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For the problem of low light efficiency caused by divergent and uneven energy distribution of commercial port-able LED nail lamps, we propose an optical system composed of a Lambertian LED, a total internal reflection lens and a microlens array. According to the simulation results, the optical system can project a 130mm×70mm rectangular light pattern with a uniformity of 85.17% on a screen 70mm away. In addition, the optical utilized factor of the rectangular light pattern is 87.85%, and the optical efficiency of the entire optical system is 83.93%. &nbsp

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

Several functional gastrointestinal disorders (FGIDs) have overlapping symptoms, and, consequently, developing treatment strategies based on symptomatology poses a challenge for the clinical management of complex FGIDs. The significant overlap in the symptoms of FGIDs caused by the shared pathophysiological mechanisms is both a challenge and an excellent target for therapeutic development, since treatment strategies focused on shared pathophysiological mechanisms can treat the associated underlying diseases rather than just alleviating the primary symptoms. Owing to its multi-targeted approach, traditional Chinese medicine (TCM) has garnered immense interest worldwide; however, the quality of the data demonstrating its effectiveness is generally weak. Additionally, the causal link between the intrinsic mechanisms of action of TCM and its clinical benefits remains obscure. Systems biology is characterized by holistic and dynamic research, which corresponds to the holistic, multi-targeted, and syndrome-based approach of TCM. Therefore, high-throughput analysis techniques can be employed to describe and comprehend the genesis and progression of diseases, as well as the impacts of TCM on the organism, which may aid in elucidating the pathogenic mechanisms of the diseases as well as the mechanism of action of TCM.

The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality negative information. Capturing negative signals in positive and unlabeled data is challenging for recommendation systems. Most studies have used specific data and proposed negative sampling methods suitable to the data characteristics. Existing negative sampling strategies cannot automatically select suitable approaches for different data. However, this one-size-fits-all strategy often makes potential positive samples considered as negative, or truly negative samples considered as potential positive samples and recommend to users. In this way, it will not only turn down the recommendation result, but even also have an adverse effect. Accordingly, we propose a novel negative sampling model, Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation (RHNSR), which can combine multiple sampling strategies and dynamically adjust the proportions used by different sampling strategies. In addition, ensemble learning, which integrates various model sampling strategies for obtaining an improved solution, was applied to RHNSR. Extensive experiments were conducted on three real-world recommendation datasets, and the experimental results indicated that the proposed model significantly outperformed state-of-the-art baseline models and revealed significant improvements in precision and hit ratio (49.02% and 37.41%, respectively).

The glycoprotein CD44 is a key regulator of malignant behaviors in breast cancer cells. To date, hyaluronic acid (HA)-CD44 signaling pathway has been widely documented in the context of metastatic bone diseases. Core 1 β1,3-galactosyltransferase (C1GALT1) is a critical enzyme responsible for the elongation of O-glycosylation. Aberrant O-glycans is recognized as a hallmark in cancers. However, the effects of C1GALT1 on CD44 signaling and bone metastasis remain unclear. In this study, IHC analysis indicated that C1GALT1 expression positively correlates with CD44 in breast cancer. Silencing C1GALT1 accumulates the Tn antigen on CD44, which decreases CD44 levels and osteoclastogenic signaling. Mutations in the O-glycosites on the stem region of CD44 impair its surface localization as well as suppress cell–HA adhesion and osteoclastogenic effects of breast cancer cells. Furthermore, in vivo experiments demonstrated the inhibitory effect of silencing C1GALT1 on breast cancer bone metastasis and bone loss. In conclusion, our study highlights the importance of O-glycans in promoting CD44-mediated tumorigenic signals and indicates a novel function of C1GALT1 in driving breast cancer bone metastasis. Implications: Truncation of GalNAc-type O-glycans by silencing C1GALT1 suppresses CD44-mediated osteoclastogenesis and bone metastasis in breast cancer. Targeting the O-glycans on CD44 may serve as a potential therapeutic target for blocking cancer bone metastasis.

Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution is often used to enhance the resolution of the seismic image by adding a model driven regularization term. However, this method does not consider the features of the data, nor does it exactly describe the relationship between seismic data and the desired attribute (such as seismic reflectivity). We propose a data and model dual-driven seismic deconvolution method based on error-constrained joint sparse representation using borehole measurement and surface seismic data. The combined features of the borehole reflectivity and the surface seismic data can be obtained through joint dictionary learning. With the help of the joint dictionary, the relationship between seismic waveforms and reflectivity is captured by the sparse coefficients. We construct the regularization term of deconvolution by alternately decomposing the error of the synthesized data via sparse reconstruction and the observed seismic data. Unlike model-driven methods, the constraint term of the new method can be established by the error-constrained sparse representation. Based on this sparse representation, the initial model of reflectivity is obtained to realize the sparse deconvolution of seismic data under the constraint of borehole data features. In general, this method is a data and model dual-driven deconvolution. Synthetic and field data tests demonstrate that this method can effectively improve the resolution and accuracy of deconvolution.

