Search Results (12)

Currently, the customs inspection of wood-boring pests in timber still primarily relies on manual visual inspection, which involves observing insect holes on the timber surface and splitting the timber for confirmation. However, this method has significant drawbacks such as long detection time, high labor cost, and accuracy relying on human experience, making it difficult to meet the practical needs of efficient and intelligent customs quarantine. To address this issue, this paper develops a rapid identification system based on the peristaltic signals of wood-boring pests through the PyQt framework. The system employs a deep learning model with multi-attention mechanisms, namely the Residual Denoising Vision Network (RDVNet). Firstly, a LabVIEW-based hardware–software system is used to collect pest peristaltic signals in an environment free of vibration interference. Subsequently, the original signals are clipped, converted to audio format, and mixed with external noise. Then signal features are extracted through three cepstral feature extraction methods Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and input into the model. In the experimental stage, this paper compares the denoising module of RDVNet (de-RDVNet) with four classic denoising models under five noise intensity conditions. Finally, it evaluates the performance of RDVNet and four other noise reduction classification models in classification tasks. The results show that PNCC has the most comprehensive feature extraction capability. When PNCC is used as the model input, de-RDVNet achieves an average peak signal-to-noise ratio (PSNR) of 29.8 and a Structural Similarity Index Measure (SSIM) of 0.820 in denoising experiments, both being the best among the comparative models. In classification experiments, RDVNet has an average F1 score of 0.878 and an accuracy of 92.8%, demonstrating the most excellent performance. Overall, the application of this system in customs timber quarantine can effectively improve detection efficiency and reduce labor costs and has significant practical value and promotion prospects. Full article

The development of heterogeneous nanocatalysts with high performance is essential for improving hydrogen production through formic acid dehydrogenation, but challenging. Herein, highly dispersed Pd nanoparticles (NPs) were successfully immobilized on porous nitrogen-doped carbon cages (PNCCs) derived from zeolitic imidazole frameworks. By virtue of the synergistic effect, the optimized Pd/PNCC nanocatalytic systems exhibit an excellent catalytic kinetics toward catalyzing FA dehydrogenation with a turnover frequency (TOF) value as high as 3174 h⁻¹ at 323 K, which is 59 times relative to that of Pd nanoparticles. The exceptional activity may be ascribed to the PNCC solid support may induce a strong electronic metal–support interaction to optimize the electron configuration of Pd active sites and accelerate the kinetics of O-H bond cleavage, resulting in an enhanced catalytic performance toward FA dehydrogenation. This work will supply a novel strategy for the development of supported nanocatalysts with high performance for tremendous catalytic applications in the future. Full article

The objective of this work was to investigate the use of nanocrystalline cellulose (NCC) as a drug-delivery excipient for buccal films. Gel-like dispersions were created by blending either gel or powder NCC (gNCC or pNCC) with natural polymers (alginate, pectin, or chitosan) in water, with glycerol serving as a plasticiser. Ibuprofen (IBU) as an active pharmaceutical ingredient (API) was dissolved in a self-microemulsifying drug delivery system (SMEDDS) to improve its solubility prior to its addition to gel-like dispersions. Dispersions were dried, and resulting films were cut to 3 cm × 1.5 cm size, appropriate for buccal delivery. Rheological measurements revealed that shorter, thinner, and less crystalline nanocellulose fibres are more favourable for stronger gel properties. While overall, weaker gel structure prior to film casting also resulted in shorter disintegration time, this was not the case for NCC–chitosan films; here, the low solubility of chitosan in neutral media proved to be the main obstacle. Nevertheless, the prolonged disintegration of NCC–chitosan films did not impact the dissolution of IBU, as these films exhibited the fastest dissolution rate, followed by NCC–pectin and NCC–alginate. Furthermore, NCC properties significantly influenced the dissolution behaviour of the chitosan formulations, with gNCC favouring faster IBU release due to weaker gel formation prior to film casting. Full article

