Chargaff's second parity rule and the kinetics of DNA replication.

Comparison of Energy Management Strategies between Fuzzy Logic and Mixed-Integer Linear Programming for a Hybrid Photovoltaic–Wind Powered Reverse Osmosis Desalination System

Comprehensive review of the requirements of AC transmission grid codes and the role of FACTS devices in integrating renewable power plants

Low-power deep packet inspection: A programmable logic approach

Leveraging programmable logic controllers for machine learning applications in industrial setups

Online Performance Evaluation for Complex Systems based on Belief Rule Base with Dynamic Power-set Space

Health state assessment method for complex systems based on belief rule base with dual interpretability

A robust safety assessment method based on belief rule base with dynamic rule regulation

A novel fault diagnosis method for complex systems based on belief rule base and average causal effect

Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system

The design of pathways to synthesize valuable molecules remains a central challenge in chemistry and biotechnology. Several computational retrosynthesis tools have been developed to address this problem, but their scope is often confined only to reactions in either synthetic organic chemistry or monofunctional enzymatic chemistry. We present TridentSynth, a web-based retrosynthesis tool (https://tridentsynth.lbl.gov) to scale synthesis planning up to three different routes by also incorporating multifunctional Type I polyketide synthase (PKS) enzymes into our reaction toolkit along with organic chemistry and monofunctional enzymes. Unlike monofunctional enzymes that catalyze single transformations, PKSs function as molecular assembly lines that catalyze multiple carbon-carbon bond formation reactions between acyl-coenzyme A substrates to construct elongated carbon scaffolds. PKSs follow a modular, programmable logic that allows them to be reconfigured to make new molecules in a predictable way. These scaffolds can then be chemoenzymatically modified to eventually access a wider array of molecular targets than would be possible with just synthetic chemistry or monofunctional enzymes alone, in a manner that mimics the evolved biosynthesis routes of many useful natural products. TridentSynth assists synthetic biologists by suggesting routes to synthesize a desired molecule through an intuitive web interface that requires no local installation or programming expertise.

DNA has emerged as a versatile molecular engineering material owing to its predictable base-pairing rules, structural programmability, and excellent biocompatibility. Programmable DNA logic systems capable of integrating diverse biological signals and execute predefined logical operations, bridging molecular computing with biomedical applications. In this review, we summarize recent advances in programmable DNA reaction and logic systems for biomedical applications. We first introduce DNA logic gates responsive to diverse biomolecular and physiological inputs, followed by cascaded DNA reaction circuits and molecular classifiers for amplified signal processing and precision diagnosis. We then discuss DNA nanostructures and intelligent nanomachines that integrate sensing, computation and actuation for therapeutic regulation. Finally, we outline current challenges and future opportunities toward the development of DNA-based intelligent biomedical systems.

Diffuse correlation spectroscopy (DCS) is a powerful optical technique for non-invasive quantification of deep-tissue microvascular blood flow quantification. Conventional DCS systems are bulky and rely on external computing devices, limiting their point-of-care applicability. In this study, we implemented the proposed DCS system on a Xilinx Zynq-7000 system-on-chip (SoC) via hardware-software co-design approach. The proposed approach employed the programmable logic (PL) for a multi-tau digital autocorrelator and the processing system (PS) for real-time blood flow index (BFI) extraction via the Nelder-Mead algorithm. Arterial occlusion and breath-holding in vivo tests demonstrated strong consistency and high correlation with a conventional PC-based DCS system. Additionally, the proposed system significantly reduces instrumentation size and cost, enabling portable assessment of tissue microcirculation.

Abstract Health status assessment is crucial for ensuring the reliability and safety of complex systems. Belief rule bases (BRBs) are widely used in complex system modeling because of it advanced causal reasoning capabilities. However, traditional BRB-based assessment models often rely on fixed triangular membership functions (MFs), which limit their flexibility and inference performance. Thus, a new adaptive interval type II belief rule base (AII-BRB) is proposed. Firstly, a shape coefficient of the MF is defined, which can effectively adjust its shape according to the observed measurement data. Based on this, an adaptive interval Type II MF is constructed. Thus, the reasoning process of the BRB model changes from quantitative value reasoning to interval probability reasoning. Furthermore, the basic performance of the AII-BRB is explored to illustrate its rationality. AII-BRB model not only provides point estimate of the health status, but also provides the interval range of health status. Finally, the health status assessments of spacecraft flywheel systems and lithium batteries are used as case studies to validate the effectiveness of the AII-BRB method.

Scalable genetic circuits are essential for implementing complex functions in living cells. Toward this goal, RNA regulators can provide a much-needed parts library with added benefits of low metabolic load, design flexibility, and logic capacity. However, despite the great potential of synthetic RNA circuits, constructing such circuits with wide dynamic ranges and multiplexed regulatory cascades remains a challenge. To address this, we introduce RATEX (Ribosome-Assisted Transcriptional EXpression controller) by integrating a translation-to-transcription converter with synthetic RNA regulators, enabling a compact and scalable RNA-programmed circuit architecture. The RATEX platform repurposes a large library of well-characterized translation regulators with up to 1,492-fold gene regulation, while leveraging natural ribosome-mediated sensing of diverse environmental inputs, such as metabolites. We demonstrated multi-input logic processing with up to a 6-input OR logic gate for RNA inputs and hybrid 3-input logic gates to sense diverse metabolite and small-molecule inputs alongside RNA signals. Signal amplification with multiplexed combinatorial control of RNA outputs was achieved through multiplexed signaling cascades. Finally, the RNA- and metabolite-sensing 3-input AND gates were used to control cellular morphology and intracellular spatial organization. Together, the RATEX platform, with its scalable and modular architecture, offers a broad potential design space for synthetic biology and biotechnology.

