Search Results (965)

This paper proposes a hardware–software co-design for adaptive lossless compression based on Hybrid Arithmetic–Huffman Coding, a table-driven approximation of arithmetic coding that preserves near-optimal compression efficiency while eliminating the multiplicative precision and sequential bottlenecks that have traditionally prevented arithmetic coding deployment in resource-constrained embedded systems. The compression pipeline is partitioned as follows: flexible software on the processor core dynamically builds and adapts the prefix coding (usually Huffman Coding) frontend for accurate probability estimation and binarization; the resulting binary stream is fed to a deeply pipelined systolic hardware accelerator that performs binary arithmetic coding using pre-calibrated finite state transition tables, dedicated renormalization logic, and carry propagation mitigation circuitry instantiated in on-chip memory. The resulting implementation achieves compression ratios consistently within 0.4% of the theoretical entropy limit, multi-gigabit per second throughput in 28 nm/FinFET nodes, and approximately 68% lower energy per compressed byte than optimized software arithmetic coding, making it ideally suited for real-time embedded vision, IoT sensor networks, and edge multimedia applications. Full article

Per- and polyfluoroalkyl substances are persistent environmental contaminants increasingly implicated in neurotoxicity. Establishing causality and mechanisms relevant to Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis requires human-relevant systems that capture exposure, barrier function, and brain circuitry. We review advanced cellular platforms—iPSC-derived neuronal and glial cultures, cerebral and midbrain organoids, and chip-based microphysiological systems—that model disease-relevant phenotypes (Aβ/tau pathology, dopaminergic vulnerability, myelination defects) under controlled PFAS exposures and defined genetic risk backgrounds. Modular, fluidically coupled BBB-on-chip → brain-organoid microphysiological systems have been reported, enabling chronic, low-dose PFAS perfusion under physiological shear, real-time barrier integrity readouts such as transepithelial/transendothelial electrical resistance (TEER), quantification of PFAS partitioning and translocation, and downstream neuronal–glial responses assessed by electrophysiology and multi-omics. Across platforms, convergent PFAS-responsive processes emerge—mitochondrial dysfunction and oxidative stress, lipid/ceramide dysregulation, neuroinflammatory signaling, and synaptic/network impairments—providing a mechanistic scaffold for biomarker discovery and gene–environment interrogation with isogenic lines. We outline principles for exposure design (environmentally relevant ranges, longitudinal paradigms), multimodal endpoints (omics, electrophysiology, imaging), and cross-lab standardization to improve comparability. Together, these models advance the quantitative evaluation of PFAS neurotoxicity and support translation into risk assessment and therapeutic strategies. Full article

Self-powered microfluidic systems represent a promising direction toward autonomous and portable lab-on-chip technologies, yet conventional electrowetting platforms remain constrained by bulky high-voltage supplies and intricate control circuitry. In this work, we design a triboelectric nanogenerator (TENG)-based microfluidic system that harvests mechanical energy for droplet manipulation without any external electronics. The TENG integrates two triboelectric units with a 25° phase offset, enabling periodic high-voltage generation. Finite element simulations elucidate the electric field distributions of the TENG and microfluidic chip, validating the operating principle of the integrated microfluidic system. Experimental studies further quantify the effects of electrode geometry and rotational speed on the critical drivable droplet volume, demonstrating stable transport over linear, S-shaped, and circular trajectories. Remarkably, the droplet motion direction can be instantaneously reversed by reversing the TENG rotation direction, achieving bidirectional control without auxiliary circuitry. This work establishes a voltage-optimized, structurally tunable, and fully self-powered platform, offering a new paradigm for portable digital microfluidics. Full article

The convergence of Internet of Things (IoT), embedded microcontrollers, and robotics has significantly transformed industrial and service applications under the Industry 5.0 paradigm. IoT-enabled automation not only reduces human intervention but also improves system efficiency, safety, and adaptability across multiple domains. The growing integration of automation technologies in manufacturing lines has significantly reduced human intervention while improving productivity and operational safety. Robotic arms play a crucial role in modern industrial environments, particularly for repetitive, hazardous, or precision-demanding tasks. This study presents a cost-effective robotic arm system for product selection, sorting and processing in automated production lines. The system operates in both automatic and manual modes and utilizes an ESP32-based controller, radio frequency identification (RFID) modules, and low-cost sensors to identify and transport products on a conveyor. A mobile, IoT-enabled interface provides remote real-time monitoring and control, while integrated safety mechanisms, current-voltage protections, and emergency stop circuitry enhance operational reliability. Using cost-effective components to reduce total cost, the system has been successfully validated through experiments to reduce labor dependency and operational errors, proving its scalability and economic viability for industrial automation. Compared to similar systems, this study presents an Industry 5.0 approach for low-cost IoT-based automated production lines. Full article

