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Accurate identification of the tool wear state during the machining process is of great significance to improve product quality and benefit. The wear states of the same tool type and machining material have similarities during the machining process. By mining the data value of the historical machining process and analyzing the similarity of the procedure, the subsequent machining process can be predicted with the help of transfer learning. Therefore, this study proposes a tool wear prediction scheme based on feature-based transfer learning to realize the accurate prediction of the tool wear state. The genetic algorithm (GA) is used to select a subset of sensor features that are highly correlated with tool wear. Then, the source domain and target domain are constructed on the basis of the selected sensor features of the historical tool and the new tool during the machining process, respectively. In addition, features in the life cycle of the new tool are completed by feature-based transfer learning. After feature transfer, the maximum mean square discrepancy (MMD) method is used to evaluate the similarity of features, and the optimal feature subset is selected according to the evaluation result. Finally, the particle swarm-optimized support vector machine (PSO-SVM) model is applied to predict the tool wear states during the new tool machining. The effectiveness of the proposed tool wear scheme is verified by the cutting force and wear data of the tool life cycle under three different milling parameter combinations. Results with high accuracy show the advantages of the feature-based transfer learning method for tool wear state prediction.

Tool wear is an important consideration for Computerized Numerical Control (CNC) machine tools as it directly affects machining precision. To realize the online recognition of tool wear degree, this research develops a tool wear monitoring system using an indirect measurement method which selects signal characteristics that are strongly correlated with tool wear to recognize tool wear status. The system combines support vector machine (SVM) and genetic algorithm (GA) to establish a nonlinear mapping relationship between a sample of cutting force sensor signal and tool wear level. The cutting force signal is extracted using time domain statistics, frequency domain analysis, and wavelet packet decomposition. GA is employed to select the sensitive features which have a high correlation with tool wear states. SVM is also applied to obtain the state recognition results of tool wear. The gray wolf optimization (GWO) algorithm is used to optimize the SVM parameters and to improve prediction accuracy and reduce internal parameters’ adjustment time. A milling experiment on AISI 1045 steel showed that when comparing with SVM optimized by commonly used optimization algorithms (grid search, particle swarm optimization, and GA), the proposed tool wear monitoring system can accurately reflect the degree of tool wear and achieves strong generalizability. A set of vibration signals are adopted to verify the presented research. Results show that the proposed tool wear monitoring system is robust.

Concerns have been raised in recent years regarding co-selection for antibiotic resistance among bacteria exposed to heavy metals, particularly copper and zinc, used as growth promoters for some livestock species. In this study, 25 IncHI2 plasmids harboring oqxAB (20/25) /bla CTX-M (18/25) were found with sizes ranging from ∼260 to ∼350 kb and 22 belonged to the ST3-IncHI2 group. In addition to bla CTX-M and oqxAB , pcoA - E (5/25) and silE - P (5/25), as well as aac ( 6 ′) -Ib-cr (18/25), floR (16/25), rmtB (6/25), qnrS1 (3/25) and fosA3 (2/25), were also identified on these IncHI2 plasmids. The plasmids carried pco and sil contributed to increasing in the MICs of CuSO 4 and AgNO 3 . The genetic context surrounding the two operons was well conserved except some variations within the pco operon. The ~32 kb region containing the two operons identified in the IncHI2 plasmids was also found in chromosomes of different Enterobacteriaceae species. Further, phylogenetic analysis of this structure showed that Tn7-like transposon might play an important role in cross-genus transfer of the sil and pco operons among Enterobacteriaceae. In conclusion, co-existence of the pco and sil operons, and oqxAB/bla CTX-M as well as other antibiotic resistance genes on IncHI2 plasmids may promote the development of multidrug-resistant bacteria.