Global Health Capacity Building (GHCB) initiatives are central to strengthening health systems and workforce readiness in Fragile and Conflict-Affected Settings (FCAS). While many evaluation frameworks exist, most were developed for stable contexts and offer limited guidance on how to adapt evaluation processes to environments rife with political instability, limited infrastructure, population mobility, and rapidly shifting conditions. As innovative learning modalities, such as online and blended learning modalities expand in FCAS, there is a critical need for evaluation approaches that are context-responsive, process-oriented, and tailored to fragile settings. This paper presents the Evaluation of Capacity Building (eCAP) framework, an evidence-informed framework for evaluating GHCB in FCAS. The eCAP framework was developed through a 5-year iterative process by the Global Health Institute at the American University of Beirut, as informed by 3 sequential phases: (1) 3 systematic reviews exploring evaluation methods for GHCB in low- and middle-income countries and in the MENA region; (2) evaluation of 5 case studies implemented in the region; and (3) a synthesis of outcomes and process-related results between 2019 and 2024. The eCAP framework conceptualizes the evaluation lifecycle in FCAS as a dynamic and adaptive process across three interconnected phases: (1) understanding the program (modality, population, context, level of evaluation, and program logic); (2) implementation (logistics, recruitment, engagement, tool selection, and timing of data collection), and (3) analysis, feedback, and learning. Without prescribing standardized indicators, the framework emphasizes decision-making principles for evaluation that enable adaptation to contextual constraints and other field-based realities. The framework is useful for diverse FCAS, and it has demonstrated feasibility and utility in capturing short-to-medium term outcomes while preserving methodological rigor under challenging and unstable conditions. The eCAP framework addresses a key gap in the evaluation of GHCB initiatives in FCAS by offering a structured yet adaptable approach grounded in ample field-based evidence. The framework provides practical guidance for researchers, implementers, and funders seeking to design and evaluate capacity building initiatives in complex humanitarian environments. Ultimately, this framework has implications for strengthening evaluation practices, improving programmatic learnings, and guiding policy and funding decisions related to capacity building in FCAS.

Abstract Secure data obfuscation requires balancing perceptual transparency, computational efficiency, and architectural deployability. Conventional spatial-domain steganography achieves high capacity but lacks structural abstraction and hardware-oriented design. We introduce SteganoSNN, a neuromorphic steganographic framework that integrates spike-based temporal encoding with field programmable gate array (FPGA)-accelerated embedding for secure and energy-aware multimedia data hiding. Audio samples are transformed into spike-count regimes using a Leaky Integrate-and-Fire (LIF) spiking neuron model, followed by modulo-based symbol transformation and deterministic spike-index mapping. The spike-to-Spike Index (SI) abstraction decouples source symbols from embedded bit patterns, introducing a temporal encoding layer prior to least significant bit (LSB) embedding in red, green, blue, alpha (RGBA) images. The proposed system is implemented in Python using NEST and realised on a PYNQ-Z2 FPGA through a hybrid processing-system/programming-logic co-design. Post-implementation analysis reports total on-chip power consumption of approximately 1.41 W, with programmable logic contributing only a small fraction of overall energy usage. The proposed framework supports an embedding capacity of 8 bits per pixel (bpp) while maintaining high perceptual fidelity on the DIV2K 2017 dataset, achieving peak signal-to-noise ratio (PSNR) values between 40.4 dB and 41.35 dB and structural similarity index (SSIM) above 0.97. Real-time feasibility is demonstrated through detailed latency and throughput evaluation on full-High Definition (HD) images. Unlike purely LSB-based approaches, SteganoSNN introduces a neuromorphic temporal representation layer that enables hardware-efficient symbol abstraction without increasing embedding depth. The results establish spike-based encoding as a viable architectural paradigm for secure and resource-aware steganography in edge-Artificial Intelligence (AI) and embedded systems.

This paper presents the implementation of a distance protection relay using a programmable logic controller (PLC). Power systems widely use distance relays to detect and isolate faults based on the impedance between the relay and the fault location. By leveraging a PLC, the design benefits from flexibility, real-time processing, and integration with a monitoring system. The paper is mostly about using voltage and current inputs to figure out impedance, finding fault zones, and putting protection logic into place using the Function Block Diagram (FBD) in PLC programming environments. The proposed system is reliable, cost-effective, and easy to modify for various protection schemes

Insight into effects of torrefaction on biomass: Chemical composition, pyrolysis behavior and products distribution.

Accurate and low-latency beam position monitoring is essential for pencil beam scanning (PBS) proton therapy. The Unscented Kalman Filter (UKF) improves dynamic tracking compared to conventional centroid-based methods. However, the millisecond-level latency of its software implementation renders it unsuitable for deterministic real-time interlock systems [1]. This work presents a hardware implementation of a Square-Root Unscented Kalman Filter (SR-UKF) for a 128-channel strip ionization chamber beam monitor. A dynamic 9-strip region-of-interest (ROI) selection module is introduced to reduce measurement dimensionality and suppress background noise. Numerical stability is ensured by propagating the Cholesky factor of the covariance matrix. Implemented on the programmable logic of a Zynq-7100 FPGA using a pipelined dataflow structure, the architecture achieves a deterministic processing latency of 27.93 μs per frame. Experiments using 70–240 MeV proton beam datasets with controlled noise injection demonstrate sub-millimeter tracking accuracy and stable performance under low-SNR conditions.