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Sensor data that is often collected in the Internet of Things (IoT) or any smart city environment should be protected against security and privacy concerns. Often, sensor data that is shared by devices in smart cities contains sensitive or private information that can often be shared over different networks and by different smart applications. In the last decade, the area of Privacy-Preserving Data Mining (PPDM) has received a lot of attention as the amount of data received and collected daily is huge. Unfortunately, PPDM mostly applies to binary data. To improve the usefulness of PPDM, we present a more usable version for smart cities called Privacy-Preserving Utility Mining (PPUM), in the form of a Maximal Sensitive Utility-Maximal Sensitive ConflIct (MSU-MSI) algorithm. MSU-MSI finds any conflicting items that may contain sensitive itemsets with high-utility and sanitizes them, stripping them of sensitive and private information while maintaining utility. Any transactions encountered that contain sensitive itemsets are first fed through sanitization processes. This is followed by calculating the total number of items that conflict, and then removing them so sanitization processes can operate more efficiently so as to not redo known sanitization processes. We conduct an in-depth experimental analysis, where our detailed methodology is compared directly with state-of-the-art frameworks such as MSU-MIU, MSU-MAU, HHUIF and MSCIF. Our proposed MSU-MSI shows a higher performance in missing cost, in particular when dealing with highly dense or highly sparse datasets. Moreover, our novel framework is shown to achieve an excellent performance with regards to similarity in database structure and database utility. [ABSTRACT FROM AUTHOR]

SDS22 and Inhibitor-3 (I3) are two ancient regulators of protein phosphatase 1 (PP1) that regulate multiple essential biological processes. Both SDS22 and I3 form stable dimeric complexes with PP1; however, and atypically for PP1 regulators, they also form a triple complex, where both proteins bind to PP1 simultaneously (SPI complex). Here we report the crystal structure of the SPI complex. While both regulators bind PP1 in conformations identical to those observed in their individual PP1 complexes, PP1 adopts the SDS22-bound conformation, which lacks its M1 metal. Unexpectedly, surface plasmon resonance (SPR) revealed that the affinity of I3 for the SDS22:PP1 complex is 10-fold lower than PP1 alone. We show that this change in binding affinity is solely due to the interaction of I3 with the PP1 active site, specifically PP1's M2 metal, demonstrating that SDS22 likely allows for PP1 M2 metal exchange and thus PP1 biogenesis. [ABSTRACT FROM AUTHOR]

To improve the average remaining energy of the network, reduce the routing overhead, extend the network lifecycle and reduce the average end-to-end delay, a new multi-frequency communication routing protocol for mobile Internet of things device terminal wireless network is proposed. Construct the topology of a multi-frequency wireless communication network and measure and analyze its routing; According to the routing measurement results, the communication routing cluster head is elected; The RPBUC algorithm is used to establish the wireless communication energy consumption model, and the greedy algorithm is used to establish the wireless network communication data transmission mechanism. Based on the wireless network communication energy consumption model and the wireless network communication data transmission mechanism, the ant colony algorithm is used to select the optimal wireless network multi-frequency communication routing protocol. The experimental results show that the average network residual energy of the protocol is greater than 0.01 J, the routing overhead is kept within 0.2, the network life cycle is prolonged, and the average end-to-end delay is effectively reduced. [ABSTRACT FROM AUTHOR]

Cyberbullying is disturbing and troubling online misconduct. It appears in various forms and is usually in a textual format in most social networks. Intelligent systems are necessary for automated detection of these incidents. Some of the recent experiments have tackled this issue with traditional machine learning models. Most of the models have been applied to one social network at a time. The latest research has seen different models based on deep learning algorithms make an impact on the detection of cyberbullying. These detection mechanisms have resulted in efficient identification of incidences while others have limitations of standard identification versions. This paper performs an empirical analysis to determine the effectiveness and performance of deep learning algorithms in detecting insults in Social Commentary. The following four deep learning models were used for experimental results, namely: Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Data pre-processing steps were followed that included text cleaning, tokenization, stemming, Lemmatization, and removal of stop words. After performing data pre-processing, clean textual data is passed to deep learning algorithms for prediction. The results show that the BLSTM model achieved high accuracy and F1-measure scores in comparison to RNN, LSTM, and GRU. Our in-depth results shown which deep learning models can be most effective against cyberbullying when directly compared with others and paves the way for future hybrid technologies that may be employed to combat this serious online issue. [ABSTRACT FROM AUTHOR]

