The Internet of Bio-Nano Things is a promising technology to reduce turnover and increase resolution of medical data collection. Therefore, tiny devices, so-called Nano Machines (NMs) are placed inside the human body, where the collect, process and transmit information to devices outside the human body. Interfaces between in-body and the external electrical domain are crucial to the realization of this technology. In this paper, we propose sweat glands, a pre-existing infrastructure, as a novel interface through which NMs can transmit information from the inside to the outside of the human body. We provide a detailed mathematical model of the envisioned communication system and evaluate its communication performance using different types of detectors (e.g., neural network based detector).

Histopathologists are experiencing a digital revolution in their field thanks to the digitization of Whole Slide Images (WSIs), which are microscope slides of tissue that can measure gigapixels in size. With so much high resolution data at their disposal, computer vision techniques can now be used to automate laboratory processes, create visual standards, and increase analysis throughput, all of which reduce the workload of pathologists [1]. The ”gold” standard in neuropathology, particularly for Alzheimer’s Disease—is pathological diagnosis made by looking at White Matter Inclusions (WSIs) in brain tissue. Semi-quantitative scoring in accordance with the standards established by the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) is necessary in order to arrive at a pathology diagnosis. As to these criteria, the diagnosis of Alzheimer’s disease relies heavily on the density of neuritic (cored) Amyloid-β plaques. In order to support in the classification of Alzheimer’s disease, recent studies by Tang et al. [2] and Wong et al. [3] examine the use of Deep Learning (DL) algorithms to WSIs of brain regions that have been histochemically labelled. According to the results from [2], there is a link between semi-quantitative scoring performed by a skilled neuropathologist and scoring generated by a DL, coupled with strong precision and recall metrics. By using the methods of [2] to data from an alternative brain bank, Vizcarra et al. [4] validate it and discover a similar connection between DL and semi-quantitative scoring. The methodology’s successful validation shows potential for incorporating DL into the neuropathology pipeline. While this can shorten the time neuropathologists need to score WSIs, two studies [2], [3] point out a specific problem—the predominant Aβ morphologies are quite rare, with diffuse plaques predominating. Classified as cored plaques or Cerebral Amyloid Angiopathy (CAA), only 1-2% of candidate samples found using conventional computer vision algorithms meet these criteria. Consequently, the significantly imbalanced datasets that are employed may have a negative impact on the DL’s performance. Additionally, trying to increase minority class instances by extra annotation by highly qualified neuropathologists would be prohibitively costly and time-consuming. Therefore, we aim to address the problem of unbalanced datasets for Aβ morphology in brain tissue immune histochemically stained WSIs. As part of our approach, we want to investigate how minority examples of Aβ morphologies, like cored plaques and CAA, may be synthesized using generative modelling, namely Generative Adversarial Networks (GANs), in order to balance the dataset. By doing this, we might also shorten the amount of time needed to classify high-quality datasets. To the best of our knowledge, this is the initial attempt at generatively modeling for the distribution of Aβ morphologies in images. Our objective is to respond to the following questions:Is it possible to develop a GAN architecture that can synthesize unique, high-quality instances of Aβ morphologies that are identical to real samples?Does the GAN prevent the problem of memorization and is it able to sufficiently address the modes of the real data distribution?Are there ever instances of low quality? If so, is it possible to eliminate this through a selection process?Can we use datasets balanced by GAN-oversampling minority data to increase the reliability of downstream classifiers for Alzheimer’s disease?The paper is as follows in the next section we will see the background related to our study. In Section 3, the related works are presented. In Section 4, the materials and methods used are discussed. In Section 5, the experimental analysis along with results are presented and we conclude the paper in Section 6 with some conclusions and future works.

Omnidirectional images (ODIs) capture 360-degree scenes but require extremely high resolution to provide a truly immersive experience. However, most existing ODIs are insufficient in resolution, leading to degraded visual quality. While prior methods like OSRT have made progress in addressing this issue, they still struggle to fully exploit the geometric properties of equirectangular projection (ERP), particularly in handling geometric distortions and preserving fine details. To overcome these challenges, we propose a Multi-scale Fractal Analysis and Self-adaptive Spherical Harmonics (MFASH) method, which builds upon the OSRT framework to enhance the performance of omnidirectional image super-resolution (ODISR). MFASH introduces fractal geometry to adaptively capture varying levels of detail, self-adaptive spherical harmonics for flexible geometric representation, and nonlinear diffusion equations for edge preservation and noise reduction. By integrating these advanced mathematical models into the OSRT architecture, our method better handles the complexities of ODIs, including geometric distortions and texture preservation. Extensive experiments demonstrate that MFASH outperforms state-of-the-art methods in both quantitative metrics and visual quality, especially in reconstructing fine details and complex textures in ODIs.

Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteristics, attracting considerable attention from researchers. Previous studies have explored LLMs' graph reasoning abilities through various techniques, such as different encoding methods for graph structures and the use of carefully designed prompts. However, a critical factor has been mostly overlooked: the prompt sequential order in which graph descriptions are presented to the models. In this study, we present the first comprehensive analysis of how the order of graph descriptions impacts LLM performance. Specifically, we comprehensively evaluate four graph description orders across six graph problems using six mainstream LLMs. The results reveal that: (1) ordered graph descriptions significantly improve LLMs' comprehension of graph structures; (2) the robustness of LLMs to graph description order varies across different tasks; and (3) the impact of graph order on performance is closely related to the inherent characteristics of tasks. This study provides a critical advancement in the application of LLMs for solving graph-related problems, paving the way for future research to optimize model performance through strategic graph description ordering.

Pulse-Width-Modulated (PWM) thermo-optic tuning in silicon photonics requires power supplies with low quiescent current, compact size, and fast PWM output voltage. However, the transient performance of conventional outputcapacitorless low-dropout regulators (OCL-LDOs) is limited by their loop bandwidth and slew rate, which prevents them from achieving fast PWM tuning. Inspired by application-specific designs (e.g., GPU, TPU, etc.) that sacrifice general performance for specific performance improvements, this paper presents a gate-switching (GS) LDO that is optimized for fast PWM tuning instead of general transient response. In the proposed design, the speed of PWM tuning is independent of the global feedback loop of the LDO and is only limited by the discharging speed of the pass transistor gate, which is accelerated through a synchronized gate discharging (SGD) block. This design has been implemented in a 65 nm standard CMOS process, occupying an active area of 2800 µm 2. With a unity-gain-frequency (UGF) of 27.8 MHz, this design achieves an average settling time of 15 ns when transitioning between zero load current and the maximum output current, significantly faster than its 1/UGF value. With a 1.2 V input voltage, it can generate a 1-MHz, 1-Vpp output voltage with a duty ratio ranging from 2.5% to 99.5% with a 50 Ω heater load.

Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and consequently on the outcome. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. The results indicate that AEC is more robust and stable against the impact of collinearity when it explains AI models compared with the state of arts XAI method.

The increasing complexity and diversity of natural language inputs have posed significant challenges to the contextual understanding capabilities of contemporary language models. Dynamic Pattern Alignment (DPA) emerges as a novel approach that dynamically aligns detected semantic patterns within input data with the model's internal representations, thereby enhancing semantic transfer and context-aware processing. This study presents a comprehensive examination of DPA's integration into an open-source language model, detailing the algorithmic framework and modifications to the model architecture. An automated testing environment was employed to evaluate performance gains and context consistency, utilizing diverse datasets and standardized preprocessing techniques. The experimental results demonstrate that DPA significantly improves semantic retention across contextually dynamic inputs, reduces computational load, and enhances response times. Comparative analyses with baseline models demonstrate DPA's superiority in semantic transfer and contextual relevance. These findings suggest that DPA offers a promising advancement in the development of context-aware language models, with potential applications extending beyond the current scope.

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.

Multi-jet impingement-based cooling manifolds, functioning as integrated networks for liquid delivery and effusion, have become increasingly important due to the rising thermal management demands of high-performance computing chips. As these chips generate significant thermal load during operation, efficient cooling solutions are essential to maintain performance and prevent thermal-related reliability issues. This study focuses on the conceptual design and assessment of an innovative manifold, with an emphasis on its thermal and hydraulic performance. A 3D-printed serpentine manifold with distributed inlets and outlets is considered, where hot and cold liquids are separated by partition walls, resulting in a compact and lid-compatible design. The manifold is designed to cool a heated area of 10 mm × 10 mm, with the heat flux reaching up to approximately 415 W/cm². To assess the performance, steady-state temperature and pressure drop across the manifold are experimentally studied under different coolant flow rates and heat fluxes. The thermo-hydrodynamic performance of the cooler is compared with existing similar designs in the literature, focusing on single-phase coolant operation. Additionally, the study extends the analysis to include two-phase operation, exploring the effectiveness of the manifold. This innovative, compact manifold design has the great potential to act as an integrated heat spreader, offering a promising cooling solution for high heat flux applications while maintaining heat transfer efficiency.