Flood control briefings are critical emergency response documents that provide timely decision support for urban safety and regional development under climate change challenges. However, existing large language models (LLMs) face significant difficulties in domain-specific adaptation, content controllability, and logical consistency when processing complex water conservancy data. This study aims to develop a robust automated text generation method that ensures high accuracy and logical rigor for flood prevention in the Lixiahe region. We propose an IP-CoT method that integrates Chain-of-Thought (CoT) reasoning for structured information extraction and an Inverse Prompting (IP) mechanism with beam search to optimize content relevance using the DeepSeek-R1 model. Validated on a constructed dataset comprising flood control records from the Lixia River network from 2010 to 2024, the proposed method achieved an accuracy rate of 95.32% in the verification of emotional attributes, which is 2% to 15% higher than most traditional models. Additionally, in the verification of thematic attributes, fluency and diversity were improved, showing significant enhancements compared to the baseline model. This approach significantly enhances the quality and efficiency of domain-specific text generation, providing a reliable intelligent solution for modernizing regional flood control decision-making systems.

Background One of the goals of livestock genomics research is to identify the genetic differences responsible for variation in phenotypic traits, particularly those of economic importance. Characterizing the genetic variation in livestock species is an important step towards linking genes or genomic regions with phenotypes. The completion of the bovine genome sequence and recent advances in DNA sequencing technology allow for in-depth characterization of the genetic variations present in cattle. Here we describe the whole-genome resequencing of two Bos taurus bulls from distinct breeds for the purpose of identifying and annotating novel forms of genetic variation in cattle. Results The genomes of a Black Angus bull and a Holstein bull were sequenced to 22-fold and 19-fold coverage, respectively, using the ABI SOLiD system. Comparisons of the sequences with the Btau4.0 reference assembly yielded 7 million single nucleotide polymorphisms (SNPs), 24% of which were identified in both animals. Of the total SNPs found in Holstein, Black Angus, and in both animals, 81%, 81%, and 75% respectively are novel. In-depth annotations of the data identified more than 16 thousand distinct non-synonymous SNPs (85% novel) between the two datasets. Alignments between the SNP-altered proteins and orthologues from numerous species indicate that many of the SNPs alter well-conserved amino acids. Several SNPs predicted to create or remove stop codons were also found. A comparison between the sequencing SNPs and genotyping results from the BovineHD high-density genotyping chip indicates a detection rate of 91% for homozygous SNPs and 81% for heterozygous SNPs. The false positive rate is estimated to be about 2% for both the Black Angus and Holstein SNP sets, based on follow-up genotyping of 422 and 427 SNPs, respectively. Comparisons of read depth between the two bulls along the reference assembly identified 790 putative copy-number variations (CNVs). Ten randomly selected CNVs, five genic and five non-genic, were successfully validated using quantitative real-time PCR. The CNVs are enriched for immune system genes and include genes that may contribute to lactation capacity. The majority of the CNVs (69%) were detected as regions with higher abundance in the Holstein bull. Conclusions Substantial genetic differences exist between the Black Angus and Holstein animals sequenced in this work and the Hereford reference sequence, and some of this variation is predicted to affect evolutionarily conserved amino acids or gene copy number. The deeply annotated SNPs and CNVs identified in this resequencing study can serve as useful genetic tools, and as candidates in searches for phenotype-altering DNA differences.

Enhancing the soluble expression of heterologous proteins in chassis microorganisms is critical for fundamental biological research and synthetic biology-driven industrial applications. Current methods for designing DNA sequences to ensure high soluble expression often rely excessively on high-frequency codons while overlooking optimal codon context, leading to suboptimal outcomes. To address these limitations, we developed an integrated deep learning framework combining a synonymous codon generation (SCG) model and a gene expression level prediction (GELP) model. The SCG model captures codon usage patterns in using large-scale genomic data, whereas the GELP model leverages gene expression data to prioritize sequences with high soluble expression potential. We validated our approach by optimizing the DNA sequences of two industrial enzymes, α-glucan phosphorylase (αGP) and isoamylase (IA), achieving significant and reproducible improvements in soluble expression (mean 12.2-16.9-fold, = 3 and 2.6-3.4-fold, = 4), confirmed by one-way ANOVA and one-sample -tests. This study provides a useful tool for designing DNA sequences that confer high soluble expression and for understanding the relationship between DNA sequence and protein expression. Notably, SCG-GELP reveals a core-avoiding codon optimization strategy that substantially enhances soluble protein yield.