本研究旨在探究女同志與前伴侶分手後的關係連結、社會支持與分手調適之關聯性。研究對象招募自我認同為女同志者，研究工具為：「分手後關係連結量表」、「社會支持量表」與「分手調適量表」等三項，問卷預試後由網路蒐集到938份有效問卷，並以獨立樣本t檢定、單因子變異數分析、皮爾森積差相關及多元迴歸進行分析。研究結果發現：（1）分手距今時間愈短、目前沒有伴侶與協商分手者，更傾向與前任伴侶聯繫；（2）20歲以上、交往時間愈長、分手次數愈多、目前有伴侶者，所獲得之社會支持較高；（3）20歲以下、交往時間短、分手次數較少者，分手後較具困惑感；（4）分手次數較多、分手距今較久、目前有伴侶者，有更正向之分手調適；（5）分手後與前任聯繫程度愈高者，具較高程度之痛苦、困惑與懷疑自我價值，及較多之愧疚與自責；與前任聯繫程度較低者，較易走出傷痛、肯定與瞭解自我價值及回到原本生活；（6）「分手後關係連結」與「社會支持」對「分手調適」多數向度具顯著預測力。最後根據結果提出建議，提供實務工作及未來研究者進行參考。 The purpose of this study was to investigate the association among relational boundaries with an ex-partner, social support and adjustment to a breakup among lesbian individuals. The participants, who identify as lesbian were assessed with Post-Breakup-Connectedness-Attitudes Scales, Scale of Social Support and Scale of breakup adjustment. After the pretest, 938 online questionnaires were collected in LGBT community and analyzed by descriptive statistics, one-way ANOVA, Person’s correlation and multiple regression. The results demonstrated that: (1) individuals who were not currently in a relationship, had a shorter time since the breakup, and negotiated the breakup were more likely to maintain contact with their ex-partner; (2) individuals aged over 20, with a longer dating time, more breakups, and currently in a relationship tended to have more social support; (3) individuals aged below 20, with a shorter dating time and fewer breakups, tended to feel more confused after a breakup; (4) individuals with more breakups, a longer time since the breakup, and currently in a relationship tended to have a positive adjustment after a breakup; (5) the more contact with ex-partner, the more likely it was to experience more pain, confuse, self-doubt, guilt and self-blame, while the less contact with ex-partner, the more likely it was to experience self-affirmation and to get through pain, to understand self-worth, and to return to normal life; (6) relational boundaries and social support among lesbians individuals significantly predicted breakup adjustment. Finally, suggestions for counselors and further researchers are offered. &nbsp

When gel nails are UV cured, the reaction heat results in a rise in nail temperature, which causes the user to feel pain. In this study, four layers of single-color gel nails were used to measure and analyze the temperature rise of each layer of gel resin during UV curing. The measurement results show that the temperature variations of the base gel and color gel layers were 1.55°C and 1.56°C, respectively, and the temperatures of the builder and sealer rose higher, with an average of 3.96°C and 3.54°C, respectively. The results show that the temperature rise was related to the thickness of the resin applied by the nail technician and varied from time to time, which could be solved by using Internet of Things (IoT) technology. Using a microprocessor and PC to analyze the temperature rise curve in the cloud is conducive to finding how the resin is cured in real-time and adjusting the intensity and duration of the UV light accordingly to keep the temperature variation within the threshold. &nbsp

With the development of decentralized networks, smart contracts, especially those for ERC tokens, are attracting more and more Dapp users to implement their applications. There are some functions in ERC token contracts that only a specific group of accounts could invoke. Among those functions, some even can influence other accounts or the whole system without prior notice or permission. These functions are referred to as contract backdoors. Once exploited by an attacker, they can cause property losses and harm users’ privacy. In this work, we propose Pied-Piper, a hybrid analysis method that integrates datalog analysis and directed fuzzing to detect backdoor threats in Ethereum ERC token contracts. First, datalog analysis is applied to abstract the data structures and identification rules related to the threats for preliminary static detection. Then, directed fuzzing is applied to eliminate false positives caused by the static analysis. We first evaluated Pied-Piper on 200 smart contracts, which are injected with different types of backdoors. It reported all problems without false positives, and none of the injected problems was missed. Then, we applied Pied-Piper on 13,484 real token contracts deployed on Ethereum. Pied-Piper reported 189 confirmed problems, four of which have been assigned unique CVE ids while others are still in the review process. Each contract takes 8.03 seconds for datalog analysis on average, and the fuzzing engine can eliminate the false positives within one minute.

