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Purpose: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Patients with early-stage TNBCs have distinct likelihood of distant recurrence. Current therapeutic guidance is still limited.Methods: We extracted transcriptome data for 189 pathologically confirmed pT1-2N0M0 TNBC patients at Fudan University Shanghai Cancer Center. Candidate mRNAs were filtered, which was followed by differential expressed mRNAs analysis, survival analysis, and LASSO Cox regression model. All-subsets regression program was used for constructing a multi-mRNA signature in the training set (n=159); the accuracy and prognostic value were then validated using an independent validation set (n=158). Results: Here, we profiled the transcriptome data from 189 early-stage TNBC patients along with 50 paired normal tissues. Early-stage TNBCs are featured of basal-like and immune-suppressed subtype and homologous recombination ability deficiency. We developed a prognostic signature contained seven mRNAs from transcriptome data (ACAN, KRT5, TMEM101, LCA5, RPP40, LAGE3, CDKL2). In both the training (n=159) and validation cohorts (n=158), the signature could identify patients with relatively high recurrence risks and serve as an independent prognostic factor. The signature had better prognostic value than traditional clinicopathological features in both sets. Among the seven mRNAs, TMEM101 is highly expressed in TNBC and represents a potential therapeutic target. Inhibition of TMEM101 impaired tumor progression.Conclusions: Our 7-mRNA signature could accurately predict recurrence risks of early-stage TNBCs. Clinical and genomic low risk TNBC patients may have the opportunity to avoid adjuvant chemotherapy

Alternating direction method of multipliers (ADMM) and adaptive moment estimation (ADAM) are two optimizers of paramount importance in convex optimization (CO) and deep learning (DL), respectively. Numerous state-of-the-art algorithms for solving inverse problems are achieved by carefully designing a convex criterion, typically composed of a data-fitting term and a regularizer. Even when the regularizer is convex, its mathematical form is often sophisticated, hence inducing a math-heavy optimization procedure and making the algorithm design a daunting task for software engineers. Probably for this reason, people turn to solve the inverse problems via DL, but this requires big data collection, quite time-consuming if not impossible. Motivated by these facts, we propose a new framework, termed as ADMM-ADAM, for solving inverse problems. As the key contribution, even just with small/single data, the proposed ADMM-ADAM is able to exploit DL to obtain a convex regularizer of very simple math form, followed by solving the regularized criterion using simple CO algorithm. As a side contribution, a state-of-the-art hyperspectral inpainting algorithm is designed under ADMM-ADAM, demonstrating its superiority even without the aid of big data or sophisticated mathematical regularization.

Online handwritten diagram recognition (OHDR) has attracted considerable attention for its potential applications in many areas, but it is a challenging task due to the complex 2D structure, writing style variation, and lack of annotated data. Existing OHDR methods often have limitations in modeling and learning complex contextual relationships. To overcome these challenges, we propose an OHDR method based on graph neural networks (GNNs) in this paper. In particular, we formulate symbol segmentation and symbol recognition as node clustering and node classification problems on stroke graphs and solve the problems jointly under a unified learning framework with a GNN model. This GNN model is denoted as Instance GNN since it gives the symbol instance label as well as the semantic label. Extensive experiments on two flowchart datasets and a finite automata dataset show that our method consistently outperforms previous methods with large margins and achieves state-of-the-art performance. In addition, we release a large-scale annotated online handwritten flowchart dataset, CASIA-OHFC, and provide initial experimental results as a baseline.

Dopamine functioning is an essential mechanism underlying addictive behaviors. This paper evaluates the association of Internet gaming disorder (IGD) with the catechol‐O‐methyltransferase (COMT) val158met polymorphism and examines the roles of impulsivity and reinforcement sensitivity in this association. Using diagnostic interviews, this study recruited 69 participants with IGD and 138 participants without. All participants underwent diagnostic interviews for IGD and an evaluation for the COMT val158met polymorphism, impulsivity, and reinforcement sensitivity. Among participants with the Val/Val genotype, the odds ratio (95% confidence interval) for IGD was 2.09 (1.15–3.80). The IGD–Val/Val genotype association was mediated by impulsivity and fun‐seeking. The Val/Val genotype is indicative of low frontal functioning and is a predictive factor of IGD, with this effect being confounded by impulsivity and fun‐seeking. Interventions targeting impulsivity and fun‐seeking might attenuate the risk of IGD, particularly among individuals with the Val/Val genotype. Additional studies are necessary to elucidate the possible role of dopamine functioning.

