DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition

Effect of Cu doping on the microstructure and mechanical properties of AlTiVN-Cu nanocomposite coatings

Cu phase has been incorporated into hard coatings to form nanocomposite structure, which not only enhanced the hardness but also the toughness due to excellent ductility of copper. In this study, a single Al67Ti33-V-Cu spliced target was used to prepare the AlTiVN-Cu nanocomposite coatings, and the effect of Cu doping on microstructure and mechanical properties of AlTiVN-Cu coatings has been investigated. The results showed that the deposition rate linearly increased from 3.8 to 13.4 nm/min when Cu content increased from 2.6 to 46.7 at.%. AlTiVN-Cu coatings exhibited a Ti-Al-V-N solid-solution phase with strong (111) preferred orientation at low Cu contents below 8.3 at.%. When Cu content increased above 22.6 at.%, Cu atoms grew up into metallic crystallites and strongly suppressed crystal growth of nitride coatings due to repeated nucleation. With increasing Cu content, the microstructure transferred from compact columnar to dense featureless, and then to coarse columnar structure. AlTiVN-Cu(2.6 at.%) coating exhibited a super hardness of 41.1 GPa and an excellent toughness with a high H3/E*2 ratio of 0.24

Influence of lubricious oxides formation on the tribological behavior of Mo-V-Cu-N coatings deposited by HIPIMS

The variations of microstructure, mechanical properties, and oxidation behavior of Mo-V-Cu-N coatings are directly correlated to the chemical compositions, which significantly affects their tribological behavior. The aim of this work was to characterize Mo-V-Cu-N coatings with different chemical compositions deposited by high power impulse magnetron sputtering (HIPIMS) using single Mo-V-Cu segmental target, and to investigate the correlations between the lubricative oxides formed on coating surfaces with the variation of tribological behavior at different temperatures. The oxidation of Mo-V-Cu-N coatings started at 400 °C with the lubrication oxides of Mo-O and Cu-Mo-O were formed, which led to the decrease in coefficients of friction and wear rates of the coatings. It was found that the rapid outward diffusion of Mo and Cu atoms took place preferentially at around the growth defects (e.g. microparticles and pores). The incorporation of V atoms into Mo-Cu-N coatings enhanced the oxidation resistance at temperatures below 400 °C. At 500 °C, all the fcc B1-MoN and VN phases disappeared due to the severe oxidation, and the V2O5 phase was first appeared. Even though a relatively low coefficient of friction was obtained at 500 °C, the wear resistance of Mo-V-Cu-N coatings was decreased due to the severe oxidation and loss of mechanical strength

Influence of pulse frequency on microstructure and mechanical properties of Al-Ti-V-Cu-N coatings deposited by HIPIMS

As an important parameter of HIPIMS, pulse frequency has significant influence on the microstructure and mechanical properties of the deposited coatings, especially for the multi-component coatings deposited by using a spliced target with different metal sputtering yields. In this study, a single Al67Ti33-V-Cu spliced target was designed to prepare Al-Ti-V-Cu-N coatings by using high power impulse magnetron sputtering (HIPIMS). The results showed that the peak target current density decreased from 0.75 to 0.24 A∙cm−2 as the pulse frequency increased, along with the microstructure transferred from dense structure to coarse column structure. The pulse frequency has significant influence on chemical compositions of Al-Ti-V-Cu-N coatings, especially for Cu content increasing from 6.2 to 11.7 at.%. All the coatings exhibited a single solid-solution phase of Ti-Al-V-N, and the preferred orientation changed from (111) to (220) when the pulse frequency increased above 200 Hz. The decrease in peak target current density at high pulse frequencies resulted in a sharp decrease in the coating hardness from 35.2 to 16.4 GPa, whereas the relaxation of compressive residual stress contributed to an improvement in adhesion strength from 43.3 to 79.6 N

Assessing vulnerability for inhabitants of Dhaka City considering flood-hazard exposure

