High Hit Research Articles

Optimizing targeting strategies for lithotripsy through in-vitro and in vivo studies with consideration of respiratory regularity

BackgroundThis work aimed to identify a method to achieve improved stone targeting and safety in shockwave lithotripsy by accounting for respiration.MethodsWe set up an electromotive device simulating renal movement during respiration to place artificial stones within the phantom gel, measuring stone weight changes before and after shockwave exposure and the cavitation damage. We conducted clinical trials using respiratory masks and sensors to monitor and analyze patient respiration during shockwave lithotripsy.ResultsThe in vitro efficiency of lithotripsy was higher when adjusted for respiration than when respiration was not adjusted for. Slow respiration showed the best efficiency with higher hit rates when not adjusted for respiration. Cavitation damage was also lowest during slow respiration. The clinical study included 52 patients. Respiratory regularity was maintained above 90% in regular respiration. When respiration was regular, the lithotripsy rate was about 65.6%, which stayed at about 40% when respiration was irregular. During the lithotripsy, the participants experienced various events, such as sleep, taking off their masks, talking, movement, coughing, pain, nervousness, and hyperventilation. The generation of shockwaves based on respiratory regularity could reduce pain in patients.ConclusionThese results suggest a more accurate lithotripsy should be performed according to respiratory regularity.

MADRL-based UAV swarm non-cooperative game under incomplete information

Unmanned Aerial Vehicles (UAVs) play increasing important role in modern battlefield. In this paper, considering the incomplete observation information of individual UAV in complex combat environment, we put forward an UAV swarm non-cooperative game model based on Multi-Agent Deep Reinforcement Learning (MADRL), where the state space and action space are constructed to adapt the real features of UAV swarm air-to-air combat. The multi-agent particle environment is employed to generate an UAV combat scene with continuous observation space. Some recently popular MADRL methods are compared extensively in the UAV swarm non-cooperative game model, the results indicate that the performance of Multi-Agent Soft Actor-Critic (MASAC) is better than that of other MADRL methods by a large margin. UAV swarm employing MASAC can learn more effective policies, and obtain much higher hit rate and win rate. Simulations under different swarm sizes and UAV physical parameters are also performed, which implies that MASAC owns a well generalization effect. Furthermore, the practicability and convergence of MASAC are addressed by investigating the loss value of Q-value networks with respect to individual UAV, the results demonstrate that MASAC is of good practicability and the Nash equilibrium of the UAV swarm non-cooperative game under incomplete information can be reached.

Application of Genetic Algorithm in Optimization Simulation of Industrial Waste Land Reuse

In order to better understand the application of optimization simulation for industrial waste land reuse, the author proposes an application study based on nonlinear genetic algorithm in the optimization simulation of industrial waste land reuse. The author takes the landscape renovation and reuse of industrial waste sites as the research object, and through research on the current situation of landscape renovation and reuse of industrial waste sites both domestically and internationally, as well as on-site inspections, attempts to use landscape design techniques to deal with this once glorious but destructive industrial landscape that has already declined. Secondly, a genetic algorithm for enhancing the timeliness of industrial waste land reuse is proposed, which is based on random walks, combine users' long-term and short-term preferences to calculate the most suitable Top-N industrial waste land reuse optimization model for the current period. Finally, the two algorithms proposed by the author were experimentally validated on the dataset. In the CiteU Like dataset, the best performance was achieved at a=0.4, while in the JD dataset, the best performance was achieved at a=0.6. When k=6, the hit rate significantly decreases by about 50%. The URT-R genetic algorithm exhibits a high recommendation hit rate in recommendations targeting timeliness. The author analyzed the different characteristics of industrial waste reuse in scenic areas and optimized their essence and transformation methods, further improving the transformation and renewal methods of industrial waste land in the process of urban development in China. I hope to provide useful references for future research on related topics and practices.

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes.

Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low-data scenarios. For the first time, we extend the application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4 %), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.

Enhanced RBE of Particle Radiation Depends on Beam Size in the Micrometer Range.