Data representation learning is one of the most important problems in machine learning. Unsupervised representation learning becomes meritorious as it has no necessity of label information with observed data. Due to the highly time-consuming learning of deep-learning models, there are many machine-learning models directly adapting well-trained deep models that are obtained in a supervised and end-to-end manner as feature abstractors to distinct problems. However, it is obvious that different machine-learning tasks require disparate representation of original input data. Taking human action recognition as an example, it is well known that human actions in a video sequence are 3-D signals containing both visual appearance and motion dynamics of humans and objects. Therefore, the data representation approaches with the capabilities to capture both spatial and temporal correlations in videos are meaningful. Most of the existing human motion recognition models build classifiers based on deep-learning structures such as deep convolutional networks. These models require a large quantity of training videos with annotations. Meanwhile, these supervised models cannot recognize samples from the distinct dataset without retraining. In this article, we propose a new 3-D deconvolutional network (3DDN) for representation learning of high-dimensional video data, in which the high-level features are obtained through the optimization approach. The proposed 3DDN decomposes the video frames into spatiotemporal features under a sparse constraint in an unsupervised way. In addition, it also can be regarded as a building block to develop deep architectures by stacking. The high-level representation of input sequential data can be used in multiple downstream machine-learning tasks, we evaluate the proposed 3DDN and its deep models in human action recognition. The experimental results from three datasets: 1) KTH data; 2) HMDB-51; and 3) UCF-101, demonstrate that the proposed 3DDN is an alternative approach to feedforward convolutional neural networks (CNNs), that attains comparable results.

Although text detection methods have addressed several challenges in the past, there is a dearth of effective methods for text detection in deformable images, such as images containing text embedded on cloth, banners, rubber, sports jerseys, uniforms, etc. This is because deformable regions contain surfaces of arbitrarily shapes, which lead to poor text quality. This paper presents a new method for deformable text detection in natural scene images. It is observed that although the shapes of characters change in a deformable region, the pixel values and spatial relationship between the pixels do not change. This motivated us to explore extraction of Maximally Stable Extremal Regions (MSER) in an image in which pixels that share common features are grouped into components. The unique character shape variations led us to explore harmonic features to represent the component shape variations, using which a classifier classifies text and non-text components from the output of the MSER step. Additionally, the objective of developing a lightweight method with low computational cost motivated us to introduce a modified Pixel Aggression Network (PAN) for text deformable text detection at a component level. Comprehensive experiments which include experiments on our Deformable Text Dataset (DTD) and standard natural scene text datasets, namely, MSRATD-500, ICDAR 2019 MLT, Total-Text, CTW1500, ICDAR 2019 ArT and DSTA1500 datasets show that the proposed model outperforms the existing methods for our dataset as well as the standard datasets.

Most of the existing ubiquitous clinic recommendation (UCR) systems adopt linear mechanisms to aggregate the attribute-level performances of a clinic to evaluate the overall performance. However, such linear mechanisms may not be able to explain the choices of all patients. To solve this problem, the modified mixed binary nonlinear programming (MMBNLP)–feedforward neural network (FNN) approach is proposed in this study. In the proposed methodology, first, the existing MBNLP model is modified to improve the successful recommendation rate using a linear recommendation mechanism. Subsequently, an FNN is constructed to fit the relationship between the attribute-level performances of a clinic and its overall performance, thereby providing possible ways to further enhance the recommendation performance. The results of a regional experiment showed that the MMBNLP–FNN approach improved the successful recommendation rate by 30%.

The success of mediation is affected by many factors, such as the context of the quarrel, the personality of both parties, and the negotiation skill of the mediator, which lead to uncertainty for the work of prediction. This article takes a different approach from that of previous legal prediction research. It analyzes and predicts whether two parties in a dispute can reach an agreement peacefully through the conciliation of mediation. With the inference result, we can know whether mediation is a more practical and time-saving method to solve the dispute. Existing works about legal case prediction mostly focus on prosecution or criminal cases. In this work, we propose a long short-term memory (LSTM)–based framework, called LSTMEnsembler, to predict mediation results by assembling multiple classifiers. Among these classifiers, some are powerful for modeling the numerical and categorical features of case information, for example, XGBoost and LightGBM. Some are effective for dealing with textual data, for example, TextCNN and BERT. The proposed LSTMEnsembler aims to not only combine the effectiveness of different classifiers intelligently but also to capture temporal dependencies from previous cases to boost the performance of mediation prediction. Our experimental results show that our proposed LSTMEnsembler can achieve 85.6% for F-measure on real-world mediation data.

Current therapy does not provide significant benefits for patients with chronic stroke. Pre-clinical studies suggested that autologous adipose-derived stem cells have benefits for the treatment of chronic stroke. This Phase I open-label study was conducted to demonstrate the safety and efficacy of autologous adipose-derived stem cells (GXNPC1) in chronic stroke. Three patients with chronic stroke were treated with stereotactic implantation of autologous adipose-derived stem cells (1 × 108 cells). The primary endpoints of safety evaluation included adverse events, over a 6 months post-implantation period. The secondary endpoints included improvements in neurological functions. Evolutional change of brain parenchyma was also followed with magnetic resonance imaging (MRI). All three participants improved significantly at 6 months follow-up. The extent of improvement from pre-treatment was: National Institutes of Health Stroke Scale improved 5-15 points, Barthel Index: 25-50 points, Berg balance scale 0-21 points and Fugl-Meyer modified sensation 3-28 points. All three patients had signal change along the implantation tract on MRI one month after surgery. There is no related safety issue through 6 months observation. Clinical measures of neurological symptoms of these patients with chronic stroke improved at 6 months without adverse effects after implantation of autologous adipose-derived stem cells (GXNPC1), which might be correlated with post-implantation changes on brain MRI. Clinical Trial Registration-URL: https://clinicaltrials.gov/ct2/show/NCT02813512?term=ADSC&cond=Stroke&cntry=TW&draw=2&rank=1 Unique identifier: NCT02813512.