To achieve radio frequency (RF) power detection, gain control, and circuit protection, this paper presents n+ GaAs/AuGeNi-Au thermocouple-type RF microelectromechanical system (MEMS) power sensors based on dual thermal flow paths. The sensors utilize a conversion principle of RF power-heat-voltage, where a thermovoltage is obtained as the RF power changes. To improve the heat transfer efficiency and the sensitivity, structures of two heat conduction paths are designed: one in which a thermal slug of Au is placed between two load resistors and hot junctions of the thermocouples, and one in which a back cavity is fabricated by the MEMS technology to form a substrate membrane underneath the resistors and the hot junctions. The improved sensors were fabricated by a GaAs monolithic microwave integrated circuit (MMIC) process. Experiments show that these sensors have reflection losses of less than −17 dB up to 12 GHz. At 1, 5, and 10 GHz, measured sensitivities are about 63.45, 53.97, and 44.14 µV/mW for the sensor with the thermal slug, and about 111.03, 94.79, and 79.04 µV/mW for the sensor with the thermal slug and the back cavity, respectively.

There has been a substantial gap between drying antibiotic pipeline and ongoing antibiotic resistance crisis, necessitating approaches to revitalize existing antimicrobials to meet unmet clinical demand for viable treatments. Herein, a lignan compound, magnolol, was identified that profoundly potentiates colistin (CS) to eradicate Gram negative bacteria and curb the development of resistance under host-mimicking condition. The mechanistic study showed that magnolol is able to disrupt PmrA/B two component signaling by dissociating the PmrA regulator protein from its cognate DNA including eptA and arnT . This action blocks the PmrA/B-dependent protective modifications of lipopolysaccharide (LPS) to reduce the net charges of bacterial membrane, thereaby facilitating its electrostatic interaction with CS. MAG-facilitated enhancement of CS binding promotes the formation of toroidal pores in the bacterial membrane, which in turn triggers rapid bacterial death by inducing lethal cytoplasmic contents leakage. In sum, this work not only illustrates the great potential of untapped phytoconstitutes such as magnolol in confronting antibiotic resistance but also reveals that silencing PmrA/B signaling as a favorable strategy to potentiate CS activity in vivo .

It is significant to ensure stable machining quality that accurately monitors the tool wear. Sensor signals are mainly used to predict tool wear. Therefore, complete sensor signals are crucial for tool wear prediction. To obtain comprehensive information on the process for improving prediction accuracy and ensuring the effectiveness of the prediction model when sensor data are missing, this paper proposes a tool wear prediction scheme through prioritization of sensor combinations under missing data. In this prediction scheme, the optimal feature subset of the sensor combination is obtained using kernel principal component analysis (KPCA) optimized by maximum information coefficient (MIC); that is, the MIC is employed to select the gamma value of the KPCA. Meanwhile, random forests are utilized to determine the prioritization of sensor combinations based on the obtained optimal feature subset. With the occurrence of signal loss, prediction errors exist. Thus, the sensor combination that replaces the sensor with missing data is selected according to the determined priority order. The effectiveness of the proposed scheme is verified through sensor combinations based on the cutting force, vibration, and acoustic emission during the milling process of TC18.

The present work studies the effect of three variables (spindle speed, feed rate, and depth of cut) towards surface roughness by adopting orthogonal design and surrogate model. Experiment in hard turning of AISI 1045 steel with YT5 tool were carried out. The analysis of variance (ANOVA) and the regression model suggest that the feed rate has great effect on the surface roughness compared to the other two variables. The contour plot and the surface plot based on the regression model show the correlation between the response (surface roughness) and all possible pairwise combinations of the three variables. In order to get the desired surface roughness, the optimum cutting parameters are obtained by developing an optimization method.