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In recent years, blueberry root rot has been caused mainly by Fusarium commune, and there is an urgent need for a green and efficient method to control this disease. To date, research on Schizophyllum commune has focused on antioxidant mechanisms, reactive dye degradation, etc., but the mechanism underlying the inhibition of pathogenic microorganisms is still unclear. Here, the control effects of S. commune on F. commune and blueberry root rot were studied using adversarial culture, tissue culture, and greenhouse pot experiments. The results showed that S. commune can dissolve insoluble phosphorus and secrete various extracellular hydrolases. The results of hyphal confrontation and fermentation broth antagonism experiments showed that S. commune had a significant inhibitory effect on F. commune, with inhibition rates of 70.30% and 22.86%, respectively. Microscopy results showed distortion of F. commune hyphae, indicating that S. commune is strongly parasitic. S. commune had a significant growth-promoting effect on blueberry tissue-cultured seedlings. After inoculation with S. commune, inoculation with the pathogenic fungus, or inoculation at a later time, the strain significantly reduced the root rot disease index in the potted blueberry seedlings, with relative control effects of 79.14% and 62.57%, respectively. In addition, S. commune G18 significantly increased the antioxidant enzyme contents in the aboveground and underground parts of potted blueberry seedlings. We can conclude that S. commune is a potential biocontrol agent that can be used to effectively control blueberry root rot caused by F. commune in the field. [ABSTRACT FROM AUTHOR]

Objectives: To assess the performance bias caused by sampling data into training and test sets in a mammography radiomics study. Methods: Mammograms from 700 women were used to study upstaging of ductal carcinoma in situ. The dataset was repeatedly shuffled and split into training (n = 400) and test cases (n = 300) forty times. For each split, cross-validation was used for training, followed by an assessment of the test set. Logistic regression with regularization and support vector machine were used as the machine learning classifiers. For each split and classifier type, multiple models were created based on radiomics and/or clinical features. Results: Area under the curve (AUC) performances varied considerably across the different data splits (e.g., radiomics regression model: train 0.58–0.70, test 0.59–0.73). Performances for regression models showed a tradeoff where better training led to worse testing and vice versa. Cross-validation over all cases reduced this variability, but required samples of 500+ cases to yield representative estimates of performance. Conclusions: In medical imaging, clinical datasets are often limited to relatively small size. Models built from different training sets may not be representative of the whole dataset. Depending on the selected data split and model, performance bias could lead to inappropriate conclusions that might influence the clinical significance of the findings. Advances in knowledge: Performance bias can result from model testing when using limited datasets. Optimal strategies for test set selection should be developed to ensure study conclusions are appropriate. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) technology has made remarkable strides in the music industry, profoundly impacting various aspects of our lives. This progress has extended to revolutionizing industries, streamlining processes, and enhancing personalization in music. AI's expanding capabilities have driven computer vision, natural language processing, and machine learning breakthroughs. As music enthusiasts increasingly turn to online platforms, the need for effective recommendation systems has become more pressing. The exponential growth of musical content poses a challenge in discovering music tailored to individual tastes. This paper proposed a personalized music recommendation system powered by deep learning, explicitly utilizing convolutional neural networks (CNNs) to address the challenges posed by expanding music libraries and diverse song resources. In today's era of ever-growing music databases and increasing content diversity, more than traditional retrieval methods are needed to efficiently connect users with their preferred track. Traditional music platforms rely solely on retrieval mechanisms, necessitating manual inputs such as artist names and titles to locate specific songs. However, this approach needs in-depth music data analysis and delivers personalized recommendations generated by various deep learning-based algorithms. This paper bridges this gap by harnessing deep learning algorithms for comprehensive music data analysis and personalized song suggestions. The proposed CNN-based recommendation system explores the domains of deep learning and content-based approaches, empowering users to discover their favorite tunes swiftly. This innovative mechanism leverages CNNs and deep learning algorithms to offer efficient, personalized, and data-driven music recommendations, transforming the user experience with their music collections. In experimental results, the proposed CNN model is compared with various deep learning algorithms, including DNN, RNN, and LSTM, as well as traditional algorithms like SVM, KNN, RF, and DT. Impressively, the CNN model achieved the highest accuracy at 95%, surpassing all other algorithms. [ABSTRACT FROM AUTHOR]

China's social credit system can be examined as a governance tool which sorts citizenship behaviors into trustworthy and untrustworthy categories as part of the regime's long‐standing effort to cultivate a loyal citizenry. Based on a data set comprised of central‐level official documents, national model citizen lists, and media reports, this study qualitatively examines how the Chinese state constructs "good" and "bad" citizen ideal types. Contrary to media depictions of the system as digital totalitarianism, political behaviors are not the sole criterion for sorting citizens into categories. In fact, the state constructs "bad" (untrustworthy) citizens as those who engage in a wide range of behaviors, including financial and professional misconduct. Simultaneously, the state also uses the system to construct and cultivate "good" (trustworthy) citizens as those who publicly demonstrate loyalty to the regime. Theoretically, this study sheds light on how the world's most powerful authoritarian regime governs through a system that distributes material and symbolic capital. [ABSTRACT FROM AUTHOR]

A huge amount of water at supercritical conditions exists in Earth's interior, where its dielectric properties play a critical role in determining how it stores and transports materials. However, it is very challenging to obtain the static dielectric constant of water, ϵ0, in a wide pressure–temperature (P–T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we introduce a neural network dipole model, which, combined with molecular dynamics, can be used to compute P–T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that ϵ0 may vary by one order of magnitude in Earth's upper mantle, suggesting that the solvation properties of water change dramatically at different depths. Although ϵ0 and the molecular dipole moment increase with an increase in pressure along an isotherm, the dipolar angular correlation has its maximum at 5 GPa–7 GPa, which may indicate that hydrogen bonds become weaker at high pressure. We also calculated the frequency-dependent dielectric constant of water in the microwave range, which, to the best of our knowledge, has not been calculated from first principles, and found that temperature affects the dielectric absorption more than pressure. Our results are of great use in many areas, e.g., modeling water–rock interactions in geochemistry. The computational approach introduced here can be readily applied to other molecular fluids. [ABSTRACT FROM AUTHOR]

Deep learning accelerators have domain-specific architectures, this special memory hierarchy and working mode could bring about new crucial security vulnerabilities. Neural network reuse PE resources layer by layer, after a layer finished, accelerator will give an interrupt to inform host processor dispatch the next layer. By snooping on the interrupt signal patterns, we can capture specific deep neural network (DNN) models and launch hardware trojan attacks. In this paper, we propose Int-Monitor, a novel neural network model triggered hardware trojan in DNN accelerators. By implanting a well-designed interrupt monitor between the host processor and DNN accelerator, this backdoor can capture specific DNN models and trigger the trojan to attack DNN bias buffers. By attacking the global bias buffer, this trojan can prevent the activation of neurons in a DNN model. As result, the network forward propagation will be invalid and the accelerator will deny service. Runtime experiments on LeNet, Resnet, YOLOv2 and YOLOv4tiny DNN models show Int-Monitor can successfully attack the FPGA-based DNN accelerator SoCs. RTL synthesis and implementation show this trojan takes only small hardware overhead and negligible power consumption. It brings 0.5%, 0.2% hardware overhead and 0.622%, 0.187% power consumption on average in the SIMD and NVDLA accelerators. Unlike previous trojan using specially dedicated input data as a trigger, this novel trojan can enable hackers utilize DNN model as a trigger. This mechanism can make its escape from data pre-processing and data encryption. [ABSTRACT FROM AUTHOR]