Stacked nanosheet nFETs considering a six-stack four-finger transistor array are studied and optimized by validated TCAD simulation. Stacked Si nanosheet (NS) nFETs have lower parasitics than nFinFETs in the same six-fin/stack four-finger transistor array layout. With the same electron mobility, back-end-of-line (BEOL), equivalent oxide thickness (EOT) of 1.4 nm, and gate length of 30 nm, the stacked Si NSs have $1.1\times $ cut-off frequency (240 versus 215 GHz) and $1.15\times $ maximum oscillation frequency (290 versus 251 GHz) when compared to FinFETs due to larger transconductance increase than capacitance increase and output conductance decrease. With the optimized EOT of 0.8 nm and gate length of 18 nm, the stacked Si NSs can achieve cut-off frequency of 340 GHz and maximum oscillation frequency of 370 GHz. Furthermore, considering the higher electron mobility on {100} surfaces of NSs than {110} sidewalls of FinFETs as suggested by the ${I} _{D}$ – ${V}_{\text {GS}}$ fitting, the cut-off frequency and maximum oscillation frequency of stacked Si NSs can reach 380 and 390 GHz, respectively.

A 6T-SRAM bitcell with the footprint of only two transistors is demonstrated by stacking four n-type vertical gate-all-around transistors (VFET) on two pFinFETs. The local interconnects are all within the footprint of the two bottom pFinFETs. The bitcell area is $0.014~\mu \text{m}^{{2}}$ considering the experimentally achievable metal pitch of 52 nm and bottom pFinFETs of 5 nm node. It can be further reduced to $0.009~\mu \text{m}^{{2}}$ considering the metal pitch of 30 nm. The minimum operating voltage is analyzed considering the dominant work function variation. The fin height of pFETs and the gate length of nFETs are used to optimize the read/write static noise margin (RSNM/WSNM) for the same bitcell area. The cell ratio is insensitive to pFET fin height and nFET gate length, leading the read stability to be less affected by pFET fin height and nFET gate length as compared to the write stability. The minimum operating voltage is reduced to 0.59 V using pFET fin height of 15 nm and nFET gate length of 20 nm to balance the read/write SNM without assist circuit techniques. The minimum operating voltage can be further improved to 0.56 V by optimizing the threshold voltage of −0.27 and 0.27 V for pFETs and nFETs, respectively. Moreover, using the negative bitline (NBL) technique can provide an additional 320 mV reduction on the write minimum operating voltage.

Climatic factors and global warming have great impacts on the tourists' behaviors. The goal of this study is to explore how they affect the total number of visitors, and the Huisun National Forest Park (HNFP) in Taiwan was selected as the research site. This study comprehensively evaluated the direct and indirect effects of climate change on the total number of visitors. Regression models were built to examine variations in the total number of visitors, including potential future climate scenarios in four global warming scenarios, and a questionnaire survey was conducted to investigate tourists' behaviors. The majority of the respondents were male, aged 17–24 years, and students with an educational level of (junior) college. The results show that climate change may not decrease on the total number of visitors to HNFP if only temperature and precipitation variations are considered. The climate-induced factors also have less effects on the travel intention for the tourists who have not visited. The visitors were most concerned about landscape transformation under climate change. Therefore, the managers should pay more attention to the conservation and maintenance of the area's landscapes to avoid reduced revisit intention and frequency. Since the total number of visitors is expected to increase, the HNFP managers need to modify the policies accordingly. The outcomes can be used for developing strategies in response to climate change.

Requirements of compressed sensing techniques targeted at miniaturized hyperspectral satellite applications include lightweight onboard hardware, high-speed sensing, low sampling rate for compressing the massive volume of typical hyperspectral data, and noise robustness for reliable data transmission to the ground station. We achieve all these aims via deep learning, and neural networks resulted from which can be implemented on-chip, thereby allowing light hardware implementation. Our neural networks were trained from small-scaled data, but, even so, the resulting encoder achieves a very low sampling rate and very high speed. Unlike typical network training, the input–output pairs are not square but stripe-like images, partly because compressed acquisition does not allow performing compression after obtaining complete data cube and partly because stripe-like acquisition well matches the popular pushbroom hyperspectral sensing schemes. Even with such hard restriction caused by nontraditional training, the resulting decoder still reconstructs the image with high accuracy. To match the requirement of pushbroom sensing, a lightweight encoder is proposed to compress the stripe-like images immediately. Meanwhile, multiscale feature fusion block (MFB) and aggregation (MFA) modules are proposed to form our decoder for enhancing the feature representation of the compressed acquisitions. Furthermore, we achieve joint spatial/spectral super-resolution (SR) progressively, ensuring accurate hyperspectral reconstruction via a low-rank-driven decoder. The encoder and decoder are trained in an end-to-end manner, where noise robustness is forced during the training stage. Comprehensive experiments demonstrate the superiority of the proposed hyperspectral compressed sensing method, as well as its one-shot transfer learning (OTL)-based extension, both quantitatively and qualitatively.

