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BackgroundIn China, Atractylodes-containing Chinese medicines are widely used as adjuvant therapy to neoadjuvant chemotherapy (NAC) in individuals diagnosed with advanced gastric cancer (AGC). Nevertheless, the findings concerning its effectiveness are still restricted. The aim from this research was to examine the efficiency and security Atractylodes macrocephala-containing traditional Chinese medicine together with NAC in the management of AGC.MethodsLiterature was systematically searched across 8 electronic databases until September 20, 2023. Two researchers conducted a thorough review of the selected studies. The primary outcome measures included the objective response rate (ORR), disease control rate (DCR), quality of life (QOL), adverse drug reactions (ADRs), and levels of peripheral blood lymphocytes. The relevant effect estimates are as follows as risk ratios (RR) or mean differences (MD) with corresponding 95% confidence intervals (CI). Credibility of information was evaluated using the GRADE analyzer.ResultsThe results showed that solely on the basis of the accessible literature examined in NAC patients, individuals who received the therapeutic regimen containing Atractylodis Macrocephalae Chinese herbal preparations demonstrated a superior overall response rate (Relative Risk: 1.41, 95% confidence interval: 1.27-1.57, P < 0.001); DCR (RR: 1.20, 95% confidence interval: 1.13-1.27, P < 0.001), as compared to QOL (RR: 1.43, 95% confidence interval: 1.30-1.57, P < 0.001, MD: 8.47, 95% confidence interval: 7.16 - 9.77, P < 0.001); the proportions of CD3+ T-cells, CD4+ T-cells, CD8+ T-cells, CD4+CD8+ T-cells were increased; and the incidence of adverse reactions was decreased. Subgroup analyses showed that oral administration of all the traditional Chinese medicines containing Atractylodes macrocephala could improve tumor efficacy. Regardless of the duration of therapy of ≥8 weeks or

BackgroundDue to the rising standard of living and advances in public health and medical care in recent years in China, the health-related quality of life (HRQoL) has been increasingly acknowledged as an important part of health management of adults. This study aimed to analyze the HRQoL of Chinese adults and identify the influencing factors, proposing specific recommendations for improvement.MethodsA cross-sectional study was conducted among 1,291 selected adults from four provinces spanning different regions in China aged ≥18 years from July 2021 and January 2022. The EuroQol-5D-5L (EQ-5D-5L) was used to conduct the HRQoL survey, and a general questionnaire was administered to collect demographic characteristics, general information, and health behaviors of participants. The health utility value was measured, and one-way analysis of variance was performed. The Tobit regression model was employed to analyze the factors influencing the HRQoL of Chinese adults.ResultsThe mean health utility values and visual analog scale scores for adults were 0.9400 ± 0.1197 and 84.09 ± 14.392, respectively. Notably, 60.3% of respondents reported no difficulties in any of the five dimensions of EQ-5D. However, a substantial proportion faced challenges in anxiety/depression (27%) and pain/discomfort (26.2%). Tobit regression model revealed that age, marital status, educational level, diet, sleep, mental state, mood, and chronic diseases significantly impact the HRQoL of Chinese adults.ConclusionThe HRQoL among Chinese adults is generally satisfactory, but pay particular attention on areas such as pain, psychological anxiety, chronic diseases, and negative emotions is needed. The factors such as stress associated with marriage and the demands of high-skilled occupations might influence the overall health of the population. According to our findings, public health strategies to improve HRQoL should be developed to promote relatively healthy environments and lifestyles for older adults. Moreover, proactive measures are crucial for mitigating the potential health impacts associated with marital stress and high-skilled employment.

A combination of morphological traits and DNA data (COI and 28S rRNA partial sequences) was used to study the genus Mongoloniscus Verhoeff, 1930 from China. Four new species are described: M. crenatus Jiang, Li & Huang, sp. nov., M. orientalis Jiang, Li & Huang, sp. nov., M. polyacanthum Jiang, Li & Huang, sp. nov., and M. parvus Jiang, Li & Huang, sp. nov. Following an in-depth examination of the Mongoloniscus species, Lucasioides vannamei (Arcangeli, 1927), comb. nov. (from Mongoloniscus) is proposed, and M. chevronus Yang & An, 2021, syn. nov. is synonymized with Koreoniscus racovitzai (Arcangeli, 1927). A restrictive criterion for recognizing the genus Mongoloniscus is also provided in the present study.

In this paper, we consider a fractional Nernst-Planck-Poisson-Navier-Stokes system in $ \mathbb{R}^{3} $. First, we obtain a priori estimates by using energy estimates. Then, we construct an iterative solution sequence by solving the approximate problem and obtaining the local existence and uniqueness of the classical solution. Finally, combining the local existence with a priori estimates, the global existence and uniqueness of the classical solution with small initial data are obtained.