Currently, concrete structures have increasingly higher requirements for the shear capacity of beams, and ultrahigh-performance concrete (UHPC) beams are increasingly widely used. To facilitate the design of UHPC beams, this paper constructs a UHPC beam shear strength prediction model. First, static shear tests were conducted on 6 UHPC beam specimens with a length of 2 m and a cross-sectional size of 200 mm × 300 mm to explore the effects of the UHPC strength, shear span ratio, hoop ratio, and steel fiber content on the shear resistance and failure morphology of the UHPC beams. Based on the results of this study and a static load experiment of 102 UHPC beams in the literature, the construction includes the shear span ratio (λ), beam section width (b), beam section height (h), hoop ratio (ρSV), UHPC compressive strength (fc), steel fiber volume fraction (Vf), and the UHPC beam shear capacity (Vex) 7 parameter database. Based on the construction of the database, 1200 BPNN models were trained through trial and error. The models were evaluated using the correlation coefficient R, root mean square error RMSE, and a20-index indicators, and the optimal BPNN model (6-15-8-1) was determined based on the ranking of RMSE. After the optimal BPNN is optimized by a genetic algorithm, the prediction performance of the model is improved. The correlation coefficient between the predicted value and the experimental value is R2 = 0.98667, and RMSE = 7.38. This model can reliably predict the shear strength of UHPC beams and provide designers with a reference for the design of UHPC beams. Finally, after sensitivity analysis, the influence of each input parameter on the UHPC shear capacity is determined. [ABSTRACT FROM AUTHOR]

Fusarium graminearum is an important wheat pathogen and a producer of deoxynivalenol (DON). Biosynthesis of DON is suppressed by ammonium and induced by arginine and polyamines. To better understand ammonium repression of DON biosynthesis, in this study, we functionally characterized three ammonium permease (MEP) genes in F. graminearum. All the mep deletion mutants were normal in growth on V8 agar. Whereas deletion of MEP1 had no detectable phenotypes, the mep2 and mep3 mutants had defects in hyphal growth under ammonium limiting conditions and infection of wheat heads, with the latter having less severe defects. Deletion of MEP2 but not MEP3 affected nitrogen repression of DON biosynthesis and genes involved in nitrate metabolism. The mep2 mep3 double mutant had more severe defects in nitrogen repression than the mep2 mutant and was defective in ascospore releasing. Mutant alleles of MEP2 with truncated C‐terminal cytoplasmic tail (CT) failed to complement the mep2 mutant. Expression of a dominant active allele of RAS2 partially rescued the defects of mep2 in nitrogen repression. Taken together, these results suggest that Mep2 acts as the major sensor of ammonium availability in F. graminearum and its CT region functions in nitrogen repression via RAS2 and downstream signalling pathways. [ABSTRACT FROM AUTHOR]

The dry–wet cycle greatly impacts the anchoring effect of prestressed anchor cables in expansive soil. Moreover, the stress relaxation of the soil and prestress loss of anchor cables are correlated. This is due to the special expansion and contraction characteristics of this soil. To explore the influence of the dry–wet cycle, this study experimentally analyses the stress change mechanism of the anchor supporting structure in expansive soil; meanwhile, the coupled expansive soil-anchor stress model for the dry–wet cycle case is derived. Expansive soil possesses unique viscoelastic-plastic mechanical characteristics. These were simulated using a calculation framework based on the Bingham model. This was used to describe the coupling effect between expansive soil and anchor cable with equal strain. Dry–wet cycle tests were set up under three different conditions: moisture contents, the amplitude, and number of the wet–dry cycles, which verified the accuracy of the model. The experimental results show that the number and amplitude of dry–wet cycles determine the number and amplitude of cycles of anchor stress change, respectively, and the initial water content determines the reference value of stress change. The primary inference is that the number of dry–wet cycles has the greatest influence on stress attenuation of anchor, as shown in the experiment, where the stress attenuation of stress anchor was 88.12% of initial stress after three dry–wet cycles. [ABSTRACT FROM AUTHOR]

In this paper, a multi-reconfigurable filter which consists of three identical varactor-graphene-loaded coaxial substrate integrated waveguide (SIW) resonators is presented. The center frequency and transmission amplitude of the proposed filter is independently reconfigurable by means of electrically controlling the values of the varactor capacitance and the graphene resistance, respectively. Tuners and their bias circuits are mounted on the surfaces of the SIW resonator to adjust the tuning of resonant frequency and unloaded quality factor, which enable to realize the frequency and amplitude of the presented multi-reconfigurable filter. In addition, the proposed filter applies a cascaded triplet topology to generate a transmission zero at the upper side in order to enhance skirt selectivity and out-of-band rejection. The circular shape of the resonator provides great flexibility and produces an additional compactness of the filter. The proposed multi-reconfigurable coaxial SIW filter is designed and fabricated, with high- Q varactors and graphene thin sheets as frequency and amplitude tuning elements, respectively, exhibiting the following reconfigurable characteristics: (a) a wide tuning range of 0.58â€"1.01 GHz with a constant 1 dB fractional bandwidth of 2 ± 0.2%. (b) A continuous tuning range of amplitude at any center frequency in the achievable frequency band. For instance, the proposed filter can be tuned with an amplitude range of 3.02â€"19.8 dB by varying the value of the graphene sheet resistance at 1.01 GHz. (c) Free combination of the arbitrary center frequency and attenuation level. The proposed filter with tunable filtering and attenuating responses has widely potential in reconfigurable wireless communication systems and radar systems due to its high integration and versatility. [ABSTRACT FROM AUTHOR]

This article presents a frequency–amplitude independently tunable bandpass filter (BPF). The filter uses an electrically tunable coaxial substrate integrated waveguide (SIW) resonator, which has a compact size and a constant fractional bandwidth (FBW). The surface-gap coaxial SIW resonator is loaded with eight varactors and a thin graphene sheet at the top and bottom, respectively, which results in possessing independent tuning characteristics of frequency and quality factors. The variable capacitance values of the varactor are used to achieve frequency reconfigurability, and the graphene located on the bottom surface of the resonator is equivalent to a tunable lumped resistor used to control the $Q$ -factor. In addition, detailed steps of BPF design are provided. For validation, a prototype third-order varactor–graphene-loaded multitunable coaxial SIW BPF is fabricated. The measurements show that within the tunable range of the varactors, the proposed filter can be tuned from 0.68 to 1.03 GHz with 5% ± 0.2% 1-dB FBW and the insertion loss of passband can be continuously tuned over 2.68–6.37 dB. The frequency tuning and amplitude tuning functions are not dependent on each other. Over the frequency tuning range, the transmission amplitude at an arbitrary center frequency can be dynamically tuned by controlling the bias voltage applied to the graphene. [ABSTRACT FROM AUTHOR]

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement. [ABSTRACT FROM AUTHOR]

The shunt active power filter (SAPF) system oscillation is a massive threat to the security and stability of the power grid. This study classifies SAPF oscillation into two categories according to the difference in mechanisms. The SAPF oscillation in one category is caused by the resonant characteristics of a switching noise filter and is called external loop amplification. The SAPF oscillation in the other category is induced by the presence of a capacitor in the load current for SAPF and is called self-excited oscillation. Unlike previous studies, this study tried to reveal the internal relationship between the two kinds of SAPF oscillation, present a general shunt virtual-damping-based SAPF oscillation suppression strategy covering the previous resonant damping method, and provide the discrete domain stability criterion of the control system. The sampling frequency was at least six times the resonant frequency. The stability region was enlarged with an increase in the sampling frequency and narrowed with a rise in the resonant frequency. As to the harmful self-excited oscillation problem, this study proposes a composite control strategy combining selective harmonic compensation and grid-side current feedback. Moreover, this study considers the more general resistance–inductance–capacitance load situations and analyzes the stability of the SAPF–Thyristor Switched Capacitor (TSC) hybrid compensation system. Simulations and experiments demonstrated that the proposed compound control method can reduce the primary harmonics of the system by more than 90% and has a better oscillation suppression performance than previous suppression methods. In particular, if we selected the TSC series reactance rate following more than 6%, self-excited oscillation could usually be avoided. [ABSTRACT FROM AUTHOR]

