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Abstract The direct laser writing (DLW) of photoluminescent metal clusters is inspiring intensive research in functional glasses. However, understanding the influence of the host structure on cluster formation and visualizing DLW-induced clusters at the atomic scale remains challenging. In this work, we develop a highly photosensitive fluorophosphate glass through fluorine incorporation. The addition of fluorine establishes a conducive environment for Ag+ ions before DLW and enhances the availability of reducing agents and diffusion pathways during DLW. These advantages facilitate the formation of Ag clusters under low-energy single-pulsed DLW. Increasing laser energy results in a combination of Ag clusters and glasses defect, forming a dot + ring photoluminescent pattern. Atom probe tomography (APT), a technique capable of mapping the elemental spatial distribution and identifying clustering, is employed to gain more information on laser-induced clusters. Comparison of APT results between samples without and with DLW reveals the formation of Ag clusters after laser writing. The design concept and characterization enrich the understanding of Ag cluster behavior in glasses. This knowledge opens the possibility of rational design of clusters confined in glasses and inspires their synthesis for various applications.

Abstract Objectives To develop and validate radiomics model based on computed tomography (CT) for preoperative prediction of CN0 status in patients with papillary thyroid carcinoma (PTC). Methods A total of 548 pathologically confirmed LNs (243 non-metastatic and 305 metastatic) two distinct hospitals were retrospectively assessed. A total of 396 radiomics features were extracted from arterial-phase CT images, where the strongest features containing the most predictive potential were further selected using the least absolute shrinkage and selection operator (LASSO) regression method. Delong test was used to compare the AUC values of training set, test sets and cN0 group. Results The Rad-score showed good discriminating performance with Area Under the ROC Curve (AUC) of 0.917(95% CI, 0.884 to 0.950), 0.892 (95% CI, 0.833 to 0.950) and 0.921 (95% CI, 868 to 0.973) in the training, internal validation cohort and external validation cohort, respectively. The test group of CN0 with a AUC of 0.892 (95% CI, 0.805 to 0.979). The accuracy was 85.4% (sensitivity = 81.3%; specificity = 88.9%) in the training cohort, 82.9% (sensitivity = 79.0%; specificity = 88.7%) in the internal validation cohort, 85.4% (sensitivity = 89.7%; specificity = 83.8%) in the external validation cohort, 86.7% (sensitivity = 83.8%; specificity = 91.3%) in the CN0 test group.The calibration curve demonstrated a significant Rad-score (P-value in H-L test > 0.05). The decision curve analysis indicated that the rad-score was clinically useful. Conclusions Radiomics has shown great diagnostic potential to preoperatively predict the status of cN0 in PTC.

The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FPS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation.

BackgroundPrevious studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. ObjectiveThis study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. MethodsThis longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. ResultsOf the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. ConclusionsThis study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders.

Cadmium (Cd) contamination poses a substantial threat the environment, necessitating effective remediation strategies. Phytoremediation emerges as a cost-efficient and eco-friendly approach for reducing Cd levels in the soil. In this study, the suitability of A. venetum for ameliorating Cd-contaminated soils was evaluated. Mild Cd stress promoted seedling and root growth, with the root being identified as the primary tissue for Cd accumulation. The Cd content of roots ranged from 0.35 to 0.55 mg/g under treatment with 10–50 µM CdCl2·2.5 H2O, and the bioaccumulation factor ranged from 28.78 to 84.43. Transcriptome sequencing revealed 20,292 unigenes, and 7507 nonredundant differentially expressed genes (DEGs) were identified across five comparison groups. DEGs belonging to the “MAPK signaling pathway-plant,” “monoterpenoid biosynthesis,” and “flavonoid biosynthesis pathway” exhibited higher expression levels in roots compared to stems and leaves. In addition, cytokinin-related DEGs, ROS scavenger genes, such as P450, glutathione-S-transferase (GST), and superoxide dismutase (SOD), and the cell wall biosynthesis-related genes, CSLG and D-GRL, were also upregulated in the root tissue, suggesting that Cd promotes root development. Conversely, certain ABC transporter genes, (e.g, NRAMP5), and some vacuolar iron transporters, predominantly expressed in the roots, displayed a strong correlation with Cd content, revealing the mechanism underlying the compartmentalized storage of Cd in the roots. KEGG enrichment analysis of DEGs showed that the pathways associated with the biosynthesis of flavonoids, lignin, and some terpenoids were significantly enriched in the roots under Cd stress, underscoring the pivotal role of these pathways in Cd detoxification. Our study suggests A. venetum as a potential Cd-contaminated phytoremediation plant and provides insights into the molecular-level mechanisms of root development promotion and accumulation mechanism in response to Cd stress.