Background: Adult acquired flatfoot deformity (AAFD) and hallux valgus (HV) are common foot and ankle deformities. Few studies have reported the changes in radiographic parameters of HV after reconstructive surgery for AAFD. This study aimed to evaluate the changes in radiographic parameters of HV and analyze the risk factors for increased HV after correction of AAFD. Methods: Adult patients with flexible AAFD who underwent similar bony procedures including medializing calcaneal osteotomy and Cotton osteotomy were included. Radiographic parameters were measured on weightbearing radiographs preoperatively, postoperatively, and at the final follow-up. Patients were divided into hallux valgus angle (HVA) increased and HVA nonincreased groups; logistic regression analysis was performed to identify risk factors affecting increased HV. Results: Forty-six feet of 43 patients were included. After AAFD reconstructive surgery, the tibial sesamoid position improved by 1 grade, but the HVA increased 4 degrees in average. Further, 21 of 46 feet (46%) showed an HVA increase ≥5 degrees immediately after AAFD correction surgery. Preoperative talonavicular coverage angle Conclusion: In this case series, using plain radiographs to measure standard parameters of foot alignment, we found the association between AAFD correction and HV deformity measures somewhat paradoxical. Correction of overpronation of the hindfoot and midfoot appears to improve the first metatarsal rotational deformity but may also increase HVA. A lower preoperative talonavicular coverage angle was associated with an increase of the HVA after surgery. Level of Evidence: Level IV, case series study.

Background Long-term care (LTC) services are a professional service-driven (PSD) system; to deliver appropriate care services to residents, health care providers first need to collect appropriate patient data and make a professional assessment. A well-designed LTC information system should therefore consider the information requirements of multidisciplinary health care providers to adequately support their care services. Objectives This study proposed a modified service blueprint—the PSD service blueprint—for visualizing interdisciplinary service providers' input and output information requirements, which correspond to their service activities. Methods The PSD service blueprint comprises five layers and seven elements. We also present a case study to illustrate the blueprint's application to daily LTC services. Results Our proposed approach could clearly illustrate the daily care activities, service providers (main actors), actors' input and output information, and suggestions for LTC information system–related applications. Conclusion The proposed PSD service blueprint can not only gather interdisciplinary LTC service providers' information system requirements but also act as a mapping tool for visualizing the care service process.

The panel-level redistribution-layer (RDL)-first fan-out packaging for hybrid substrate is studied. Emphasis is placed on the process, materials, design, and fabrication of: 1) heterogeneous integration of one large chip and one small chip with 50- $\mu \text{m}$ pitch (minimum); 2) fine metal linewidth and spacing RDL-first substrate on a temporary panel carrier; 3) ordinary build-up package substrate on a panel; 4) hybrid substrate which is by soldering the RDL-first substrate on the build-up substrate; and 5) chips to hybrid substrate bonding and underfilling. Reliability assessment by thermomechanical simulation includes thermal cycling of the heterogeneous integration of the two-chip package on the hybrid substrate that is performed by a nonlinear temperature- and time-dependent finite-element analysis.

Amyloid‐β (Aβ) deposition in the brain has been implicated in the development of Alzheimer's disease (AD), and neuroinflammation generates AD progression. Therapeutic effects of anti‐inflammatory approaches in AD are still under investigation. Curcumin, a potent anti‐inflammatory and antioxidant, has demonstrated therapeutic potential in AD models. However, curcumin's anti‐inflammatory molecular mechanisms and its associated cognitive impairment mechanisms in AD remain unclear. The high‐mobility group box‐1 protein (HMGB1) participates in the regulation of neuroinflammation. Herein, we attempted to evaluate the anti‐inflammatory effects of chronic oral administration of curcumin and HMGB1 expression in APP/PS1 transgenic mice AD model. We found that transgenic mice treated with a curcumin diet had shorter escape latencies and showed a significant increase in percent alternation, when compared with transgenic mice, in the Morris water maze and Y‐maze tests. Additionally, curcumin treatment could effectively decrease HMGB1 protein expression, advanced glycosylation end product‐specific receptor (RAGE), Toll‐like receptors‐4 (TLR4) and nuclear factor kappa B (NF‐κB) in transgenic mice hippocampus. However, amyloid plaques detected with thioflavin‐S staining in transgenic mice hippocampus were not affected by curcumin treatment. In contrast, curcumin significantly decreased GFAP‐positive cells, as assessed by immunofluorescence staining. Taken together, these data indicate that oral administration of curcumin may be a promising agent to attenuate memory deterioration in AD mice, probably inhibiting the HMGB1‐RAGE/TLR4‐NF‐κB inflammatory signalling pathway.