Abstract Aims Relationship between body mass index (BMI), frailty, and clinical adverse events remains unclear in patients with heart failure (HF) with preserved ejection fraction (HFpEF) in different patient populations. We aimed to compare the association of BMI, frailty, and clinical adverse events between a US cohort from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) study and a Chinese cohort from the Heart Failure Registry of Patient Outcomes (HERO) study. Methods and results We used data of 1715 participants enrolled from America in the TOPCAT study and 1487 patients with HFpEF in the Chinese registry study, the HERO. We evaluated the relationship between BMI and frailty using multivariate restricted cubic spline logistic regression. Association between frailty and BMI categories and primary outcomes including HF hospitalization, aborted sudden death, and cardiovascular death, all‐cause mortality, and HF hospitalization were analysed by Cox proportional hazards models. The patients' mean age was 72 ± 11 years for both study populations, with 50% and 46% female for the TOPCAT study and the HERO study, respectively. Patients in the TOPCAT study had a higher mean BMI (33.9 vs. 24 kg/m2), with 72.3% vs. 52.9% defined as moderately to severely frail (frailty index > 0.3). In the TOPCAT study, risk of frailty rose as BMI increased, but not in the HERO study. Patients with frailty were at significant higher risk for the primary composite outcomes [hazard ratio (HR) 1.84 (95% confidence interval: 1.46–2.32)], all‐cause mortality [HR 1.73 (1.34–2.25)], and HF hospitalization [HR 1.83 (1.40–2.40)] in the TOPCAT study. The corresponding numbers in the HERO study were 1.26 (1.01–1.57), 2.21 (1.45–3.35), and 1.15 (0.81–1.37), respectively. The association of frailty with clinical outcomes did not vary with BMI categories in the two studies. Conclusions BMI distribution and association between BMI and frailty risk were different between the two study populations. Frailty was associated with clinical adverse events and this association was consistent across different BMI categories in both studies.

Exalloniscus Stebbing, 1911 is investigated from China, and eleven species of the genus are now recorded from China. Five of them are described as new: E. duospinatus Li & Jiang, sp. nov., E. curvispinatus Li & Jiang, sp. nov., E. triangulus Li & Jiang, sp. nov., E. tridentatus Li & Jiang, sp. nov. and E. taitii Li & Jiang, sp. nov. A map of China showing the recorded localities of Exalloniscus members is provided.

Abstract Since the impressive superior performance demonstrated by deep learning methods is widely used in histopathological image analysis and diagnosis, existing work cannot fully extract the information in the breast cancer images due to the limited high resolution of histopathological images. In this study, we construct a novel intermediate layer structure that fully extracts feature information and name it DMBANet, which can extract as much feature information as possible from the input image by up-dimensioning the intermediate convolutional layers to improve the performance of the network. Furthermore, we employ the depth-separable convolution method on the Spindle Structure by decoupling the intermediate convolutional layers and convolving them separately, to significantly reduce the number of parameters and computation of the Spindle Structure and improve the overall network operation speed. We also design the Spindle Structure as a multi-branch model and add different attention mechanisms to different branches. Spindle Structure can effectively improve the performance of the network, the branches with added attention can extract richer and more focused feature information, and the branch with residual connections can minimize the degradation phenomenon in our network and speed up network optimization. The comprehensive experiment shows the superior performance of DMBANet compared to the state-of-the-art method, achieving about 98% classification accuracy, which is better than existing methods. The code is available at https://github.com/Nagi-Dr/DMBANet-main .

The structures and chemical bond evolution of ditantalum doped carbon clusters Ta2Cn−/0 (n = 1–7) were studied via size-selected anion photoelectron spectroscopy and theoretical calculations. It is found that Ta2C−/0 has a triangular structure and Ta2C2−/0 has a quasi-rhombus structure with C2v symmetry. Ta2C3− has a quasi-planar structure with a carbon atom and a C2 unit interacting with two tantalum atoms, and the lowest-energy isomer of neutral Ta2C3 has a triangular bipyramid structure with three carbon atoms around the Ta2 unit. Ta2C4−/0 has two C2 units connected with the Ta2 unit in parallel. Two isomers of Ta2C5− are observed, where both isomers have one carbon atom and two C2 units bound to the Ta2 unit in different ways. The most stable structure of neutral Ta2C5 has one carbon atom added on top of the Ta2C4 cluster. The most stable structures of Ta2C6-7−/0 can be viewed as a C2 unit and a C3 unit capping a butterfly like Ta2C4 structure, respectively. Molecular orbital analysis shows that neutral Ta2C3 has a large gap between its highest occupied molecular orbital and lowest unoccupied molecular orbital. Chemical bonding analysis reveals that the Ta–Ta interactions in Ta2Cn−/0 (n = 1–7) clusters are slightly weaker than the Ta–Ta interaction in bare Ta2 due to the participation in forming multicenter bonds. [ABSTRACT FROM AUTHOR]

