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Achieving the desired color temperature and color rendering index in light sources is challenging because of the deficiency of certain color components, notably cyan in the illumination spectra. In this investigation, we successfully synthesized orthogonal Ca5Ga6O14: Bi3+ (CGO: Bi3+) phosphors with space group Cmc21, featuring two distinct Ca(1) and Ca(2) sites. Under the excitation of 335 nm, a broad cyan emission with a 495 nm peak was observed. Gaussian peak fitting revealed two sub-peaks at 445 and 500 nm, originating from the emission of Bi3+ occupying Ca(1) and Ca(2) sites, respectively. As Bi3+ doping concentration increased, the luminescence experienced a red shift, attributed to the cooperative impact of crystal field splitting and electron cloud rearrangement. Through the substitution of Sr2+ ions, we achieved a 2.15 times enhancement in emission intensity and an increase in lifetime from 0.451 to 0.551 μs. This substitution also led to a consistent increase in the relative emission percentage of Bi(1) from 46.5% to 73.1%, while that of Bi(2) decreased from 70.5% to 44.9%. It is likely a result of some Bi3+ ions originally situated in the Ca(2) site migrating to the Ca(1) site. Finally, the introduction of Na+ ions for charge compensation resulted in a 20.18% increase in the luminescence intensity and an enhanced quantum efficiency of 84.21%. This CGO: Bi3+ phosphor holds promise as a potential cyan phosphor for white light sources. [ABSTRACT FROM AUTHOR]

The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for detecting malicious traffic. Although it is a labor-intensive and time-consuming process to gather large labeled datasets, the majority of prior studies on the classification of malicious traffic use supervised learning approaches and provide decent classification results when a substantial quantity of labeled data is available. This paper proposes a semi-supervised learning approach for classifying malicious IIoT traffic. The approach utilizes the encoder–decoder model framework to classify the traffic, even with a limited amount of labeled data available. We sample and normalize the data during the data-processing stage. In the semi-supervised model-building stage, we first pre-train a model on a large unlabeled dataset. Subsequently, we transfer the learned weights to a new model, which is then retrained using a small labeled dataset. We also offer an edge intelligence model that considers aspects such as computation latency, transmission latency, and privacy protection to improve the model's performance. To achieve the lowest total latency and to reduce the risk of privacy leakage, we first create latency and privacy-protection models for each local, edge, and cloud. Then, we optimize the total latency and overall privacy level. In the study of IIoT malicious traffic classification, experimental results demonstrate that our method reduces the model training and classification time with 97.55% accuracy; moreover, our approach boosts the privacy-protection factor. [ABSTRACT FROM AUTHOR]

Network security situational awareness is based on the extraction and analysis of big data, and by understanding these data to evaluate the current network security status and predict future development trends, provide feedback to decision-makers to make corresponding countermeasures, and achieve security protection for the network environment. This article focuses on artificial intelligence, summarizes the related definitions and classic models of network security situational awareness, and provides an overview of artificial intelligence. Starting from the method of machine learning, it specifically introduces the research status of neural-network-based network security situational awareness and summarizes the research work in recent years. Finally, the future development trends of network security situational awareness are summarized, and its prospects. [ABSTRACT FROM AUTHOR]

BACKGROUND: The survival rate of experimental animals is a very important index in chemical toxicity evaluation experiments. The calculation of nematode survival rate is used in many experiments. OBJECTIVE: Traditional survival rate quantification methods require manual counting. This is a time-consuming and laborious work when using 384-well plate for high-throughput chemical toxicity assessment experiments. At present, there is a great need for an automatic method to identify the survival rate of nematodes in the experiment of chemical toxicity evaluation. METHODS: We designed an automatic nematode survival rate recognition method by combining the bright field experimental image of nematodes and the dark field image of nematodes which is captured after adding Propidium Iodide dye, and used it to calculate the nematode survival rate in different chemical environments. Experiment results show that the survival rate obtained by our automatic counting method is very similar to the survival rate obtained by manual counting. RESULTS: Through several different chemical experiments, we can see that chemicals with different toxicity have different effects on the survival rate of nematodes. And the survival rate of nematodes under different chemical concentrations has an obvious gradient trend from high concentration to low concentration. In addition, our method can quantify the motility of nematodes. There are also significant differences in the motility of nematodes cultured in different chemical environments. Moreover, the nematode motility under different chemical concentrations showed an obvious gradient change trend from high concentration to low concentration. CONCLUSION: Our study provides an accurate and efficient nematode survival rate recognition method for chemical toxicology research. [ABSTRACT FROM AUTHOR]

