使用生物反应器酶促合成酸酐（英）

用装有固定化脂肪酶的生物反应器系统对有效地合成蓖麻油酸酐进行了尝试。合成酸酐试图证明：通过控制反应混合物的最适含水量,反应是否能达到目标的缩合度。结果表明,使用生物反应器,反应能迅速地进行,在生物反应器内进行重复间歇反应是可行的,这样与不同脂肪酶相比较,每次脂肪酶的用量均能大大减少。     

Electrochemistry and spectro-electrochemistry of dithizonatophenylmercury(II)

The reactions between dithizone (H₂Dz, (1)) or potassium dithizonate (K⁺HDz⁻, (2)) and phenylmercury(II) chloride gives PhHg(HDz), (3). Complex (3) is photochromic. In dichloromethane, the blue photo-exited state of (3) exhibits a first order relaxation process to regenerate the orange ground state with rate constant 0.00053 s⁻¹. The half life of this relaxation is ca. 1300 s. Electrochemically, on cyclic voltammetry time scale, the oxidations of (1) and (3) are different. A comparative voltammetric and spectro-electrochemical study of (1) and (3) in CH₂Cl₂ containing 0.1 mol dm⁻³ [N(ⁿBu)₄][B(C₆F₅)₄] revealed that the mercapto group of (1) can be oxidised in two one-electron transfer steps. A disulphide is first produced and then in a second oxidation step, HDz⁺ is formed. In contrast, complex (3) shows only one ligand-based oxidation step. Upon complexation with phenylmercury the free mercaptan group of (1) becomes a stable “metal thioether”, Hg–S–C, which effectively prevents disulphide formation in (3) upon electrochemical oxidation. Both (1) and (3) shows two reduction steps. The electrochemical fingerprint of blue photo-excited (3) is identical to that of the orange ground state as no new functional groups are introduced upon irradiation; only bond rotation occurs. The different electronic spectra for each of the redox states of (3), obtained from spectro-electrochemical measurements, revealed that only the (3)/(3⁻) couple exhibits electrochromic properties.     

Model‐Based Recognition of Mid‐Infrared Sensor Fouling in Paracetamol Crystallization

A procedure for the recognition of undesired solid deposition on the optical element of an attenuated total reflectance probe for online mid‐infrared spectroscopy during crystallization processes is presented. Unlike other approaches, the method exclusively uses intelligent spectral analysis for solid recognition and does not require additional devices. Indirect hard modeling enables the separation of the solid material's contribution to the mixture spectra from those contributions of the dissolved material and the solvents, expressed through the predicted component weights. The detection of the solid component weight is promising for implementation into process monitoring or control.     

Arithmetic Optimization with Ensemble Deep Transfer Learning BasedMelanoma Classification

Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.     

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.     

Malnutrition in food-surplus areas: experience from nutritional surveillance for decentralized planning in Haiti

In Haiti, a novel approach to nutritional surveillance was developed on the basis of a low-cost, simple-to-repeat set of household surveys in all nine administrative departments using sentinel community sites. This system allows each department to independently conduct follow-up surveys as needed. The results of the first round of surveys conducted in 1995 show lower malnutrition rates in typical food-deficient departments and high levels of malnutrition in several food-surplus areas. Further analyses underscore the importance of variables related to child-care practices and of care-enabling factors such as household food security, health environment, and caregivers' time and education. These findings challenge the traditional thinking among a majority of Haitian policy makers who look at the malnutrition problem solely from the perspective of local food production.