Melanoma cells in the brain may use similar mechanisms for adapting to injury and/or disease (that is, through continued reallocation of energy, matter, and information) as other cell types do to create an environment in which cancer cells can grow and sustain themselves within the confines of the brain. These adaptable mechanisms include the ability to reactivate dormant neural crest-derived migration and communication pathways. Unlike some other types of cancers that invade neural tissue as a simple invasion, melanomas are capable of achieving limited molecular, metabolic, and electrical similarity to the neural circuitry of the brain. Melanomas achieve this limited similarity through both vascular co-optation and mimicking synaptic functions, as well as through their engagement of redox-coupled metabolic pathways and feedback-regulated signal transduction pathways. The result is the creation of a metastable tumor–host system, where the relationship between tumor and host is defined by the interaction of stabilizing and destabilizing forces; forces that define the degree of coherence, vulnerability, and persistence of the tumor–host system. In this review, we integrate molecular, electrophysiological, and anatomical data to develop a single unifying hypothesis for the functional integration of melanoma cells into the neural tissue of the brain. Additionally, we describe how neural crest-based regulatory pathways are reactivated in the adult brain and how tumor–host coherence is developed as a function of the shared thermodynamic and informational constraints placed on both tumor and host. We also describe how our proposed conceptual model allows for the understanding of therapeutic interventions as selective disruptions of the neural, metabolic, and immunological couplings that support metastatic adaptation. Full article

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective. Full article

The principle of non-Foster broadband matching of electrically small (high-Q) transmitting antennas has been around for more than a few decades. However, there have been only a handful of experimentally proven examples, with highly limited scope of application. The main obstacle is the inherent instability of non-Foster elements, since those are actually active circuits with positive feedback. This research investigates the idea that a common non-Foster transmitter antenna matching network can be replaced with the arrangement of multiple frequency-coherent sources, rendering positive feedback unnecessary. Two different approaches were analyzed, one with a coherent RF current source, the other one with a coherent voltage source. The viability of this idea has been verified by simulations and experimental results. Unlike non-Foster matching, the proposed principle cannot support instability because it does not contain any positive feedback loops. In addition, it offers multiple-octave operational bandwidth, while the efficiency is limited only by the linear operation of the used circuitry, thus remaining at least equal to that of its non-Foster equivalent. While the proof-of-concept experiments were conducted in the lower HF (up to 20 MHz) frequency band, where common antennas are electrically small and highly reactive, there are no physical obstacles to scaling the same principle of operation to higher frequencies. Full article

Nilaparvata lugens is a migratory pest with high fecundity and outstanding drug resistance, which poses a devastating danger to rice production. This study investigated the reproductive regulation mechanism of N. lugens, specifically silencing the trehalose-6-phosphate synthase gene (TPS) via RNAi to elucidate how TPS governs the trehalose metabolic network through modulation of trehalose biosynthesis. Insect fecundity hinges on the synchronized progression of oogenesis and the tightly controlled expression of vitellogenin (Vg). In N. lugens, this coordination is orchestrated by an integrated molecular network that converges juvenile hormone signaling (JH), 20-hydroxyecdysone pathways (20E), insulin/IGF signaling (IIS), and the target of rapamycin cascade (TOR), collectively dictating the reproductive output of the species. Using TPS knockdown as the entry point, this study dissects the lipid-metabolic circuitry of N. lugens and uncovers how hormonal signaling cascades orchestrate reproduction by precisely modulating vitellogenin (Vg) and its cognate receptor VgR. Synthesized double-stranded terpene synthase genes (dsTPSs) can degrade mRNA, inhibit protein translation, and ultimately lead to the silencing of TPS genes, simultaneously crippling energy provision and hormonal signaling to orchestrate a multi-pronged suppression of reproduction. This dual-action intervention offers a promising molecular target for environmentally friendly management of N. lugens. Full article

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy, yet the transcriptional hierarchies linking endocrine signaling to tumor progression remain poorly defined. Here, we integrated gene-expression profiles from two independent cohorts (TCGA-THCA and GSE33630) to identify consensus transcriptional master regulators (TMRs) driving PTC. After normalization and differential expression analysis, we reconstructed regulon networks with ARACNe-AP, inferred TMR activity using VIPER, and integrated evidence across datasets via Fisher’s meta-analysis. This cross-cohort strategy yielded 50 shared TMRs, predominantly from the Zinc Finger, Forkhead, ETS, and nuclear receptor families. Network topology highlighted PBX4, GATAD2A, BHLHE40, HEY2, and TEAD4 as upstream regulators controlling other TMRs. Functional enrichment revealed activation of NOTCH, MAPK, PI3K, and TGF-β signaling and enrichment of early and late estrogen-response programs, uncovering a noncanonical role of SMAD9 in TGF-β signaling. Together, these findings delineate the transcriptional and hormonal circuitry underlying thyroid tumorigenesis, providing a regulatory framework for biomarker-driven therapies based on network activity states. Full article