This article presents a technique for load-modulated balanced amplifiers (LMBAs) to achieve a continuum of load modulation (LM) ratio. This has been possible in prior arts using asymmetrical auxiliary devices. In contrast, the proposed technique employs identical auxiliary devices. A LM ratio is enabled by a specific coupling factor of the power-combining coupler at the output. The proposed LMBA technique is demonstrated through the design, implementation, and measurement of a concurrent wideband LMBA operating across 3.1–4.2 GHz. The prototype achieves a peak output power of $ 48.1\pm 0.6~\text {dBm}$ , with drain efficiency of 47–58% at peak power and 47–61% at 10-dB power back-off. The concurrent wideband performance is demonstrated by a characterization with 900-MHz new radio (NR) signal with 10-dB peak-to-average power ratio (PAPR). At 37.8 dBm of average output power, the measurement yields 44% drain efficiency and −44-dBc adjacent channel leakage ratio (ACLR). [ABSTRACT FROM AUTHOR]

• Proposed an automated modal identification method based on the MVMD framework, enhancing the efficiency of modal identification. • A new fusion parameter combining power spectral cross-entropy with reconstruction error has been developed, resolving the accuracy issue in setting MVMD decomposition parameters. • Validated the method's accuracy, robustness, and practicality through numerical models and a subway tunnel model test. This paper introduces a fully automated modal identification algorithm based on the Multivariate Variational Mode Decomposition (MVMD) of free vibration responses to determine structural modal parameters. Addressing the challenge of setting MVMD parameters, we introduce a fusion parameter combining power spectral cross-entropy with reconstruction error as an adaptive fitness function in the optimization algorithm, enabling optimal parameter selection. Then, modal frequencies, damping ratios, and shapes of structures can be extracted from autonomously decomposed Intrinsic Mode Functions by employing the principle of modal superposition and least squares fitting without manual parameter adjustments. Validated by a four-degree-of-freedom numerical model, the method demonstrated accurate, automatic modal parameter identification. The method was further applied to a subway tunnel structure model experiment. Comprehensive modal identification was conducted on tunnel structures under varying degrees of damage. The results validate the proposed method's effectiveness and reveal the damaged segment structure's multimodal parameter variation patterns and surrounding soil. [ABSTRACT FROM AUTHOR]

Nanoplastics (NPs) have attracted increasing attention within terrestrial ecosystems. However, our understanding of their impacts on the intricate anaerobic methanogenesis processes occurring in paddy soils microbial communities remains limited with respect to nanoplastics shape, function, and metabolic effects. Herein, we explored the effects of polystyrene nanoplastics (PS-NPs) and microplastics (PS-MPs) on anaerobic methanogenesis in a typical paddy soil. The results show that PS-NPs delayed methane production and the time to reach peak acetate content in incubation process of paddy soils, and the methanogenic rate increased rapidly after 13 days, with a maximum increase of 87.97%. However, PS-MPs had no marked effect on CH 4 , CO 2 and acetate production. In addition, PS-NPs affected soil physicochemical properties by reducing pH and increasing electrical conductivity. Acetoclastic methanogens were enriched and the relative abundance of the genes ackA, pta, ACSS, cdhC, cdhD and cdhE in the acetoclastic pathways were significantly increased under PS-NPs exposure. In addition, PS-MPs had significant effect on the microbial community structure but no effect on methanogenic pathways of the paddy soils. This study provides important insights into the response of key microorganisms, functional genes and methanogenesis pathways to NPs during anaerobic methanogenesis in paddy soils. [Display omitted] • PS-NPs had a significant effect on anaerobic methanogenesis in paddy soils. • The methanogenic lag-phase time was positively correlated with the level of PS-NPs. • PS-NPs affected methanogenic pathways in paddy soils. • Acetoclastic methanogens were enriched in the presence of PS-NPs. [ABSTRACT FROM AUTHOR]

[Display omitted] • OPFRs and their metabolites exhibited seasonal variation in seawater rather than in sediments in the PRE. • In comparison to vertical migration, horizontal migration accounted for the majority of OPFR transport. • The majority of the riverine-input OPFRs in the PRE have transitioned to the open sea. • Multi-scenario control strategies for the ecological risk of EHDPHP have been proposed. Estuaries serve as crucial filters for land-based pollutants to the open sea, but there is a lack of information on the migration and fate of organophosphate flame retardants (OPFRs) within estuaries. This study focused on the Pearl River Estuary (PRE) by examining the co-occurrence of OPFRs and their metabolites and quantifying their transport fluxes using a mass balance model. The seawater concentrations of OPFRs and their metabolites exhibited significant seasonal variations (p < 0.01), while the sediment concentrations of OPFRs reflected the long-term distributional equilibrium in the PRE. The concentration of Σ 9 OPFRs in seawater showed a relentless dilution from the entrance to the offshore region in the normal and wet seasons, which was significantly in accordance with the gradients of pH, dissolved oxygen (DO), and salinity (p < 0.05). Furthermore, horizontal migration dominated the transport of OPFRs, and the inventory assessment revealed that both the water column and sediment were important reservoirs in the PRE. According to the estimated fluxes from the mass balance model, riverine input emerged as the principal pathway for OPFR entry into the PRE (1.55 × 105, 6.28 × 104, and 9.00 × 104 kg/yr in the normal, dry and wet seasons, respectively), whereas outflow to the open sea predominantly determined the main fates of the OPFRs. The risk quotient (RQ) results showed that EHDPHP (0.835) in water posed medium ecological risk, while other OPFRs and metabolites presented relatively lower risk (RQ < 0.1). The risk control effects were evaluated through scenario simulations of mathematical fitting between controllable source factors and the RQ of risky OPFR. The risk of EHDPHP in the PRE could be effectively reduced by restricting its concentrations in entrance region (<9.31, 8.67, and 12.7 ng/L in the normal, dry and wet seasons, respectively) of the PRE. This research offers foundational insights into environmental management and pollution control strategies for emerging pollutants in estuaries. [ABSTRACT FROM AUTHOR]

Background: Placenta accreta (PA) is a major cause of maternal morbidity and mortality in modern obstetrics, few studies have explored the underlying molecular mechanisms. Methods: In our study, transcriptome and proteome profiling were performed in placental tissues from ten participants including five cases each in the PA and control groups to clarify the pathogenesis of PA. Results: We identified differential expression of 37,743 transcripts and 160 proteins between the PA and control groups with an overlap rate of 0.09%. The 33 most-significant transcripts and proteins were found and further screened and analyzed. Adhesion-related signature, chemotaxis related signatures and immune related signature were found in the PA group and played a certain role. Sum up two points, three significant indicators, methyl-CpG-binding domain protein 2 (MeCP2), podocin (PODN), and apolipoprotein D (ApoD), which participate in "negative regulation of cell migration", were downregulated at the mRNA and protein levels in PA group. Furthermore, transwell migration and invasion assay of HTR-8/SVneo cell indicated the all of them impaired the migration and invasion of trophoblast. Conclusion: A poor correlation was observed between the transcriptome and proteome data and MeCP2, PODN, and ApoD decreased in transcriptome and proteome profiling, resulting in increased migration of trophoblasts in the PA group, which clarify the mechanism of PA and might be the biomarkers or therapy targets in the future. [ABSTRACT FROM AUTHOR]