The serine proteases CAP1/Prss8 and CAP3/St14 are identified as ENaC channel-activating proteases in vitro, highly suggesting that they are required for proteolytic activation of ENaC in vivo. The present study tested whether CAP3/St14 is relevant for renal proteolytic ENaC activation and affects ENaC-mediated Na⁺ absorption following Na⁺ deprivation conditions. CAP3/St14 knockout mice exhibit a significant decrease in CAP1/Prss8 protein expression with altered ENaC subunit and decreased pNCC protein abundances but overall maintain sodium balance. RNAscope-based analyses reveal co-expression of CAP3/St14 and CAP1/Prss8 with alpha ENaC in distal tubules of the cortex from wild-type mice. Double CAP1/Prss8; CAP3/St14-deficiency maintained Na⁺ and K⁺ balance on a Na⁺-deprived diet, restored ENaC subunit protein abundances but showed reduced NCC activity under Na⁺ deprivation. Overall, our data clearly show that CAP3/St14 is not required for direct proteolytic activation of ENaC but for its protein abundance. Our study reveals a complex regulation of ENaC by these serine proteases on the expression level rather than on its proteolytic activation. Full article

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%. Full article

Pediatric neurocritical care (PNCC) is a rapidly growing field. Challenges posed by the COVID-19 pandemic on trainee exposure to educational opportunities involving direct patient care led to the creative solutions for virtual education supported by guiding organizations such as the Pediatric Neurocritical Care Research Group (PNCRG). Our objective is to describe the creation of an international, peer-reviewed, online PNCC educational series targeting medical trainees and faculty. More than 1600 members of departments such as pediatrics, pediatric critical care, and child neurology hailing from 75 countries across six continents have participated in this series over a 10-month period. We created an online educational channel in PNCC with over 2500 views to date and over 130 followers. This framework could serve as a roadmap for other institutions and specialties seeking to address the ongoing problems of textbook obsolescence relating to the rapid acceleration in knowledge acquisition, as well as those seeking to create new educational content that offers opportunities for an interactive, global audience. Through the creation of a virtual community of practice, we have created an international forum for pediatric healthcare providers to share and learn specialized expertise and best practices to advance global pediatric health. Full article

In this study, CsPbBr₃@SiO₂ perovskite nanocrystal composites (CsPbBr₃@SiO₂ PNCCs) were synthesized by a benzyl bromide nucleophilic substitution strategy. Homogeneous halide exchange between CsPbBr₃@SiO₂ PNCCs and Cl⁻ solution (aqueous phase) was applied to the determination of Cl⁻ in sea sand samples. Fast halide exchange with Cl⁻ in the aqueous phase without any magnetic stirring or pH regulation resulted in the blue shift of the photoluminescence (PL) wavelength and vivid PL color changes from green to blue. The results show that the PL sensing of Cl⁻ in aqueous samples could be implemented by using the halide exchange of CsPbBr₃@SiO₂ PNCCs. A linear relationship between the PL wavelength shift and the Cl⁻ concentration in the range of 0 to 3.0% was found, which was applied to the determination of Cl⁻ concentration in sea sand samples. This method greatly simplifies the detection process and provides a new idea for further broadening PL sensing using the CsPbBr₃ PNC halide. Full article

Nicotinamide mononucleotide (NMN) is a key intermediate in the nicotinamide adenine dinucleotide (NAD+) biosynthesis. Its supplementation has demonstrated beneficial effects on several diseases. The aim of this study was to characterize NMN deamidase (PncC) inactive mutants to use as possible molecular recognition elements (MREs) for an NMN-specific biosensor. Thermal stability assays and steady-state fluorescence spectroscopy measurements were used to study the binding of NMN and related metabolites (NaMN, Na, Nam, NR, NAD, NADP, and NaAD) to the PncC mutated variants. In particular, the S29A PncC and K61Q PncC variant forms were selected since they still preserve the ability to bind NMN in the micromolar range, but they are not able to catalyze the enzymatic reaction. While S29A PncC shows a similar affinity also for NaMN (the product of the PncC catalyzed reaction), K61Q PncC does not interact significantly with it. Thus, PncC K61Q mutant seems to be a promising candidate to use as specific probe for an NMN biosensor. Full article