The General AntiParticle Spectrometer is a balloon-borne experiment designed to search for low-energy cosmic-ray antinuclei as a potential indirect signature of dark matter. Over the course of at least three long-duration flights over Antarctica, it will explore the sub-$250 \mathrm{M}\mathrm{e}\mathrm{V}/\mathit{n}$ energy range with sensitivity to antideuterons and antihelium, while also extending antiproton measurements below 100 $\mathrm{M}$$\mathrm{e}\mathrm{V}$. The instrument features a tracker built from more than one thousand lithium-drifted silicon detectors, each read out by a dedicated custom integrated circuit. With the first flight scheduled for the austral summer of 2025, a new prototype chip, ANTARES4, has been developed using a commercial 65 $\mathrm{n}$$\mathrm{m}$ complementary metal-oxide semiconductor process for use in the second flight. It integrates eight independent analog channels, each incorporating a low-noise charge-sensitive amplifier with dynamic signal compression, a CR–RC shaping stage with eight selectable peaking times, and on-chip calibration circuitry. The charge-sensitive amplifier uses metal-oxide semiconductor feedback elements with voltage-dependent capacitance to support the wide input energy range from 10 $\mathrm{k}$$\mathrm{e}\mathrm{V}$ to 100 $\mathrm{M}$$\mathrm{e}\mathrm{V}$. Four alternative feedback implementations are included to compare performance and design trade-offs. Leakage current compensation up to 200 $\mathrm{n}$$\mathrm{A}$ per detector strip is provided by a Krummenacher current–feedback network. This paper presents the design and architecture of ANTARES4, highlighting the motivations, design drivers, and performance requirements that guided its development. Full article

The rapid development of the Internet of Things (IoT) has imposed increasingly stringent power consumption requirements on receiver design. Unlike phase-locked loops (PLLs), free-running oscillators eliminate power-hungry loop circuitry. However, the inherent frequency offset of free-running oscillators introduces uncertainty in the intermediate frequency (IF), preventing the receiver from aligning with the desired channel. To address this, we present a reconfigurable channel receiver employing a free-running oscillator and frequency estimation for low-power IoT applications. The proposed receiver first captures a specific preamble sequence corresponding to the desired channel through multiple parallel sub-channels implemented in the digital baseband (DBB), which collectively cover the expected IF frequency range. The desired IF frequency is estimated using the proposed preamble-based frequency estimation (PBFE) algorithm. After frequency estimation, the receiver switches to a single-channel mode and tunes its passband center frequency to the estimated IF frequency, enabling high-sensitivity data reception. Measurement results demonstrate that the PBFE algorithm achieves reliable frequency estimation with a minimum IF signal-to-noise ratio (SNR) of 2 dB and an estimation error below 22 kHz. In single-channel mode, with a residual frequency offset of 30 kHz, an 8-point energy accumulation decoding scheme achieves a bit error rate (BER) of 10⁻³ at an IF SNR of 5.2 dB. Compared with the case of the original 50 kHz IF frequency offset, the required SNR is improved by 4.1 dB. Full article

Background/Purpose: Skilled motor performance depends on the action–observation networks (AONs), which supports the internal simulation of perceived movements. While expertise effects are well-documented in sports, neuroimaging evidence in golf is scarce, particularly on temporal dynamics across swing phases. This study examines how golf expertise modulates AON activation and functional connectivity during temporally distinct swing phases (pre-hitting vs. hitting) and assesses implications for predictive-coding models of motor skill. Methods: Fifty-seven participants (elite golfers: n = 28; controls: n = 29) underwent functional magnetic resonance imaging (fMRI) scanning while viewing golf swing videos segmented into pre-hitting and hitting phases. Data analysis employed generalized linear models (GLMs) with two-sample t-tests for group comparisons and generalized psychophysiological interaction (gPPI) to assess functional connectivity using GLM-identified activation clusters as seeds. Results: (1) Compared to controls, elite golfers showed stronger activation in right insula and posterior cingulate cortex during pre-hitting, and in right cerebellum and bilateral postcentral cortex during hitting phases. The hitting > pre-hitting contrast revealed enhanced bilateral postcentral gyrus activation in golfers. (2) gPPI analysis demonstrated significant group × phase interaction in functional connectivity between right postcentral gyrus and left precuneus. Conclusions: Elite golf expertise dynamically retunes AON across swing phases, shifting from anticipatory interoceptive processing to impact-centered sensorimotor–parietal circuitry. These findings refine predictive-coding models of motor skill and identify the postcentral–precuneus loop as a potential target for neurofeedback interventions aimed at optimizing golf performance. Full article