To compare the outcomes of patients with hematological malignancies who received ATG-Fresenius (ATG-F) 20 mg/kg versus those who received ATG-Genzyme (ATG-G) 10 mg/kg in an unrelated donor hematopoietic stem cell transplantation (HSCT) procedure, a total of 186 patients who underwent their first allogeneic HSCT with an unrelated donor were retrospectively analyzed. One hundred and seven patients received ATG-F, and seventy-nine patients received ATG-G. Multivariate analysis showed that the type of ATG preparation had no effect on neutrophil engraftment (P = 0.61), cumulative incidence of relapse (P = 0.092), nonrelapse mortality (P = 0.44), grade II-IV acute graft-versus-host disease (GVHD) (P = 0.47), chronic GVHD (P = 0.29), overall survival (P = 0.795), recurrence-free survival (P = 0.945) or GVHD-free relapse-free survival (P = 0.082). ATG-G was associated with a lower risk of extensive chronic GVHD and a higher risk of cytomegaloviremia (P = 0.01 and HR = 0.41, P

Purpose Language networks are reorganized during glioma growth, leading to varying language performance in patients with gliomas located in language-eloquent areas. Therefore, pre-treated language performance reflects the neuroplasticity potential. Different domains of language processing, such as speech expression, repetition, and comprehension, involving different neural networks. We analyzed the effects of patient factors and tumor characteristics on the pre-treated performance to investigate neuroplastic potential of different language domains. Methods Patient age, sex, education level, tumor grade, language pathway involvement, T1 contrast enhanced (C+), and FLAIR (T2) volume were selected as variables. The correlation with abnormal language performance was verified using univariate and multivariate logistic regression. Results In total, 104 patients were enrolled in this study. 44% of patients had repetitive abnormalities, 34.9% had comprehension abnormalities, and 32.1% had expression abnormalities. The proportion of language pathway involvement and normal language performance was 60% in grade 2 and 3 gliomas and 16% in grade 4 gliomas. Tumor grade (p = 0.006) and T2 volume (p = 0.008) were associated with abnormal performance in the expressive domain, education level (p = 0.004) and T1 C + volume (p = 0.049) in the repetitive domain, and education level (p = 0.013), T2 volume (p = 0.011), and tumor grade (p = 0.089) in the comprehensive domain. Conclusion Different clinical and radiological factors affected the abnormal performance of the three language domains, indicating their functional connectivity and neuroplastic potential are inherently varied. The dynamic interactions between patient factors, tumor characteristics, and language processing should be considered when resecting left hemispheric gliomas.

Feature selection focuses on selecting important features that can improve the accuracy and simplification of the learning model. Nevertheless, for the ordered data in many real-world applications, most of the existing feature selection algorithms take the single-measure into consideration when selecting candidate features, which may affect the classification performance. Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms.

Advances in virtual reality technology have greatly benefited the acrophobia research field. Virtual reality height exposure is a reliable method of inducing stress with low variance across ages and demographics. When creating a virtual height exposure environment, researchers have often used haptic feedback elements to improve the sense of realism of a virtual environment. While the quality of the rendered for the virtual environment increases over time, the physical environment is often simplified to a conservative passive haptic feedback platform. The impact of the increasing disparity between the virtual and physical environment on the induced stress levels is unclear. This paper presents an experiment that explored the effect of combining an elevated physical platform with different levels of virtual heights to induce stress. Eighteen participants experienced four different conditions of varying physical and virtual heights. The measurements included gait parameters, heart rate, heart rate variability, and electrodermal activity. The results show that the added physical elevation at a low virtual height shifts the participant's walking behaviour and increases the perception of danger. However, the virtual environment still plays an essential role in manipulating height exposure and inducing physiological stress. Another finding is that a person's behaviour always corresponds to the more significant perceived threat, whether from the physical or virtual environment.

Objectives: To investigate the association of polygenic risk score (PRS) and bladder cancer (BC) risk and whether this PRS can be offset by a healthy lifestyle. Methods: Individuals with BC (n = 563) and non-BC controls (n = 483 957) were identified in the UK Biobank, and adjusted Cox regression models were used. A PRS was constructed based on 34 genetic variants associated with BC development, while a healthy lifestyle score (HLS) was constructed based on three lifestyle factors (i.e., smoking, physical activity, and diet). Results: Overall, a negative interaction was observed between the PRS and the HLS (P = 0.02). A 7% higher and 28% lower BC risk per 1-standard deviation (SD) increment in PRS and HLS were observed, respectively. A simultaneous increment of 1 SD in both HLS and PRS was associated with a 6% lower BC risk. In addition, individuals with a high genetic risk and an unfavourable lifestyle showed an increased BC risk compared to individuals with low genetic risk and a favourable lifestyle (hazard ratio 1.55, 95% confidence interval 1.16–1.91; P for trend

Suppliers significantly affect the effectiveness of sustainable supply chain management. Hence, it is extremely important to evaluate and select suppliers scientifically and objectively. Based on the theory of triple bottom line (economic, social, and environmental dimension) and a balanced scorecard, a measureable supplier evaluation framework in a sustainable supply chain is first formulated. Second, to reduce the defects of the single weight method, the subjective and objective weights of evaluation indicators are determined by combining the fuzzy best-worst method (BWM) and the entropy method, and then the combination weights are obtained through linear weighting. Third, the grey relational technique for order performance by similarity to ideal solution (TOPSIS) method is further adopted to evaluate and rank the suppliers. Finally, a case study illustrates and demonstrates the availability of the proposed supplier evaluation index system and evaluation method. Subsequently, some suggestions are proposed according to the results.