The mechanism for enhanced precipitation strengthening due to the addition of copper to Al-9.0Zn-1.5Mg (wt.%) alloys has been investigated by means of tensile test, transmission electron microscopy, anomalous small-angle X-ray scattering and three-dimensional atom probe. With the addition of copper increasing from 0% to 1.0%, and then 2.6%, the strength increment of peak-aged alloys due to precipitation strengthening increases from 274.8 MPa to 329.3 MPa, and then 343.0 MPa. Based on the copper-induced change of the parameters of clusters, GPI zones and η′ precipitates coexisting in the aged alloys, the enhanced precipitation strengthening has been discussed in detail by considering their coherent, modulus and short-order strengthening. In the alloy without copper, the coherent strengthening from η′ precipitates play a main role in precipitation strengthening. In copper-containing alloys, the contribution of clusters to strengthening become significant. The addition of copper decreases coherent strengthening and modulus strengthening from η′ precipitates, but increases modulus strengthening from GPI zones and clusters, and short-range order strengthening from clusters more apparently, and consequently enhances strength of the alloys after aging.

Abstract In this work we revisit the existence of a strong first order electroweak phase transition (SFOEWPT) and recent m W precision measurement in the Type-I and Type-II 2HDMs. The O $$ \mathcal{O} $$ (100) GeV new scalars in 2HDMs are favored by SFOEWPT, which is necessary for electroweak baryogenesis, and observed m W shift as well. We find that under current constraints, both Type-I and Type-II 2HDM can explain the SFOEWPT, Z-pole, Higgs precision measurements and m W precision measurement of CDF-II at same time, and all these precision measurements are sensitive to heavy Higgs mass splitting in 2HDM. The allowed regions are ∆m A/C ∈ (−400, 400) GeV, tan β ∈ (1, 50), and ∆m A/C ∈ (−200, 300) GeV, tan β ∈ (1, 10) for Type-I and Type-II 2HDM respectively. Furthermore future lepton collider measurements on Higgs and Z boson properties can explore this scenario in more detail or even rule out it.

Salt stress is a serious abiotic stress that primarily inhibits plant growth, resulting in severe yield losses. Our previous research found that flavonoids play important roles in A. venetum salt stress tolerance. In response to salt stress, we noted that the flavonoid content was depleted in A. venetum. However, the detailed mechanism is still not clear. In this study, the expression patterns of three flavonoids synthetase genes, AvF3H, AvF3’H, and AvFLS were systemically analyzed under salt stress in A. venetum seedlings. The salt tolerance of transgenic Arabidopsis plants was improved by heterologous overexpression of these synthetase genes. The NBT and DAB staining results as well as H2O2 and O2•- content analysis revealed that under salt stress, ROS molecules were reduced in transgenic plants compared to WT plants, which corresponded to the activation of the antioxidant enzyme system and an increase in total flavonoid content, particularly rutin, eriodictyol, and naringerin in transgenic plants. External application of flavonoids reduced ROS damage in WT plants just like what we observed in the transgenic plants (without the external application). Additionally, our transcriptome analysis demonstrated that auxin and jasmonic acid biosynthesis genes, as well as signaling transduction genes, were primarily activated in transgenic plants under salt stress, leading to activation of the cell wall biosynthesis or modification genes that promote plant growth. As a result, we investigated the mechanism through flavonoids enhance the salt tolerance, offering a theoretical foundation for enhancing salt tolerance in plants.