This review offered a comprehensive analysis of tendon and ligament injuries, emphasizing the crucial role of tendon-derived stem cells (TDSCs) in tissue engineering as a potential solution for these challenging medical conditions. Tendon and ligament injuries, prevalent among athletes, the elderly, and laborers, often result in long-term disability and reduced quality of life due to the poor intrinsic healing capacity of these avascular structures. The formation of biomechanically inferior scar tissue and a high rate of reinjury underscore the need for innovative approaches to enhance and guide the regenerative process. This review delved into the complexities of tendon and ligament structure and function, types of injuries and their impacts, and the limitations of the natural repair process. It particularly focused on the role of TDSCs within the context of tissue engineering. TDSCs, with their ability to differentiate into tenocytes, are explored in various applications, including biocompatible scaffolds for cell tracking, co-culture systems to optimize tendon-bone healing, and graft healing techniques. The review also addressed the challenges of immunoreactivity post-transplantation, the importance of pre-treating TDSCs, and the potential of hydrogels and decellularized matrices in supporting tendon regeneration. It concluded by highlighting the essential roles of mechanical and molecular stimuli in TDSC differentiation and the current challenges in the field, paving the way for future research directions.

This study explored 1-year follow-up of Parmaco-invasive strategy with half-dose recombinant human prourokinase (PHDP) in patients with acute ST-segment elevation myocardial infarction (STEMI). The follow-up endpoints were major adverse cardiovascular events (MACEs) occurring within 30 days and 1 year, as well as postoperative bleeding events. The study ultimately included 150 subjects, with 75 in the primary percutaneous coronary intervention (PPCI) group and 75 in the PHDP group. This study found that the PHDP group had a shorter FMC-reperfusion time (42.00 min vs 96.00 min, P 0.05). The PHDP facilitates early treatment of infarct-related vessels, shortens FMC-reperfusion time, and does not increase the risk of MACEs.

Tigecycline is a last-resort drug used to treat serious infections caused by multidrug-resistant bacteria. tet(X4) is a recently discovered plasmid-mediated tigecycline resistance gene that confers high-level resistance to tigecycline and other tetracyclines. Since the first discovery of tet(X4) in 2019, it has spread rapidly worldwide, and as a consequence, tigecycline has become increasingly ineffective in the clinical treatment of multidrug-resistant infections. In this study, we identified and analyzed tet(X4)-positive Escherichia coli isolates from duck farms in Hunan Province, China. In total, 976 samples were collected from nine duck farms. Antimicrobial susceptibility testing and whole-genome sequencing (WGS) were performed to establish the phenotypes and genotypes of tet(X4)-positive isolates. In addition, the genomic characteristics and transferability of tet(X4) were determined based on bioinformatics analysis and conjugation. We accordingly detected an E. coli strain harboring tet(X4) and seven other resistance genes in duck feces. Multi-locus sequence typing analysis revealed that this isolate belonged to a new clone, and subsequent genetic analysis indicated that tet(X4) was carried in a 4608-bp circular intermediate, flanked by ISVsa3-ORF2-abh elements. Moreover, it exhibited transferability to E. coli C600 with a frequency of 10-5. The detection of tet(X4)-harboring E, coli strains on duck farms enhances our understanding of tigecycline resistance dynamics. The transferable nature of the circular intermediate of tet(X4) contributing to the spread of tigecycline resistance genes poses a substantial threat to healthcare. Consequently, vigilant monitoring and proactive measures are necessary to prevent their spread.

