The nanocomposites with reversible cross-linking covalent bonds were prepared by reacting furfurylamine (FA)-modified diglycidyl ether of bisphenol A (DGEBA) and furfuryl-functionalized aniline trimer-modified graphene (TFAT-G) with bismaleimide (BMI) via Diels-Alder (DA) reaction. The successful synthesis of the TFAT modifier is confirmed by NMR hydrogen spectroscopy and IR spectroscopy tests. The structure and properties of TFAT-G epoxy nanocomposites are characterized by SEM, DSC, tensile and resistivity CES. The results show that TFAT-G was uniformly dispersed in the resin, and 1wt% TFAT-G composites increase to 233% for tensile strength, 63% for elongation at break, 66.1% for modulus and 7.8℃ for Tg. Overall, the graphene/self-healing resin nanocomposites have both good self-healing function and electrical conductivity by adding 1% modified graphene and still maintain the original 83% strength and 89% electrical conductivity after one cycle of heating repair.

This article analyzes the Franco-Spanish invasion of Vietnam from 1858 to 1860. Research is based on documents such as correspondence, reports, archives and diplomatic treaties as well as monographs from the time France intended to invade Vietnam through missionary activities until the end of the Franco-Spanish invasion. The article focuses on three basic issues that are the causes of the alliance, the evolution of the French and Spanish coalition invasion in Vietnam and its impact on stakeholders. The research results on the Franco-Spanish invasion of Vietnam indicated that the invasion carried out by this alliance was more special than that of the other French coalitions before. The invasion was based on an alliance without any commitment to the interests and responsibilities of the parties. The goals of France and Spain in this invasion were different. That led to conflict between France and Spain during the invasion and the fall of the alliance. France achieved its goal through an invasion that turned Vietnam into a colony, while Spain had barely achieved its desire to have commercial and missionary interests in the area.

Introduction: Novel robotic surgery systems (da Vinci Xi) are superior to classical open and laparoscopic techniques with its clear and three-dimensional view. We aimed to present the first case low anterior resection of rectal cancer and vaginal specimen extraction with Da Vinci Xi.Case: A 75-year-old female patient with rectum adenocarcinoma was undergone robotic-assisted low anterior resection (LAR) of the rectum, vaginal removal of the specimen, colorectal anastomosis and loop ileostomy. The operation time was 190 minutes. There were no postoperative complications. Pathological tumor stage was stage pT1N0 with negative proximal, distal and radial resection margins. The patient was discharged on the third postoperative day.Conclusion: Robot-assisted LAR, total mesorectal excision, vaginal removal of the specimen, colorectal anastomosis, and loop ileostomy can be performed easily and safely with Da Vinci Xi at early stage rectal cancer. And the vaginal extraction of the specimen avoids us from a traditional abdominal incision.

The selectivity of electrochemical sensors to ascorbic acid (AA), dopamine (DA), and uric acid (UA) remains an open challenge in the field of biosensing. In this study, the selective mechanisms for detecting AA, DA, and UA molecules on the graphene and graphene oxide substrates were illustrated through the charge population analysis from the DFT calculation results. Our substrate models contained the 1:10 oxygen per carbon ratio of reduced graphene oxide, and the functionalized configurations were selected according to the formation energy. Geometry optimizations were performed for the adsorption of AA, DA, and UA on the pristine graphene, epoxy-functionalized graphene, and hydroxyl-functionalized graphene at the DFT level with vdW-DF2 corrections. From the calculations, AA was bound to both epoxy and hydroxyl-functionalized GO with relatively low adsorption energy, while DA was adsorbed stronger to the electronegative epoxy groups. The strongest adsorption of UA to both types of functional groups corresponded to the largest amount of electron transfer through the pi orbitals of UA. Local electron loss created local electric fields that opposed the electron transfer during an oxidation reaction. Our analysis agreed with the results from previous experimental studies and provide insight into other electrode modifications for electrochemical sensing.

Among many other themes, Leonardo da Vinci’s Manuscript B contains several drawings of centrally planned churches, some of which are represented using a plan view paired with a bird’s-eye view. The use of a bird’s-eye instead of an elevation represents an innovative depiction technique, which allows to combine the immediacy of the perspective view with the measurability of the façades, and therefore to describe the three-dimensionality of the buildings. To understand the reasons behind the use of this original technique, the edifices’ shape and classify them we decided to use 3D digital reconstruction techniques, for their ability to avoid misunderstandings in the reconstruction process and in the results. This article describes the method to create the digital models of sixteen churches. A Visual programming language (VPL) script was used as 3D base for modelling the churches achieved from a classification code expressing the aggregative rules of the churches. Then, the geometric process for the construction of the plan and its relationship with the elevation measures was studied for each church. Finally, this information was used for the completion of the 3D models, distinguishing more output variants each time there was an inconsistency between plan and perspective view, a variability of one architectural element or an uncertainty.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. In order to promote the development of platforms enabling to the discovery of compounds with anti-bacterial activity, Fourier Transformed Mid-Infrared (FT-MIR) spectroscopy associated with the machine learning algorithms were used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli cells. According to the plant tissue (seeds, dry and fresh leaves, and flowers) and the solvents (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning, phenol content, antioxidant, and antimicrobial activity were obtained. Principal component analysis of spectra, allowed to discriminate compounds that inhibit the E. coli growth, according to the conventional assay. The supervised classification models enabled the prediction of the compound's impact on the E. coli growth, with the accuracy for the test data: Partial least squares-discriminant analysis of 94%; Support vector machines of 89%; k-Nearest neighbor of 72%; and a Backpropagation network of 100%. According to the promising results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