Abstract Axion-like particles (ALPs) appear in various new physics models with spon- taneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investi- gate such light ALPs through the ALP-strahlung production processes pp → W ± a, Za with the sequential decay a → γγ at the 14 TeV LHC with an integrated luminosity of 3000 fb −1 (HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3 GeV to 5 GeV. The obtained bounds are stronger than the existing limits on the ALP-photon coupling.

Rail Conveyor is a highly energy-efficient and cost-effective bulk material transportation system, which merges the benefits of both belt conveyor technology and railway. Reducing vibration and noise is a key problem in the design of rail conveyor system, which has great social and economic significance for the realization of green environmental protection and sustainable development of bulk materials transportation. This article describes the field experimental test of vibration and noise. The vibration acceleration of rail conveyor was tested under different belt speed and different carriage spacing and different wheel materials of support carriage, and the noise of rail conveyor was tested under different belt speed and different test distance. Experimental results and analysis show that: With the increase of belt speed, the vibration and noise of wheel and rail of rail conveyor increase greatly, and the wheel rail vibration and noise decrease with the increase of the distance between the support carriages, and the wheel material has a great influence on the vibration.

BackgroundApathy is among the most prevalent and incapacitating non-motor symptoms of Parkinson’s disease (PD). PD patients with apathy (PD-A) have been reported to have abnormal spontaneous brain activity mainly in 0.01–0.08 Hz. However, the frequency-dependence of brain activity in PD-A remains unclear. Therefore, this study aimed to examine whether abnormalities in PD-A are associated with specific frequency bands.Materials and methodsOverall, 28 patients with PD-A, 19 PD patients without apathy (PD-NA), and 32 gender-, age-matched healthy controls (HCs) were enrolled. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data, demographic information, and neuropsychological assessments, including apathy, depression, anxiety and cognitive function for every participant. The amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in the conventional (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz), and slow-5 (0.01–0.027 Hz) frequency bands based on statistical parametric mapping (SPM12) and RESTplus V1.25. Two-sample t-tests were performed to compare the differences among the three groups.ResultsPD-A reduced ALFF in the right anterior cingulate gyri in the slow-5 band and decreased fALFF in the right middle frontal gyrus in the conventional band, compared to patients with PD-NA. However, PerAF, ReHo, and DC could not distinguish PD-A from PD-NA in the three bands. PD-A had higher ALFF and fALFF in the left middle occipital gyrus and lower fALFF in the bilateral insula in the slow-5 band compared to the HCs. Furthermore, abnormal DC value in hippocampus and parahippocampus was observed separately in the conventional band and in the slow-4 band between PD-A and HCs. Moreover, PD-A and PD-NA showed lower ReHo in cerebellum in the three bands compared to the HCs.ConclusionOur study revealed that PD-A and PD-NA might have different neurophysiological mechanisms. Concurrently, the ALFF in the slow-5 band and fALFF in the conventional band were sensitive in differentiating PD-A from PD-NA. The influence of apathy on the disease can be considered in the future research on PD, with the effects of frequency band taken into account when analyzing spontaneous brain activities in PD-A.