Bacterial exonuclease III (ExoIII), widely acknowledged for specifically targeting double-stranded DNA (dsDNA), has been documented as a DNA repair-associated nuclease with apurinic/apyrimidinic (AP)-endonuclease and 3′→5′ exonuclease activities. Due to these enzymatic properties, ExoIII has been broadly applied in molecular biosensors. Here, we demonstrate that ExoIII (Escherichia coli) possesses highly active enzymatic activities on ssDNA. By using a range of ssDNA fluorescence-quenching reporters and fluorophore-labeled probes coupled with mass spectrometry analysis, we found ExoIII cleaved the ssDNA at 5′-bond of phosphodiester from 3′ to 5′ end by both exonuclease and endonuclease activities. Additional point mutation analysis identified the critical residues for the ssDNase action of ExoIII and suggested the activity shared the same active center with the dsDNA-targeted activities of ExoIII. Notably, ExoIII could also digest the dsDNA structures containing 3′-end ssDNA. Considering most ExoIII-assisted molecular biosensors require the involvement of single-stranded DNA (ssDNA) or nucleic acid aptamer containing ssDNA, the activity will lead to low efficiency or false positive outcome. Our study revealed the multi-enzymatic activity and the underlying molecular mechanism of ExoIII on ssDNA, illuminating novel insights for understanding its biological roles in DNA repair and the rational design of ExoIII-ssDNA involved diagnostics.

Modern power systems are gradually evolving into low-inertia systems due to the integration of high penetration of renewable energy and power electronic equipment. For the natural inertia on the rotor, doubly-fed induction generator (DFIG) based wind farms are expected to provide inertia support for systems. The paper proposed an analytical model of DFIG with df/dt inertia control for studying frequency dynamics, and investigates the optimal design of virtual inertia from wind farms, as well as its equivalent inertia contribution to system. The inertia response differences between DFIG and synchronous generator (SG) are discussed and analyzed for the first time, and then to accurately describe the frequency response with low cost, an analytical model of DFIG with inertia control is developed considering wind turbine aerodynamic characteristics. The explicit expression of virtual inertia provided by DFIG is derived according to the kinetic energy equilibrium, and the key factors that dominate the magnitude of virtual inertia are illustrated. Based on the system frequency response model including wind farms, taking the system frequency nadir (SFN) and the time of reaching nadir (ToN) as targets, a multi-objective optimal configuration model of virtual inertia is constructed and solved through MOPSO. The time domain simulations of a generic two machine four bus system and modified IEEE 9-bus test system are both performed in MATLAB/Simulink, the accuracy and efficiency of our proposed DFIG model on system frequency dynamics research are demonstrated, the pareto frontiers of virtual inertia configuration under different working conditions are optimized and analyzed as well as the impacts of different parameters on inertia evaluation.

Considering the granularity of embedded data in the design of reversible data hiding scheme has important research significance for the permission control and management of multi-granularity information. To broaden the application possibilities of encrypted data in cloud environments, the researchers propose a fine-grained reversible data hiding method leveraging the cipher-text redundancy of ElGamal encryption. Initially, prior to the encryption process, pixels are organized into a full binary tree based on fine-grained access permissions. Subsequently, a chaotic sequence generator is employed to assign distinct embedding keys to each layer of the full binary tree according to the access permissions. Following this, an XOR operation is conducted between the embedding key and the corresponding secret information in each layer to derive the target features of the cipher-text, facilitating subsequent fine-grained data hiding. Throughout the ElGamal encryption process, iterative manipulation of the random variable ensures alignment between the cipher-text output and the target feature, enabling the embedding of secret information across different layers. This approach facilitates the fine-grained blind extraction of secret information from an encrypted state, thereby expanding the potential applications of cipher-text by extracting information without revealing the original data. Furthermore, the scheme enhances information security through distributed storage and conceals the presence of information hiding by leveraging the separability of lossless decryption and information extraction. Simulation results demonstrate that secret information of three granularities can be embedded and extracted without interference within a three-layer full binary structure, with a maximum embedding capacity of up to 1.75 bpp.

Abstract This study modeled the habitat distribution of Pterocarpus santalinus, a valuable rosewood species, across China under current and future climate scenarios (SSPs126, SSPs245, and SSPs585) using MaxEnt. Our findings reveal that the current suitable habitat, spanning approximately 409,600 km2, is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, and Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan–Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for P. santalinus is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of P. santalinus in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.

Abstract Background Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. Methods ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. Results 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P

Abstract Background One noteworthy concern within the realm of education is the level of engagement demonstrated by students. Among the factor that can have a crucial role in this domain is teacher support, especially emotional support which has an impact on several aspects of learners’ education. Furthermore, various studies have investigated the relationship between Emotional Intelligence (EI) and learners’ engagement. Methods Accordingly, this study investigated the possible role of trait EI and the emotional support of teachers and how these constructs may work to associate learners’ engagement. For this objective, a total of 309 Chinese students across different colleges and universities in 5 provinces of Beijing, Shanghai, Jiangsu, Hubei, and Shaanxi were enrolled. They were 126 females and 183 males, with ages ranging from 18 to 30 years old (Mean = 24.6). Results The results of this research through running Structural Equation Modeling (SEM) demonstrated that teachers’ emotional support and trait EI both can associate students’ learning engagement. The final measurement model shows that about 73% of changes in learners’ engagement can be associated by their trait EI and teachers’ emotional support. Conclusions This study underscores the importance of emotional support from teachers and the trait of EI in relation to students’ engagement in learning. Both factors were shown to play a significant role in associating student engagement. Moreover, this study could potentially have wider impacts on members of academic teams.