Turbine guide vane faces extremely high thermal load, especially on the leading edge region. In order to enhance cooling performance, the present study investigates the effects of leading-edge film cooling arrangements by adiabatic and aero-thermal coupled simulation. The three-row cooling design of the 25° inclined angle with the trenched holes is used to replace five-row showerhead holes. The SST k − ω γ − R e θ model is employed after conducting the numerical validation. The results show that at coolant mass flow of 0.00171 kg/s, three-row showerhead film cooling with trenched holes can provide higher laterally averaged adiabatic cooling effectiveness than three-row round holes. It can also improve cooling performance in most of the leading-edge regions, compared with five-row showerhead cooling arrangements. The three-row cooling design with trenched holes can still obtain the highest area-averaged effectiveness on either the suction side or the pressure side of the leading edge. Besides, the trenched holes are also beneficial to the reduction of the aerodynamic losses. In the aero-thermal coupled simulation, the improvement of the three-row cooling design with trenched holes decreases because of the internal cooling effect. It is also proved that the transverse trench would not lead to a local high temperature region on the metal surface. The trenched configuration above can be used on the first-stage turbine vane to increase turbine inlet temperature, improve engine efficiency, and prolong engine life. [ABSTRACT FROM AUTHOR]

• A random forest model is proposed for cell-type-specific cohesin-mediated chromatin loops prediction. • Chromatin state is responsible for cell-type-specificity of chromatin loops. • Chromatin state combines with the binding of key factors can predict cell-type-specific loops accurately. Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness, safety, and convenience of in‐class transition (iCT) from intravenous bortezomib‐based induction to ixazomib‐based oral regimens. Methods: This retrospective real‐world study was conducted in 16 Chinese hospitals between October 2017 and April 2023 and analyzed newly diagnosed (NDMM) and first‐line relapsed multiple myeloma (FRMM) patients who attained at least a partial response from bortezomib‐based induction therapy, followed by an ixazomib‐based oral regimen for 2 year or until disease progression or intolerable toxicity. Results: The study enrolled 199 patients, median age: 63 years old, male 55.4%, 53% as high risk (HR), and 47% as standard risk. Cytogenetic risk stratification by metaphase fluorescence in situ hybridization (M‐FISH), based on the Mayo Clinic risk stratification system. The median duration of total PI therapy was 11 months, with ixazomib‐based treatment spanning 6 months. At the 20‐month median follow‐up, 53% of patients remained on therapy. The 24‐month PFS rate was 84.3% from the initiation of bortezomib‐based induction and 83.4% from the start of ixazomib‐based treatment. Overall response rate (ORR) was 100% post‐bortezomib induction and 90% following 6 cycles of the ixazomib‐based regimen. Based on the Sankey diagrams, 89.51% of patients maintained or improved their disease response after 2 cycles of iCT, 6 cycles (90.14%), and 12 cycles (80%). The HR level of Mayo was found to be a significant independent factor in a worse remission (hazard ratio (HR) 2.55; p = 0.033). Ixazomib's safety profile aligned with previous clinical trial data, with 49% of patients experiencing at least one AE of any grade. The most common AEs included peripheral neuropathy, nausea and vomiting, diarrhea, thrombocytopenia, and granulocytopenia. Conclusion: In the real‐world Chinese MM population, NDMM and FRMM patients responded favorably to PI‐based continuous therapy, demonstrating substantial response rates. The ixazomib‐based iCT allows for sustained PI‐based treatment, offering promising efficacy and tolerable AEs. [ABSTRACT FROM AUTHOR]

Objective: Long non-coding RNAs (lncRNAs) and microRNAs (miRs) are differentially expressed in ovarian cancer (OC) cells and influence OC progression. This study intended to explore the underlying roles of LINC00115 and miR-30a in OC. Methods: Gene Expression Omnibus database was used to find OC microarray datasets and bioinformatics analysis predicted the potential molecular mechanism of OC. OC stem cells (OCSCs) surface marker was isolated from human OC cell line and identified. CD133+ OCSCs were transfected with LINC00115, miR-30a and SOX9 alone or together to detect sphere-forming ability and apoptosis of OCSCs. Caspase-3 activity and DNA damage in cell supernatant were detected. The levels of CD44, NANOG, POU5F1, LINC00115, CD133, miR-30a and SOX9 were measured. Then sh-LNC00115-treated OCSCs were added with Wnt/β-catenin activator SKL2001 to observe the changes of cell stemness and activity. Finally, animal models were established to evaluate the effect of LINC00115 on OCSC in vivo. Results: LINC00115 and SOX9 were highly expressed in OC, while miR-30a was lowly expressed. After silencing LINC00115 or overexpressing miR-30a, the sphere-forming rate of CD133+ OCSC and levels of CD133, CD44, NANOG and POU5F1 decreased, while apoptotic rate, Caspase-3 activity and histone-related DNA damage increased. SOX9 reversed these trends. Additionally, LINC00115 could bind to miR-30a and miR-30a could target SOX9. SKL2001 partially reversed cell stemness and activity in sh-LNC00115-treated OCSCs. Finally, silencing LINC00115 could inhibit OCSCs growth in vivo. Conclusion: LINC00115 promoted stemness and inhibited apoptosis of OCSCs by upregulating SOX9 and in activating the Wnt/β-catenin pathway through competitively binding to miR-30a. [ABSTRACT FROM AUTHOR]

Named data networking (NDN) is one representation and implementation of information-centric networking (ICN) and is considered to be among the most promising designs for the next generation of network architecture. The introduction of NDN into vehicular ad-hoc networks (VANETs) and utilization of its content-centric characteristic to improve data transmission and distribution in VANETs has become a research hotspot in recent years. However, research on mobility support of NDN-based VANETs still faces many challenges. To solve the issue of data transmission path breaking due to Consumer mobility in NDN-based VANETs, this article proposes a novel vehicle tracking-based Data packet forwarding scheme (VTDF) to improve the successful delivery rate of Data packets in mobile environments. In this approach, the urban road structure is divided into complex multi-junction and straight lane scenarios and Data packets are forwarded according to vehicle movement information. Simulations indicated that this vehicle tracking scheme provides a lower average data transmission delay, shorter handover delay between roadside units, and higher data delivery rate for Consumers compared to the standard methods. [ABSTRACT FROM AUTHOR]

The swirling flow is used to increase mixing and decrease nitrogen oxide emission in the combustor. Meanwhile, the swirling incoming flow has a significant effect on both aerodynamic and heat transfer characteristics of the downstream first-stage turbine. This paper conducts a numerical investigation on the influences of the inlet swirl core on the film cooling performance of a leading-edge model. Besides, the trenched holes are employed to improve the cooling performance under conditions of different swirl numbers and blowing ratios. The results show that the inlet swirl benefits the generation of downstream detrimental vortex structures and increases the mixing between coolant and mainstream. Although the trenched holes cannot absolutely change the flow patterns of swirling mainstream, they can guarantee the lateral spread of coolant and suppress the downstream coolant rotation. At different swirl numbers, the trenched cases all provide a better cooling performance, especially on the rows away from the stagnation line. Besides, the trenched cases show incremental cooling performance with the blowing ratio increasing from 1.0 to 3.0. However, for the round holes, the highest averaged cooling effectiveness is obtained at blowing ratio of 2.0, which is still lower than the trenched cases. [ABSTRACT FROM AUTHOR]

There are different methods to enhance forced convective heat transfer. In the current study, porous blocks are used on the hot wall of a two-dimensional channel. Use of porous media can enhance the heat transfer by improving the flow mixing with subsequent increment in the effective thermal conductivity of the domain at the position of the used permeable media. However, a cavity-like flow appears between the blocks, which deteriorates heat transfer there. In this study, a bleeding condition by suction is applied to improve heat transfer there. Effects of some important parameters including porous media permeability, porous blocks relative height, flow Reynolds number, and the bleeding coefficient on the thermal performance and pressure loss are investigated. The outcomes reveal that the bleeding condition effect on heat transfer mainly depends on the bleeding coefficient. For example, the effect of bleeding condition on the total average Nusselt number is negligible for the bleeding coefficient (σ) less than 0.01, while it could improve the average Nusselt number by 36% at σ = 0.05. Furthermore, increasing the permeability of porous blocks, the effect of the bleeding condition becomes more highlighted from the performance number point of view. [ABSTRACT FROM AUTHOR]

