Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce efficient and low-power hardware designs. The inference process of a Deep Neural Network (DNN) is considered a computationally intensive process that requires hardware accelerators to operate in real-world scenarios due to the low latency requirements of real-time applications. As a result, High-Level Synthesis (HLS) tools are gaining popularity since they provide attractive ways to reduce design time complexity directly in register transfer level (RTL). In this paper, we implement a MobileNetV2 model using a state-of-the-art HLS tool in order to conduct a design space exploration and to provide insights on complex hardware designs which are tailored for DNN inference. Our goal is to combine design methodologies with sparsification techniques to produce hardware accelerators that achieve comparable error metrics within the same order of magnitude with the corresponding state-of-the-art systems while also significantly reducing the inference latency and resource utilization. Toward this end, we apply sparse matrix techniques on a MobileNetV2 model for efficient data representation, and we evaluate our designs in two different weight pruning approaches. Experimental results are evaluated with respect to the CIFAR-10 data set using several different design methodologies in order to fully explore their effects on the performance of the model under examination.

The future of drug delivery offers immense potential for the creation of nanoplatforms based on nanogels. Nanogels present a significant possibility for pharmaceutical advancements because of their excellent stability and effective drug-loading capability for both hydrophobic and hydrophilic agents. As multifunctional systems, composite nanogels demonstrate the capacity to carry genes, drugs, and diagnostic agents while offering a perfect platform for theranostic multimodal applications. Nanogels can achieve diverse responsiveness and enable the stimuli-responsive release of chemo-/immunotherapy drugs and thus reprogramming cells within the TME in order to inhibit tumor proliferation, progression, and metastasis. In order to achieve active targeting and boost drug accumulation at target sites, particular ligands can be added to nanogels to improve the therapeutic outcomes and enhance the precision of cancer therapy. Modern “immune-specific” nanogels also have extra sophisticated tumor tissue-editing properties. Consequently, the introduction of a multifunctional nanogel-based drug delivery system improves the targeted distribution of immunotherapy drugs and combinational therapeutic treatments, thereby increasing the effectiveness of tumor therapy.

Background: Healthcare systems worldwide face growing challenges in anticipating and managing patient surges, particularly in times of public health crises, natural disasters, or seasonal peaks. The ability of healthcare organisations to forecast and respond to such demand fluctuations—referred to as organisational readiness for patient capacity surge—has become a critical determinant of service continuity and patient outcomes. Despite the urgency, there remains a lack of consolidated evidence on how healthcare authorities measure, evaluate, and operationalise this readiness. This systematic review aims to identify and synthesise existing literature that presents case studies, methodologies, and strategic frameworks used to evaluate organisational preparedness for patient surge capacity. It also explores resource allocation mechanisms, hospital capacity planning algorithms, and temporary facility strategies documented in healthcare settings. Methods: The review was conducted across two major scientific repositories, i.e., PubMed and Web of Science (WoS). A set of four structured search queries were formulated to capture the breadth of the topic, focusing on demand forecasting, hospital capacity planning, workforce models, and resource management within the context of healthcare surge demand. The search was limited to publications from the last 10 years (2014–2024) to ensure the inclusion of contemporary practices and technologies. Results: A total of 142 articles were selected for detailed analysis. The articles were categorised into six thematic groups: (i) empirical case studies on healthcare surge management; (ii) hospital resources and capacity scaling; (iii) ethical frameworks guiding surge response; (iv) IT-driven algorithms and forecasting tools; (v) policy evaluations and actionable lessons learned; and (vi) existing systematic reviews in related domains. Notably, several articles provided evidence-based frameworks and simulation models supporting predictive planning, while others highlighted real-world implementation of temporary care facilities and staff redeployment protocols. Conclusions: The review underscores the fragmented yet growing body of literature addressing the multidimensional nature of surge preparedness in healthcare. While algorithmic forecasting and capacity modelling are advancing, gaps remain in standardising metrics for organisational readiness and incorporating ethical considerations in surge planning. Limitations of this review include potential selection bias and the subjective categorisation of articles. Future research should aim to develop integrative frameworks that couple technical, operational, and ethical readiness for patient surge scenarios.

During the past decade, model order reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on balanced truncation (BT) offer very good error estimates and can provide compact models with any desired accuracy over the whole range of frequencies (from dc to infinity). However, in most applications the circuit is only intended to operate at specific frequency windows, which means that the reduced-order model can become unnecessarily large to achieve approximation over all frequencies. In this article, we present a frequency-limited approach which, combined with an efficient low-rank sparse implementation of the extended Krylov subspace (EKS) method, can handle large input models and provably leads to reduced-order models that are either smaller or exhibit better accuracy than full-frequency BT.

Electromigration (EM) has become one of the most significant challenges considering longterm reliability in integrated circuit design. The problem is caused by the large current density in circuit interconnections. However, in most cases, we are interested in the EM stress at specific points of the interconnect, such as vias and boundaries. As a result, Model Order Reduction (MOR) techniques can provide attractive methodologies to reduce the complexity of the original systems. System-theoretic techniques like Balanced Truncation (BT) offer very reliable bounds for the approximation error, compared to moment-matching methods. In this paper, we apply a computationally efficient low-rank BT procedure based on the extended Krylov subspace method, that can handle large-scale models and significantly accelerate the EM stress analysis. Experimental results on the industrial IBM power grid benchmarks demonstrate that our method can achieve a speedup up to 238× over a standard transient analysis method and a speedup up to 15× over COMSOL, while exhibiting negligible error.