Synthesis of N-Hydroxysuccinimide Esters, N-Acylsaccharins, and Activated Esters from Carboxylic Acids Using I2/PPh3.

A method for the syntheses of isolable, active esters is described in which carboxylic acids are treated with triphenylphosphine, iodine, and triethylamine. Active esters accessible in this way include N-hydroxysuccinimide esters, N-hydroxyphthalimide esters (N-(acyloxy)phthalimides), N-acylsaccharins, pentafluorophenol esters, pentachlorophenol esters, N-hydroxybenzotriazole esters, and hexafluoro-2-propanol esters. The approach can be similarly applied toward the formation of N-acylsaccharins and N-acylimidazoles. The method is suitable for the formation of isolable active esters of aromatic and aliphatic activated acids as well as α-amino acid derivatives. These products are widely used reagents in organic synthesis, peptide synthesis, medicinal chemistry, and chemical biology (e.g., for bioconjugations). The method has broad substrate scope, uses simple and inexpensive reagents, avoids the use of carbodiimides or other coupling agents, and occurs at room temperature. Additionally, the diastereomers of compound Boc-Ala-NHCHPh are demonstrated to be distinguishable by 1H NMR (in DMSO-d6), allowing for a straightforward NMR method to establish the degree of racemization of activated esters of Boc-Ala or amide bond formations using Boc-Ala.

TLT: Recurrent fine-tuning transfer learning for water quality long-term prediction.

The water quality long-term prediction is essential to water environment management decisions. In recent years, although water quality prediction methods based on deep learning have achieved excellent performance in short-term prediction, these methods are unsuitable for long-term prediction because the accumulation use of short-term prediction will easily introduce noise. Furthermore, The long-term prediction task requires a large amount of data to train the model to obtain accurate prediction results. For some monitoring stations with limited historical data, it is challenging to fully exploit the performance of deep learning models. To this end, we introduce a transfer learning framework into water quality prediction to improve the prediction performance in data-constrained scenarios. We propose a deep Transfer Learning based on Transformer (TLT) model to enable time dependency perception and facilitate long-term water quality prediction. In TLT, we innovatively introduce a recurrent fine-tuning transfer learning method, which can transfer the knowledge learned from source monitoring stations to the target station, while preventing the deep learning model from overfitting the source data during the pre-training phase. So, TLT can fully exert the performance of deep learning models with limited samples. We conduct experiments on data from 120 monitoring stations in major rivers and lakes in China to verify the effectiveness of TLT. The results show that TLT can effectively improve the long-term prediction accuracy of four water quality indicators (pH, DO, NH3-N, and CODMn) from monitoring stations with limited samples.

A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism.

The water environment plays an essential role in the mangrove wetland ecosystem. Predicting water quality will help us better protect water resources from pollution, allowing the mangrove ecosystem to perform its normal ecological role. New approaches to solve such nonlinear problems need further research since the complexity of water quality data and they are easily affected by the noise. In this paper, we propose a water quality prediction model named CNN-LSTM with Attention (CLA) to predict the water quality variables. We conduct a case study on the water quality dataset of Beilun Estuary to predict pH and NH3-N. Linear interpolation and wavelet techniques are used for missing data filling and data denoising, respectively. The hybrid model CNN-LSTM is highly capable of resolving nonlinear time series prediction problems, and the attention mechanism captures longer time dependence. The experimental results show that our model outperforms other ones, and can predict with different time lags in a stable manner.

A water quality prediction method based on the multi-time scale bidirectional long short-term memory network.