High-linear energy transfer (LET) radiation, such as heavy ions is associated with a higher relative biological effectiveness (RBE) than low-LET radiation, such as photons. Irradiation with low- and high-LET particles differ in the interaction with the cellular matter and therefore in the spatial dose distribution. When a single high-LET particle interacts with matter, it results in doses of up to thousands of gray (Gy) locally concentrated around the ion trajectory, whereas the mean dose averaged over the target, such as a cell nucleus is only in the range of a Gy. DNA damage therefore accumulates in this small volume. In contrast, up to hundreds of low-LET particle hits are required to achieve the same mean dose, resulting in a quasi-homogeneous damage distribution throughout the cell nucleus. In this study, we investigated the dependence of RBE from different spatial dose depositions using different focused beam spot sizes of proton radiation with respect to the induction of chromosome aberrations and clonogenic cell survival. Human-hamster hybrid (AL) as well as Chinese hamster ovary cells (CHO-K1) were irradiated with focused low LET protons of 20 MeV (LET = 2.6 keV/µm) beam energy with a mean dose of 1.7 Gy in a quadratic matrix pattern with point spacing of 5.4 × 5.4 µm2 and 117 protons per matrix point at the ion microbeam SNAKE using different beam spot sizes between 0.8 µm and 2.8 µm (full width at half maximum). The dose-response curves of X-ray reference radiation were used to determine the RBE after a 1.7 Gy dose of radiation. The RBE for the induction of dicentric chromosomes and cell inactivation was increased after irradiation with the smallest beam spot diameter (0.8 µm for chromosome aberration experiments and 1.0 µm for cell survival experiments) compared to homogeneous proton radiation but was still below the RBE of a corresponding high LET single ion hit. By increasing the spot size to 1.6-1.8 µm, the RBE decreased but was still higher than for homogeneously distributed protons. By further increasing the spot size to 2.7-2.8 µm, the RBE was no longer different from the homogeneous radiation. Our experiments demonstrate that varying spot size of low-LET radiation gradually modifies the RBE. This underlines that a substantial fraction of enhanced RBE originates from inhomogeneous energy concentrations on the µm scale (mean intertrack distances of low-LET particles below 0.1 µm) and quantifies the link between such energy concentration and RBE. The missing fraction of RBE enhancement when comparing with high-LET ions is attributed to the high inner track energy deposition on the nanometer scale. The results are compared with model results of PARTRAC and LEM for chromosomal aberration and cell survival, respectively, which suggest mechanistic interpretations of the observed radiation effects.

Serial powering characterisation for the CMS Inner Tracker at the High Luminosity LHC

The CMS tracking system will be completely replaced in order to operate in the extreme conditions of the High Luminosity LHC. The new tracker will consist of two main subdetectors: the Inner Tracker, containing pixel modules, and the Outer Tracker, consisting of strip and macro-pixel modules. In the Inner Tracker, silicon sensors will be read out by the CMS Readout Chip (CROC). The chip, built in 65 nm CMOS technology, is able to withstand high radiation doses (500 Mrad) and hit rates (3 GHz/cm2 on the innermost tracking layer) during operation. Moreover, it must handle a fine sensor granularity (2500 µm2 pixels) and operate at low detection thresholds (1000 e-). The Inner Tracker will make use of the serial powering scheme to provide the ∼60 kW required by ∼4000 modular units. The Shunt-LDO (SLDO) regulator is the part of the CROC responsible for providing the power by draining a constant current in time, independently of the chip power consumption. Tests performed with Single Chip Cards (SCC) in serial configurations are reported in this paper.

