging a large semantic knowledge base on the edge server. Formulated as an online optimization problem, it aims to simultaneously minimize semantic loss and adhere to specified transmission latency thresholds. To address this, we design a soft actor-critic-based deep reinforcement learning system with a carefully designed reward for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the optimization objectives. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups using open-source datasets.

An identity and data fiduciary model for source validation in social networks is needed, one that protects users' privacy and independence, while ensuring that data being transmitted is authentic and human-generated. Such a model will enable new types of reputation scoring services to emerge, utilizing data on the networks based on the pseudonymous user's posts and transaction history.

Question Generation (QG) is an essential area in Natural Language Processing (NLP) that aims to create questions from a given text. This paper reviews the evolution of QG methods, from early rule-based systems to contemporary deep learning techniques, and explores potential future advancements. By examining the strengths and weaknesses of each approach, we provide a comprehensive understanding of the progress in QG and propose directions for future research.

Deep learning methods have significantly advanced the analysis of satellite imagery, greatly enhancing the efficiency of screening extensive datasets. However, progress in the automatic identification of elements under the surface (subsoil features), detectable only through proxies like soil and vegetation alterations, has been limited. This challenge is further compounded by the significant influence of seasonal variability and land use management on their visibility. Automatic identification often requires multitemporal observations to maximise the likelihood of obtaining favourable visibility conditions and to strengthen the reliability of each identification. Additionally, the limited availability of publicly accessible specialised datasets has impeded progress in applying deep learning approaches. This paper presents the first publicly available dataset of multitemporal Sentinel-2 imagery for identifying palaeochannels, a specific category of subsoil features. We established a baseline for semantic segmentation by evaluating three architectures with various parameter configurations, specifically designed to address the unique challenges of segmenting these elongated and branched features under diverse landscape conditions at different times of the year. The results offer preliminary insights into addressing the varying visibility of subsoil features by applying deep learning to analyse temporal series. The dataset is available on Hugging Face and IIT Dataverse repositories.

This study presents the design and performance evaluation of a solar photovoltaic (PV)-based microgrid system for the Ola Fathia community in Mowe, Ogun State, Nigeria. The aim is to address the electricity access challenges faced by this peri-urban community through a reliable and sustainable energy solution. Using advanced modeling tools like PVsyst, the research involved a detailed analysis of the community's energy demand, solar resource assessment, and optimal system design. The proposed microgrid system, with a capacity of 92.65 kW comprising 371 solar panels and battery storage, was evaluated for its technical and economic performance. Results indicate that the solar PV microgrid can reliably meet the community's energy needs, offering significant cost savings and financial viability. The technical evaluation confirmed the system's efficiency and reliability, while the economic analysis highlighted its affordability. Social acceptance and the potential socioeconomic benefits, including job creation and improved quality of life, were also examined. Key challenges such as dependency on sunlight, energy storage limitations, and initial capital costs were identified and addressed. This research underscores the feasibility and effectiveness of solar PV-based microgrids in providing clean, reliable, and affordable electricity to underserved communities. It offers valuable insights into the technical, economic, and social aspects of deploying renewable energy systems in peri-urban areas of Nigeria. The findings support the broader goal of achieving universal access to sustainable electricity, contributing to sustainable development and environmental sustainability.

Time series data, which consists of observations collected sequentially over time, plays a pivotal role in various fields, including finance, economics, and environmental science. This paper delves into the mathematical foundations underpinning time series data, focusing on key concepts such as stationarity, autocorrelation, and time series models. By exploring how time series analysis is used to predict weather patterns and analyze long-term climate trends, this paper illustrates the power of mathematical modeling in uncovering insights about the world around us. Notably, the application of time series models in weather forecasting has revealed vital information about phenomena like El Niño and La Niña, which have profound impacts on global climate. This work underscores the significance of time series analysis in understanding and addressing environmental challenges.

Commercial Unmanned Aircraft Systems (UAS) have a wide range of applications, including package delivery, inspection as well as search and rescue missions. To operate Unmanned Aircraft Vehicles (UAV) Beyond Visual Line of Sight (BVLOS), reliable long-distance communication is essential. Although the cellular network is a potential solution, it still faces issues such as signal loss and increased handover at higher altitudes. To mitigate these issues, our work proposes the usage of two cellular links from different providers, which are prioritised according to Quality of Service (QoS) prediction. We evaluate multiple AI-based model architectures for the prediction, and find that the model consisting of Gated Recurrent Units (GRU) and convolutional layers outperforms the others. The models are trained and tested on real-world data showing a reduction in latency peaks, thereby increasing connection resilience. Additionally, the prediction pipeline is designed to be executable on the UAV side and is not limited to a specific geographical area, making it applicable to real-world scenarios. Finally, we present a pre-flight path planning algorithm that considers QoS when calculating the flight path in order to further enhance the communication. To support the research community, we publicly share the dataset used to obtain our results.

Understanding the limitations of Large Language Models (LLMs) in code generation is crucial for optimizing their use in software development. This paper explores these limitations, discusses their impact on coding practices, and oers insights into potential solutions and areas for further research.