In order to mitigate carbon emissions and address environmental concerns, an increasing number of industries have started investing in green technologies to produce environmentally friendly products. This research develops a game theoretical model to examine the phenomenon of manufacturer encroachment in the context of carbon emission reduction and asymmetric information. Our analysis reveals that when the manufacturer does not encroach, a reduction in carbon emissions benefits all members of the supply chain. However, under encroachment, only the manufacturer stands to benefit from the reduction in carbon emissions. Moreover, with a low direct selling cost, we recommend that the manufacturer should aim to achieve a lower level of carbon emissions under encroachment compared to non-encroachment scenarios. Another key finding is that the benefits of encroachment for the manufacturer depend on the relative costs of direct selling and carbon emission reduction. Encroachment is advantageous to the manufacturer when either the direct selling cost or the carbon emission reduction cost is low. On the other hand, encroachment benefits the retailer only when both the direct selling cost and the carbon emission reduction cost are high. Furthermore, we demonstrate that it is in the retailer's best interest to voluntarily share demand information with the manufacturer, particularly when the direct selling cost is either low or high. By sharing demand information, the retailer can enhance the overall performance of the supply chain. Overall, this study provides valuable insights for managers regarding encroachment and carbon emission decisions in the context of low carbon manufacturing, carbon cap-and-trade policies, and asymmetric information. These findings can guide decision-making processes and help optimize the performance of supply chains in environmentally conscious settings. [ABSTRACT FROM AUTHOR]

Cellulose is a highly abundant biomass resource and can be used as raw material for green electronics. The carbon nanotubes (CNTs) doped cellulose-based nanocomposite film (CNTs/Cellulose) was prepared in this paper. The cellulose in CNTs/Cellulose belongs to the type I crystal form. The CNTs are filled in the fiber network and covered on the cellulose fiber surface. The maximum thermal degradation temperature of CNTs/Cellulose is 142 °C higher than that of cellulose. A flexible piezoelectric nanogenerator (PENG) with a high current of milliampere level was made from CNTs/Cellulose. The PENG shows an output current of 6.19 mA and a current density of 1.55 mA/cm2. Furthermore, the PENG can successfully drive electronic devices such as LED lights, electronic watches and calculators under its own gravity without applying additional force. This work provides a new idea for the development of novel PENG. [Display omitted] ● The CNTs/Cellulose-PENG's output current is 6.19 mA at a pressure of 500 N. ● For CNTs/Cellulose-PENG, there is a linear relationship between stress and current. ● This PENG can drive the LED, watch and calculator to work without additional force. [ABSTRACT FROM AUTHOR]

The increasing population and energy consumption have led to a greater emphasis on managing and storing different energy sources. Researchers are now focusing on integrating renewable energy into energy systems and improving efficiency through accurate modeling and calculations. This article presents the optimal operation of an integrated energy system that incorporates wind energy as a renewable source. The study focuses on enhancing the modeling accuracy of equipment such as combined heat and power units and storage devices to align the obtained results with real-world scenarios. To effectively manage electrical and thermal loads, a load management program is implemented. Furthermore, the uncertainty of wind speed is addressed using the interval-valued grey decision Theory method. Two sensitivity analyses are conducted to further investigate the system's performance. Firstly, the impact of incorporating storage resources and load management programs on the overall operating cost is examined. The results demonstrate a significant 22.87% improvement in the objective function when both storage devices and load management programs are employed simultaneously. Secondly, a sensitivity analysis is performed to evaluate the objective function's response to changes in the uncertainty budget parameters and wind speed deviation. The findings highlight the greater impact of wind speed deviation on the objective function compared to changes in the uncertainty budget. The problem is formulated as a mixed integer linear programming problem, and the GAMS software and CPLEX solver are utilized to obtain the relevant results. The findings emphasize the potential of integrating wind energy into an integrated energy system and underscore the importance of accurate modeling, load management, and the consideration of storage resources. These insights contribute to the optimization of energy system operations and the advancement of sustainable energy practices. • Optimal operation of a multiple energy system with electric, natural gas, and thermal loads. • Integration of wind energy and uncertainty modeling using the IGDT method. • Accurate modeling of CHP units, boilers, and storage devices. • Load management programs to reduce peak hour demand. [ABSTRACT FROM AUTHOR]

We report a biocompatible nanomedicine (GFCT) that possesses both H 2 O 2 self-supply and GSH-elimination properties for achieving highly synergistic enhanced CDT and chemotherapy effects by integrating iron-doped calcium phosphate nanoparticles with hypoxia-sensitive prodrug (tirapazamine). Such a biocompatible and biodegradable nanomedicine provides a promising nanotheranostics for efficient tumor microenvironment-specific synergistic therapies. [Display omitted] • A biocompatible iron-doped calcium phosphate-based nanomedicine is proposed for synergistic chemotherapy/chemodynamic therapy. • The engineered GFCT presents H 2 O 2 self-supply and GSH-elimination properties for enhanced CDT efficacy. • This work provides an effective strategy via the integration of TME regulation with TPZ-induced chemotherapy. Chemodynamic therapy (CDT) has been exploited as an emerging therapeutic modality for cancer treatment. However, insufficient intracellular H 2 O 2 levels and ultrahigh glutathione (GSH) in the tumor microenvironment (TME) severely restrict CDT efficacy. To address these concerns, in this work, a self-reinforcing biodegradable nanotherapeutic was developed to modulate the TME to amplify CDT. The nanotherapeutic (GOD-FeCaP@TPZ, abbreviated GFCT) was fabricated through a mild one-step biomineralization process using the enzyme catalyst glucose oxidase (GOD) as a template to form biodegradable iron-doped calcium phosphate nanoparticles, followed by the loading of the prodrug tirapazamine (TPZ). When GFCT decomposes and releases Fe3+ in the acidic TME, the delivered GOD can effectively catalyze the oxidization of glucose to enhance the subsequent Fenton-like reaction by promoting intracellular acidity (via the production of gluconic acid) and improving the generation of H 2 O 2. Moreover, the released iron constantly depletes GSH by self-cyclic valence alterations in Fe(III) and Fe(II) and then triggers H 2 O 2 to produce •OH through an Fe(II)-mediated Fenton-like reaction. Moreover, the elevated level of hypoxia induced by GOD-mediated glucose oxidization can activate the prodrug TPZ for effective chemotherapy, thus achieving remarkable synergistic therapeutic outcomes. This work provides another paradigm to augment antitumor efficacy via modulation of the TME. [ABSTRACT FROM AUTHOR]

This article examines how market actors are actively engaged in state control of the Internet in China by studying the emerging industry of Internet-opinion management in that country. It presents an ecology of this industry, identifies three main market actors whose competitiveness is deeply rooted in the Chinese political context and identifies three stages of state-market collaboration. This article sheds light on how the rise of big data has strengthened state capacity for Internet control. It provides original evidence for how the profit motive drives Chinese data companies and media organisations to seek active involvement in the institutional construction of Internet-opinion control. This article also contributes to the literature on repression and contentious politics. It demonstrates that by relying on the market, authoritarian states are able to turn advanced technology into a repressive tool, which makes it more difficult for their citizens to use the Internet to mobilise. [ABSTRACT FROM AUTHOR]