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article.

Under the background of further promoting the construction of "golden class" in universities, how to use teaching innovation and promote the cultivation of learners is the focus of educational researchers. Guided by the theory of deep learning, based on MOOC resource platform and teaching management platform, this study constructs a "3-stage-2 platform" blended learning mode for deep learning in three stages: before class, in class and after class, puts forward teaching strategies to promote deep learning, and based on this mode, takes the computer network course as an example to carry out the blended teaching design and practice. The results show that the model has a certain role in promoting learners' knowledge mastery and ability training, and good emotional experience is conducive to learners' deep learning. Keywords: Deep learning, mixed teaching, teaching design.

Liver transplantation (LT) for small infants6 months old is rare but becoming common as perioperative care improves. In Taiwan, living donor LT (LDLT) has expanded indications but is rarely performed for this age group because of unfavorable outcomes in the literature. We evaluated LDLT outcomes of patients6 months old.We identified infants6 months old undergoing LDLT between 2004 and 2019 at our hospital. Variables related to recipients, donors, surgeries, and outcomes were analyzed.Nine patients were identified. Indications for LT were biliary atresia (n = 2), Alagille syndrome (n = 1), protein C deficiency (n = 1), and acute liver failure (n = 5), including two patients with neonatal hemochromatosis, one with herpes simplex hepatitis, one with giant cell hepatitis with autoimmune hemolytic anemia, and one with hemophagocytic lymphohistiocytosis. Median age and weight at LT were 129 days and 4.8 kg, respectively. Graft types included left lateral segment (LLS, n = 4), hyper-reduced LLS (n = 4), and monosegment (n = 1). The median graft-to-recipient weight ratio was 4%. The median follow-up period was 14 months (range, 8 days to 127 months) with two mortalities, and two patients were totally weaned off immunosuppressants. Adjuvant therapies were required for patients with giant cell hepatitis and hemophagocytosis. Preoperative reconstructive imaging for estimating graft thickness facilitated surgical planning.Although LDLT is difficult to perform for small infants, outcomes are favorable and mainly dependent on underlying causes in addition to technical innovations.

An accurate driving cycle prediction is a vital function of an onboard energy management strategy (EMS) for a battery/ultracapacitor hybrid energy storage system (HESS) in electric vehicles. In this paper, we address the requirements to achieve better EMS performances for a HESS. First, a long short-term memory-based method is proposed to predict driving cycles under the framework of a model predictive control (MPC) algorithm. Secondly, the performances of three EMSs based on fuzzy logic, MPC, and dynamic programming are systematically evaluated and analyzed. For online implementation, the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based EMS. Thirdly, the impact of battery aging on EMS performances is explored; it indicates that battery aging consciousness can slightly extend battery life. Finally, a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.

In this paper, we address the fixed-time consensus tracking problem of second-order leader-follower multi-agent systems with nonlinear dynamics under directed topology. The consensus tracking algorithm consists of distributed observer and observer-based decentralized controller. The fixed-time distributed observer guarantees that each follower estimates the leader’s state under directed topology within a fixed time, where the upper bound of convergence time is independent on the initial conditions. The fixed-time decentralized controller makes each follower converge to the leader’s state in fixed time via tracking the distributed observer’s state and overcome the nonlinear dynamics without adding linear control terms. Finally, the numerical example is provided to illustrate the effectiveness of the results.

Most existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.

This study aimed to evaluate the belief of frustration intolerance of individuals with internet gaming disorder (IGD) and its association with depression among them. We recruited 69 participants with IGD and 138 controls (69 regular gamers and other non‐gamers). IGD is diagnosed based on DSM‐5 (DSM stands for diagnostic and statistical manual of mental disorders) criteria through psychiatric interviews. They had completed the questionnaire for the belief of frustration intolerance, depression, and severity of IGD. The participant with IGD had a higher score on the frustration discomfort scale and its subscales, discomfort intolerance, entitlement, emotional intolerance, and achievement. Further regression analysis demonstrated an independent association between entitlement and IGD in control of depression. The frustration intolerance is also associated with depression and the severity of IGD among the IGD group. The discomfort intolerance and achievement were the most associated factors of depression. Frustration intolerance is a crucial irrational belief of IGD. It contributes to the severity of IGD and depression among individuals with IGD. The frustration intolerance, particularly for discomfort intolerance, entitlement, and achievement, should be assessed and intervened while treating individuals with IGD.