Background. Inflammatory breast cancer (IBC) is one of the most rare and aggressive subtypes of primary breast cancer (BC). Our study aimed to explore hub genes related to the pathogenesis of IBC, which could be considered as novel molecular biomarkers for IBC diagnosis and prognosis. Material and Methods. Two datasets from gene expression omnibus database (GEO) were selected. Enrichment analysis and protein-protein interaction (PPI) network for the DEGs were performed. We analyzed the prognostic values of hub genes in the Kaplan-Meier Plotter. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) was used to find candidate small molecules capable to reverse the gene status of IBC. Results. 157 DEGs were selected in total. We constructed the PPI network with 154 nodes interconnected by 128 interactions. The KEGG pathway analysis indicated that the DEGs were enriched in apoptosis, pathways in cancer and insulin signaling pathway. PTEN, PSMF1, PSMC6, AURKB, FZR1, CASP9, CASP6, CASP8, BAD, AKR7A2, ZNF24, SSX2IP, SIGLEC1, MS4A4A, and VSIG4 were selected as hub genes based on the high degree of connectivity. Six hub genes (PSMC6, AURKB, CASP9, BAD, ZNF24, and SSX2IP) that were significantly associated with the prognosis of breast cancer. The expression of CASP9 protein was associated with prognosis and immune cells infiltration of breast cancer. CASP9- naringenin (NGE) is expected to be the most promising candidate gene-compound interaction for the treatment of IBC. Conclusion. Taken together, CASP9 can be used as a prognostic biomarker and a novel therapeutic target in IBC. [ABSTRACT FROM AUTHOR]

According to the actual situation of the comprehensive office building and the functional requirements of the building energy consumption monitoring and management system, the office building energy consumption monitoring and management system is designed by using the hardware resource configurable Shanke PLC(SKPLC) as the data collector. The system uses data bus technology and field data acquisition technology to achieve the building energy consumption data acquisition and management. Practice has proved that energy-saving effect is good.

Background. There is limited knowledge about the role of cancer-associated fibroblasts (CAF) in the tumor microenvironment of triple-negative breast cancer (TNBC). Methods. Three hundred and thirty-five TNBC samples from four datasets were retrieved and analyzed. In order to determine the CAF subtype by combining gene expression profiles, an unsupervised clustering analysis was adopted. The prognosis, enriched pathways, immune cells, immune scores, and tumor purity were compared between CAF subtypes. The genes with the highest importance were selected by bioinformatics analysis. The machine learning model was built to predict the TNBC CAF subtype by these selected genes. Results. TNBC samples were classified into two CAF subtypes (CAF+ and CAF-). The CAF- subtype of TNBC was linked to the longer overall survival and more immune cells than the CAF+ subtype. CAF- and CAF+ were enriched in immune-related pathways and extracellular matrix pathways, respectively. Bioinformatics analysis identified 9 CAF subtype-related markers (ADAMTS12, AEBP1, COL10A1, COL11A1, CXCL11, CXCR6, EDNRA, EPPK1, and WNT7B). We constructed a robust random forest model using these 9 genes, and the area under the curve (AUC) value of the model was 0.921. Conclusion. The current study identified CAF subtypes based on gene expression profiles and found that CAF subtypes have significantly different overall survival, immune cells, and immunotherapy response rates. [ABSTRACT FROM AUTHOR]