Product Design is getting nowadays new challenges on developing new products, since the industry is in a rush to introduce products into the marketplace, where customers demand products faster, cheaper, and free of failures. In the meantime, global companies are always trying to improve their Product Design process to get advantages over their competitors using proven tools like FEMA and mixing methodologies like Fuzzy theories with FMEA. Even today using all this tools and combination of methodologies there is a gap to address and it is required a robust risk analysis solving current issues in the electronic industry. This document aims to reveal a novel integrated method, where Failure Mode and Effect Analysis, Pythagorean Fuzzy Sets and Dimensional Analysis are cohesive into a model that minimize the uncertainty of the ranking and prioritization over the Failure Mode and Effect Analysis execution, helping to identify risks within an accurate grade over possible failures in the Product Design process. A real practical example is used to show the proposed method, where it is identified a robust methodology integration and solid and results using a sensitivity analysis.

The Serra da Estrela Natural Park (NPSEs) in Portugal stands out as a well-preserved region abundant in medicinal plants, particularly known for their pharmaceutical applications in diabetes prevention and treatment. This comprehensive review explores these plants' botanical diversity, traditional uses, pharmacological applications, and chemical composition. The NPSEs boast a rich diversity with 138 medicinal plants across 55 families identified as traditionally and pharmacologically used against diabetes globally. Notably, the Asteraceae and Lamiaceae families are prevalent in anti-diabetic applications. In vitro studies reveal their significant inhibition of carbohydrate-metabolizing enzymes, and certain plant co-products regulate genes involved in carbohydrate metabolism and insulin secretion. In vivo trials demonstrate anti-diabetic effects, including glycaemia regulation, insulin secretion, antioxidant activity, and lipid profile modulation. Medicinal plants in NPSE exhibit various activities beyond anti-diabetic, such as antioxidant, anti-inflammatory, antibacterial, anticancer, and more. Chemical analysis identifies over fifty compounds like phenolic acids, flavonoids, terpenoids, and polysaccharides responsible for their efficacy against diabetes. The findings underscore the potential of NPSE medicinal plants as anti-diabetic candidates, urging further research to develop effective plant-based anti-diabetic drugs, beverages, and supplements.

In the frame of a broader project, we were interested at comparing the amino acid profile in a specific variety of onion, Rossa da inverno sel. Rojo Duro, produced in two different Italian sites: Cannara (Umbria region) and Imola (Emilia Romagna region). In both places, onions were cultivated and harvested in the same way, and irrigated by water sprinkler method. A further group of Cannara onions, growth by microirrigation, was also evaluated. After the extraction of free amino acid mixture from onion samples, an ion-pairing RP-HPLC method allowed the separation and the evaporative light scattering detection of almost all underivatized proteinogenic amino acids. However, only the peaks corresponding to Leu, Phe, Trp, were present in all the investigated samples and unaffected from matrix interfering peaks. The application of the beeswarm/box plots with the ANOVA/TukeyHSD statistical approach revealed a content of Leu and Phe markedly influenced by the geographical origin of the onions, while not by the irrigation procedure. The developed HPLC method was validated in terms of specificity, linearity, LOD and LOQ, accuracy and precision, before the quantitative assay of Leu, Phe and Trp in the onion samples. Although further studies are necessary, these preliminary findings can represent a good starting point for considering the quantity of specific amino acids in the Rossa da inverno sel. Rojo Duro variety as a fingerprint of its geographical origin. In principle, the developed approach might be applied to other onion varieties, thus contributing to their characterization and traceability, also contributing to limit commercial frauds.

The processed tomato is one of the major food products of Italy and characterized today many Italian Regions, in northern and southern Italy, even though the firms in the industry have had difficulties, in recent years, due to an increase in the cost of raw materials. Furthermore, tomato processing firms are often characterized by significant investment in fixed assets and working capital, with an consequent increase in equity and/or debt financing, with consequent increasing of the risk of bankruptcy, as happens to many firms in the sector in recent years. The aim of the research is than to analyze the risk of bankruptcy of tomato processing companies applying financial ratios. To achieve this goal, the research consider the annual data of a sample of 17 tomato processing firms operating in the Inter Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”; firms’ data are divided into still-active (not-distressed) and failed (distressed) firms, with the aim of analyzing the differences between financial data and management practices of the two groups of firms to evaluate the sustainability of the business cycle. The company data suggest that larger firms, with adequate financial structure, have been able to withstand the tomato market crisis in recent years, where distressed firms are on average smaller and suffer from a higher recourse to debt capital with lower profit margins than not-distressed firms. The research could then be applied by entrepreneurs, managers, bankers and public operator to define good practices of management to be achieved and measured with financial ratios even in order reduce the risk of distress for firms operating in tomato sector.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: 1) classification models (Linear Discriminant Analysis, LDA Adaptive Regression Splines, MARS, Support Vector Machine, SVM, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, PLS) and 3) a combined model of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN> SVM > MARS was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R= 0.996), while other models showed goodness of fit as following MARS> SVM> MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) =0.986 and Q2 =0.973 were observed with the PLS model than OPLS-DA. These results demonstrate the usefulness of the DSC technique combined with chemometrics for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.