With the development of information technology, the high-speed private data stream encryption transmission system is more and more widely used in the field of education. The high-speed private data stream encryption transmission system can realize high-speed and safe data transmission and protect the privacy and security of user data. To protect the security of private data in the online education of specialty "traffic engineering", an encryption transmission system for high-speed private data stream is designed. After the online education system and core modules of traffic engineering are analyzed, the system framework is designed through the codec module and the high-speed privacy data stream processing control and transmission module. The sliding window segmentation method is used to extract the privacy data in the education system and send it to the coding sub-module. Based on a homomorphic encryption algorithm, a private data stream encryption and decryption model is established. The key encoding is designed by the vector quantization coding method. The private data is authenticated according to the characteristics of the key, and the data encryption transmission is completed through the microcontroller. The experimental results show that the encrypted data of the designed system has only 0.01 Gbit loss, and the encryption time is only 93ms. The data dimension displayed after encryption is relatively scattered, and the distribution probability of encrypted data is between 1500–2000, which can effectively encrypt the private data in the online education system of transportation engineering specialty, and improve the security of high-speed private data flow in the online education system of transportation engineering specialty. [ABSTRACT FROM AUTHOR]

There are some problems in the quality evaluation of network distance education, such as the large error in the quantitative results of the evaluation indicators, the low accuracy of the quality evaluation and the large error in the indicator's weight distribution. Therefore, a quantitative evaluation method of NDE reliability based on Back Propagation (BP) neural networks and fuzzy comprehensive evaluation is proposed. From the perspective of students, teachers and schools, the primary and secondary indicators of teaching evaluation are constructed, and the reliability evaluation indicators system of teaching quality is established. The fuzzy comprehensive evaluation method is used to calculate the membership degree of the evaluation indicators, the consistency verification method is used to determine the consistency of the indicator's attributes, and the quantitative research of the indicators data is completed by the weight calculation method to improve the accuracy of the quantitative results. BP neural network is used to construct the input layer, hidden layer and output layer of network distance education quality reliability evaluation indicators, calculate the weight of evaluation indicators, transform the input evaluation indicators data through linear conversion function, construct the indicators evaluation model, and correct the evaluation results with the help of loss function to improve the accuracy of quality evaluation results. The experimental results show that the evaluation result error of the proposed method is relatively small, with an error of less than 0.3%, and the highest evaluation accuracy reaches 96.3%, which can effectively improve the quantitative evaluation effect of the reliability of network remote teaching quality. [ABSTRACT FROM AUTHOR]

To improve the ability of natural language information processing in mobile communication networks, a system based on recurrent neural networks (RNN) is designed. The feedback connection is transformed into a hidden layer pointing to the input layer. According to the physiological process of human speech and the acoustic characteristics of a speech signal, a model is designed using an excitation model, channel model, and radiation model, respectively. The experimental results show that the information processing accuracy of the designed system is high and the processing time is short. [ABSTRACT FROM AUTHOR]

Tropical cyclones that originate from the Indian Ocean affect the Indian Sub-Continent. Heavy rainfall and flooding occur because of these cyclones. South Odisha was affected by Cyclonic Storm Daye in September 2018 and Cyclonic Storm Titli was occurred in August affecting Andhra Pradesh and Odisha as well. The Eastern portion of India was affected by the Cyclonic Storm Fani in April 2019. In May 2020, West Bengal was affected by the Amphan which is a Super Cyclonic Storm and in the same year Tamil Nadu was affected by the very severe Cyclonic Storm Nivar in November 2020. These are just a few of the notable cyclonic events in the Indian Sub-Continent. These cyclonic events cause a dramatic change in a very short time from dry soil to exceptional flooding. In this proposed work, we are attempting to create an observations-driven prediction model to quantify the soil moisture variations daily, predict county-based meteorology and evaluate the cause of cyclones and heavy rainfall in certain areas of India. In our work, we applied a deep learning-based methodology to predict soil moisture. For the prediction model, we fused Feed Forward Neural Networks with the Gated Recurrent Unit (GRU) model and present the prediction results. We have used climatic as well as environmental data published by the Indian Meteorological Department (IMD) Warning from 2011. The collected data is time-series data. Comparisons and the relationship that exists between soil moisture and meteorological data are made and analyzed. The soil moisture of the South Indian states Karnataka, Andhra Pradesh and Tamil Nadu are predicted from weather data using a hybrid deep learning model. The evaluations of the proposed work using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and R-squared (R2) against Non-hybrid Neural Network models such as Artificial Neural Networks (ANN), Convolutional Neural Networks, and Gated Recurrent Unit (GRU) models is analyzes where our model has given better results. [ABSTRACT FROM AUTHOR]

Lung abnormality in humans is steadily increasing due to various causes, and early recognition and treatment are extensively suggested. Tuberculosis (TB) is one of the lung diseases, and due to its occurrence rate and harshness, the World Health Organization (WHO) lists TB among the top ten diseases which lead to death. The clinical level detection of TB is usually performed using bio-medical imaging methods, and a chest X-ray is a commonly adopted imaging modality. This work aims to develop an automated procedure to detect TB from X-ray images using VGG-UNet-supported joint segmentation and classification. The various phases of the proposed scheme involved; (i) image collection and resizing, (ii) deep-features mining, (iii) segmentation of lung section, (iv) local-binary-pattern (LBP) generation and feature extraction, (v) optimal feature selection using spotted hyena algorithm (SHA), (vi) serial feature concatenation, and (vii) classification and validation. This research considered 3000 test images (1500 healthy and 1500 TB class) for the assessment, and the proposed experiment is implemented using Matlab®. This work implements the pretrained models to detect TB in X-rays with improved accuracy, and this research helped achieve a classification accuracy of >99% with a fine-tree classifier. [ABSTRACT FROM AUTHOR]