The rail conveyor is a new type of energy-saving system for the long-distance transportation of bulk materials. Operating noise is an urgent problem that the current model faces. It will cause noise pollution and affect the health of workers. In this paper, the factors causing vibration and noise are analyzed by modeling the wheel-rail system and the supporting truss structure. Based on the built test platform, the system vibration of the vertical steering wheel, the track support truss, and the track connection were measured, and the vibration characteristics at different positions were analyzed. Based on the established noise and vibration model, the distribution and occurrence rules of system noise under different operating speeds and fastener stiffness conditions were obtained. The experimental results show that the vibration amplitude of the frame near the head of the conveyor is the largest. The amplitude under the condition of 2 m/s running speed at the same position is 4 times that under the condition of 1 m/s. At different welds of the track, the width and depth of the rail gap have a great influence on the vibration impact, which is mainly due to the impact of the uneven impedance at the track gap, and the greater the running speed, the more obvious the vibration impact. The simulation results show the trend of noise generation, the speed of the trolley, and the stiffness of the track fasteners have a positive effect on the generation of noise in the low-frequency region. The research results of this paper will play an important role in the noise and vibration analysis of rail conveyors and help to optimize the structure design of the track transmission system.

Abstract The electroweak phase transition can be made first order by extending the Standard Model (SM) Higgs sector with extra scalars. The same new physics can explain the matter-antimatter asymmetry of the universe by supplying an extra source of CP violation and sphaleron processes. In this paper we study the existence of strong first order electroweak phase transition (SFOEWPT) in the type-I and type-II two Higgs doublet models (2HDM). We focus on how the SFOEWPT requirements constraint the spectrum of non-SM Higgs. Through the parameter space scan, we find that SFOEWPT suggests an upper limit on the masses of heavy Higgs m A / H / H ± $$ {m}_{A/H/{H}^{\pm }} $$ , which is around 1 TeV. High temperature expansion and Higgs vacuum uplifting is used for an analytical understanding of our results. After taking into account the probe ability on SFOEWPT from theoretical constraints, Higgs and Z-pole precision measurements up to the one-loop level at future Higgs & Z factories, sizeable loop corrections require m A / H ± − m H $$ {m}_{A/{H}^{\pm }}-{m}_H $$ ∈ (100, 250) GeV to meet SFOEWPT condition for Type-II 2HDM, and m A / H ± − m H $$ \left|{m}_{A/{H}^{\pm }}-{m}_H\right| $$ ∈ (100, 350) GeV or m A − m H / H ± $$ \left|{m}_A-{m}_{H/{H}^{\pm }}\right| $$ ∈ (100, 350) GeV for Type-I 2HDM.

Abstract Background To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). Method One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient. Results The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined. Conclusions Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.

In this paper, we regard policyholders, insurance companies, and government departments to be an anti-fraud supervision system, and we explore the supervision of motor vehicle insurance fraud from the perspective of a tripartite game. Taking into consideration the bad reputation records of policyholders as a state variable, through continuous accumulation in effective time, it creates a continuous growth-type warning effect on policyholders, and thus, effectively curbs policyholder fraud and false supervision by insurance companies. At the same time, by considering the influence of random factors on the anti-fraud game of motor vehicle insurance, in this paper, we establish a stochastic differential game model to explore the optimal strategy, the optimal income level, and the expectation and variance of the insured’s bad reputation record stock under the conditions of with and without government supervision. Finally, through a simulation analysis, it is found that the game with government supervision is more conducive to reduce the insured’s fraud intensity, and the simulation proves the impact of different parameters on system stability.

The existence of conveyor foreign objects poses a serious threat to the service life of conveyor belts, which will cause abnormal damage or even tearing, so fast and effective detection of conveyor foreign objects is of great significance to ensure the safe and efficient operation of belt conveyors. Considering the need for the foreign object detection algorithm to operate in edge computing devices, this paper proposes a hybrid compression method that integrates network sparse, structured pruning, and knowledge distillation to compress the network parameters and calculations. Combined with a Yolov5 network for practice, three structured pruning strategies are specifically proposed, all of which are proven to have achieved a good compression effect. The experiment results show that under the pruning rate of 0.9, the proposed three pruning strategies can achieve more than 95% compression for network parameters, more than 90% compression for the computation, and more than 90% compression for the size of the network model, and the optimized network is able to accelerate inference on both Central Processing Unit (CPU) and Graphic Processing Unit (GPU) hardware platforms, with a maximum speedup of 70.3% on the GPU platform and 157.5% on the CPU platform, providing an excellent real-time performance but also causing a large accuracy loss. In contrast, the proposed method balances better real-time performance and detection accuracy (>88.2%) when the pruning rate is at 0.6~0.9. Further, to avoid the influence of motion blur, a method of introducing prior knowledge is proposed to improve the resistance of the network, thus strongly ensuring the detection effect. All the technical solutions proposed are of great significance in promoting the intelligent development of coal mine equipment, ensuring the safe and efficient operation of belt conveyors, and promoting sustainable development.

BackgroundImmunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer.PurposeTo develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI.AssessmentThe patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging.Statistical TestsIndependent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysisResultsThe radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794–0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794–0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845–0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use.Data ConclusionRadiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.

We examine the lepton-specific 2HDM as a solution of muon g−2 anomaly under various theoretical and experimental constraints, especially the direct search limits from the LHC and the requirement of a strong first-order phase transition in the early universe. We find that the muon g−2 anomaly can be explained in the region of 32

Aiming at the problem that mining conveyor belts are easily damaged under severe working conditions, the paper proposed a deep learning-based conveyor belt damage detection method. To further explore the possibility of the application of lightweight CNNs in the detection of conveyor belt damage, the paper deeply integrates the MobileNet and Yolov4 network to achieve the lightweight of Yolov4, and performs a test on the exiting conveyor belt damage dataset containing 3000 images. The test results show that the lightweight network can effectively detect the damage of the conveyor belt, with the fastest test speed 70.26 FPS, and the highest test accuracy 93.22%. Compared with the original Yolov4, the accuracy increased by 3.5% with the speed increased by 188%. By comparing other existing detection methods, the strong generalization ability of the model is verified, which provides technical support and empirical reference for the visual monitoring and intelligent development of belt conveyors.

Abstract A light stop around the weak scale is a hopeful messenger of natural supersymmetry (SUSY), but it has not shown up at the current stage of LHC. Such a situation raises the question of the fate of natural SUSY. Actually, a relatively light stop can easily be hidden in a compressed spectra such as mild mass degeneracy between stop and neutralino plus top quark. Searching for such a stop at the LHC is a challenge. On the other hand, in terms of the argument of natural SUSY, other members in the stop sector, including a heavier stop $$\tilde{t}_2$$ t ~ 2 and lighter sbottom $$\tilde{b}_1$$ b ~ 1 (both assumed to be left-handed-like), are also supposed to be relatively light and therefore searching for them would provide an alternative method to probe natural SUSY with a compressed spectra. In this paper we consider quasi-natural SUSY which tolerates relatively heavy colored partners near the TeV scale, with a moderately large mass gap between the heavier members and the lightest stop. Then W / Z / h as companions of $$\tilde{t}_2$$ t ~ 2 and $$\tilde{b}_1$$ b ~ 1 decaying into $$\tilde{t}_1$$ t ~ 1 generically are well boosted, and they, along with other visible particles from $$\tilde{t}_1$$ t ~ 1 decay, are a good probe to study compressed SUSY. We find that the resulting search strategy with boosted bosons can have better sensitivity than those utilizing multi-leptons.

Many standard model extensions, including composite Goldstone Higgs models, predict vector-like fermionic top-partners at the TeV scale. The intensive search programmes by ATLAS and CMS focus on decays into a 3rd generation quark and an electroweak boson (W,Z,h). However, underlying models of partial compositeness contain additional states that give rise to exotic top partner decays. We consider a well-motivated scenario in which a charge-2/3 top-partner decays into a pseudo-scalar, T→ta, with a→gg or bb¯ dominating below the tt¯ threshold. We show that the constraints on the top partner mass from QCD pair production are substantially weakened, still allowing a top partner mass as light as 400 GeV.