Research on Simulation Approximate Solution Strategy for Complex Kinematic Models

Abstract In order to meet the needs of military, road construction, multimedia industry and other aspects, UAVs are gradually given more functions. As the basic function of UAV applications, the fixed-point delivery problem model has higher and higher accuracy requirements. However, in the actual scene, the UAV delivery problem is affected by the interaction of various factors such as flight height, air resistance, and dive angle, which makes it difficult to achieve high stability and high hit accuracy. This paper will analyze the complex motion model based on the fixed-point delivery of explosives by UAV, study the relationship between the stability of UAV delivery and the hit accuracy, and analyze the influence of relevant parameters on the problem by using modeling. In this paper, a multivariate nonlinear continuous time change model is proposed, and a continuous time slice discretization idea operation model is introduced to approximate the time slice splitting inside the UAV launch motion. Secondly, the design quantified evaluation index reaction the initial velocity of the explosive, the launch Angle, the height off the ground and other parameters to analyze the model. Finally, the best scheduling strategy is obtained and verified by using the idea of variable traversal and trial- and-error simulation. The experimental results show that the variation of UAV flying height, speed, depression and other interference factors is consistent with the prediction of score and hit accuracy, according to the environment setting of this model, when the UAV is 300 meters above the ground and 290 meters away from the target horizontal position, the delivery speed is 250m/s, and the pitch angle is about 27°, the fixed-point delivery of explosives is the best strategy.

Labeled graph partitioning scheme for distributed edge caching

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data resulted in low cache hit rates and high data access latency. This limitation stemmed from existing graph partitioning schemes that overlooked the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user’s historical query subgraphs and then performed labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers had a high hit rate for frequent user queries while satisfying a minimum edge cut. Evaluation on real datasets from various application domains demonstrated that LGPE achieved approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performed well in terms of edge cuts in special graph cases.

The Application of Digital Multimedia Technology in the Innovative Mode of English Teaching in institutions of higher education

Abstract Innovative English teaching resources will inevitably result from the use of media technology in the classroom. This article will examine the new approach to teaching English in college from the standpoint of teaching resources. In order to establish a new mode of English teaching in higher education institutions based on personalized recommendation of teaching resources, this paper proposes two recommendation algorithms: one is content-based, and the other is collaborative filtering. The algorithm is enhanced by combining it with the design of multimedia English teaching resources. The influence of hit rate and MAE values prior to and following the cooperative filtering recommendation method is examined. The distinction between the pre-and post-test results of the experimental and control courses is analyzed and compared to conclude the examination of the impact of the novel model of teaching college English. The analysis indicates that the improved recommendation algorithm has a 4.85% higher hit rate than the traditional collaborative filtering algorithm. The improved recommendation algorithm is more accurate than the traditional recommendation algorithm, as suggested by this.

Einsatz von Shutter-Brillen im Tennis – Einfluss von (Bild-)Frequenz und Dunkelphasenanteil auf die Zielschlagpräzision

Purpose. The use of shutter glasses for perception training in sports requires knowledge of adequate load dosage. The study analyses the influence of (image-)frequency or “duty ratio” (dark phase percentage) on target stroke accuracy in tennis. Material and Methods. With a total number of 22 tennis players (18m, 4f, mean age = 23.6, SD = ± 2.7 years) the target hit precision was conducted. The tennis players had to return the balls played to them by a ball machine using the forehand volley and to make direct hits to a bull’s eye (high scores = high target hit precision). The results with different (randomised) conditions (strobe rates level II = 5.3 Hz, level V = 2.3 Hz, lev- el VIII = 1.0 Hz; dark phase level II = 88 ms, level V = 341 ms, level VIII = 909 ms) of currently used shutter glasses (Nike Vapor Strobe) and without occlusion have been compared. Keywords intermittent vision, stroboscopic visual training, shutter lens glasses, sports vision training, tennis

Genetic Subtyping Using Whole Exome Sequencing across Large B Cell Lymphoma Entities in a Series of 109 DLBCL and Plasmablastic Lymphoma Cases