In "Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification", Dutta et al. from Saudi Arabia develop an intelligent rider optimization algorithm with deep learning enabled HSI classification model, named IRODL-HSIC technique. Dans "Modèle de classification optimal et entièrement connecté basé sur les réseaux de neurones profonds pour des images hyperspectrales prises par des drones", Hamza et al. de l'Arabie saoudite présentent un nouveau modèle de classification d'images acquises par drones (UAVDL-ICM), basé sur l'approche d'apprentissage en profondeur (DL) pour l'environnement de l'industrie 4.0. Hyperspectral Remote Sensing (HRS) is an emerging, multidisciplinary paradigm with a variety of applications that are built on the principle of material spectroscopy, radiative transfer, and imaging spectroscopy. [Extracted from the article]

Background: Twitter is a popular social networking site where short messages or "tweets" of users have been used extensively for research purposes. However, not much research has been done in mining the medical professions, such as detecting the occupations of users from their biographical contents. Mining such professions can be used to build efficient recommender systems for cost-effective targeted advertisements. Moreover, it is highly important to develop effective methods to identify the occupation of users since conventional classification methods rely on features developed by human intelligence. Although, the result may be favorable for the classification problem. However, it is still extremely challenging for traditional classifiers to predict the medical occupations accurately since it involves predicting multiple occupations. Hence this study emphasizes predicting the medical occupational class of users through their public biographical ("Bio") content. We have conducted our analysis by annotating the bio content of Twitter users. In this paper, we propose a method of combining word embedding with state-of-art neural network models that include: Long Short Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit, Bidirectional Encoder Representations from Transformers, and A lite BERT. Moreover, we have also observed that by composing the word embedding with the neural network models there is no need to construct any particular attribute or feature. By using word embedding, the bio contents are formatted as dense vectors which are fed as input into the neural network models as a sequence of vectors. Result: Performance metrics that include accuracy, precision, recall, and F1-score have shown a significant difference between our method of combining word embedding with neural network models than with the traditional methods. The scores have proved that our proposed approach has outperformed the traditional machine learning techniques for detecting medical occupations among users. ALBERT has performed the best among the deep learning networks with an F1 score of 0.90. Conclusion: In this study, we have presented a novel method of detecting the occupations of Twitter users engaged in the medical domain by merging word embedding with state-of-art neural networks. The outcomes of our approach have demonstrated that our method can further advance the process of analyzing corpora of social media without going through the trouble of developing computationally expensive features. [ABSTRACT FROM AUTHOR]

There has been an alarming rise in human monkeypox cases during these past few months in countries where the disease is not endemic. The recent COVID-19 pandemic and the connection of the monkeypox virus with the smallpox-causing variola virus makes it highly likely to be a candidate for another human health emergency. The transmission mode is predominantly via sexual contact, especially among men who have sex with men (MSM); anogenital lesions are the most typical presentation. Although it is a disease with a self-limiting course, some patients require admission for severe anorectal pain, pharyngitis, eye lesions, kidney injury, myocarditis, or soft tissue superinfections. Antiviral therapy has been advocated, of which tecovirimat is promising in patients with comorbidities. Vaccines will be the mainstay for the present and future control of the disease. [ABSTRACT FROM AUTHOR]

The Internet of Things is made up of a set of embedded devices such as light detection sensors, ranging (LiDAR) sensors, and millimetre-wave sensors. These sensors generate a massive amount of data in which limited communication capacity is available to share a massive amount of data to Fog-based Roadside Units (RSU) for data process and analysis service. Fog-based RSU has become an emerging paradigm in intelligent transportation but needs research attention to design intelligent decision-making methods for data communication and computation at Fog-based RSU. To address these issues, we design a two-level Quantum based D2D Computation, Communication ( Q D C 2 ) approach. First, design a bandwidth allocation strategy based on spatial importance score factors to resolve embedded devices' data transmission issues. Second, design an adaptive equilibrium service offloading strategy based on device-centric measurements to assess the computation capacity and performance rate for resolving Fog-node computation consistency issues. Additionally, Fog-based RSU is interconnected with LAN helps to optimize service latency. Simulation results show that our approach achieved a high service reliability rate (79.56%), low error rate (0.9%), and an execution delay of 22.5 s for 15 devices compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In order to improve the quality of online distance education and students' online learning, an intelligent online distance education decision-making method based on cloud computing is proposed. Using computing, we provide decision-making resources for online education decision-making through the resource layer; adopt the G1 deviation maximization method to calculate the combination weight, and determine the optimal decision-making scheme in combination with bipolar binary semantics and cloud model; moreover, we provide human–computer interaction windows to view the decision-making scheme at the application layer; and complete the optimal decision-making for intelligent online education by providing the management function of cloud computing services. Experimental results show that this method can effectively obtain a decision-making scheme for network education. After the application of this method, the students' learning ability and academic performance have been significantly improved. [ABSTRACT FROM AUTHOR]