The increasing complexity of human language and the need for precise interpretation across various applications have posed significant challenges for language models, particularly in aligning their outputs with intended instructions. Addressing this issue through a novel back-and-forth weight propagation method has shown substantial promise in enhancing alignment accuracy, response coherence, and training efficiency. The proposed method leverages an iterative feedback mechanism that refines the model's internal representations, leading to more reliable and consistent outputs, even across diverse and complex instructional prompts. Experimental results demonstrated significant improvements in error rate reduction, scalability across different model sizes, and overall model fidelity. The findings suggest that this approach not only enhances the interpretative capabilities of language models but also offers practical benefits for resource-constrained environments, making it a valuable addition to both open source and commercial model development efforts. The research lays the groundwork for future advancements in model alignment, with implications for a wide range of real-world applications that require accurate and coherent language processing.

This paper presents an I/O interface with Xtalk Minimizing Affine Signaling (XMAS), which is designed to support high-speed data transmission in die-to-die communication over silicon interposers or similar high-density interconnects susceptible to crosstalk. The operating principles of XMAS are elucidated through rigorous analyses, and its advantages over existing signaling are validated through numerical experiments. XMAS not only demonstrates exceptional crosstalk removing capabilities but also exhibits robustness against noise, especially simultaneous switching noise. Fabricated in a 28-nm CMOS process, the prototype XMAS transceiver achieves an edge density of 3.6 TB/s/mm and an energy efficiency of 0.65 pJ/b. Compared to the single-ended signaling, the crosstalk-induced peak-to-peak jitter of the received eye with XMAS is reduced by 75 % at 10 GS/s/pin data rate, and the horizontal eye opening extends to 0.2 UI at a bit error rate < 10−12 .

This paper presents the design, modeling, and feasibility study of a magnetic resonance (MR)-conditional, steerable neurosurgical robot for the intracranial delivery of therapeutic agents. Immunotherapy is an emerging technique for brain tumor treatment but faces challenges due to low cell trafficking with systemic infusions, particularly in the case of large tumors. To address this limitation, we have developed a novel robotic system capable of delivering therapeutic agents throughout a brain tumor. The robot comprises a straight, rigid outer tube and a flexible inner tube that can navigate along curved trajectories in multiple directions. Integrated into the system is a customdesigned injection mechanism, which consists of a motorized syringe, a long hydraulic transmission tube, and local syringes. This integration with the robot's nonmagnetic actuation system enables precise navigation to various locations within the tumor and targeted delivery of therapeutic agents at each site, thus maximizing their trafficking in the tumor. The robotic system is designed to be MR-conditional, enabling the use of intraoperative MR imaging to guide the delivery process and ensure accurate and sufficient delivery of therapeutic agents. Along with the design and manufacturing, we also present the modeling and characterization of the robot's motion capability, evaluation of open-loop motion control, and assessment of MR-compatibility. Additionally, we demonstrate therapeutic delivery in phantom models, collectively validating the feasibility of robot-assisted, MRI-guided therapeutic delivery.

Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circumstances such as the destruction of cellular communication and electricity infrastructure, lack of line of sight (LoS), and difficulty of localization under the rubble. In this article, we discuss key enablers that can boost communication during disasters, namely, satellite and aerial platforms, redundancy, silencing, and sustainable networks aided with wireless energy transfer (WET). The article also highlights how these solutions can be implemented in order to solve the failure of communication during disasters. Finally, it sheds light on unresolved challenges, as well as future research directions.

The lifetime of a battery pack consisting of many cells in series is determined by the weakest cell. Heterogeneous degradation of the battery cell, as a result of inherent discrepancy between cells, accelerates the aging of the weakest cell, which cannot be overcome by conventional passive and active balancing techniques. Therefore, this paper presents a methodology for charging series-reconfigurable Lithium-ion battery packs. To mitigate the negative effects of unregulated temperature increases, thermal gradients, state-of-charge imbalances, and other cell-tocell variations, we formulate and evaluate a charging strategy that addresses temperature and charge homogenization and temperature tracking. Model Predictive Control (MPC) was used to optimally homogenize the charge and temperature in the reconfigurable battery. Comparative simulations were carried out on the passive and the proposed reconfigurable battery, showing the superior perormance of reconfigurable battery dealing with battery cells with different aging conditions. Moreover, the scalability of the proposed method is verified on a string of 100 serial cells with low computational time. Finally experiments were conducted for four different case studies to verify the proposed MPC-based charge and temperature homogenization and control.

Deep neural networks (DNNs) have emerged as powerful predictors, demonstrating impressive performance across a spectrum of applications. They efficiently map high-dimensional input data to an output. However, a notable drawback is the lack of transparency and interpretability in DNN outputs. In critical domains such as healthcare, finance, public safety, and autonomous systems, transparency in DNN output is paramount for informed decision-making and trustworthiness. Recognizing and quantifying uncertainty in outputs can significantly improve decision-making capabilities. In crowd counting, where deep learning models are prevalent, uncertainty estimation is vital for robust decision-making in resource allocation, event planning, and public safety. However, existing work often overlooks the associated uncertainty in crowd count predictions, focusing solely on estimation. In this work, we propose a method to not only estimate the count but also the uncertainty in these estimated counts, considering both data uncertainty and model uncertainty. Furthermore, to enhance the calibration of model uncertainty, we utilize adaptive dropout rates within a Monte Carlo dropoutbased Bayesian framework. By utilizing isotonic regression, we calibrate overall predictive uncertainty, covering both data and model uncertainties. This leads to well-calibrated confidence intervals for the model predictions. Through extensive simulations on widely used datasets, our method sets a new benchmark.