Safety and efficacy of low-power pure-cut hot snare polypectomy for small nonpedunculated colorectal polyps compared with conventional resection methods: A propensity score matching analysis.

Objectives: Cold snare polypectomy (CSP) is widely performed for small colorectal polyps. However, small colorectal polyps sometimes include high-grade adenomas or carcinomas that require endoscopic resection with electrocautery. This study aimed to evaluate the efficacy and safety of a novel resection technique, hot snare polypectomy with low-power pure-cut current (LPPC-HSP) for small colorectal polyps, compared with CSP and conventional endoscopic mucosal resection (EMR). Methods: Records of patients who underwent CSP, EMR, or LPPC-HSP for nonpedunculated colorectal polyps less than 10 mm between April 2021 and March 2022 were retrospectively evaluated. We analyzed and compared the treatment outcomes of CSP and EMR with those of LPPC-HSP using propensity score matching. Results: After propensity score matching of 396 pairs, an analysis of CSP and LPPC-HSP indicated that LPPC-HSP had a significantly higher R0 resection rate (84% vs. 68%; p < 0.01). Delayed bleeding was observed in only two cases treated with CSP before matching. Perforation was not observed with either treatment. After propensity score matching of 176 pairs, an analysis of EMR and LPPC-HSP indicated that their en bloc and R0 resection rates were not significantly different (99.4% vs. 100%, p = 1.00; 79% vs. 81%, p = 0.79). Delayed bleeding and perforation were not observed with either treatment. Conclusions: The safety of LPPC-HSP was comparable to that of CSP. The treatment outcomes of LPPC-HSP were comparable to those of conventional EMR for small polyps. These results suggest that this technique is a safe and effective treatment for nonpedunculated polyps less than 10 mm.

Exploring the potential of spiking neural networks in biomedical applications: advantages, limitations, and future perspectives.

In this paper, a comprehensive exploration is undertaken to elucidate the utilization of Spiking Neural Networks (SNNs) within the biomedical domain. The investigation delves into the experimentally validated advantages of SNNs in comparison to alternative models like LSTM, while also critically examining the inherent limitations of SNN classifiers or algorithms. SNNs exhibit distinctive advantages that render them particularly apt for targeted applications within the biomedical field. Over time, SNNs have undergone extensive scrutiny in realms such as neuromorphic processing, Brain-Computer Interfaces (BCIs), and Disease Diagnosis. Notably, SNNs demonstrate a remarkable affinity for the processing and analysis of biomedical signals, including but not limited to electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) data. This paper initiates its exploration by introducing some of the biomedical applications of EMG, such as the classification of hand gestures and motion decoding. Subsequently, the focus extends to the applications of SNNs in the analysis of EEG and ECG signals. Moreover, the paper delves into the diverse applications of SNNs in specific anatomical regions, such as the eyes and noses. In the final sections, the paper culminates with a comprehensive analysis of the field, offering insights into the advantages, disadvantages, challenges, and opportunities introduced by various SNN models in the realm of healthcare and biomedical domains. This holistic examination provides a nuanced perspective on the potential transformative impact of SNN across a spectrum of applications within the biomedical landscape.

Initial treatment for surgery-naïve desmoid tumors by high intensity focused ultrasound.

Introduction: Desmoid tumor (DT) is a rare proliferative disease occurring in connective tissues, characterized by high infiltration and recurrence rates. While surgery remains the primary treatment, its recurrence risk is high, and some extra-abdominal desmoid tumors are inoperable due to their locations. Despite attempts with radiotherapy and systemic therapy, the efficacy remains limited. Methods: We used low-power cumulative high-intensity focused ultrasound (HIFU) therapy as an initial treatment for desmoid tumor patients either ineligible or unwilling for surgery. Low-power cumulative HIFU employs slower heat accumulation and diffusion, minimizing damage to surrounding tissues while enhancing efficacy. Results: Fifty-seven non-FAP desmoid tumor patients, previously untreated surgically, underwent low-power cumulative HIFU therapy. Among them, 35 had abdominal wall DT, 20 had extra-abdominal DT, and 2 had intra- abdominal DT, with an 85% median ablation ratio. Abdominal wall DT patients showed significantly better response rates (91.4% vs. 86%) and disease control rates (100% vs. 32%) than that of non-abdominal wall DT patients. Median event- free survival time was not reached after a median follow-up duration of 34 months. Discussion: With its high response rate, durable efficacy, and mild adverse effects, our findings suggest that low-power cumulative HIFU presents a promising novel treatment for desmoid tumors, particularly abdominal wall DT patients.

Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids.

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 µs and ultra-low power consumption of 15 µW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner.

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Realization of Extremely High-Gain and Low-Power in nMOS Inverter Based on Monolayer WS2 Transistor Operating in Subthreshold Regime.

In this work, we report an n-type metal-oxide-semiconductor (nMOS) inverter using chemical vapor deposition (CVD)-grown monolayer WS2 field-effect transistors (FETs). Our large-area CVD-grown monolayer WS2 FETs exhibit outstanding electrical properties including a high on/off ratio, small subthreshold swing, and excellent drain-induced barrier lowering. These are achieved by n-type doping using AlOx/Al2O3 and a double-gate structure employing high-k dielectric HfO2. Due to the superior subthreshold characteristics, monolayer WS2 FETs show high transconductance and high output resistance in the subthreshold regime, resulting in significantly higher intrinsic gain compared to conventional Si MOSFETs. Therefore, we successfully realize subthreshold operating monolayer WS2 nMOS inverters with extremely high gains of 564 and 2056 at supply voltage (VDD) of 1 and 2 V, respectively, and low power consumption of ∼2.3 pW·µm-1 at VDD = 1 V. In addition, the monolayer WS2 nMOS inverter is further expanded to the demonstration of logic circuits such as AND, OR, NAND, NOR logic gates, and SRAM. These findings suggest the potential of monolayer WS2 for high-gain and low-power logic circuits and validate the practical application in large areas.

Reconfigurable Vertical Phototransistor with MoTe2 Homojunction for High-Speed Rectifier and Multivalued Logical Circuits.

The most reported two-dimensional (2D) reconfigurable multivalued logic (RMVL) devices primarily involve a planar configuration and carrier transport, which limits the high-density circuit integration and high-speed logic operation. In this work, the vertical transistors with reconfigurable MoTe2 homojunction are developed for low-power, high-speed, multivalued logic circuits. Through top/bottom dual-gate modulation, the transistors can be configured into four modes: P-i-N, N-i-P, P-i-P, and N-i-N. The reconfigurable rectifying and photovoltaic behaviors are observed in P-i-N and N-i-P configurations, exhibiting ideal diode characteristics with a current rectification ratio over 105 and sign-reversible photovoltaic response with a photoswitching ratio up to 7.44 × 105. Taking advantage of the seamless homogeneous integration and short vertical channel architecture, the transistor can operate as an electrical switch with an ultrafast speed of 680 ns, surpassing the conventional p-n diode. The MoTe2 half-wave rectifier is then applied in high-frequency integrated circuits using both square wave and sinusoidal waveforms. By applying an electrical pulse with a 1/4 phase difference between two input signals, the RMVL circuit has been achieved. This work proposes a universal and reconfigurable vertical transistor, enabled by dual-gate electrostatic doping on top/bottom sides of MoTe2 homojunction, suggesting a high integration device scheme for high-speed RMVL circuits and systems.

A 640 nA IQ Output-Capacitor-Less Low Dropout (LDO) Regulator with Sub-Threshold Slew-Rate Enhancement for Narrow Band Internet of Things (NB-IoT) Applications.