When the existing methods are used to encrypt the lightweight infrared image, there is no denoising processing for the lightweight infrared image, which has the problems of low image signal-to-noise ratio, low encryption efficiency and poor encryption effect. A dual chaotic encryption method for lightweight infrared image is proposed. The discrete orthogonal wavelet transform is used to process the lightweight infrared image. The wavelet coefficients of the lightweight infrared image are estimated by improving the block singular value decomposition. The estimated high-frequency image and low-frequency image are transformed by inverse wavelet transform to complete the denoising of the lightweight infrared image. The wavelet coefficients are estimated by threshold method or minimum mean square error method, and then the noise free lightweight infrared image is obtained by inverse wavelet transform. The lightweight infrared image is encrypted by double scrambling of pixel position and bit, and the dual chaotic encryption of lightweight infrared image is realized. The experimental results show that the security coefficient of the image encrypted by this method is more than 0.8. This method has high image signal-to-noise ratio, high encryption efficiency and good encryption effect. [ABSTRACT FROM AUTHOR]

Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy. [ABSTRACT FROM AUTHOR]

Image reconstruction is a mathematical process, where the image is compressed into a small representation and derived from this form. The general use of the reconstruction technique finds a place in noise removal from images obtained in medicine or other areas of life. In this paper, we propose a heuristic validation mechanism for training different types of neural networks in the problem of image reconstruction. The main idea is based on finding some important areas on image by heuristic algorithm and train network until a certain level of entropy of these areas is achieved. The mathematical model of this technique is described and supported by experimental results on different datasets with complex analysis of different heuristics. Proposed approach shows that it can reduce the average time of training process using convolutional neural networks. [ABSTRACT FROM AUTHOR]

Mandelbrot set, which was provided as a highlight in fractal and chaos, is studied by many researchers. With the extension of Mandelbrot set to generalized M set with different kinds of exponent k (k − M set), properties are hard to understand when k is a complex number. In this paper, fractal property of generalized M set with complex exponent z is studied. First, a relation is constructed between generalized M set with complex and real exponent. Then, distribution of z − M set on complex plane is researched. Meanwhile, symmetry of generalized M set is proved. Finally, graphics, generated by escape time algorithm, are the validated results of this paper. [ABSTRACT FROM AUTHOR]

Recently, Content Delivery Networks (CDN) have become more and more popular. The technology itself is ahead of academic research in this area. Several dimensions of the technology have not been adequately investigated by academia. These dimensions include outline management, security, and standardization. Discovering and highlighting aspects of this technology that may have or have not been covered by academic research is the first step toward helping academia bridge the gap with industry or even go one step further to lead industry in the right direction. This suggests a comprehensive survey on research works in this regard. The literature in this area has already come up with some surveys and taxonomies, but some of them are outdated or do not cover every aspect of CDN while others fail to detect existing trends or to develop a holistic roadmap for research on the technology. Furthermore, none of the existing surveys aim at enlightening the dark aspects of the technology that have not been subject to academic research. In this survey, we first extract the lifecycle of a CDN as suggested by the existing research. Then, we investigate previous relevant works on each phase of the lifecycle to clarify where the research is currently located and headed. We show how CDN technology tends to converge with emerging paradigms such as cloud computing, edge computing, and machine learning, which are more mature in terms of academic research. This helps us determine the right direction for further research by revealing the deficiencies in existing works. [ABSTRACT FROM AUTHOR]

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster. [ABSTRACT FROM AUTHOR]

Securing a wireless sensor system is a hard task for researchers today. Strengthening the authentication system before connection establishment is the right way to enhance the architecture and also to provide secured communication from eavesdroppers. In this paper, we present a novel algorithm to enhance the security of data using a hybrid model which uses an adaptive encoding technique alongside Chaotic Hopfield Neural Network. The proposed computation upgrades the security of a key shared between any nodes. Our experimental results show that the security of transmitted data is better than traditional algorithms. Moreover, we also show that the computational time for the proposed algorithm is less than known traditional algorithms. [ABSTRACT FROM AUTHOR]

Current accounting methods for small and medium-sized enterprises (SMEs) have long running times and low user satisfaction. Therefore, a method for the selection of accounting models for SMEs based on accounting market big data (AMBD) is proposed in this paper. Firstly, some indicators such as the current ratio, quick ratio, asset-liability ratio, accounts receivable turnover rate, and other indicators taken from the solvency, operating capacity, profitability, and growth capacity of a company are selected to set up an AMBD constraint system. Then, the principal component analysis method is used to achieve the classification of the constraints of the AMBD. Finally, by combining particle swarm optimization with ant colony optimization, the optimal accounting model is obtained through iteration. Experimental results show that the proposed method has high efficiency and user satisfaction, and achieves a high coefficient of rationality. Furthermore, the method incorporates the constraints found in the AMBD, and meets the selection requirements of the SME accounting model. [ABSTRACT FROM AUTHOR]