In modern automotive technology, the majority of energy produced by internal combustion engines is lost as waste heat, primarily through heat dissipation from exhaust gasses. This inefficiency has significant implications for fuel consumption and environmental impact, especially in an era of increasing energy demand and concerns about pollution. Thermoelectric generators (TEGs) offer a promising solution by converting waste heat directly into electrical energy, thereby improving fuel efficiency and reducing emissions with the benefit of not introducing moving parts or complex systems. First, this paper provides a comprehensive review of TEGs in automotive waste heat recovery, exploring their underlying principles, material advancements, and most optimal conditions for the best power output and efficiency. Next, key design challenges are addressed, including heat exchanger optimization, thermoelectric material efficiency, leg geometry, and system integration, all to determine the method in which TEGs can be used to convert the most possible heat to electric power as efficiently as possible. Finally, the paper discusses potential enhancements, research gaps, and future directions in TEG development.

Remote photoplethysmography (rPPG) is an active area of research that has extended beyond heart rate (HR) estimation to estimate more complex cardiac signals such as HR variability and blood oxygen saturation (SpO 2). As these applications extend to more challenging domains, the SNR of the physiological signal is increasingly important. This research is particularly useful for enhancing the accessibility and impact of rPPG technologies in consumer electronic devices, such as RGB cameras. Almost all videos recorded with consumer devices use prevailing codec settings, and such video compression is a source of signal degradation. While prior works have assessed the performance of rPPG algorithms under various video compression rates, they have not explored how specific compression configurations can be strategically optimized to better preserve the integrity of rPPG signals. This paper aims to address this gap by identifying a close connection between the projection plane from the principled plane-orthogonal-to-skin (POS) algorithm to the YCbCr color space commonly used in compression. Namely, these projection planes are equivalent, which is a crucial connection that motivates the consideration of alternative color spaces for video compression in physiological monitoring applications. Improved rPPG signal preservation at low bitrates can be achieved using the RGB24 color space for compression compared to YUV444 and YUV420. These results suggest opportunities to reduce error in rPPG applications by treating the video compression color space as a tunable setting in a system's design.

The expansion of transformer-based architectures has introduced challenges in balancing computational efficiency with task-specific performance, particularly as models grow in scale and complexity. Contextual gradient pruning offers a novel solution through the dynamic selection of parameters based on gradient magnitudes, ensuring that critical linguistic and semantic elements are retained while reducing computational overhead. The methodology introduces a sparsification objective function that aligns parameter reduction with evolving training dynamics, enabling adaptability without compromising representational capacity. Experiments conducted on a state-of-the-art open-source language model demonstrate the method's ability to maintain robust performance across perplexity, token prediction accuracy, and downstream task benchmarks. Results reveal that computational efficiency improves significantly, with reductions in floating-point operations exceeding 60% at higher sparsity levels, while linguistic coherence and rare word coverage remain largely preserved. Comparative analysis highlights the superiority of gradient-informed pruning over conventional static approaches, showcasing its scalability and robustness across diverse architectures. Scalability is further evidenced through consistent improvements in inference latency, energy consumption, and model transferability, making the approach suitable for both small-scale and resource-intensive deployments. The analysis of adversarial robustness and attention head utilization provides deeper insights into the model's adaptive capabilities under varying sparsity conditions. Additionally, the method demonstrates potential in enabling broader accessibility to language models through its ability to tailor sparsity configurations to specific computational environments. The findings position contextual gradient pruning as a transformative advancement in efficient language model optimization, addressing key challenges associated with sustainability and scalability. By combining theoretical rigor with practical efficacy, the study contributes a significant step forward in the pursuit of more adaptable and resourceconscious natural language processing systems.