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO2) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

Partial discharge (PD) is the main cause of insulation breakdown in high voltage electrical equipment, which poses a potential threat to the safety and reliability of equipment. Traditional PD detection methods have limitations in liquid insulation systems. In this paper, an interferometric method for detecting PD in liquid is proposed. Through the experimental platform based on the Michelson interference method, the PD in the water sample is excited by the voltage applied by the lightning surge generator, and the interference fringes distorted are collected by the CCD camera. The experimental results show that the peak value of the phase distribution recovered from interferogram increases when the voltage increases. In addition, when the liquid contains particles that degrade the insulation properties, the effect on the insulation properties can be judged according to the interferometry. This study verifies the feasibility and effectiveness of PD detection in liquid based on Michelson interferometry, and provides a new idea for condition monitoring and fault diagnosis of liquid insulation system.

Accurate and fast classification of FDEs is crucial to power systems situation awareness and stability control. However, various database used in recent studies makes it hard to directly compare different classification methods. To relieve the lack of a standardized database that can be used as a benchmark, this work proposes an open-source package which generates frequency events based on PowerFactory named PF-FEDG. PF-FEDG can produce various labeled FDEs with random parameters such as generator trips, load disconnections, line outages, frequency ramp ups, frequency ramp downs, and frequency oscillations. Furthermore, the package includes three reference FDEs classifiers based on deep learning techniques. These models are tested on the IEEE 39-bus system and IEEE 118-bus system and achieve high performance. The package generating capability and the reference classifiers can be used by the community as benchmarks for comparison and development of new algorithms for FDEs detection. The code of PF-FEDG is available on GitLab.

Dobinin K is a novel eudesmane sesquiterpenoids compound isolated from the root of Dobinea delavayi and displays potential antiplasmodial activity in vivo. Here, we evaluate the antiplasmodial activity of dobinin K in vitro and study its acting mechanism. The antiplasmodial activity of dobinin K in vitro was evaluated by concentration-, time-dependent, and stage-specific parasite inhibition assay. The potential target of dobinin K on Plasmodium falciparum was predicted by transcriptome analysis. Apoptosis of P. falciparum was detected by Giemsa, Hoechst 33258, and TUNEL staining assay. The reactive oxygen species (ROS) level, oxygen consumption, and mitochondrial membrane potential of P. falciparum were assessed by DCFH-DA, R01, and JC-1 fluorescent dye, respectively. The effect of dobinin K on the mitochondrial electron transport chain (ETC) was investigated by enzyme activity analysis and the binding abilities of dobinin K with different enzymes were learned by molecular docking. Dobinin K inhibited the growth of P. falciparum in a concentration-, time-dependent, and stage-specific manner. The predicted mechanism of dobinin K was related to the redox system of P. falciparum. Dobinin K increased intracellular ROS levels of P. falciparum and induced their apoptosis. After dobinin K treatment, P. falciparum mitochondria lost their function, which was presented as decreased oxygen consumption and depolarization of the membrane potential. Among five dehydrogenases in P. falciparum ETC, dobinin K displayed the best inhibitory power on NDH2 activity. Our findings indicate that the antiplasmodial effect of dobinin K in vitro is mediated by the enhancement of the ROS level in P. falciparum and the disruption of its mitochondrial function.

With the deepening of national efforts toward green energy transformation, the power system is evolving into one characterized by “double high”—a high proportion of new energy integration and a high level of power electronic systems. This results in a more complex system topology, necessitating improvements in various prevention and control measures. Traditional model-based methods for locating power oscillation disturbance sources in power systems are no longer sufficient to meet the operational demands of modern power systems. With the rapid development of wide-area measurement systems (WAMS), there is growing interest in disturbance source localization using system response data. System dynamics provide a wealth of easily extractable data that can accurately reflect the power system’s behavior under normal conditions. This paper proposes a numerical method for locating disturbance sources, combining energy functions with normal distribution identification, based on power oscillation mechanisms and system response data. The method identifies potential disturbance sources, including small random load fluctuations and large forced power oscillations. The innovation lies in the introduction of a 3 Sigma value criterion to pinpoint the disturbance source location, addressing the limitations of traditional energy function methods that require manual intervention. By quantifying the localization of power oscillation disturbance sources, this method significantly improves both efficiency and accuracy.

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.