An ultra-low quiescent current output-capacitor-less low dropout (OCL-LDO) regulator for power-sensitive applications is proposed in this paper. To improve the gain of the OCL-LDO feedback loop, the error amplifier employs a combination of a cross-coupled input stage for boosting the equivalent input transconductance and a negative resistance technique to improve the gain. Meanwhile, in order to address the issue of transient response of the ultra-low quiescent current OCL-LDO, a sub-threshold slew-rate enhancement circuit is proposed in this paper, which consists of a transient signal input stage and a slew-rate current increase branch. The proposed OCL-LDO is fabricated in a 0.18 µm CMOS process with an effective area of 0.049 mm2. According to the measurement results, the proposed OCL-LDO has a maximum load current of 100 mA and a minimum quiescent current of 640 nA at an input voltage of 1.2 V and an output voltage of 1 V. The overshoot and undershoot voltages are 197 mV and 201 mV, respectively, and the PSR of the OCL-LDO is -72.4 dB at 1 kHz when the load current is 100 µA. In addition, the OCL-LDO has a load regulation of 7.6 µV/mA and a line regulation of 0.87 mV/V.

Comparative Analysis of Clinical Outcomes of High-power, Short-duration Ablation versus Low-power, Long-duration Ablation Strategy in Patients with Atrial Fibrillation: A Comprehensive Umbrella Review of Meta-analyses.

Atrial fibrillation (AF) affects around 33 million people worldwide, rendering it a common cardiac arrhythmia. Catheter ablation (CA) has evolved as a leading therapeutic intervention for symptomatic AF. This umbrella review systematically evaluates existing systematic reviews and meta-analyses to assess the safety, efficacy, and potential of high-power, short-duration (HPSD) ablation as an alternative therapy option for AF. A thorough exploration was undertaken across PubMed, the Cochrane Library, and Embase to identify pertinent studies for inclusion in this umbrella review. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method was employed to assess the overall certainty of the evidence comprehensively, and the quality of the incorporated reviews was meticulously evaluated through use of the AMSTAR 2 tool, the Cochrane Collaboration tool, and the Newcastle-Ottawa scale. In this study, we initially identified 35 systematic reviews and meta-analyses, narrowing them down to a final selection of 11 studies, which collectively integrated data from 6 randomized controlled trials and 26 observational studies. For primary efficacy outcomes, the HPSD approach led to a non-significant decrease in the risk of atrial tachyarrhythmia recurrence (risk ratio [RR], 0.88; 95% confidence interval [CI], 0.70-1.12; I 2 = 90%; P = .31) and a significantly reduced risk of AF recurrence (RR, 0.53; 95% CI, 0.42-0.67; I 2 = 0%; P < .00001) compared to the low-power, long-duration (LPLD) approach. In terms of primary safety outcomes, the HPSD approach significantly reduced the risk of esophageal thermal injury (ETI) (RR, 0.71; 95% CI, 0.61-0.83; I 2 = 0%; P < .00001) and facilitated a non-significant decrease in the risk of other major complications (RR, 0.87; 95% CI, 0.73-1.03; I 2 = 0%; P = .10). In conclusion, HPSD therapy is safer and more effective than LPLD therapy, facilitating decreased AF recurrence rates along with reductions in ETI, total procedure duration, ablation number, ablation time, fluoroscopy time, and acute pulmonary vein reconnection.

Real-Time Tunable Gas Sensing Platform Based on SnO2 Nanoparticles Activated by Blue Micro-Light-Emitting Diodes.

Micro-light-emitting diodes (µLEDs) have gained significant interest as an activation source for gas sensors owing to their advantages, including room temperature operation and low power consumption. However, despite these benefits, challenges still exist such as a limited range of detectable gases and slow response. In this study, we present a blue µLED-integrated light-activated gas sensor array based on SnO2 nanoparticles (NPs) that exhibit excellent sensitivity, tunable selectivity, and rapid detection with micro-watt level power consumption. The optimal power for µLED is observed at the highest gas response, supported by finite-difference time-domain simulation. Additionally, we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO2 NPs. The noble metals induce catalytic interaction with reducing gases, clearly distinguishing NH3, H2, and C2H5OH. Real-time gas monitoring based on a fully hardware-implemented light-activated sensing array was demonstrated, opening up new avenues for advancements in light-activated electronic nose technologies.

Dual oxidative stress biomarkers co-recognition in periodontal microenvironment: A flexible and low-power consumption nanozyme sensing platform.

