Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental h...
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The capture,regeneration,and conversion of CO_(2) from ambient air and flue gas streams are critical aspects of mitigating global *** sorbents for CO_(2) absorption are very promising as they have high mass transfer a...
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The capture,regeneration,and conversion of CO_(2) from ambient air and flue gas streams are critical aspects of mitigating global *** sorbents for CO_(2) absorption are very promising as they have high mass transfer areas without energy input and reduce emissions and minimize corrosion as compared to liquid ***,precisely tunable solid CO_(2) sorbents are difficult to ***,we demonstrate the high-throughput production of hydrogel-based CO_(2)-absorbing particles via liquid *** wrapping a liquid jet consisting of an aqueous solution of cross-linkable branched polyethylenimine(PEI)with a layer of suspension containing hydrophobic silica nanoparticles,monodisperse droplets with a silica nanoparticle coating layer was formed in the air.A stable Pickering emulsion containing PEI droplets was obtained after these ejected droplets were collected in a heated oil *** droplets turn into mm-sized particles after thermal curing in the *** diameter,PEI content,and silica content of the particles were systematically varied,and their CO_(2) absorption was measured as a function of *** regeneration of the particles enabled cyclic testing,revealing a CO_(2) absorption capacity of 6.5±0.5 mol kg^(−1)solid PEI in pure CO_(2) environments and 0.7±0.3 mol kg^(−1)solid PEI for direct air *** thousands of particles were produced per second at a rate of around 0.5 kg per hour,with a single *** process can be further scaled by *** complete toolbox for the design,fabrication,testing,and regeneration of functional hydrogel particles provides a powerful route toward novel solid sorbents for regenerative CO_(2) capture.
MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(...
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MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on ***,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser *** a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper ***,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive *** heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.
Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance...
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Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of arti...
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In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (IoT) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze se...
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ISBN:
(纸本)9798350393354
In this work, an earthquake prediction system utilizing machine learning (ML) techniques and Internet of Things (IoT) technologies is presented, using accelerometer data from the ADXL335 sensor. In order to analyze seismic patterns, the system records multi-axis accelerations. Various machine learning models are then used for predictive analytics. This technology seeks to predict probable seismic events by combining sensor data with sophisticated algorithms, assisting early warning systems for disaster readiness. The ADXL335 accelerometer is the central component of the Earthquake Prediction System described in this work. It records accelerations on the X, Y, and Z axes and converts them into analogue signals for further processing. These data streams are transmitted for feature extraction by utilizing IoT infrastructure, with an emphasis on seismic patterns that may indicate future earthquake events. To evaluate the accelerometer data and produce predicted insights, the system incorporates a variety of machine learning models, such as decision trees and support vector machines. The goal is to support disaster management plans by enabling early detection and warning of seismic activity through this combination of sensor technology and advanced analytics. A wide variety of machine learning models, such as decision trees, support vector machines, and recurrent neural networks, are used to derive actionable insights. These algorithms produce predictive analytics to support catastrophe management methods by carefully analyzing accelerometer data. The ultimate objective is to enable more proactive disaster mitigation planning by facilitating early detection and alerts of seismic activity. This system, which combines advanced analytics with sensor technology, is a critical step in strengthening disaster management systems. Its capacity to predict seismic events may help minimize the effects of earthquakes on impacted areas, help with evacuation plans, and provide timely a
Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula....
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Boolean satisfiability (SAT) is widely used as a solver engine in electronic design automation (EDA). Typically, SAT is used to determine whether one or more groups of variables can be combined to form a true formula. All solutions SAT (AllSAT) is a variant of the SAT problem. In the fields of formal verification and pattern generation, AllSAT is particularly useful because it efficiently enumerates all possible solutions. In this paper, a semi-tensor product (STP) based AllSAT solver is proposed. The solver can solve instances described in both the conjunctive normal form (CNF) and circuit form. The implementation of our method differs from incremental enumeration because we do not add blocking conditions for existing solutions, but rather compute the matrices to obtain all the solutions in one pass. Additionally, the logical matrices support a variety of logic operations. Results from experiments with MCNC benchmarks using CNF-based and circuit-based forms show that our method can accelerate CPU time by 8.1x (238x maximum) and 19.9x (72x maximum), respectively.
Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and lo...
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In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In resp...
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The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern gener...
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