The motor movement performed by different body parts affects the synaptic potential at different brain cortices, which can be observed by the electroencephalogram (EEG) signal. The recorded EEG signals can be used to decode the imagined motor task. The EEG signals are non-stationary and transient and contain time, frequency, and space information. Extracting this information and processing them with the latest machine learning and deep learning algorithms can be useful for brain–computer interfacing and other human–machine interaction techniques. EEG signal contains negative values. Hence, nonnegative matrix factorization can be used to provide a meaningful explanation of information within EEG signals. Sparseness in feature vectors is another essential factor to consider while identifying the structures in an input signal. In this work, we propose a novel motor imagery classification model that extracts the weights for predefined motor imagery features from EEG signals and classifies them using a convolution neural network (CNN). Sparse nonnegative matrix factorization is used to extract the fundamental feature vectors for different motor imagery events, which are further used to extract the combined weight matrix of unknown motor imagery events. The designed CNN classifies the extracted weight matrix in the corresponding classes. The acquired EEG signals from all the channels are processed simultaneously using the CNN, which helps extract spatial information from the signals. BCI Competition IV dataset IIa and BCI Competition III dataset IVa are used to validate the proposed method. The proposed method has been compared with existing methods and validates their superiority in terms of average accuracy. The classification accuracy for two types and four types of motor imagery signals is 99.53% and 94.58%, respectively. Empirical results show that EEG signals' sparseness characteristics can be considered an effective feature for motor imagery classification. [ABSTRACT FROM AUTHOR]

Sensitive information in images is can be leaked during attacks, resulting in the malicious acquisition of personal information. To improve the robustness of attacking defence for video images, a digital watermarking algorithm based on locality-sensitive hashing (LSH) is designed in this paper. Video signals are decomposed using a one-dimensional wavelet transform. According to the Yeung Mintzer (Y-M) algorithm, a marker watermark W1 is embedded in the low-frequency subband to identify any image tampering. A data string of hash function values and the exclusive OR (XOR) result of identification watermark W2 are embedded into the HH high-frequency subband, which was used to identify and counter pseudo-authentication attacks such as collage and Vector Quantization (VQ). The singular value decomposition (SVD) algorithm is used to decompose the hash-mapped watermark and adaptively adjust embedding strength of the watermark. The position-sensitive hash algorithm which is proposed in this work has good invisibility for embedding digital watermarks into images, with an average accuracy of approximately 97% for feature matching of digital images. The PSNR value of the image embedded with the watermark is approximately 49 dB. At the 50th minute of the experiment, the regulatory factor value of the research method was 0.3. Under different attack modes, the correlation coefficient between the watermark extracted by this method and the original watermark image is greater than 0.85. Due to the low compression quality of JPGE, the correlation coefficient between the watermark and the initial watermark is greater than 0.6, and its error rate is less than 0.10 bits, showing the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

Selling as a profession and sales management as an organizational function have undergone major changes that were driven by the evolutionary journey of the marketing discipline. The extant value cocreation paradigm is recrafting the purpose of selling. This paper explores the conceptual development of a selling paradigm that is more responsible to its customers. Specifically, in Business-to-Business (B2B) context, the cost of any selling behavior that compromises customer interest could be of disastrous proportions. Building on the theoretical foundations of self-regulation and job demands-resources theory, this paper develops a conceptual model of responsible selling by integrating self-leadership literature with the emerging paradigm of value-based selling. The conceptual model outlines several research propositions for empirical validation and discusses its potential implications for sales managers and sales organizations. [ABSTRACT FROM AUTHOR]

Infection by HIV‐1 requires protein–protein interactions involving gp120, CD4 and CCR5. We have previously demonstrated that the transferred nuclear Overhauser effect (TRNOE), in combination with asymmetric deuteration of a protein and a peptide ligand can be used to detect intermolecular interactions in large protein complexes with molecular weights up to ~ 100 kDa. Here, using this approach, we reveal interactions between tyrosine residues of a 27‐residue peptide corresponding to the N‐terminal segment of the CCR5 chemokine receptor, and a dimeric extended core YU2 gp120 envelope protein of HIV‐1 complexed with a CD4‐mimic miniprotein. The TRNOE crosspeaks in the ternary complex were assigned to the specific Tyr protons in the CCR5 peptide and to methyl protons of isoleucine, leucine and/or valine residues of gp120. Site directed mutagenesis combined with selective deuteration and TRNOE resulted in the first discernment by a biophysical method of specific pairwise interactions between gp120 residues in the bridging sheet of gp120 and the N‐terminus of CCR5. A surface representation of the gp120 bridging sheet indicating in red residues that interact with aromatic residues in the N‐terminus of the chemokine receptor CCR5 (Nt‐CCR5). Specific pairwise interactions involving these residues were determined, using TRNOE, selective deuteration and site directed mutagenesis on a gp120/CD4 mimic/Nt‐CCR5 complex. [ABSTRACT FROM AUTHOR]