We study a metal organic chemical vapor deposition regrowth process for electrically driven photonic-crystal surface-emitting lasers (PCSELs). The photonic-crystal (PC) structure consisting of air ...

Background/Aims Endoscopic ultrasonography (EUS)-guided tissue acquisition requires a long learning curve. We aimed to compare the skill maturation curves between fine needle aspiration (FNA) and biopsy (FNB) for tissue acquisition. Methods The initial 60 procedures performed by the trainee endosonographer (30 FNA vs. 30 FNB) were consecutively enrolled. The difference in procedure performance was compared between the two groups. Learning curves were assessed. Twenty additional cases were subsequently enrolled to assess the consistency of performance in the FNB group. Results The FNB group acquired larger tissue samples (2.35 vs. 0.70 mm2; p

Objective A role of diffusion-weighted imaging (DWI) in the diagnosis of cerebral venous thrombosis (CVT) is not wellunderstood. This study evaluates the effectiveness of DWI in the diagnosis of CVT. Methods Literature search was conducted in electronic databases for the identification of studies which reported the outcomes of patients subjected to DWI for CVT diagnosis. Random-effects meta-analyses were performed to achieve overall estimates of important diagnostic efficiency indices including hyperintense signal rate, the sensitivity and specificity of DWI in diagnosing CVT, and the apparent diffusion coefficient (ADC) of DWI signal areas and surrounding tissue. Results Nineteen studies (443 patients with 856 CVTs; age 40 years [95% confidence interval (CI), 33 to 43]; 28% males [95% CI, 18 to 38]; symptom onset to DWI time 4.6 days [95% CI, 2.3 to 6.9]) were included. Hyperintense signals on DWI were detected in 40% (95% CI, 26 to 55) of the cases. The sensitivity of DWI for detecting CVT was 22% (95% CI, 11 to 34) but specificity was 98% (95% CI, 95 to 100). ADC values were quite heterogenous in DWI signal areas. However, generally the ADC values were lower in DWI signal areas than in surrounding normal areas (mean difference−0.33×10-3 mm2/s [95% CI, −0.44 to −0.23]; p

BACKGROUND Although external fixator is standard for managing staged treatment of open tibial fracture, the main disadvantage of this device is too bulky to be tolerated by most patients for longtime use. The purposes of this pilot study were to compare the biomechanical properties of a novel low-profile external fixator (LP-ESF) with a traditional ESF and also to evaluate its performance in patients with Gustilo type IIIb tibial open fractures. METHODS A prospective clinical pilot study started from January 2015 to December 2017, and 18 patients with Gustilo type IIIb open tibial fractures underwent the fixation with a novel LP-ESF system. The biomechanical properties of the LP-ESF were compared with the Synthes External Fixation System according to the standard ASTM F1541-02. These patients were divided into two groups according to the size of bony defect. The postoperative clinical outcomes were subsequently collected. RESULTS The biomechanical properties of the LP-ESF were comparable with those of Synthes External Fixation System and had an improved the axial/torsional stiffness and ultimate strength. In the clinical study, all patients with LP-ESF had fracture union. The duration of application of LP-ESF was 3.5 to 18 months until fracture union. In 10 of 18 patients, their fractures were immobilized with the LP-ESF until bone union, and no pin tract infection and no chronic osteomyelitis were recorded. The 36-Item Short Form Health Survey life quality and health survey were good to excellent in these patients. Notably, the LP-ESF allowed a patient with severe bone and soft-tissue defects to preserve the leg and joints function. CONCLUSION In this study, we found that the novel LP-ESFs had improved clinical outcomes. The long-term LP-ESF application seems to be tolerable in our patients. This novel approach permits better controls in deep infection and faster healing of fractures, and thus may provide a viable alternative treatment for Gustilo type IIIb open tibial fractures.

BACKGROUND To evaluate the value of locoregional radiation therapy (LRRT) in de novo metastatic nasopharyngeal carcinoma (mNPC) and identify suitable candidates for additional LRRT after palliative chemotherapy (PCT). METHODS Patients with de novo mNPC received platinum-based chemotherapy for a minimum of four cycles with or without definitive LRRT via intensity-modulated radiation therapy (IMRT) were all candidates for this study. RESULTS A total of 168 patients were included for this analysis. Additional LRRT was associated with significantly longer median OS (69.5 vs. 17.8 months, p

Ecosystem service assessment has become an emerging lobbying tool in recent years, relatively, less studies use non-economic methods to explore the society’s demand for ecosystem services from the ...

Significance: Endothelial cells lining the lumen of blood vessels play an important role in the regulation of cardiovascular functions through releasing both vasoconstricting and vasodilating facto...

