Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

The application of improved signal summing method into the spacecraft force limited vibration test

This paper provides an improved signal summing method for the spacecraft force limited vibration test system with eight force transducers. The key point for this method is to change the combination way of the signals coming out of the eight force transducers while the formulas inside the signal conditioning amplifier have been used skillfully. This method had been successfully adopted in the spacecraft force limited vibration test and the accuracy requirements of key force and moment signals have been met. And this method has been proved to be a very powerful tool for providing the critical force and moment data used to determine the force limited profile during the spacecraft dynamic test

Multiparametric MR imaging in diagnosis of chronic prostatitis and its differentiation from prostate cancer

AbstractChronic prostatitis is a heterogeneous condition with high prevalence rate. Chronic prostatitis has overlap in clinical presentation with other prostate disorders and is one of the causes of high serum prostate specific antigen (PSA) level. Chronic prostatitis, unlike acute prostatitis, is difficult to diagnose reliably and accurately on the clinical grounds alone. Not only this, it is also challenging to differentiate chronic prostatitis from prostate cancer with imaging modalities like TRUS and conventional MR Imaging, as the findings can mimic those of prostate cancer. Even biopsy doesn't play promising role in the diagnosis of chronic prostatitis as it has limited sensitivity and specificity. As a result of this, chronic prostatitis may be misdiagnosed as a malignant condition and end up in aggressive surgical management resulting in increased morbidity. This warrants the need of reliable diagnostic tool which has ability not only to diagnose it reliably but also to differentiate it from the prostate cancer. Recently, it is suggested that multiparametric MR Imaging of the prostate could improve the diagnostic accuracy of the prostate cancer. This review is based on the critically published literature and aims to provide an overview of multiparamateric MRI techniques in the diagnosis of chronic prostatitis and its differentiation from prostate cancer

All-small-molecule organic solar cells with over 14% efficiency by optimizing hierarchical morphologies

The high efficiency all-small-molecule organic solar cells (OSCs) normally require optimized morphology in their bulk heterojunction active layers. Herein, a small-molecule donor is designed and synthesized, and single-crystal structural analyses reveal its explicit molecular planarity and compact intermolecular packing. A promising narrow bandgap small-molecule with absorption edge of more than 930 nm along with our home-designed small molecule is selected as electron acceptors. To the best of our knowledge, the binary all-small-molecule OSCs achieve the highest efficiency of 14.34% by optimizing their hierarchical morphologies, in which the donor or acceptor rich domains with size up to ca. 70 nm, and the donor crystals of tens of nanometers, together with the donor-acceptor blending, are proved coexisting in the hierarchical large domain. All-small-molecule photovoltaic system shows its promising for high performance OSCs, and our study is likely to lead to insights in relations between bulk heterojunction structure and photovoltaic performance.Funding Agencies|Ministry of Science and Technology of ChinaMinistry of Science and Technology, China [2016YFA0200700, 2016YFF0203803]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [51961135103, 51973043, 21822503, 21534003, 21721002]; Beijing Nova ProgramBeijing Municipal Science &amp; Technology Commission [Z17110001117062]; Youth Innovation Promotion Association; K.C.Wong Education Foundation; Chinese Academy of SciencesChinese Academy of Sciences; Swedish Research Council VRSwedish Research Council [2018-06048, 2018-05484]</p

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements

BackgroundTesticular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.PurposeBased on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.Materials and methodsThe study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.ResultsThe deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation &gt;0.9) in determining TV.ConclusionThe MRI-based deep learning model is an accurate and reliable tool for measuring TV

Incorporating uric acid into the CHA2DS2-VASc score improves the prediction of new-onset atrial fibrillation in patients with acute myocardial infarction

Abstract Background New-onset atrial fibrillation (NOAF) is a common cardiac arrhythmia observed in patients with acute myocardial infarction (AMI) and is associated with worse outcomes. While uric acid has been proposed as a potential biomarker for predicting atrial fibrillation, its association with NOAF in patients with AMI and its incremental discriminative ability when added to the CHA2DS2-VASc score are not well established. Methods We conducted a retrospective analysis of 1000 consecutive patients with AMI without a history of atrial fibrillation between January 2018 and December 2020. Continuous electrocardiographic monitoring was performed during the patients’ hospital stay to detect NOAF. We assessed the predictive ability of the different scoring models using receiver operating characteristic (ROC) curves. In addition, we employed the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) analyses to assess the incremental discriminative ability of uric acid when added to the CHA2DS2-VASc score. Results Ninety-three patients (9.3%) developed NOAF during hospitalisation. In multivariate regression analyses, the adjusted odds ratio (OR) for NOAF was 1.439 per one standard deviation increase in uric acid level (95% confidence intervals (CI):1.182–1.753, p < 0.001). The ROC curve analysis revealed that the AUC for uric acid was 0.667 (95% CI:0.601–0.719), while the AUC for the CHA2DS2-VASc score was 0.678 (95% CI:0.623–0.734). After integrating the uric acid variable into the CHA2DS2-VASc score, the combined score yielded an improved AUC of 0.737 (95% CI:0.709–0.764, p = 0.009). Furthermore, there was a significant improvement in both IDI and NRI, indicating an incremental improvement in discriminative ability (IDI = 0.041, p < 0.001; NRI = 0.627, p < 0.001). Conclusion Our study suggests that uric acid level is an independent risk factor for the development of NOAF after AMI. Furthermore, the incorporation of uric acid into the CHA2DS2-VASc score significantly improves the discriminative ability of the score in identifying patients at high risk for NOAF

A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System

Aiming at the impact of the uncertainty of source load on the optimal scheduling in an integrated energy system (IES), in this paper, based on hybrid resolution modeling and hybrid instruction cycle scheduling technology, three time scales of day-ahead, intra-day rolling and real-time feedback optimization scheduling models are established, respectively, with the objectives of the economic optimal daily operation of the system, the minimum sum of the operation cost of energy purchase and wind curtailment penalty cost in the rolling control time domain, and the minimum adjustment amount of equipment output power. Then, the chaotic gravitational search algorithm (CGSA) is used to solve the problem, and the composite coordination optimization operation strategy of IES with mixed time scales based on CGSA is proposed. In the example, the comparison between the multi-timescale scheduling plan and the actual output, the comparison of the system scheduling results under different strategies and the comparison of different optimization algorithms show that the proposed optimization operation strategy is beneficial to optimize the energy flow distribution, reduce the system operation cost, improve the IES economy and optimization speed

Effects of Echo Time on IVIM Quantification of the Normal Prostate

Abstract The two-compartment intravoxel incoherent motion (IVIM) theory assumes that the transverse relaxation time is the same in both compartments. However, blood and tissue have different T2 values, and echo time (TE) may thus have an effect on the quantitative parameters of IVIM. The purpose of this study was to investigate the effects of TE on IVIM-DWI-derived parameters of the prostate. In total, 17 healthy volunteers underwent two repeat examinations. IVIM-DWI data were scanned 6 times with variable TE values of 60, 70, 80, 90, 100, and 120 ms. The ADC of a mono-exponential model and the D, D*, and f parameters of the IVIM model were calculated separately for each TE. Repeat measures were assessed by calculating the coefficient of variation and Bland-Altman limits of agreement for each parameter. Spearman’s rho test was used to analyse relationships between IVIM indices and TE. Our results showed that TE had an effect on IVIM quantification, which should be kept constant in the examination protocol at each individual institution. Alternatively, an extended IVIM could be used to eliminate the effect of the TE value on the quantitative parameters of IVIM. This may be helpful for guiding clinical research, especially for longitudinal studies