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. [ABSTRACT FROM AUTHOR]

In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data coming from different sensors is first converted into an image database using the Gamian angular field. The images are trained using VGG16, where XAI technology and hyper-parameter optimization are introduced. Thus, analyzing the impact of the different input values in the output and understanding the different weights of a deep learning model used in the learning process helps us to increase interpretation of the overall process of IoT systems. Extensive testing was conducted to demonstrate the performance of our developed model on two separate IoT datasets. Results show the efficiency of the proposed approach compared to the baseline approaches in terms of both runtime and accuracy. [ABSTRACT FROM AUTHOR]

Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit. [ABSTRACT FROM AUTHOR]

NMR is a powerful tool for studying structural details of protein/peptide complexes exhibiting weak to medium binding ( KD > 10 μ m). However, it has been assumed that intermolecular nuclear Overhauser effect ( NOE) interactions are difficult to observe in such complexes. We demonstrate that intermolecular NOEs can be revealed by combining the 13C-edited/13C-filtered experiment with the transferred NOE effect ( TRNOE). Due to the TRNOE phenomenon, intermolecular NOE cross peaks are characterized by both the chemical shifts (CSs) of the protein protons and the average CSs of the peptide protons, which are dominated by the CSs of the protons of the free peptide. Previously, the TRNOE phenomenon was used almost exclusively to investigate the conformation of small ligands bound to large biomolecules. Here, we demonstrate that TRNOE can be extended to enable the study of intermolecular interactions in small- and medium-sized protein complexes. We used the 13C-edited/13C-filtered TRNOE experiment to study the interactions of the chemokine regulated upon activation, normal T cell, expressed and secreted ( RANTES) with a 27-residue peptide, containing two sulfotyrosine residues, representing the N-terminal segment of the CCR5 receptor ((Nt- CCR5(1-27). The TRNOE phenomenon led to more than doubling of the signal-to-noise ratios ( SNRs) for the intermolecular NOEs observed in the 13C-edited/13C-filtered experiment for the 11.5-kDa monomeric RANTES/Nt- CCR5(1-27) complex. An even better improvement in the SNR was achieved with dimeric Nt- CCR5(1-27)/ RANTES (23 kDa), especially in comparison with the spectra measured with a 1 : 1 protein to peptide ratio. In principle, the isotope-edited/isotope-filtered TRNOE spectrum can discern all intermolecular interactions involving nonexchangeable protons in the complex. [ABSTRACT FROM AUTHOR]

Weak protein-protein and protein-ligand interactions play important roles in biological recognition. In many cases, simplification of structural studies of large protein complexes is achieved by investigation of the interaction between the protein and a weakly binding segment of its protein ligand. Detection of pairwise interactions in such complexes is a major challenge for both X-ray crystallography and nuclear magnetic resonance. We demonstrate that transferred nuclear Overhauser effect (TRNOE), in combination with asymmetric deuteration of a protein and a peptide ligand can be used to detect intermolecular interactions in large protein complexes with molecular weights up to ~ 100 kDa. Using this approach, we revealed interactions between tyrosine residues of a 27-residue peptide (deuterated at Ile and Val residues) corresponding to the N-terminal segment of the human C-C chemokine receptor 5 (CCR5) chemokine receptor, and a 43 kDa construct of gp120 envelope protein of human immunodeficiency virus type 1 (deuterated on all aromatics) complexed with a cluster of differentiation 4-mimic miniprotein. The complex was present mostly as a dimer as determined by T2 relaxation measurements. The TRNOE crosspeaks in the ternary complex were assigned to the specific Tyr protons in the CCR5 peptide and to methyl protons, predominantly of isoleucine residues, and also of leucine and/or valine residues of gp120. The TRNOE/asymmetric deuteration method benefits from the sensitivity of the homonuclear NOESY experiment and does not suffer the sensitivity losses associated with isotope-edited/isotope-filtered approaches that rely on magnetization transfer between protons and heteronuclei that are bonded to them. The technique can be widely applied for studying large protein complexes that exhibit fast off-rates. [ABSTRACT FROM AUTHOR]