Continually disclosed vulnerabilities reveal that traditional computer architecture lacks the consideration of security. This article proposes a security-first architecture, with an Active Security Processor (ASP) integrated to conventional computer architectures. To reduce the attack surface of ASP and improve the security of the whole system, the ASP is physically isolated from Computation Processor Units (CPU) with an asymmetric address space, which enables both ASP and CPU to run their operating system and applications independently in their own memory space. Furthermore, the ASP, which has the highest privilege (Super Root) of the whole system, possesses two advantageous features. First, the ASP can efficiently access all CPU resources and collect multi-dimensional information to monitor malicious behaviors, meanwhile, the CPU cannot access the ASP's private resources in any way. Second, instead of being scheduled by CPUs, the ASP can actively manage the security mechanisms employed in either CPUs or the ASP. Based on the security-first architecture, we introduce several typical security tasks running on ASP. With different considerations in terms of system overhead, complexity and performance, we also explore four typical system-level implementations for integrating the ASP to the security-first architecture. The first-generation ASP was designed and implemented based on the 40nm technology, and a security computer system was implemented based on it. Evaluations on this real hardware platform demonstrate that the security-first architecture can protect the system effectively with minor performance impacts on computing workloads. [ABSTRACT FROM AUTHOR]

Time series data are sequences of values that are obtained by sampling a signal at a fixed frequency, and time series classification algorithms distinguish time series into different categories. Among many time series classification algorithms, subseries-based algorithms have received widespread attention because of their high accuracy and low computational complexity. However, subseries-based algorithms consider the similarity of subseries only by shape and ignore semantic similarity. Therefore, the purpose of this paper is to determine how to solve the problem that subseries-based time series classification algorithms ignore the semantic similarity between subseries. To address this issue, we introduce the deep graph kernel technique to capture the semantic similarity between subseries. To verify the performance of the method, we test the proposed algorithm on publicly available datasets from the UCR repository and the experimental results prove that the deep graph kernel has an important role in enhancing the accuracy of the algorithm and that the proposed algorithm performs quite well in terms of accuracy and has a considerable advantage over other representative algorithms. [ABSTRACT FROM AUTHOR]

What role do emotions play in state repression? Building upon ethnographic observation in one Beijing petition bureau, this paper explores the emotional labour performed by grassroots officials to demobilize social dissent. The petition system serves as an official channel through which the Chinese government receives complaints and grievances from citizens. Notwithstanding its institutional inefficiency in addressing petitioners' requirements, this system plays a critical role in maintaining social stability. I investigate the process by which frontline petition officials manage petitions. I argue that channelling petitioners' emotions has become one of these officials' core functions. Petition officials have developed three types of emotional strategies – emotional defusing, emotional constraint and emotional reshaping – to absorb petitioners' complaints. This study of emotional repression offers a fresh perspective on the affective dimension of contentious politics and also contributes to the theoretical discussion on how authoritarian regimes deal with dissent. [ABSTRACT FROM AUTHOR]

The aim of this study is to identify the dynamic explicit and implicit information factors which displayed on the webpage of platforms that influence backers' investment decision-making behavior. We analyze the connections among these factors by collecting the longitudinal dataset from reward-based crowdfunding platform. Based on ELM model, we establish Fixed Estimation Panel Data Model respectively according to explicit and implicit factors and take Funding Status (crowdfunding results) as the moderating variable to observe the goal gradient effect. Results indicate that most variables in the central route affect backers' investment behavior positively, while most variables in the periphery route have a negative impact on backers' investment behavior. The Funding Status has a significant negative moderating effect on the explicit variables, and has no significant moderating effect on the implicit information variables of the project. In addition, we upgrade the econometric method used by previous scholars, which could improve the accuracy of the FE model. Furthermore, we find strong support for the herding effect in reward-based crowdfunding and the intensity tends to decrease before the funding goal draws near. [ABSTRACT FROM AUTHOR]

Pod dehiscence brings much loss for modern agricultural production, and multiple pod dehiscence components have been identified in many plant species. However, the pod dehiscence regulation factors in soybean are limited. In this study, we investigate the function of GmDIR26, a close homologues gene of pod dehiscence genes GmPdh1, PvPdh1, and CaPdh1, in the regulation of pod dehiscence in soybean. The secondary and tertiary structure analysis reveals that GmDIR26 protein has a similar structure with GmPdh1, PvPdh1, and CaPdh1 proteins. Synteny analysis of soybean and chickpea genomes shows that the genomic region surrounding GmDIR26 and CaPdh1 might be evolved from the same ancestor, and these two genes might have similar function. GmDIR26 shows an increased expression pattern during pod development and reaches a peak at beginning seed stage. Meanwhile, GmDIR26 exhibits high expression levels in dorsal suture and pod wall, but low expression pattern in ventral suture. In addition, GmDIR26 shows higher expression levels in pod dehiscence genotype than that in pod indehiscence accessions. Overexpression of GmDIR26 in soybean increases pod dehiscence in transgenic plants, of which the lignin layer in inner sclerenchyma pods is thicker and looser. The expression levels of several pod dehiscence genes are altered. Our study provides important information for further modification of pod dehiscence resistance soybean and characterization of soybean pod dehiscence regulation network. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in artificial intelligence and deep neural networks (DNNs) have produced an explosive demand for computing platforms equipped with customized domain-specific accelerators. However, DNN accelerators have security vulnerabilities. Researchers have previously explored DNN attack and defense technologies that mainly focus on training and inference algorithms or model structure robustness. The problem of how to design a secure accelerator architecture has received relatively little attention, especially with the rapid development of FPGA-based heterogeneous computing SoCs. To mitigate this bottleneck, we propose Nacc-Guard, a lightweight DNN accelerator architecture which can effectively defend against neural network bit-flip attacks and memory Trojan attacks. By utilizing a linear randomization encryption algorithm based on stream cipher Trivium, interrupt signal confused coding, and hash-based message authentication code, Nacc-Guard can not only guarantee the integrity of the uploaded DNN file but also ensure buffer data confidentiality. To evaluate Nacc-Guard, NVDLA and a SIMD accelerator coupling with a RISC-V Rocket and ARM processor is implemented at RTL. Experimental evaluation shows that Nacc-Guard has a 3 × hardware overhead reduction compared with conventional AES. Experiments on VGG, ResNet50, GoogLeNet, and YOLOv4-tiny validate that this framework can successfully ensure secure DNN inference with negligible performance loss. It achieves a 3.63 × speedup and 35% energy reduction over the AES baseline. [ABSTRACT FROM AUTHOR]

Dark septate endophytes (DSEs) inhabit plant roots and soil in ecosystems and host plants worldwide. DSE colonization is influenced by cultivars, soil factors, and specific habitat conditions. The regular diversity of DSEs in blueberries in Guizhou, China, is still unclear. In this study, four cultivars (Gardenblue, Powderblue, O’Neal, and Legacy) in three areas (Gaopo, Majiang, and Fenggang) in Guizhou were used to identify DSEs by morphological and molecular biological methods and to clarify the relationship between DSE diversity and DSE colonization and soil factors of cultivated blueberries in Guizhou. The DSEs isolated from cultivated blueberry roots in 3 areas in Guizhou Province were different, belonging to 17 genera, and the dominant genera were Penicillium, Phialocephala, and Thozetella. DSEs isolated from Majiang belonged to 12 genera and 16 species, those from Gaopo belonged to 7 genera and 15 species, and those from Fenggang belonged to 5 genera and 7 species. Among the different blueberry varieties, 11 genera were isolated from O’Neal, 12 genera were isolated from Powderblue, 11 genera were isolated from Legacy and 13 genera were isolated from Gardenblue. Coniochaeta is endemic to O’Neal, Chaetomium and Curvularia are endemic to Powderblue, and Thielavia is endemic to Legacy. Correlation analysis showed that DSE diversity was significantly correlated with DSE colonization and soil factors. [ABSTRACT FROM AUTHOR]