AlGaAs/InGaAs high-electron mobility transistors (HEMTs) are grown by molecular beam epitaxy (MBE). The studied HEMTs use two AlAs layers as etch-stop layers in the selective-etch recessed-gate fabrication of the HEMTs. The influence of passivation using silicon nitride on HEMTs is examined. Passivation improves the dc, high-frequency, and power characteristics of AlGaAs/InGaAs HEMTs. The passivated HEMT has a maximum extrinsic transconductance of 207 mS/mm, a unity-current-gain frequency (fT) of 13 GHz, and a maximum oscillation frequency (fmax) of 26 GHz. Furthermore, the variation of dc characteristics of the passivated HEMT with temperature is reduced.

Background/purpose The palate has become a popular site for the placement of temporary anchorage devices (TADs) owing to its bone quantity and quality. This study aimed to investigate total and cortical bone thicknesses in the whole palate as well as palatal width using a standard grid system and cone-bean computed tomography (CBCT) images. Materials and methods The CBCT images of 43 samples were selected. The total bone and cortical bone thicknesses of the palate were surveyed on 64 points per patient. The palatal width was measured. The difference between the age and sex groups was analyzed. Results The total palatal bone thickness in the adult group ranged from 9.85 ± 2.04 to 1.87 ± 0.79 mm. In the adolescent group, we found one-third of the incisor roots in the area 3 mm distal to the incisive foramen and 8 mm lateral to the mid-palatal suture. The cortical bone thickness in adults was significantly thicker in the posterior paramedian area than that in adolescents. Conclusion The thickest vertical bone is located in the zone 3 mm distal to the incisive foramen and 4–8 mm lateral to the midpalate. The zone 6 mm posterior to the incisive foramen and 2–8 mm lateral to the midpalate exhibited optimal thickness and was away from the incisor roots. This region could be a safe zone for adolescent patients to place TADs. When TADs are to be inserted at the posterior palate, the 2-mm paramedian area should be the first region of choice.

In this study, a novel gradient grating period guided-mode resonance (GGP-GMR) biosensor is proposed, and albumin was successfully detected as a test model. The GGP-GMR sensor exhibits a gradient resonant wavelength along the device. At a fixed incident wavelength, light resonates at a specific period such that the light is reflected at this period (or resonant period) and transmitted to other periods. Thus, for a charge-coupled device under the GGP-GMR sensor, a dip-like intensity distribution was measured with a specific pixel of minimum intensity corresponding to the resonant period. The variation in sample concentrations caused changes in the refractive index (RI) on the sensor surface, which induced a shift in the resonant period and in the pixel corresponding to the minimum intensity. A GGP-GMR sensor can achieve a limit of detection of $8.58\times 10^{{-4}}$ RIU within a detection range of sucrose solutions of 0%–60% concentrations that corresponds to the RI from 1.333 to 1.442. In a buffer solution, albumin was successfully detected to measure the clinically relevant concentration. The simple design and easy fabrication of the GGP-GMR sensor combined with its simple readout and optical path design facilitates system miniaturization for handheld devices and its integration with smartphones, which could be beneficial for future point-of-care applications.

The purpose of this study was to investigate the complementary effects of polyphenolic compounds from pine bark extract (PE) as a strong antioxidative substrate on the symptoms of inattention and impulsivity in children with attention-deficit hyperactivity disorder (ADHD). This was a randomized, double-blind, crossover, placebo-controlled study that included two experimental units (4 weeks with PE supplementation and 4 weeks with placebo supplementation) separated by a 2-week washout period. ADHD participants were supplemented with 25 mg or 50 mg PE. We recruited 20 participants (17 boys and 3 girls) with a mean age of 10.0 ± 2.1 years. PE supplementation caused a significant reduction in the inattention and hyperactivity-impulsivity items of SNAP-IV. During the period of PE supplementation, the item of commissions in the Continuous Performance Test III (CPT III) significantly decreased, which was used to evaluate the symptoms of inattention and impulsivity. In addition, the erythrocytic reduced glutathione/oxidized glutathione ratio significantly increased, and the plasma TBARs level significantly decreased after 4 weeks of PE supplementation. However, there was no significant correlation between CPT III (commission) and antioxidative status indictors. PE supplementation may have potential effects of ameliorating inattention and impulsivity, and elevating the antioxidative status in children with ADHD.

Purpose We aimed to construct a nomogram to predict personalized post‐recurrence survival (PRS) among colorectal cancer liver metastasis (CRLM) patients with post‐hepatectomy recurrence. Methods Colorectal cancer liver metastasis patients who received initial hepatectomy and had subsequent recurrence between 2001 and 2019 in Sun Yat‐sen University Cancer Center from China were included in the study. Patients were randomly assigned to a training cohort and a validation cohort on a ratio of 2:1. Univariable analysis was first employed to select potential predictive factors for PRS. Then, the multivariable Cox regression model was applied to recognize independent prognostic factors. According to the model, a nomogram to predict PRS was established. The nomogram's predictive capacity was further assessed utilizing concordance index (C‐index) values, calibration plots, and Kaplan–Meier curves. Results About 376 patients were finally enrolled, with a 3‐year PRS rate of 37.3% and a 5‐year PRS rate of 24.6%. The following five independent predictors for PRS were determined to construct the nomogram: the largest size of liver metastases at initial hepatectomy, relapse‐free survival, CEA level at recurrence, recurrent sites, and treatment for recurrence. The nomogram displayed fairly good discrimination and calibration. The C‐index value was 0.742 for the training cohort and 0.773 for the validation cohort. Patients were grouped into three risk groups very well by the nomogram, with 5‐year PRS rates of 45.2%, 23.3%, and 9.0%, respectively (p, Independent prognostic factors for post‐recurrence survival were identified in colorectal cancer liver metastasis patients. A prognostic nomogram based on these factors was built and validated, which had relatively good discrimination and calibration ability.