The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. These techniques and definitions, although robust are sometimes difficult to achieve for large social net-works. In this paper, we look at applying ant colony opti-mization (ACO) to two known versions of social network anonymization, namely k-label sequence anonymity, known to be NPhard for k = 3. We also apply it to the more recent work of [23] and Label Bag Anonymization. Ants of the artificial colony are able to generate successively shorter tours by using information accumulated in the form of pheromone trails deposited by the edge colonies ant. Computer simu-lations have indicated that ACO are capable of generating good solutions for known harder graph problems. The contributions of this paper are two fold: we look to apply ACO to k-label sequence anonymity and k=label bag based anonymization, and attempt to show the power of ap-plying ACO techniques to social network privacy attempts. Furthermore, we look to build a new novel foundation of study, that although at its preliminary stages, can lead it ground breaking results down the road. [ABSTRACT FROM AUTHOR]

Abstract: When remote sports injuries are traditionally monitored, the feedback is not timely, and there are some problems such as low running speed and poor accuracy of sports injury monitoring. Therefore, this paper designs a remote sports injury monitoring method based on a wireless sensor network. Firstly, the terminal node of the sports injury monitoring process is designed, and three-terminal devices are tied to the experimental object's body to collect motion information, to realize the collection of human motion information based on ZigBee wireless sensor network; Secondly, the USB module circuit interface is designed to realize the series connection of each line, and the local processing ability of network nodes is used to make a centralized decision. Then, the skeleton coordinate system is constructed, and the rotation of the human skeleton is measured by an inertial sensor. Through a variety of posture fitting, the error of remote sports injury monitoring is reduced from the two directions of joint error and muscle error. Finally, the training sample set is learned through the BP algorithm, the fitness function of the genetic algorithm is obtained, the external structural parameters of the adaptive neural network model are adjusted, the discrimination deviation and fitness function are calculated, the adaptive neural network model with the best generalization ability is output, and the local processing ability of remote sports injury monitoring method is improved combined with wireless sensor network technology, The design of remote sports injury monitoring method based on wireless sensor network is realized. The experimental results show that the accuracy of the method is 99%, the average time delay is 1 s, and the accuracy of the method is 92% even with noise. Therefore, the method can effectively improve the running speed of the remote sports injury monitoring method and improve the accuracy of sports injury monitoring. [ABSTRACT FROM AUTHOR]

Keywords: psoriasis; isomorphic phenomenon; Koebner phenomenon; reverse Koebner phenomenon EN psoriasis isomorphic phenomenon Koebner phenomenon reverse Koebner phenomenon 1 2 2 01/06/22 20210801 NES 210801 A 49-year-old male presented in the outpatient department with an asymptomatic eruption on the lower back of a month's duration. The localization of the psoriasis lesion on the recent injured site pointed towards a Koebner isomorphic phenomenon. [Extracted from the article]

Internet‐based information exchange has resulted in the propagation of false and misleading information, which is highly detrimental to individuals and humankind. Due to the speed and volume of social media news production, supervised artificial intelligence algorithms require many annotated data, which is difficult, costly, and time‐consuming. To address this issue, we offer a novel federated semi‐supervised framework based on self‐ensembling that utilizes the linguistic and stylometric information of annotated news articles and searches for hidden patterns in unlabeled data to denoise labels. Self‐ensembling predicts the labels of unlabeled data by using the outcomes of network‐in‐training from earlier epochs. These cumulative predictions should be a stronger predictor for unknown labels than the output of the most recent training epoch; hence, they may be utilized as a substitute for the labels of unlabeled data. The approach is distinctive in collecting all of the outputs from the neural network's past training periods. It utilizes them as an unsupervised target against which to assess the current output prediction of unlabeled articles. We intend to create a dataset centred on denoising to forward the study. The dataset is mapped using (1) the shifting focus time from published news articles and (2) the semi‐supervised method based on coincidence contexts for a neural contrast embedding model for learning low‐dimensional continuous vectors that generate a focus time‐based query in sequential news articles for temporal comprehension. The model achieved 0.83% F‐measure with lexicon expansion semi‐supervised learning. [ABSTRACT FROM AUTHOR]

Edge service computing is an emerging paradigm for computing, storage, and communication services to optimize edge framework latency and cost based on mobile edge computing (MEC) devices. The devices are battery-enabled and have limited communication and computation resources. X consolidation is a major issue in distributed heterogeneous MEC orchestrations, where X represents the task scheduling/device selection/channel selection/offloading strategy. The network entities need to enhance network performance under uncertain circumstances for such orchestrations. Haphazard X consolidation leads to abnormal resource and energy usage, quality of service (QoS) and latency of the edge framework. However, this study concentrates on analysing the impact of reinforcement learning-based edge resource consolidation models. The models are classified according to functionality, including device resource management, service request allocation, device selection, and offloading types. Finally, the article discusses and highlights some unresolved challenges for further study on MEC orchestration to enhance offloading strategy and resource management, as well as device and channel selection efficiency. [ABSTRACT FROM AUTHOR]

Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human–computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the Harris Hawks Optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%. [ABSTRACT FROM AUTHOR]

