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Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

Multi-criteria decision making (MCDM) is used to determine the best alternative among various options. It is of great importance as it hugely affects the efficiency of activities in life, management, business, and engineering. This paper presents the results of a multi-criteria decision-making study when using powder-mixed electrical discharge machining (PMEDM) of cylindrically shaped parts in 90CrSi tool steel. In this study, powder concentration, pulse duration, pulse off time, pulse current, and host voltage were selected as the input process parameters. Moreover, the Taguchi method was used for the experimental design. To simultaneously ensure minimum surface roughness (RS) and maximum material-removal speed (MRS) and to implement multi-criteria decision making, MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and MAIRCA (Multi-Attributive Ideal–Real Comparative Analysis) methods were applied. Additionally, the weight calculation for the criteria was calculated using the MEREC (Method based on the Removal Effects of Criteria) method. From the results, the best alternative for the multi-criteria problem with PMEDM cylindrically shaped parts was proposed.

The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.

Background: Breast cancer (BC) is the most common cancer and the leading cause of cancer death in women. Hereditary BC risk accounts for 25% of all cases. Pathological variants in known BC precursor genes explain only about 30% of hereditary BC cases, while the underlying genetic factors in most families remain unknown. Identifying hereditary cancer risk factors will help improve genetic counseling, cancer prevention, and cancer care. Methods: Here, we used whole-exome sequencing (WES) to identify genetic variants in 105 Vietnamese patients with BC and 50 healthy women. BC-associated variants were screened by the Franklin software and the criteria of the American College of Medical Genetics and Genomics (ACMG) and evaluated based on in silico analysis. Results: In total, 56 variants were identified in 37 genes associated with BC, including ACVR1B, APC, AR, ARFGEF1, ATM, ATR, BARD1, BLM, BRCA1, BRCA2, CASP8, CASR, CHD8, CTNNB1, ESR1, FAN1, FGFR2, HMMR, KLLN, LZTR1, MCPH1, MLH1, MSH2, MSH3, MSH6, NF1, PMS2, PRKN, RAD54L, RB1CC1, RECQL, SLC22A18, SLX4, SPTBN1, TP53, WRN, and XRCC3 in 41 patients. Among them, 12 variants were novel, and 10 variants were assessed as pathogenic/likely pathogenic by ACMG and ClinVar. Variants of uncertain significance (VUS) were evaluated using in silico prediction software to predict whether they are likely to cause the disease in patients. Conclusions: This is the first WES study to identify BC-associated genetic variants in Vietnamese patients, providing a comprehensive database of BC susceptibility gene variants. We suggest using WES as a tool to identify genetic variants in BC patients for risk prediction and treatment guidance.

Cutting regime parameters play an important role in determining the efficiency of the grinding process and the quality of the ground parts. In this study, the influences of the cutting parameters, including the cutting depth (ae), the feed rate (Fe) and the wheel speed (RPM) on the grinding time when grinding tablet shape punches by a cubic boron nitride (CBN) wheel on a CNC (Computerized Numerical Control) milling machine are investigated. The Taguchi technique based on orthogonal array and analysis of variance (ANOVA) was then applied to design the number of experiments and evaluate the influence of cutting depth, feed rate and wheel speed on the grinding time. The results show that among the three cutting parameters, the most influential parameter on the grinding time is the cutting depth. The second influential parameter on the grinding time is the feed rate. The least influential parameter on grinding time is the wheel speed. In addition, the optimal condition of cutting parameters obtained for grinding tablet shape punches by cubic boron nitride wheels on a CNC milling machine are a cutting depth of 0.03 mm, wheel speed of 5000 rpm and feed rate of 3500 mm/min. This optimum cutting parameters ensure the least grinding time.

In the current study, an optimization process of powder-mixed electrical discharge machining (PMEDM) process when machining cylindrically shaped parts made of hardened 90CrSi steel is reported. In this study, SiC powder was mixed into the Diel MS 7000 dielectric solution. Additionally, graphite was chosen as the electrode material. The multi-objective functions were minimizing the surface roughness (SR) and electrode wear rate (EWR) and maximizing the material removal rate (MRR). The used input parameters of the optimization process included the powder concentration, the pulse-on time, the pulse-off time, the pulse current, and the servo voltage. A combination between the Taguchi method and the grey relation analysis (GRA) method with the support of Minitab R19 software was used to design the experiment and analyze the results. It was found that the optimal set of process parameters that can satisfy the above responses are Cp of 0.5 g/L, Ton of 8 µs, Toff of 8 µs, IP of 5 A, and SV of 4 V.