Background: Diffuse Large B Cell Lymphomas are still a significantly heterogeneous disease with few molecularly defined entities. Recently genomic based algorithms have been proposed for DBCL NOS disease classification, including the Lymphgen tool 1. Other large B cell lymphoma entities, including High Grade BCL Dual Hit or plasmablastic lymphoma remain poorly characterized according to this molecular based clustering methods. Here we sought to apply this tool for disease classification in a series of 109 large B cell lymphoma cases derived from GELTAMO clinical trials (NCT01848132 and NCT2015-005390-21) and retrospective multicentric series to confirm its application across different large B cell lymphoma entities. Methods: We retrieved FFPE and liquid biopsy diagnostic samples from 109 cases and performed histopathological confirmation, FISH testing and whole exome sequencing (Agilent Sureselect v6) by NGS. Sequencing data and available fusion data for BCL2 and BCL6 were used to classify the cases according to Lymphgen (https://llmpp.nih.gov/lymphgen). Results: 109 cases were classified as different large B cell lymphoma entities, including 31 DLBCL NOS GCB type, 8 HGBCL/DLBCL DH/NOS, 41 DLBCL NOS non-GCB type, 22 plasmablastic lymphoma, 3 THRLBCL, 1 PMBL, 2 DLBCL NOS and 1 3B Follicular Lymphoma. After WES median Tumor Mutation Burden was 972. Overall genetic classification by Lymphgen was obtained in 53 cases (49%) with the remaining 56 cases considered unclassified (molecular subtype other/NOS). Of the genetically identified cases, 18 were classified as ST2 (33%), 15 were considered EZB (28%), 5 BN2 (10%) and 3 MCD (5%). Interestingly 12 (22%, overall, 11%) were genetically composite, including 8, ST2/EZB; 1, ST2/BN2; 1, ST2/MCD and 2 ST2/EZB/MCD. According to histopathological subtype 17 GCB type DLBCL were classified as EZB (8, 47%), ST2 (5, 30%) and BN2 (1, 6%). 3 cases (17%) were genetically composite. 6 out of 8 cases of HGBCL/DLBCL DH/NOS were classified as either EZB (2), ST2 (2) or composite EZB/ST2 (2). 19 non-GCB type DLBCL were classified as ST2 (5, 26%), BN2 (4, 21 %), EZB (3, 16 %) and MCD (3, 16%). 4 cases were genetically composite (21%). Interestingly, plasmablastic lymphomas showed ST2 genetic features, found in 5 out of 22 (23%) and composite ST2/EZB in 2 cases. Genes overrepresented in the ST2 cluster include TET2, PRRC2C, STAT3,DOCK8 and CLTC. Plasmablastic Lymphomas classified as other showed recurrent mutations in PRRC2C, PABPC1, TRRAP, PRKCB, EP300 and TP53. Conclusions: ST2 and EZB genetic groups stand as the most frequent genetic subtypes of DLBCL NOS. While ST2 is found in either GCB and non-GCB subtypes, EZB is found preferentially in cases with the GCB phenotype, including GCB type DLBCL and HGBCL/DLBCL DH. In comparison with already published series we found a relatively higher prevalence of the ST2 genetic subtype among DLBCL NOS. MCD and BN2 genetic subtypes are restricted to the non-GCB subtype, but its prevalence is relatively low. Importantly, a significant fraction of cases (~50%) is still genetically unclassified (molecular subtype NOS) using WES and FISH-derived fusion data. Proper identification of the A53 cluster and relevant dysregulated pathways in the molecular NOS group require more research. Specific large B cell lymphoma entities such as plasmablastic lymphoma show features of ST2 genetic subtype in a significant fraction of tumors. These features include JAK/STAT3 pathway activation related genes and fit well with already published data 2,3. In addition, recurrent mutations in TET2, involved in epigenetic regulation of gene expression and TRRAP, target of C-MYC transcriptional activation, were enriched in this molecular subtype of plasmablastic lymphoma. Importantly ~70% remained unclassified. Mutations in this group are recurrently found in PRRC2C as well as PABPC1, both involved in VEGFA-VEGFR2 signaling. In summary, DLBCL NOS molecular subtyping supplies exact molecular classification in ~50% of our cohort. ST2 and EZB subtypes constitute ~60% of the cases with a fraction of ~15% of composite ST2/EZB. Application of the molecular classifier to plasmablastic lymphomas shows intraspecific genetic diversity and points toward dysregulation of JAK/STAT, MAPK/Erk and VEGFA-VEGFR2 signaling as well as MYC transcriptional activation. REFERENCES 1. PMID: 32289277 2. PMID: 33225311 3. PMID: 32273478

Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”

AbstractWildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi‐model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU‐CMAQ, NACC‐CMAQ, and HYSPLIT), global models (GEFS‐Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP‐MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS‐SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.