The sound-detection method of trunk borer is a very promising method in the field of forestry prevention and control of trunk borers. However, the detection accuracy of commonly used algorithms often decreases sharply in the case of noise reverberation interference. In practical applications, the sound monitoring of trunk borers often takes place in a harsh acoustic environment. To solve this problem, we intend to introduce methods which are effective in other related acoustic fields. Unfortunately, most of the methods are not suitable for acoustic detection of trunk borers and perform extremely poorly. After trying various methods, we found that Power-Normalized Cepstral Coefficients (PNCC) performed well in some cases, while it did not in others. This is due to the difference between speech and trunk borer sound. Therefore, an improved anti-noise PNCC based on wavelet package is proposed. The dmey wavlet system always obtains the best performance. We collected the audio of the following five dry borer pests for testing. They are red palm weevil, mountain pine beetle, red necked longicorn, Asian longhorn beetle and citrus longhorn beetle. In the experimental part, we used genetic algorithm-support vector machine (GA-SVM) as a classifier to compare Mel Cepstral Coefficients (MFCC), which are the most common methods in the field of audio detection of trunk borer, PNCC and improved PNCC in a variety of noise environments. The results showed that, compared with other methods, the newly proposed method can often achieve better results. The above experiments take the audio clips made of clear pest sound mixed noise. In order to further verify the effectiveness of the method, we designed another experiment with a harsh outdoor acoustic environment. We found that the proposed method achieved 88% accuracy and the traditional PNCC achieved 78% accuracy. However, the Mel cepstrum coefficient completely lost its ability to distinguish. In sum, the proposed PNCC based on wavelet packet decomposition can be used as a detection method for trunk borer in the harsh acoustic environment. This method has many advantages, including simple extraction and strong robustness to noise. Combined with cheap audio acquisition equipment, this method can effectively improve the early warning ability of forestry borer pests. Full article

Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation. Full article

Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades in the presence of noisy environments. Developing a robust ASR system that can work in real-world noise and other acoustic distorting conditions is an attractive research topic. Many advanced algorithms have been developed in the literature to deal with this problem; most of these algorithms are based on modeling the behavior of the human auditory system with perceived noisy speech. In this research, the power-normalized cepstral coefficient (PNCC) system is modified to increase robustness against the different types of environmental noises, where a new technique based on gammatone channel filtering combined with channel bias minimization is used to suppress the noise effects. The TIDIGITS database is utilized to evaluate the performance of the proposed system in comparison to the state-of-the-art techniques in the presence of additive white Gaussian noise (AWGN) and seven different types of environmental noises. In this research, one word is recognized from a set containing 11 possibilities only. The experimental results showed that the proposed method provides significant improvements in the recognition accuracy at low signal to noise ratios (SNR). In the case of subway noise at SNR = 5 dB, the proposed method outperforms the mel-frequency cepstral coefficient (MFCC) and relative spectral (RASTA)–perceptual linear predictive (PLP) methods by 55% and 47%, respectively. Moreover, the recognition rate of the proposed method is higher than the gammatone frequency cepstral coefficient (GFCC) and PNCC methods in the case of car noise. It is enhanced by 40% in comparison to the GFCC method at SNR 0dB, while it is improved by 20% in comparison to the PNCC method at SNR −5dB. Full article

The dithiadiazolyl radical p-NCC₆F₄C₆F₄CNSSN^• (4) retains its monomericnature in the solid state with molecules linked together into chains via supramolecularCN···S interactions. Variable temperature magnetic studies on 4 show that it behaves as anear-ideal Curie paramagnet (|θ| less than 0.1 K), indicating negligible intermolecularexchange. The effective magnetic moment (1.78 μ_B) is temperature independent and inexcellent agreement with the value expected for an S = 1⁄2 paramagnet with g = 2.01(1.74μ_B). The lack of exchange coupling between radicals is attributed to the absence ofsignificant orbital overlap between radical centres. Full article