This study presents a path planning algorithm based on immune ant colony B spline interpolation to eliminate path duplication and corner inflection points, increase the smoothness of the agricultural robot's path trajectory, and enhance the path planning performance. Firstly, this study improves the convergence speed of the algorithm by introducing an adaptive factor that obeys the normal distribution to optimize the path heuristic function. Secondly, to strengthen the variety of the target path search, an immune algorithm was utilized to perform a global investigation of the appropriate routes and assign the initial pheromones of the ant colony. Then, the adaptive judgment factor is introduced based on the incentive degree function and the pheromone evaporation coefficient, and the update formula are adjusted to promote the algorithm's flexibility while suppressing the optimal regional problem. Finally, the pathways are processed using a cubic B spline interpolation method to remove the edges and minimize the peak inflection points. To verify the specific optimization performance of the algorithm in this paper, we have selected the same type of improved algorithm as a comparison and tested it in a raster map model. The simulation results demonstrate that the values of the proposed algorithm paths and the standard deviation of the tracks are smaller than the comparison algorithm, which indicates the higher performance of the algorithm in searching the optimal routes and more flexible and accurate search. Compared with the comparison algorithm, the average time-consuming reduction rate of the algorithm in this paper is between 38% and 45% in different grid maps, and the number of turns is reduced by 33% to 58%. It meets the requirements of the actual work on the robot. [ABSTRACT FROM AUTHOR]

Background: Here, we describe an innovative oncoplastic technique for small to medium volume breasts with a tumor in the lower quadrant and this technique could provide sufficient tissue to avoid visible defects after tumor removal and help reshape the natural shape of the breast. Methods: A detailed procedure for the folding flap technique is described step by step. Then, the results of a retrospective analysis of patients treated using this technique, including complications and disease recurrence rate, between January 2017 and November 2021 are reported. Aesthetic outcomes were evaluated on a 5-point scale proposed by the Paris Breast Center. Results: A total of 52 patients underwent surgery with the folding flap technique, The average operation time was 98.4 min (range, 75–120 min), and the mean bleeding volume was 56.5 mL (range, 20–100 mL). A margin-positive result was confirmed in 1 patient who underwent re-excision. Short-term postoperative complications were observed in 7 patients, including 4 with fat liquefaction, 2 with seroma, and 1 with skin redness and swelling. No flap necrosis was observed. The median follow-up time was 28.6 months (range, 9–58 months), and 2 patients experienced local recurrence. The mean aesthetic score was 4.7 points, with 36 patients scoring 5 points and 26 patients scoring 4 points, respectively. Conclusions: The folding flap technique, as an innovative and favorable oncoplastic technique for treating small- to medium-volume breasts with a tumor in the lower quadrant, could retain sufficient tissue to fill the residual cavity after the operation while improving the aesthetic outcome of the breast. [ABSTRACT FROM AUTHOR]

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. Methods: Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. Results: A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. Conclusion: TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT), where numerous smart devices associated with sensors, actuators, computers, and people communicate with shared networks, has gained advantages in many fields, such as smart manufacturing, intelligent transportation, and smart grids. However, security is becoming increasingly challenging due to the vulnerability of the IIoT to various malicious attacks. In this paper, the security issues of the IIoT are reviewed from the following three aspects: (1) security threats and their attack mechanisms are presented to illustrate the vulnerability of the IIoT; (2) the intrusion detection methods are listed from the attack identification perspectives; and (3) some defense strategies are comprehensively summarized. Several concluding remarks and promising future directions are provided at the end of this paper. [ABSTRACT FROM AUTHOR]

The heterogeneous ultra-dense network (UDN) has been widely regarded as a promising architecture for coping with the massive data traffic in future mobile networks including fifth generation mobile networks. There have been innumerable research efforts toward boosting the capacity of UDNs. Global communication path orchestration can better utilize UDN capacity; unfortunately, few studies focus on this topic. In this article, we propose a communication path orchestration architecture for heterogeneous UDNs. In this architecture, data can be transported from one interior communication node (i.e., a base station or user device) to another interior node along a hybrid device communication path (HDCP). HDCPs can be used for communications between two user devices and can be used to migrate traffic between backhaul links. Application- layer-assisted data forwarding allows HDCP to include various communication links including access links, backhaul links, and device-to-device links using licensed and unlicensed spectrum resources. HDCPs are centrally arranged to meet specific requirements and designated optimization objectives. We introduce two HDCP orchestration methods in this article. Our proposed solution can be used as a significant supplement to existing techniques for heterogeneous UDNs. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the prognostic significance of the Oncotype DX recurrence score (RS) in T1-2N1M0 estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer based on the prognostic stage in the updated American Joint Commission on Cancer, 8th edition. Methods: The Surveillance, Epidemiology, and End Results database was searched to identify ER-positive invasive ductal breast cancer in T1-2N1M0 with RS results diagnosed between 2004 and 2012. Patients with RS were categorized into low-risk (RS < 11), intermediate-risk (RS 11–25), and high-risk (RS > 25) groups. The distributions of clinical-pathological characteristics were compared among the RS risk groups using Pearson's Chi square. Breast cancer-specific survival (BCSS) and overall survival (OS) were estimated using the Kaplan–Meier method and compared across RS groups using the log-rank statistic. Cox models were fitted to assess the factors independently associated with survival. Results: The study enrolled 4059 cases categorized into prognostic stages IA to IIB. The RS risk groups were positively correlated with pathological prognostic stages (P < 0.001). The RS risk groups differed significantly in terms of BCSS and OS (P < 0.001). According to the multivariate analysis, RS risk group was an independent prognostic factor for BCSS and OS together with the pathological prognostic stage. The subgroup analysis showed similar survival rates across pathological prognostic stages in the RS low-risk group but significant differences in survival rates among pathological prognostic stages in the RS intermediate-risk group. The survival rates among the RS risk groups also differed significantly in pathological prognostic stage IA. Conclusions: Oncotype DX RS provided independent prognostic significance to complement the prognostic staging system. [ABSTRACT FROM AUTHOR]