Elastically accelerating lookup on virtual SDN flow tables for software-defined cloud gateways

In recent years, Software-Defined Networking has been gradually applied to cloud gateways to provide efficient, reliable and flexible data transmission services for various large-scale cloud platforms. However, massive concurrent accessing produces huge network traffic from tenants to cloud platforms, which aggregates at cloud gateways and brings serious performance bottlenecks regarding packet classification. To solve this problem, this paper proposes an elastically accelerated lookup method of virtual SDN flow tables for software-defined cloud gateways. The method caches active exact flows in virtue of network traffic locality, enabling most packets to directly hit the cache and bypass tuple space search, which significantly accelerates flow table lookup. Focusing on network traffic jitters, the cache adaptively adjusts its capacity according to the dynamic changes of the number of active exact flows to maintain high cache hit rates, aiming to achieve elastic acceleration of flow table lookup. Furthermore, we theoretically derive the performance metrics of our proposed method such as cache hit rates, cache yield rates and average search length, based on the Zipf distribution model of network traffic. Eventually, we evaluate the performance of our proposed elastically accelerated lookup method by experiments with real network traffic traces. Experimental results indicate that our proposed method outperforms existing cache-accelerated methods with stable cache hit rates around 80% and the speedup of average search length up to 2.84 even under network traffic jitters.

Mining the Utricularia gibba genome for insulator-like elements for genetic engineering.

Gene expression is often controlled via cis-regulatory elements (CREs) that modulate the production of transcripts. For multi-gene genetic engineering and synthetic biology, precise control of transcription is crucial, both to insulate the transgenes from unwanted native regulation and to prevent readthrough or cross-regulation of transgenes within a multi-gene cassette. To prevent this activity, insulator-like elements, more properly referred to as transcriptional blockers, could be inserted to separate the transgenes so that they are independently regulated. However, only a few validated insulator-like elements are available for plants, and they tend to be larger than ideal. To identify additional potential insulator-like sequences, we conducted a genome-wide analysis of Utricularia gibba (humped bladderwort), one of the smallest known plant genomes, with genes that are naturally close together. The 10 best insulator-like candidates were evaluated in vivo for insulator-like activity. We identified a total of 4,656 intergenic regions with expression profiles suggesting insulator-like activity. Comparisons of these regions across 45 other plant species (representing Monocots, Asterids, and Rosids) show low levels of syntenic conservation of these regions. Genome-wide analysis of unmethylated regions (UMRs) indicates ~87% of the targeted regions are unmethylated; however, interpretation of this is complicated because U. gibba has remarkably low levels of methylation across the genome, so that large UMRs frequently extend over multiple genes and intergenic spaces. We also could not identify any conserved motifs among our selected intergenic regions or shared with existing insulator-like elements for plants. Despite this lack of conservation, however, testing of 10 selected intergenic regions for insulator-like activity found two elements on par with a previously published element (EXOB) while being significantly smaller. Given the small number of insulator-like elements currently available for plants, our results make a significant addition to available tools. The high hit rate (2 out of 10) also implies that more useful sequences are likely present in our selected intergenic regions; additional validation work will be required to identify which will be most useful for plant genetic engineering.

Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent

A common issue faced by the downscaled regional ensemble prediction systems is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread under the initial conditions from the global ensemble. In this study, a novel method that adopts an adaptive approach to selecting global ensemble members for regional downscaling has been developed. Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members, which maximizes a fractions skill score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5 km and is a 12-member ensemble that is driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials were conducted in June 2020 to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error (RMSE) of the ensemble mean in wind, temperature, and precipitation fields. Further verification of the rainfall forecasts showed that there was a reduction in the Brier score and a higher hit rate in the relative operating characteristic (ROC) curve for all rainfall thresholds when adaptive selection was applied. Additionally, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 h, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction.