This paper discusses the fault-tolerant consensus problem in Unmanned Aerial Vehicle Networks (UAVNets). In recent years, the applications of UAVNets have become more and more popular. Related applications include aerial photography, geological and topographic surveys, disaster monitoring, military applications, and so on. Therefore, it is very important to build a highly reliable and fault-tolerant UAVNets. However, the network architecture of UAVNets is very different from previous network architectures. Because UAVs fly at a high speed, the topology also varies quickly. Hence, how to collect sufficient messages to reach a consensus on the network for UAVs is a challenge. In this paper, the characteristics of the distributed UAVNets will be explored first. Then, based on the characteristics of the UAVNets, a new fault-tolerant consensus protocol called the UAV Consensus Protocol (UCP) is proposed. The proposed UCP consists of two phases: the message exchanging phase and the consensus making phase. The proposed UCP can solve the consensus problem with ⌞(n O -1)/3⌟+1 rounds of message exchange in the presence of ⌞(n O -1- a O)/3⌟ Byzantine faulty UAVs and a O away UAVs, where n O is the number of AUVs in the UAVNet. Moreover, the correctness of the proposed UCP is also proved in this paper. [ABSTRACT FROM AUTHOR]

Background and Purpose Recently, we have described the use of caerulomycin A ( CaeA) as a potent novel immunosuppressive agent. Immunosuppressive drugs are crucial for long-term graft survival following organ transplantation and treatment of autoimmune diseases, inflammatory disorders, hypersensitivity to allergens, etc. The objective of this study was to identify cellular targets of CaeA and decipher its mechanism of action. Experimental Approach Jurkat cells were treated with CaeA and cellular iron content, iron uptake/release, DNA content and deoxyribonucleoside triphosphate pool determined. Activation of MAPKs; expression level of transferrin receptor 1, ferritin and cell cycle control molecules; reactive oxygen species ( ROS) and cell viability were measured using Western blotting, qRT- PCR or flow cytometry. Key Results CaeA caused intracellular iron depletion by reducing its uptake and increasing its release by cells. CaeA caused cell cycle arrest by (i) inhibiting ribonucleotide reductase ( RNR) enzyme, which catalyses the rate-limiting step in the synthesis of DNA; (ii) stimulating MAPKs signalling transduction pathways that play an important role in cell growth, proliferation and differentiation; and (iii) by targeting cell cycle control molecules such as cyclin D1, cyclin-dependent kinase 4 and p21 CIP1/WAF1. The effect of CaeA on cell proliferation was reversible. Conclusions and Implications CaeA exerts its immunosuppressive effect by targeting iron. The effect is reversible, which makes CaeA an attractive candidate for development as a potent immunosuppressive drug, but also indicates that iron chelation can be used as a rationale approach to selectively suppress the immune system, because compared with normal cells, rapidly proliferating cells require a higher utilization of iron. [ABSTRACT FROM AUTHOR]

Abstract: Enantioenriched heteroaryl ethanols and aryl heteroarylmethanols are important intermediates and structural motifs in medicinal chemistry. Asymmetric biocatalytic reduction of corresponding ketones provides a straightforward approach for preparation of these compounds. Accordingly, three newly isolated fungal strains have been described, which produced the desired heteroaryl alcohols in high enantiomeric excess (ee). A broad substrate specificity was observed within these limited number of biocatalysts as demonstrated by preparation of a variety of heteroaryl alcohols, including (S)-5-(1-hydroxyethyl)furo[2,3-c]pyridine, a key intermediate for HIV-1 reverse transcriptase inhibitor, (S)-phenyl(pyridin-2-yl)methanol, an analgesic and (S,S)-2,6-bis(1-hydroxyethyl)pyridine, a chiral building block, mostly in >99% ee and 80–92% yield. Micro-morphologically, one of the isolate was found to be similar to Penicillium funiculosum. However, its β-tubulin sequence showed only 88% sequence identity with the known β-tubulin sequences of Penicillium. It may, therefore, represent a new species of Penicillium. The other biocatalysts were identified as Alternaria alternata and Talaromyces flavus. [Copyright &y& Elsevier]

Multi-agent systems enable the division of complicated tasks into individual objects that can cooperate. Such architecture can be useful in building solutions in the Internet of Medical Things (IoMT). In this paper, we propose an architecture of such a system that ensures the security of private data, as well as allows the addition and/or modification of the used classification methods. The main advantages of the proposed system are based on the implementation of blockchain technology elements and threaded federated learning. The individual elements are located on the agents who exchange information. Additionally, we propose building an agent with a consortium mechanism for classification results from many machine learning solutions. This proposal offers a new model of agents that can be implemented as a system for processing medical data in real-time. Our proposition was described and tested to present advantages over other, existing state-of-the-art methods. We show, that this proposition can improve the Internet of Medical Thing solutions by presenting a new idea of a multi-agent system that can separate different tasks like security, or classification and as a result minimize operation time and increase accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models. [ABSTRACT FROM AUTHOR]

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi‐agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor‐based data. Multi‐agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state‐of‐the‐art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions. [ABSTRACT FROM AUTHOR]