There are controversies in the comparison of overall survival between invasive micropapillary carcinoma of the breast ( IMPC) and invasive ductal carcinoma ( IDC). The objective of this study was to compare the long-term survival outcome between non-metastatic IMPC and IDC. The Surveillance, Epidemiology, and End Results database was searched to identify women with non-metastatic IMPC and IDC diagnosed between 2001 and 2013. Comparisons of patient and tumor characteristics were performed using Pearson's chi-square. The propensity score matching method was applied with each IMPC matched to one IDC. Breast cancer-specific survival ( BCSS) and overall survival ( OS) were estimated using the Kaplan-Meier product limit method and compared across groups using the log-rank statistic. Multivariate analysis was performed through Cox models. IMPC was presented with aggressive clinical presentations such as larger tumor, more positive lymph nodes, and more advanced stage compared with IDC. A higher rate of estrogen receptor ( ER)/progesterone receptor ( PR) positivity was also observed in IMPC. With a median follow-up of 64 months, IMPC had a better BCSS ( P = 0.031) and OS ( P = 0.012) compared with IDC. In a case-control analysis IMPC was still an independent favorable prognostic factor for BCSS ( HR = 0.410, P < 0.001, 95% CI: 0.293-0.572) and OS ( HR = 0.497, P < 0.001, 95% CI: 0.387-0.637). In subgroup analysis, IMPC always showed a better survival outcome compared with IDC except in AJCC stage I and histologic grade I disease. IMPC has a better long-term survival outcome compared with IDC in spite of its highly aggressive clinical presentation. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the trends of surgical treatments among young patients in T 1 N 0-1 M 0 stage based on the Surveillance, Epidemiology, and End Results database. Patients aged less than 40 y diagnosed between 1998 and 2015 were enrolled, with tumors in T 1 N 0-1 M 0 stage and not located in the central area. Differences in clinical-pathological characteristics were evaluated using chi-square tests. Multivariate logistic regression was used to measure the various factors associated with contralateral prophylactic mastectomy (CPM). Independent prognostic factors were evaluated by Cox model. The total rate of breast-conserving surgery (BCS) was 51.6%, which declined from 64.5% in 1998 to 39.6% in 2015. The total rate of CPM was 22.7%, which increased from 3.7% in 1998 to 38.7% in 2014 despite a decline to 32.7% in 2015. Meanwhile, the rate of reconstruction increased in line with that of CPM, from 9.4% in 1998 to 35.0% in 2015. There was a trend of increasing use of implant-based reconstruction. Significant higher odds of CPM were found in recent year of diagnosis between 2010 and 2015 and in implant-based reconstruction. Patients undergoing CPM had similar survival outcomes compared with those undergoing BCS and unilateral mastectomy, whereas those undergoing BCS had better survival outcomes compared with those undergoing unilateral mastectomy. A trend of growing preference for CPM and reconstruction was observed among young patients in early stage in recent years without survival benefits. Efforts should be made to promote efficient communication and evidence-based decision-making. [ABSTRACT FROM AUTHOR]