Schisandra chinensis (SC) is reported to improve myocardial ischemia. Velocity vector imaging (VVI) is a noninvasive technique for evaluating myocardial function in humans, while few reported on the application in animals. In this study, we aimed to evaluate the improved effects of SC on the myocardium of Sprague Dawley rats having hyperthyroid heart disease (HHD) using VVI technique. HHD models were established by injecting daily with subcutaneous levothyroxine (0.5 mg/kg). Then, the SC group was administered the aqueous extract of SC (2 g/kg) once daily, while the HHD and control (CON) groups were administered the same amount of distilled water daily. All the rats were provided the same amount of food and water daily, and the intervention was stopped after 28 days. The efficacy of SC in HHD rats was evaluated by ultrasound VVI. The serum total triiodothyronine level, total thyroxine level, N-terminal pro-brain natriuretic peptide expression, p53 expression, and calmodulin (CaM) activity were assessed by western blotting, Hematoxylin-Eosin and Masson staining, and electron microscopy. The results indicated that SC significantly improved the systolic velocity, diastolic velocity, strain, systolic strain rate, and diastolic strain rate of the heart by significantly reducing p53 expression and CaM activity (P < 0.05), improving myocardial fibrosis in HHD rats. Also, VVI can be a valuable tool for the evaluation of myocardial function in HHD rats. [ABSTRACT FROM AUTHOR]

Objective: The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our clinical task is to use mammographic features to predict whether ductal carcinoma in situ (DCIS) identified at needle core biopsy will be later upstaged or shown to contain invasive breast cancer. Methods: To improve the prediction of pure DCIS (negative) versus upstaged DCIS (positive) cases, this study considers the adjunctive roles of two related classes: atypical ductal hyperplasia (ADH), a non-cancer type of breast abnormity, and invasive ductal carcinoma (IDC), with 113 computer vision based mammographic features extracted from each case. To improve the baseline Model A's classification of pure vs. upstaged DCIS, we designed three different strategies (Models B, C, D) with different ways of embedding features or inputs. Results: Based on ROC analysis, the baseline Model A performed with AUC of 0.614 (95% CI, 0.496-0.733). All three new models performed better than the baseline, with domain adaptation (Model D) performing the best with an AUC of 0.697 (95% CI, 0.595-0.797). Conclusion: We improved the prediction performance of DCIS upstaging by embedding two related pathology classes in different training phases. Significance: The three new strategies of embedding related class data all outperformed the baseline model, thus demonstrating not only feature similarities among these different classes, but also the potential for improving classification by using other related classes. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the long‐term efficacy of cannulated internal fixation in patients who sustain femoral neck fracture (FNF) and risk factors influencing the outcomes. Methods: This retrospective study analyzed data from 73 elderly patients aged ≥60 years old, hospitalized for FNF and treated with cannulated internal fixation between August 2008 and July 2016. The inclusion criteria were: (i) patients aged ≥60 years; (ii) patients with recent femoral neck fracture, with times of injury ranging from 12–72 h; and (iii) patients who underwent Garden II–IV closed reduction and internal fixation. Patients were classified based on the Garden fracture type and Pauwels angle. Clinical data and radiographs before and after the surgery were collected. Subsequently, surgery was performed with the patient in supine position, under general or lumbar epidural anesthesia. Closed reduction was performed until satisfactory fracture reduction quality was achieved. The weight of all included patients were <75 kg, and thus, minimal internal fixation was performed. The ischemic necrosis of the femoral head was diagnosed by considering the symptoms, signs, and radiological findings. Harris hip scores were used to evaluate postoperative recovery of hip function. Furthermore, relationships between Garden fracture type and necrosis rate of the femoral head, Pauwels angle and necrosis rate of the femoral head, Garden fracture type and Harris hip score, and age and Harris hip score were analyzed. Results: The mean duration of surgery was 1 ± 0.17 h and blood loss for all the patients was approximately 15 mL. The included patients were followed up for 13–128 months, with an average follow‐up of 61 months. Among the included 73 patients, 65 (89.0%) exhibited satisfactory union, seven (9.6%) had femoral head necrosis, and one (1.4%) had nonunion. For seven patients with femoral head necrosis, there were two, two, and three patients classified as Garden IV, Garden III, and Garden II, respectively, and two and five patients classified as Pauwels II and Pauwels III, respectively. Among the seven cases, four underwent hip replacement. Garden fracture type was not significantly associated with femoral head necrosis (χ2 = 0.44, P > 0.05) or Harris score (χ2 = 1.43, P > 0.05). Patients with Pauwels I (0%) and II (4.3%) fractures exhibited a significantly lower necrosis rate than those with Pauwels III fractures (41.7%) (P < 0.05). Conclusions: Cannulated internal fixation was more suitable for older Chinese patients with Pauwels I/II fractures than those with Pauwels III fractures. [ABSTRACT FROM AUTHOR]

Big data values data processing to ensure effective value-added data. With the rapid development of the cloud era, the coverage of big data has gradually expanded, and it has received wide attention from all walks of life. In the process of modern social development, big data analysis is gradually applied to the future development planning, risk evaluation and integration of market development status. With the rapid development of many fields of society, the flow of information has gradually expanded, and the Internet has developed more rapidly, prompting the application of big data in various fields. Machine learning is a multidisciplinary study of how computers use data or past experience. With the ability to independently improve specific algorithms, the computer acquires knowledge through learning and achieves the goal of artificial intelligence. Big data and machine learning are the major technological changes in the modern computer world, and these technologies have had a huge impact on all walks of life. At present, with the rapid development of the Internet, mobile communications, social networks and the Internet of Things, these networks generate large amounts of data every day, and data become the most important information resource of today. Some studies have shown that in many cases, the larger the amount of data, the better the data will be for machine learning. On this basis, this paper proposes an online client algorithm based on machine learning algorithm for IoT unstructured big data analysis and uses it in other big data analysis scenarios. Use the online data entered by the customer to implement background data mining, the parallel way to verify its efficiency through machine learning algorithms such as K-nearest neighbor algorithm. [ABSTRACT FROM AUTHOR]

The conventional image segmentation algorithm of the colorimetric sensor array is inefficient and vulnerable to the interferences of the environment. Therefore, in order to improve the conventional algorithm, an image segmentation algorithm based on fuzzy C-means clustering (FCM) algorithm is proposed in this study. Through the information of the gray-scale distribution histogram, the proposed algorithm divides the different wave-peak regions, where the pixels are relatively concentrated, into different clusters to determine the number of clusters. In addition, the gray values of these clusters are calculated to determine the initial cluster center. Next, the calculation results are used as the input of the FCM algorithm to complete the clustering segmentation of FCM. The research results show that the algorithm proposed in this study avoids the human participations of the traditional FCM algorithm. Also, based on the original algorithm, the proposed algorithm can reduce the calculation iterations, thereby improving the computational efficiency and obtaining the number of clusters with reference significance. As the results indicate, the proposed algorithm can better describe the fuzzy information in the image, thereby avoiding the problem of classifying the pixels into one category. Besides, the exponential function is used to control the influence weight of the neighboring pixels, and the adaptive weighting of the pixel grayscale is realized to improve the calculation accuracy of pixel grayscale and realize the image segmentation. [ABSTRACT FROM AUTHOR]