Dynamic wireless charging (DWC) of electric vehicles (EVs) can greatly alleviate "short driving range" concern, making it a highly feasible prospect for electrified transportation. To achieve maximum power transfer in DWC, it is essential that the primary coil (under the road) and secondary coil (below the EV) are fully aligned. Lateral misalignment (LTM) occurs when the coils fail to align properly. The energy losses caused by LTM can be compensated using conventional controllers, however, they cannot perform well in higher misalignment scenarios and exhibit overshooting, and steady-state error (SSE). In this paper, a model prediction-based approach is implemented to design a controller that nullifies the LTM effect and compensates for the reduced power transfer to the EV. This work presents a comparative study between the model predictive controller (MPC) and conventional Proportional-Integral (PI) controller in terms of performance and suitability. The MPC controller uses an optimization algorithm to select the optimal control action over the prediction horizon, minimizing SSE and reducing overshooting in the system, thereby performing exceptionally well across all degrees of LTMs. The effectiveness of the proposed MPC has been evaluated through simulations in MATLAB/Simulink and experimental tests. The proposed MPC demonstrates superior performance compared to the PI controller in both simulation and experimental evaluations.

This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to the fluctuating traffic demand. The DNB controller is enhanced to 1) use traffic density estimates instead of queues to optimize signal timings; 2) to consider the eight-phase two-ring NEMA controller configuration within the game-theoretic approach; and 3) to consider dynamically adaptable control time steps. The enhanced DNB controller is benchmarked against 1) fixed-time traffic signal control using the state-of-the-practice Webster's method and an emerging Laguna-Du-Rakha (LDR) method for computing the optimum cycle length; 2) state-of-practice actuated traffic signal control; and 3) state-of-art Reinforcement Learning (RL) traffic signal control. The controller is tested on two isolated signalized intersections demonstrating enhanced overall intersection performance compared to the baseline pre-timed and actuated controllers at various demand levels and offers comparable performance to a previously developed RL controller. Specifically, the DNB controller results in a decrease in the average vehicle delay and queue size by up to 54% and 63%, respectively compared to Webster's state-of-the-practice pre-timed control. Unlike the RL controller, the DNB controller requires no pre-training while adapting to fluctuating traffic conditions, thereby providing a flexible framework for reducing traffic congestion at signalized intersections. As such, this research contributes to the development of smarter and more responsive urban traffic control systems.

This paper presents a dynamic model of a quadrotor UAV affected by a blast wave that impinges on the vehicle. The body of a quadrotor is decomposed into four spherical motor elements connected by rods to a central spherical body. Existing models for the pressure and induced air velocity of a blast wave are adapted for this work and compared to computational fluid dynamics (CFD) simulations. The response of a single sphere to a blast wave is characterized by integrating the pressure over the sphere to obtain an impulse due to the pressure wave and drag. This analysis is extended to the multi-sphere model of the quadrotor UAV. As the blast interacts with the UAV, the non-uniform pressure over the body results in forces and moments that perturb the vehicle. Similarly, the blast-induced wind generates drag forces that are superimposed. The response of the vehicle to the blast pressure and wind is investigated by numerically integrating the equations of motion over the short duration of the blast to determine the terminal state of the UAV after 100 ms when the surroundings return to ambient conditions.

Wireless sensing has made significant progress in tasks ranging from action recognition, vital sign estimation, pose estimation, etc. After over a decade of work, wireless sensing currently stands at the tipping point transitioning from proofof-concept systems to the large-scale deployment. We envision a future service scenario where wireless sensing service providers distribute sensing models to users. During usage, users might request new sensing capabilities. For example, if someone is away from home on a business trip or vacation for an extended period, they may want a new sensing capability that can detect falls in elderly parents or grandparents and promptly alert them. In this paper, we propose CCS (continuous customized service), enabling model updates on users' local computing resources without data transmission to the service providers. To address the issue of catastrophic forgetting in model updates-where updating model parameters to implement new capabilities leads to the loss of existing capabilities-we design knowledge distillation and weight alignment modules. These modules enable the sensing model to acquire new capabilities while retaining the existing ones. We conducted extensive experiments on the large-scale XRF55 dataset across Wi-Fi, millimeter-wave radar, and RFID modalities to simulate scenarios where four users sequentially introduced new customized demands. The results affirm that CCS excels in continuous model services across all the above wireless modalities, significantly outperforming existing approaches like OneFi [1].

In recent years, deep learning [1] has revolutionized various fields, such as computer vision, natural language processing [2], and speech recognition, by achieving state-of-the-art performance on numerous complex tasks [3]. These achievements are often attributed to the availability of large-scale, labeled datasets and the capacity of deep neural networks to learn rich feature representations. However, one critical challenge remains: the performance of deep learning models significantly deteriorates when there is a domain shift—a mismatch between the data distributions of the training set (source domain) and the test set (target domain). 

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 .