Silicon-Based Physical Unclonable Functions (PUFs) exploit inherent manufacturing variations to produce a unique, random, and ideally unclonable secret key. As electronic devices are decommissioned and sent for End of Life (EOL) recycling, the encrypted critical program information remains within the device. However, conventional PUFs remain vulnerable to invasive attacks and reverse engineering that with sufficient time, resources, and effort can enable an adversary to bypass the security enclave of the system and extract this secret data. Recent research has started to explore techniques to respond to tamper attempts using electromigration (EM) and time-dependent dielectric breakdown (TDDB) to the PUF entropy source, preventing future authentication attempts with well-known semiconductor reliability failure mechanisms. This work presents a Pre-Amplifier Physical Unclonable Function (Pre-Amp PUF) with a self-corruption function designed and manufactured in a 3 nm FinFET technology. This PUF can perform a destructive read operation as an EOL anti-counterfeit measure against recycled and reused electronics. The destructive read utilizes an accelerated aging technique that exploits both Hot Carrier Injection (HCI) and Bias Temperature Instability (BTI) degradations directly at the PUF entropy source bitcell data. This work demonstrates a silicon proven ability to irreversibly corrupt the encryption key, invalidating the PUF key, and blocking future authentication attempts. By utilizing HCI and BTI aging effects rather than physical damage a PUF that can self-corrupt its own key without being detectable with imaging techniques is demonstrated for the first time. A feedback loop enables corruption of up to ~30% of the PUF entropy source, which is approximately 3× more data corruption than the prior state of the art self-corrupting PUF. Our technique reuses on-chip stable (repeatable) PUF bitcells identifying circuitry and thereby minimizes the area overhead to support this differentiated feature. Full article

Monitoring humidity is essential for the protection and long-term preservation of historical monuments and cultural heritage structures, particularly those made of stone, marble, or iron. Excess moisture can accelerate material degradation and compromise structural integrity. This paper presents an alternative, low-cost method for enhancing the sensitivity of a raindrop sensor, aiming to detect micro-droplets such as early morning dew—an important factor in environmental monitoring around such sensitive sites. The proposed method involves covering the sensor’s surface with a fine nylon mesh, such as a stocking, which allows tiny water droplets to accumulate and spread more effectively across the sensor. This modification improves the electrical conductivity between the copper tracks when droplets are present, enabling the sensor to detect moisture levels that would otherwise go unnoticed. Experimental results demonstrate that the modified sensor performs significantly better than the original, unaltered version, offering greater sensitivity and consistency in its readings. The sensor responds more reliably to low volumes of moisture without requiring internal changes to its circuitry, making it both practical and cost-effective. The outcomes of this work are encouraging, suggesting that this approach is suitable for moisture detection in both research and real-world conservation scenarios. It provides a simple and scalable solution for integrating humidity monitoring into broader environmental sensing systems. Full article

Human behavior is shaped by a pervasive motive to align with others, manifesting across a wide range of tendencies—from motor synchrony and emotional contagion to convergence in beliefs and choices. Existing accounts explain how alignment arises through predictive coding and observation–execution mechanisms, but they do not address how it is regulated in a manner that considers when alignment is adaptive and with whom it should occur. We propose a goal-directed model of social alignment that integrates computational and neural levels of analysis, to enhance our understanding of alignment as a context-sensitive decision process rather than a reflexive social tendency. Computationally, alignment is formalized as a prediction-error minimization process over the gap between self and other, augmented by a meta-learning layer in which the learning rate is adaptively tuned according to the inferred value of aligning versus maintaining independence. Assessments of the traits and mental states of self and other serve as key inputs to this regulatory function. Neurally, higher-order representations of these inputs are carried by the mentalizing network (dmPFC, TPJ), which exerts top-down control through the executive control network (dlPFC, rIFG) to enhance or inhibit alignment tendencies generated by observation–execution (mirror) circuitry. By reframing alignment as a form of social decision-making under uncertainty, the model specifies both the computations and neural circuits that integrate contextual cues to arbitrate when and with whom to align. It yields testable predictions across developmental, comparative, cognitive, and neurophysiological domains, and provides a unified framework for understanding the adaptive functions of social alignment, such as strategic social learning, as well as its maladaptive outcomes, including groupthink and false information cascades. Full article