Epidemiological characteristics of hepatocellular carcinoma (HCC) in Southern Vietnam has been well reported as in Globocan 2018 while data from the North has still not been fully presented. Therefore, we conducted this retrospective descriptive study on 198 advanced HCC patients treated at 3 major hospitals in Northern Vietnam to describe demographic features, HCC risk factors, and correlation among them in patients with advanced HCC. This information will lead to prevention efforts and provide information for allocating funds for treatment. The median age at diagnosis was 57 years (range: 19-86) and the male/female ratio was 8.9/1. The proportions of hepatitis B virus (HBV) and hepatitis C virus (HCV) infection were 81.3% and 5.6%, respectively. Hepatitis C virus infection rate was significantly higher in patients <50 years old (12.5% vs 3.3%, P = .016). There was no significant difference in age or viral hepatitis infection status by gender. Only 7.6% of patients diagnosed with advanced HCC were asymptomatic. In conclusion, with the high rate of HBV infection among patients with advanced HCC, it is necessary for increasing prevention efforts in HBV screening. Furthermore, HCV infection should be noticed in patients with advanced HCC younger than 50 years old.

A predicted alkali‐thermophilic cytochrome P450‐T2 from the DNA database of Binh Chau hot spring was synthesized, expressed and purified. The properties of the enzyme, including the optimum pH and temperature, melting temperature and half‐life at 50 °C were characterized. P450‐T2 accepts electrons from the BmCPR‐Fdx2 system. l‐Mimosine and emodin may be its putative substrates. Cytochromes P450 (CYPs or P450s) comprise a superfamily of heme‐containing monooxygenases that are involved in a variety of biological processes. CYPs have broad utilities in industry, but most exhibit low thermostability, limiting their use on an industrial scale. Highly thermostable enzymes can be obtained from thermophiles in geothermal areas, including hot springs, offshore oil‐producing wells and volcanoes. Here, we report the identification of a gene encoding for a thermophilic CYP from the Binh Chau hot spring metagenomic database, which was designated as P450‐T2. The deduced amino acid sequence showed the highest identity of 73.15% with CYP203A1 of Rhodopseudomonas palustris, supporting that P450‐T2 is a member of the CYP203A subfamily. Recombinant protein expression yielded 541 nm. The optimal temperature and pH of P450‐T2 were 50 °C and 8.0, respectively. The half‐life of P450‐T2 was 50.2 min at 50 °C, and its melting temperature was 56.80 ± 0.08 °C. It was found to accept electrons from all tested redox partners systems, with BmCPR‐Fdx2 being the most effective partner. Screening for putative substrates revealed binding of phenolic compounds, such as l‐mimosine and emodin, suggesting a potential application of this new thermophilic P450 in the production of the corresponding hydroxylated products.

Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers.

α–Mangostin, which is a natural xanthone compound, inhibits the metastasis and survival of various cancer cell types. However, its therapeutic effectiveness is limited by low water solubility and very poor absorption. There are several studies that developed the drug delivery system for α–mangostin, but they are still a remaining challenge. Drug delivery techniques are severely hampered by the breakdown of nanoparticles inside endosomes. The abrasive chemical environment in these compartments causes both the nanoparticles and the encapsulated α–mangostin to degrade throughout the course of the voyage. Intracellular defenses against external materials refer to this collective mechanism. A pH-responsive liposome named PAsp(DET-Cit)–Toc, made of lipids and a charge-conversion polymer (CCP), has been created for the targeted transport of α–mangostin in order to avoid this deteriorative outcome. The average hydrodynamic size of CCP–liposome particles is 98.59 ± 5.1 nm with a PDI of 0.098 ± 0.02 and a negative zeta potential of 22.31 ± 2.4 mV. TEM showed the shape of the spherical CCP–liposomes. α–Mangostin is successfully captured inside CCP–liposome and the loading yield reached the highest encapsulation efficiency of 83% with 150 μg/mL of α–mangostin. In the acidic condition of pH 5.0, an initial burst of α–mangostin reached 50% after 6 h in buffer solution. CCP–liposomes could escape from endosomes even after 3 h, and almost 80% of CCP–liposomes escaped after 24 h. The cell ability of α–mangostin-loaded-CCP–liposome incubated in buffer solutions of 5.0 decreased significantly and was close to free α–mangostin. Our data proved that α–mangostin-loaded CCP–liposome delivered more effectively α–mangostin into cells and prevented the degradation of α–mangostin inside cells, especially endosomal degradation.