Towards practical object detection for weed spraying in precision agriculture.

Weeds pose a persistent threat to farmers' yields, but conventional methods for controlling weed populations, like herbicide spraying, pose a risk to the surrounding ecosystems. Precision spraying aims to reduce harms to the surrounding environment by targeting only the weeds rather than spraying theentire field with herbicide. Such an approach requires weeds to first be detected. With the advent of convolutional neural networks, there has been significant research trialing such technologies on datasets of weeds and crops. However, the evaluation of the performance of these approaches has often been limited to the standard machine learning metrics. This paper aims to assess the feasibility of precision spraying via a comprehensive evaluation of weed detection and spraying accuracy using two separate datasets, different image resolutions, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is proposed to compare the performance of different detection algorithms while varying the precision of the spray nozzles. The key performance indicators in precision spraying that this study focuses on are a high weed hit rate and a reduction in herbicide usage. This paper introduces two metrics, namely, weed coverage rate and area sprayed, to capture these aspects of the real-world performance of precision spraying and demonstrates their utility through experimental results. Using these metrics to calculate the spraying performance, it was found that 93% of weeds could be sprayed by spraying just 30% of the area using state-of-the-art vision methods to identify weeds.

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency.

In-depth study of direct and derived acoustic emission parameters and their information content for classifying lubricated tribological sliding contacts

Acoustic signals (AE) have become a valuable tool for monitoring various tribological systems. This study investigates three key tribological behaviors: the initial running-in process, severe wear, and the lubrication state of journal bearings using four AE postprocessing methods. These methods are friction power-scaled quadratic mean (SRMS), power spectrum (PSD), normalized power spectrum (nPSD), and a statistical hit-rate (HR) analysis of the raw AE signal. The research draws on data from four tests conducted in three set-ups: Ring-on-Disc (RoD), Ring-on-Liner (RoL), and Journal-bearing-adapter (JBA) test configurations. AE measurements were taken at both 1 Hz and 1 kHz for the quadratic mean value (RMS), as well as at 900 kHz for short time intervals during each test to capture the raw signal data. In conjunction with other measured parameters like friction coefficient, temperature, contact potential, and wear, we establish correlations between the acoustic parameters and tribological behaviors. The findings indicate that SRMS and nPSD are valuable for identifying the end of the initial running-in phase. All four methods are effective in recognizing high wear states, with nPSD and HR even distinguishing between different wear phenomena. The lubrication state of journal bearings is best assessed using nPSD, allowing users to monitor certain geometric changes in the bearing.

RADAR Echo Recognition of Squall Line Based on Deep Learning

Squall line (SL) is a convective weather process that often causes disasters. The automatic recognition and early warning of SL are important objectives in the field of meteorology. By collecting the new-generation weather RADARs (CINRAD/SA and CINRAD/SAD) base data during 12 SL weather events occurred in Jiangsu, Shanghai, Shandong, Hebei, and other regions of China from 2019 to 2021, the dataset has a total of 49,920 samples with a window size of 40 km. The 40 km area was labeled by employing manual classification and data augmentation to construct the deep learning dataset with a positive and negative sample ratio of 1:1, of which 80% and 20% are separated as the training and test set, respectively. Based on the echo height of each elevation beam at different distances, three deep learning-based models are trained for SL automatic recognition, which include a near-distance model (M1) trained by the data in nine RADAR elevation angles within 45 km from RADARs, a mid-distance model (M2) by the data in six elevations from 45 to 135 km, and a far-distance model (M3) by the data in three elevations from 135 to 230 km. A confusion matrix and its derived metrics including receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) are introduced as the indicators to evaluate the models by the test dataset. The results indicate that the accuracy of models are over 86% with the hit rates over 87%, the false alarm rates less than 21%, and the critical success indexes (CSI) surpass 78%. All the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.95, so the three models have high hit rates and low false alarm rates for ensuring SL discrimination. Finally, the effectiveness of the models is further demonstrated through two SL events detected with Nanjing, Yancheng and Qingpu RADARs.