Semantic audiovisual stimuli have a facilitatory effect on behavioral performance and influence the integration of multisensory inputs across sensory modalities. Many neuroimaging and electrophysiological studies investigated the neural mechanisms of multisensory semantic processing and reported that attention modulates the response to multisensory semantic inputs. In the present study, we designed an functional magnetic resonance imaging (fMRI) experiment of semantic discrimination using the unimodal auditory, unimodal visual and bimodal audiovisual stimuli with semantic information. By manipulating the stimuli present on attended and unattended position, we recorded the task-related fMRI data corresponding to the unimodal auditory, unimodal visual and bimodal audiovisual stimuli in attended and unattended conditions. We also recorded the fMRI data in resting state. Then the fMRI method was used together with a graph theoretical analysis to construct the functional brain networks in task-related and resting states and quantitatively characterize the topological network properties. The aim of our present study is to explore the characteristics of functional brain networks that process semantic audiovisual stimuli in attended and unattended conditions, revealing the neural mechanism of multisensory processing and the modulation of attention. The behavioral results showed that the audiovisual stimulus presented simultaneously promoted the performance of semantic discrimination task. And the analyses of network properties showed that compared with the resting-state condition, the functional networks of processing semantic audiovisual stimuli (both in attended and unattended conditions) had greater small-worldness, global efficiency, and lower clustering coefficient, characteristic path length, global efficiency and hierarchy. In addition, the hubs were concentrated in the bilateral temporal lobes, especially in the anterior temporal lobes (ATLs), which were positively correlated to reaction time (RT). Moreover, attention significantly altered the degree of small-worldness and the distribution of hubs in the functional network for processing semantic audiovisual stimuli. Our findings suggest that the topological structure of the functional brain network for processing semantic audiovisual stimulus is modulated by attention, and has the characteristics of high efficiency and low wiring cost, which maintains an optimized balance between functional segregation and integration for multisensory processing efficiently.

Previous studies have shown that task-irrelevant auditory information can provide temporal clues for the detection of visual targets and improve visual perception; such sounds are called informative sounds. The neural mechanism of the integration of informative sound and visual stimulus has been investigated extensively, using behavioral measurement or neuroimaging methods such as functional magnetic resonance imaging (fMRI) and event-related potential (ERP), but the dynamic processes of audiovisual integration cannot be characterized formally in terms of directed neuronal coupling. The present study adopts dynamic causal modeling (DCM) of fMRI data to identify changes in effective connectivity in the hierarchical brain networks that underwrite audiovisual integration and memory. This allows us to characterize context-sensitive changes in neuronal coupling and show how visual processing is contextualized by the processing of informative and uninformative sounds. Our results show that audiovisual integration with informative and uninformative sounds conforms to different optimal models in the two conditions, indicating distinct neural mechanisms of audiovisual integration. The findings also reveal that a sound is uninformative owing to low-level automatic audiovisual integration and informative owing to integration in high-level cognitive processes.

We propose to interpret the 750 GeV diphoton excess in deflected anomaly mediation supersymmetry breaking scenarios, which can naturally predict couplings between a singlet field and vector-like messengers. The CP-even scalar component (S) of the singlet field can serve as the 750 GeV resonance. The messenger scale, which is of order the gravitino scale, can be as light as Fϕ∼O(10) TeV when the messenger species NF and the deflection parameter d are moderately large. Such messengers can induce the large loop decay process S→γγ. Our results show that such a scenario can successfully accommodate the 125 GeV Higgs boson, the 750 GeV diphoton excess and the muon g−2 without conflicting with the LHC constraints. We also comment on the possible explanations in the gauge mediation supersymmetry breaking scenario.