The lyrics generator should consider the context and the singability of the songs because every song expresses a story through the context of lyrics, and the lyrics should sound with the music well. Therefore, this study proposes a framework to generate the singable lyrics, and the context of lyrics should fit the given musical style. For the context, this study adopts the GPT-2 model which is powerful for text generation. The conditional GPT-2 model can be used to generate lyrics according to the given style. For suitable for singing, this study adjusts the structure and rhyme of lyrics through the use of a syntactic parser and a rhyme modification module. With automatic and human evaluations, the experimental results show that the proposed method can generate lyrics with high structural consistency, rhyme consistency, and originality according to the given music style.

Reading irregular text of arbitrary shape or low quality in natural scene images is a challenging task. Existing irregular scene text recognition methods mainly focus on irregular text with arbitrary shape, but rarely focus on irregular text of low quality. In this work, we propose a simple but effective method for recognizing irregular texts with arbitrary shape and low quality simultaneously. The proposed Multi-Branch guided Attention Network (MBAN) makes mutual guidance among multi-branch data in training, so as to learn invariant semantic representation between regular text images and the corresponding irregular images. Compared with the standard attention framework for text recognition, MBAN can significantly improve the performance of irregular text recognition while preserving similar performance for regular text recognition. In addition, regarding the attention drift problem encountered in standard attention network, MBAN can significantly improve the accuracy of alignment factors at each time step. We verify the effectiveness of MBAN in irregular text recognition and attention drift problem through extensive experiments. The performance of MBAN is shown to be comparable on regular datasets and superior on some irregular datasets with state-of-the-art methods.

Segmenting the region of interest (ROI) from medical images is a fundamental but challenging task due to the illumination change and imaging devices. Although many models based on the local region‐based active contour model (LR‐ACM) are proposed to deal with intensity inhomogeneity, it is still difficult for the global energy‐based ACM with local image information to accurately extract the ROI from medical images. To solve this problem, this study proposes a novel localised ACM by constructing the gradient information based on the probability scores from the fuzzy k‐nearest neighbour classifier. Different from the traditional LR‐ACMs, our model is based on local rather than global image statistics, where a probability score‐based edge detector is directly used for gradient information. The energy functional consists of localised region energy and an edge energy. By introducing a local characteristics function, the localised region energy with the probability‐score‐based edge information is formulated, which can make the evolution curve stop on the exact boundaries of ROI. The edge energy including the regularisation and the penalty terms is used to avoid the reinitialisation process and smooth the evolution curve during evolution. To maintain the signed distance property of the evolution curve, a novel potential function in the penalty term is designed, which can consistently control the diffusion direction of the evolution curve. Experiments on the medical images including the cardiac magnetic resonance imaging and the 3DIRCADb databases demonstrate that the proposed model is more robust and accurate to extract the ROI than the popular localised and region‐based ACMs. The code is available at: https://github.com/HuaxiangLiu/Localized_ERAC/.

Despite demonstrating higher photoluminescence quantum yield and better ambient and operational stability than organic–inorganic hybrid perovskites, all-inorganic perovskites encounter the problem of inferior film quality and interfacial electrical properties, which limits the resultant device performance. In this study, three polymers, P4a–c, bearing distinct pendant groups based on a similar conjugated group are synthesized and employed as an interlayer to modify the PEDOT:PSS/CsPbBr3 interface. Due to the pendant design, P4a–c possess deep-lying HOMO levels and high transparency across the visible range. The different structures of the pendant groups in P4a–c are shown to result in their different propensity in energy-level modulation and solid-state aggregation, which plays a non-trivial role in affecting the resulting device performance. Due to the more appropriate energy levels and better regulation of CsPbBr3 crystals, P4c with a polar bridge moiety is shown to better mediate the performance of the derived device. The P4c-mediated PeLED delivers six times enhanced luminance (Lmax, ∼36 000 cd m−2) and 3.6 times enhanced external quantum efficiency (EQE, 2.16%) as compared to the control device (∼6000 cd m−2, 0.60%). Notably, all the devices using P4a–c interlayers deliver a lower turn-on voltage than the control device, clearly revealing the positive role of P4a–c interlayers on diminishing the barrier across the associated interface to improve charge injection efficiency.