Sensing platforms with high interference immunity and low power consumption are crucial for the co-detection of dual oxidative stress biomarkers and clinical diagnosis of periodontitis. Herein, we constructed a bifunctional nanozyme to identify hydrogen peroxide (H2O2) and ascorbic acid (AA) with low crosstalk at zero or low bias voltage. To target H2O2 and AA, Fe(III) meso-tetra(4-carboxyphenyl) porphine (TCPP(Fe)) and Pt nanoclusters were selected as active sites respectively, and titanium carbide nanosheets were additionally introduced as a sensitizer. Due to their highly efficient catalytic properties, self-powered detection of H2O2 without bias voltage and distinguishable AA detection at 0.45 V were successfully achieved. Density functional theory calculations further confirmed the binding sites for target molecules and elucidated the sensing mechanism. On this basis, a dual-channel screen-printed electrode was fabricated to further ensure the discriminative detection of dual biomarkers at the device level. The constructed flexible, low-power consumption sensing platform was successfully applied to raw clinical samples, effectively distinguishing between healthy individuals and patients with varying degrees of periodontitis. This work is expected to provide new insights into the design of highly specific nanozymes and low-power consumption electrochemical sensing systems, which will contribute to the accurate and convenient diagnosis of periodontitis.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

An Energy Efficient and Temperature Stable Digital FLL-based Wakeup Timer with Time-Domain Temperature Compensation.

This brief presents an on-chip digital intensive frequency-locked loop (DFLL)-based wakeup timer with a time-domain temperature compensation featuring a embedded temperature sensor. The proposed compensation exploits the deterministic temperature characteristics of two complementary resistors to stabilize the timer's operating frequency across the temperature by modulating the activation time window of the two resistors. As a result, it achieves a fine trimming step (± 1 ppm), allowing a small frequency error after trimming (<± 20 ppm). By reusing the DFLL structure, instead of employing a dedicated sensor, the temperature sensing operates in the background with negligible power (2 %) and hardware overhead (< 1 %). The chip is fabricated in 40 nm CMOS, resulting in 0.9 pJ/cycle energy efficiency while achieving 8 ppm/ºC from -40ºC to 80ºC.

All-2D-Materials Subthreshold-Free Field-Effect Transistor with Near-Ideal Switching Slope.

The Boltzmann Tyranny, set by thermionic statistics, dictates the lower limit of switching slope (SS) of a MOSFET to be 60 mV/dec, the fundamental barrier for low-dissipative electronics. The large SS leads to nonscalable voltage, significant leakage, and power consumption, particularly at short channels, making transistor scaling an intimidating challenge. In recent decades, an array of steep-slope transistors has been proposed; none is close to an ideal switch with ultimately abrupt switching (SS ∼ 0 mV/dec) between the binary logic states. We demonstrated an all-2D-materials van-der-Waals-heterostructure (vdW)-based FET that exhibits ultrasteep switching (0.33 mV/dec), a large on/off current ratio (∼107), and an ultralow off current (∼0.1 pA). The "Subthreshold-Free" operation achieved by the collective behavior of functional materials enables FET switching directly from the OFF-state to the ON-state with entirely eliminated subthreshold region, behaving as the ideal logic switch. Two-inch wafer-scale device fabrication is demonstrated. Boosted by device innovation and emerging materials, the research presents an advancement in achieving the "beyond-Boltzmann" transistors, overcoming one of the CMOS electronics' most infamous technology barriers that have plagued the research community for decades.

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing.

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Efficient Integration of Ultra-low Power Techniques and Energy Harvesting in Self-Sufficient Devices: A Comprehensive Overview of Current Progress and Future Directions.

Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios.

Trustworthy Environmental Monitoring Using Hardware-Assisted Security Mechanisms.

Environmental monitoring is essential for safeguarding the health of our planet and protecting human health and well-being. Without trust, the effectiveness of environmental monitoring and the ability to address environmental challenges are significantly compromised. In this paper, we present a sensor platform capable of performing authenticated and trustworthy measurements, together with a lightweight security protocol for sending the data from the sensor to a central server anonymously. Besides presenting a new and very efficient symmetric-key-based protocol, we also demonstrate on real hardware how existing embedded security modules can be utilized for this purpose. We provide an in-depth evaluation of the performance and a detailed security analysis.

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.