In this paper, we interpret the 750 GeV diphoton excess in the Zee–Babu extension of the two-Higgs-doublet model by introducing a top partner (T)/bottom partner (B). In the alignment limit, the 750 GeV resonance is identified as the heavy CP-even Higgs boson (H), which can be sizably produced via the QCD process pp→TT¯ or pp→BB¯ followed by the decay T→Ht or B→Hb. The diphoton decay rate of H is greatly enhanced by the charged singlet scalars predicted in the Zee–Babu extension and the total width of H can be as large as 7 GeV. Under the current LHC constraints, we scan the parameter space and find that such an extension can account for the observed diphoton excess.

Top squark (stop) plays a key role in the radiative stability of the Higgs boson mass in supersymmetry (SUSY). In this work, we use the LHC Run-1 data to determine the lower mass limit of the right-handed stop in a natural SUSY scenario, where the higgsinos χ˜1,20 and χ˜1± are light and nearly degenerate. We find that the stop mass has been excluded up to 430 GeV for mχ˜10≲250 GeV and to 540 GeV for mχ˜10≃100 GeV by the Run-1 SUSY searches for 2b+ETmiss and 1ℓ+jets+ETmiss, respectively. In a small strip of parameter space with mχ˜10≳190 GeV, the stop mass can still be as light as 210 GeV and compatible with the Higgs mass measurement and the monojet bound. The 14 TeV LHC with a luminosity of 20 fb−1 can further cover such a light stop window by monojet and 2b+ETmiss searches and push the lower bound of the stop mass to 710 GeV. We also explore the potential to use the Higgs golden ratio, Dγγ=σ(pp→h→γγ)/σ(pp→h→ZZ⁎→4ℓ±), as a complementary probe for the light and compressed stop. If this golden ratio can be measured at percent level at the high luminosity LHC (HL-LHC) or future e+e− colliders, the light stop can be excluded for most of the currently allowed parameter region.

The top partner as a hallmark of the Littlest Higgs Model with T-parity (LHT model) has been extensively searched for during the Large Hadron Collider (LHC) Run-1. With the increasing mass limits on the top partner, the single production of the top partner will be dominant over the pair production. Under the constraints from the Higgs data, the electroweak precision observables and Rb, we find that the mass of T-even top partner (T+) has to be heavier than 730 GeV. Then, we investigate the observability of the single T-even top partner production through the process pp→T+j with the sequent decay T+→th in the di-photon channel in the LHT model at the LHC. We find that the mass of T+ can be excluded up to 800 GeV at 2σ level at 14 TeV LHC with the integrated luminosity L=3 ab−1.

The monitoring of conveyor belt deviation based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. Two kinds of conveyor condition monitoring algorithms are developed, applied and compared in this paper. Method 1 is to extract the edges of the conveyor belt based on the methods of edge detection and line fitting, and an analysis method is proposed to quickly determine whether the conveyor belt is off course. This method simplifies the information processing process after edge line extraction and realizes the fast and effective state detection of the conveyor belt. A conveyor belt deviation detection system was built in Method 2 with the aid of laser technology and the Labview platform. The result of deviation detection was more accurate and reliable with the addition of the laser line. It is observed that both methods can perform well for belt deviation detection, especially Method 2 that resulted in higher reliability and accuracy with faster processing speed. It provides new ideas for ensuring the safe and efficient operation of the conveyor.

Measuring the Higgs self-coupling is one of the crucial physics goals at the LHC Run-2 and other future colliders. In this work, we attempt to figure out the size of SUSY effects on the trilinear self-coupling of the 125 GeV Higgs boson in the MSSM and NMSSM after the LHC Run-1. Taking account of current experimental constraints, such as the Higgs data, flavor constraints, electroweak precision observables and dark matter detections, we obtain the observations: (1) In the MSSM, the ratio λ3hMSSM/λ3hSM has been tightly constrained by the LHC data, which can be only slightly smaller than 1 and minimally reach 97%; (2) In the NMSSM with λ0.7, a large enhancement or reduction −1.1