As an important factor affecting the mangrove wetland ecosystem, water quality has become the focus of attention in recent years. Therefore, many studies have focused on the prediction of water quality to help establish a regulatory framework for the assessment and management of water pollution and ecosystem health. To make a more accurate and comprehensive forecast analysis of water quality, we propose a method for water quality prediction based on the multi-time scale bidirectional LSTM network. In the method, we improve data integrity and data volume through data preprocessing. And the network processes input data forward and backward and considers the dependencies at multiple time scales. Besides, we use the Box-Behnken experimental design method to adjust hyper-parameters in the process of modeling. In this study, we apply this method to the water quality prediction research of Beilun Estuary, and the performance of our proposed model is evaluated and compared with other models. The experiment results show that this model has better performance in water quality prediction than that of using LSTM or bidirectional LSTM alone. Graphical Abstract Schematic of research work.

Construction of three-dimensional temperature distribution using a network of ultrasonic transducers.

Although the ultrasonic technique for measuring temperature distributions has drawn much attention in recent years, most studies that adopt this technique focus on two-dimensional (2D) systems. Mathematically, extending from 2D to 3D requires higher construction-performing algorithms, as well as more complicated, but extremely crucial, designs of ultrasonic transducer layouts. Otherwise the ill condition of governing-equation matrices will become more serious. Here, we aim at constructing 3D temperature distributions by using a network of properly-installed ultrasonic transducers that can be controlled to transmit and receive ultrasound. In addition, the proposed method is capable of performing this construction procedure in real time, thus monitoring transient temperature distributions and guarantee the safety of operations related to heating or burning. Numerical simulations include constructions for four kinds of temperature distributions, as well as corresponding qualitative and quantitative analyses. Finally, our study offers a guide in developing non-intrusive experimental methods that measure 3D temperature distributions in real time.

Energy-Efficient Patching Strategy for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are vulnerable to computer viruses. To protect WSNs from virus attack, the virus library associated with each sensor node must be updated in a timely way. This article is devoted to developing energy-efficient patching strategies for WSNs. First, we model the original problem as an optimal control problem in which (a) each control stands for a patching strategy, and (b) the objective functional to be optimized stands for the energy efficiency of a patching strategy. Second, we prove that the optimal control problem is solvable. Next, we derive the optimality system for solving the optimal control problem, accompanied with a few examples. Finally, we examine the effects of some factors on the optimal control. The obtained results help improve the security of WSNs.

Receding horizon H∞ guaranteed cost tracking control for microwave heating medium with temperature-dependent permittivity.

This paper considers the temperature spectrum tracking control of microwave heating model, in the presence of asymmetrical input saturation, nonhomogeneous Neumann boundary condition and temperature-dependent permittivity. The sufficient condition for the existence of receding horizon H∞ guaranteed cost control is proposed based on the derived finite-dimensional ordinary differential equation (ODE) error model. Furthermore, by on-line updating and solving linear matrix inequalities (LMIs) optimization problem, the constrained tracking controller can be obtained in the sense of minimizing H∞ norm and satisfying the quadratic cost performance. The proposed control strategy is implemented on a one-dimensional cavity heating model and its performance is evaluated through the simulation.

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.

Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis.

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

Ultrasonic temperature distribution reconstruction for circular area based on Markov radial basis approximation and singular value decomposition.

Temperature distribution reconstruction is of critical importance for circular area, and an ultrasonic technique is investigated to meet this demand in this paper. Considering the particularity of circular area, algorithm based on Markov radial basis approximation and singular value decomposition is proposed, while ultrasonic transducers layout and division of measured area are properly designed. The reconstruction performance is validated via numerical experiments using different temperature distribution models, and is compared with algorithm based on least square method. To study the anti-interference, various noises are adding to the theoretical value of time-of-flight. Experiment results indicate that the proposed algorithm can reconstruct temperature distribution with higher accuracy and stronger anti-interference, while without the problem of algorithm based on least square method that its reconstructions will lose much temperature information near the edge of measured area.

Detection of abnormal item based on time intervals for recommender systems.

With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

A functions localized neural network with branch gates.

In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network. FLN-bg is applied to function approximation problems and a two-spiral problem. The simulation results show that FLN-bg exhibits better performance than conventional neural networks with comparable complexity.