Globalna opasnost od poplave postupno se povećava. Iako ih je nemoguće izbjeći, gubici i šteta od opasnosti (npr. poplave, cikloni i potresi) mogu se učinkovito smanjiti smanjenjem ranjivosti kućanstava odgovarajućim mjerama. Cilj ove studije je kvantitativno mjerenje ranjivosti kućanstava obzirom na opasnosti od poplave kao alata za njihovo ublažavanje. Također je predložen jedinstveni pristup za kvantificiranje ugroženosti kućanstava obzirom na opasnosti od poplave, a kao primjer predstavljena je primjena u gradu Dhaki sklonom poplavama. Podaci su prikupljeni i sa siromašnih i bogatih područja kako bi bilo pokriveno cijelo urbano područje te kako bi se usporedila razina ugroženosti od poplava. Ukupno 300 kućanstava anketirano je strukturiranim upitnikom na temelju pet čimbenika (ekonomskih, socijalnih, okolišnih, strukturnih i institucionalnih) ugroženosti od poplava. Analitički hijerarhijski postupak (AHP) primijenjen je za mjerenje pojedinačnih rezultata ranjivosti kućanstva korištenjem relativne težine varijabli i pokazatelja uz pravilnu standardizaciju. Analitički rezultati pokazali su da je 63,06% siromašnih kućanstava i 20,02% bogatih kućanstava vrlo osjetljivo na poplave. Uz to, ovaj je rad utvrdio i procijenio čimbenike odgovorne za ranjivost kućanstava u Dhaki. Što se tiče strukturne ranjivosti, rezultati su pokazali da je 82% kućanstava u siromašnim krajevima bilo visoko ranjivo, a 95,3% kućanstava koja nisu iz siromašnih četvrti bilo je umjereno ranjivo. Društveno, 67,3% siromašnih i 78,7% kućanstava koja nisu iz siromašnih naselja bila su umjereno i slabo ranjiva. Većina kućanstava u siromašnoj i nesiromašnoj četvrti (84%, odnosno 59,3%) pokazala je visoku i umjerenu ekonomsku ranjivost. Štoviše, za 69,3% siromašnih i 65,3% nesiromašnih kućanstava institucionalna ranjivost je bila visoka. Od stanovnika siromašnih naselja, 63,3% je bilo izloženo ekološkom riziku, a 78% staništa koja nisu u siromašnim područjima bilo je u kategoriji niske ranjivosti. Uz odgovarajuću prilagodbu ovdje predložen učinkoviti alat za mjerenje ranjivosti koji je ovdje prilagođen specifičnoj lokaciji, primjenjiv je i za mjerenje ranjivosti drugih gradova u svijetu. Na temelju ove studije moglo bi se provesti buduće istraživanje s više čimbenika, varijabli i pokazatelja ljudske ranjivosti na prirodne ili umjetne opasnosti / katastrofe. Budući rad mogao bi pružiti bolju sliku stanja ranjivosti od pojedinačne / višestruke opasnosti / katastrofe.Global flood hazard is gradually increasing. Though it is impossible to avoid them, losses and damage of hazards (e.g., floods, cyclones, and earthquakes) could be efficiently reduced by reducing household vulnerability with appropriate measures. This study aims to quantitatively measure the household vulnerability of flood hazards as a mitigation tool. It also proposed a unique approach to quantify flood-hazard household vulnerability, and shows its application in the flood prone city of Dhaka as an example case. Data were collected from both slum and non-slum areas to cover the entire urban habitat, and to compare their level of flood vulnerability. A total of 300 households were surveyed by structured questionnaire on the basis of five factors (economic, social, environmental, structural, and institutional) of flood vulnerability. The analytical hierarchy process (AHP) was applied to measure individual household vulnerability scores by using the relative weightage of variables and indicators with proper standardisation. Analytical results demonstrated that 63.06% slum and 20.02% non-slum households were highly vulnerable to floods. In addition, this paper determined and assessed responsible factors for household flood vulnerability in Dhaka. For structural vulnerability, results exhibited that 82% of slum households were highly vulnerable, and 95.3% of non-slum households were moderately vulnerable. Socially, 67.3% of slum and 78.7% of non-slum households were moderately and low-vulnerable. The majority of slum and non-slum households (84% and 59.3%, respectively) showed high and moderate vulnerability with respect to economic vulnerability. Moreover, 69.3% of slum and 65.3% of nonslum household institutional vulnerability levels were high. Of slum inhabitants, 63.3% were environmentally at high risk, and 78% of non-slum habitats were in the low-vulnerability category. However, as an effective tool to measure location-specific vulnerability, it is applicable for the measuring vulnerability of other cities in the world with proper customisation. On the basis of this study, future research could be conducted with more factors, variables, and indicators of human vulnerability to natural or artificial hazards/disasters. Future work may provide a better reflection of the vulnerability status of single/multiple hazard(s)/disaster(s)

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention to multi-label image retrieval. However, in the era of massive remote sensing data, how to increase retrieval efficiency and reduce feature storage while preserving semantic information remains unsolved. Considering the powerful capability of hashing learning in overcoming the curse of dimensionality caused by high-dimensional image representation in Approximate Nearest Neighbor (ANN) search problems, we propose a new semantic-preserving deep hashing model for multi-label remote sensing image retrieval. Our model consists of three main components: (1) a convolutional neural network to extract image features; (2) a hash layer to generate binary codes; (3) a new loss function to better maintain the multi-label semantic information of hash learning contained in context remote sensing image scene. As far as we know, this is the first attempt to apply deep hashing into the multi-label remote sensing image retrieval. Experimental results indicate the effectiveness and promising of the introduction of hashing methods in the multi-label remote sensing image retrieval

Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data

Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a novel synergistic approach to estimating biomass by integrating LiDAR data with Landsat 8 imagery through a deep learning-based workflow. First the relationships between biomass and spectral vegetation indices (SVIs) and LiDAR metrics were separately investigated. Next, two groups of combined optical and LiDAR indices (i.e., COLI1 and COLI2) were designed and explored to identify their performances in biomass estimation. Finally, five prediction models, including K-nearest Neighbor, Random Forest, Support Vector Regression, the deep learning model, i.e., Stacked Sparse Autoencoder network (SSAE), and multiple stepwise linear regressions, were individually used to estimate biomass with input variables of different scenarios, i.e., (i) all the COLI1 (ACOLI1), (ii) all the COLI2 (ACOLI2), (iii) ACOLI1 and all the optical (AO) and LiDAR variables (AL), and (iv) ACOLI2, AO and AL. Results showed that univariate models with the combined optical and LiDAR indices as explanatory variables presented better modeling performance than those with either optical or LiDAR data alone, regardless of the combination mode. The SSAE model obtained the best performance compared to the other tested prediction algorithms for the forest biomass estimation. The best predictive accuracy was achieved by the SSAE model with inputs of combined optical and LiDAR variables (i.e., ACOLI1, AO and AL) that yielded an R2 of 0.935, root mean squared error (RMSE) of 15.67 Mg/ha, and relative root mean squared error (RMSEr) of 11.407%. It was concluded that the presented combined indices were simple and effective by integrating LiDAR-derived structure information with Landsat 8 spectral data for estimating forest biomass. Overall, the SSAE model with inputs of Landsat 8 and LiDAR integrated information resulted in accurate estimation of forest biomass. The presented modeling workflow will greatly facilitate future forest biomass estimation and carbon stock assessments