The data of time series are massive in quantity and not conducive to subsequent processing. Therefore, the unordered time series fuzzy clustering algorithm of adaptive incremental learning has been utilized to explore the segmentation of time series in further. The research results show that the emergence of incremental learning technology can solve such problems. Also, it can continuously accumulate and increase the data, as well as improving the learning accuracy. Incremental learning technology correctly processes, retains, and utilizes the historical results, thereby reducing the training time of new samples by using historical results. Therefore, the clustering algorithm mostly clusters the cluster-liked shape of discrete datasets and uses the hierarchical clustering algorithm, which is more suitable for measuring the similarity of time series, to replace the Euclidean distance for distance metric and hierarchical clustering. The distance matrix update method is improved to reduce the computational complexity, which proves that the algorithm has higher clustering validity and reduces the operating time of the algorithm. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The purpose of this study is to realize the precise detection of Synthetic Aperture Radar (SAR) image changes. METHODS: In this study, an intuitionistic fuzzy C-means clustering algorithm is used to accurately detect the target changes in SAR images. The change of SAR image is detected by the constructed intuitionistic fuzzy C-means clustering algorithm. Then, the effect of intuitionistic fuzzy C-means clustering algorithm, block principal component analysis (PCA) and logarithmic ratio method is compared and analyzed in the aspects of stability, accuracy, image extraction, restoration, error and work efficiency of the algorithm. RESULTS: Compared with block PCA and logarithmic ratio methods, intuitionistic fuzzy C-means clustering algorithm has obvious advantages in stability, with standard deviation of 0.010 and other two algorithms of 0.014 and 0.017. In terms of detection accuracy and error, the algorithm in this study also has a good performance, and the detection accuracy can reach 92.4%. In addition, the intuitionistic fuzzy C-means clustering algorithm is clear and efficient for SAR image target extraction and restoration. Compared with the other two algorithms, the algorithm in this study improves by at least 20% in operation speed. There is no significant difference in the detection results of the proposed algorithm for SAR images with different targets, such as objects, people, geographical environment, etc. CONCLUSION: In this study, based on intuitionistic fuzzy C-means clustering algorithm, target changes in SAR images are detected, and the operation of the algorithm is studied. The algorithm used in this study shows a relatively comprehensive and good result, and also shows that the algorithm is a comprehensive result, which requires a good operation at many levels. This research greatly improves the recognition of intuitionistic fuzzy C-means clustering algorithm and SAR image. [ABSTRACT FROM AUTHOR]

This paper uses long-term bridge monitoring data to quantitatively assess the composite action exhibited in slab-on-girder highway bridges and investigates the potential relationship between composite action and deck deterioration over negative bending regions. A three-span highway bridge in Michigan is instrumented with a structural monitoring system to observe the flexural response of the spans to vehicular loads. The monitoring system is designed to offer data for quantitative assessment of the degree of composite action in composite and noncomposite sections of the bridge spans using the position of neutral axis and the magnitude of slip strain as key response parameters correlated to composite action. It is shown that unintended nonlinear partial composite action exists in negative bending regions of the bridge. A calibrated analytical model and a finite-element model are developed based on empirical observation allowing tensile strains in the deck to be estimated under load. Estimated surface strains are compared with those with the design assumption of no composite action at the slab–girder interface. It is concluded that the observed partial composite action results in higher tensile strains in the deck which is a likely culprit to accelerated deck deterioration. [ABSTRACT FROM AUTHOR]

Background: Paclitaxel (PTX) resistance is a great obstacle for the treatment of ovarian cancer. A previous study indicated that tetrandrine (TET) could induce the apoptosis of ovarian cancer cells. This study aimed to explore the effect of TET in combination with PTX on PTX resistance in ovarian cancer cells. Materials and Methods: CCK-8 assay, flow cytometry and wound healing assays were used to detect the proliferation, apoptosis and migration of PTX-resistant SKOV3 cells (SKOV3/PTX). The expressions of Bax, Bcl-2, cleaved caspase 3, β-catenin, c-Myc, cyclin D1 and p21 in SKOV3/PTX cells were detected with Western blot. In vivo animal study was performed finally. Results: In this study, the inhibitory effects of PTX on the proliferation and migration of SKOV3/PTX cells were markedly enhanced by TET. In addition, PTX-induced apoptosis in SKOV3/PTX cells was significantly enhanced by the treatment of TET via upregulating the levels of Bax and cleaved caspase 3, and downregulating the expression of Bcl-2. Moreover, combination of TET and PTX obviously induced cell cycle arrest in SKOV3/PTX cells via increasing the level of p21 and decreasing the levels of c-Myc and Cyclin D1. Meanwhile, combination of TET with PTX significantly decreased the expression of β-catenin in SKOV3/PTX cells. In vivo experiments further confirmed that TET enhanced the anti-tumor effect of PTX in SKOV3/PTX xenograft model. Conclusion: We found that TET could enhance the sensitivity of SKOV3/PTX cells to PTX via inhibiting the β-catenin/c-Myc/Cyclin D1 signaling pathway. Therefore, PTX combined with TET might be considered as a potential approach for the treatment of PTX-resistant ovarian cancer. [ABSTRACT FROM AUTHOR]

Marine diatoms are ubiquitously distributed in both coastal and open oceans, playing an important role in global primary productivity. In coastal waters, they are exposed to various pollutants in addition to multiple environmental stressors. Here, we show the pennate diatom Nitzschia sp. isolated from the East China Sea decreased its photosynthetic performance under the combined influences of two typical marine organic pollutants (Irgarol 1051 and diuron), that are frequently used as biocides to prevent biofouling, and solar UV radiation (UVR). Nitzschia sp. was sensitive to both pollutants under visible light (PAR) without UVR, even at the lowest concentration (0.5 μg L−1) tested; higher levels led to greater reductions in its photochemical yield. When additionally exposed to UVR (280–400 nm), the inhibition of the quantum yields by the antifouling pollutants was exacerbated, reflecting a synergistic impact of the pollutants and UVR. Exposure to UVR brought about 5–20% reduction at different levels of Irgarol 1051, diuron, and their mixture, with a higher reduction percentage due to UVR observed at lower concentrations of the pollutants. Our results indicate that even low levels of antifouling agents can result in significant impacts on diatoms in the presence of solar UVR, implying that combinations of UVR and organic pollutants could be a potential method to control-target algal biofouling. [ABSTRACT FROM AUTHOR]

• Cyber-physical system architecture created to link truck loads to bridge responses. • Measured truck loads linked to bridge responses create input-output data for SHM. • Highway truck loads tracked using deep learning applied to traffic camera feeds. • Bridge monitoring triggered by detecting trucks in real-time using CNN algorithms. Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water, valleys, and other roads. Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations. Structural health monitoring (SHM) systems designed to measure bridge responses have been developed to quantitatively track the health of bridges. More recently, SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured. However, precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy. To address existing technical limitations, a cyber-physical system (CPS) framework is proposed to track truck loads in a highway corridor, to trigger SHM systems to record bridge responses, and to automate the linking of bridge response data with truck weights collected by weigh-in-motion (WIM) stations installed along the corridor but not collocated with the bridges. To link truck weights to bridge responses, computer vision methods based on convolutional neural networks (CNN) are used to automate the detection and reidentification of trucks using traffic cameras. The single-stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site; high precision is obtained with the YOLO detector exceeding 95% average precision (AP) for an intersection over union (IOU) threshold of 0.75. To reidentify the same truck at different locations in the corridor, this study adopts a CNN-based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location. The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1-score of 0.97. The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges. The triggering strategy proves to be highly efficient with 99% of the triggered data collection cycles capturing truck events at each bridge. To validate, the CPS architecture is implemented on a 20-mile highway corridor that has a WIM station already installed; four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud. In total, over 10,000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another. [ABSTRACT FROM AUTHOR]

This article proposes a unified theory on power combining with a continuum of load modulation (LM) ratios. Two well-known power amplifier (PA) techniques, Doherty and traveling-wave amplifiers, are the two extreme examples within this continuum, yielding full and zero LMs, respectively. Based on this theory, a power-combining technique is proposed, which optimizes efficiency and bandwidth at a certain power backoff (BO) level, which is often in between the two well-known architectures. As a demonstration, a PA with an LM ratio of 1.4 is designed. It achieves a peak output power of 49.3±0.5 dBm and 47%–60% drain efficiency at 8-dB power BO over 2.6–3.8 GHz. The linearizability and practical usefulness of this PA are demonstrated through linearized-modulated measurements. [ABSTRACT FROM AUTHOR]