A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network.

In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.

Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.

Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenario where access to full source domain data is not available, and instant inference on the target domain is required. Despite many efforts into TTT, there is a confusion over the experimental settings, thus leading to unfair comparisons. In this work, we first revisit TTT assumptions and categorize TTT protocols by two key factors, i.e., whether testing data is sequentially streamed and whether source model is allowed to be trained with modified loss function. Among the multiple protocols, we adopt a realistic sequential test-time training (sTTT) protocol, under which we develop a test-time anchored clustering (TTAC) approach to enable stronger test-time feature learning. TTAC discovers clusters in both source and target domains and matches the target clusters to the source ones to improve adaptation. When source domain information is strictly absent (i.e., source-free) we further develop an efficient method to infer source domain distributions for anchored clustering. Finally, self-training (ST) has demonstrated great success in learning from unlabeled data and we empirically figure out that applying ST alone to TTT is prone to confirmation bias. Therefore, a more effective TTT approach is introduced by regularizing self-training with anchored clustering, and the improved model is referred to as TTAC++. We demonstrate that, under all TTT protocols, TTAC++ consistently outperforms the state-of-the-art methods on five TTT datasets, including corrupted target domain, selected hard samples, synthetic-to-real adaptation and adversarially attacked target domain. We hope this work will provide a fair benchmarking of TTT methods, and future research should be compared within respective protocols.

Daily Schedule Recommendation in Urban Life Based on Deep Reinforcement Learning.

In our daily lives, people frequently consider daily schedule to meet their needs, such as going to a barbershop for a haircut, then eating in a restaurant, and finally shopping in a supermarket. Reasonable activity location or point-of-interest (POI) and activity sequencing will help people save a lot of time and get better services. In this article, we propose a reinforcement learning-based deep activity factor balancing model to recommend a reasonable daily schedule according to user's current location and needs. The proposed model consists of a deep activity factor balancing network (DAFB) and a reinforcement learning framework. First, the DAFB is proposed to fuse multiple factors that affect daily schedule recommendation (DSR). Then, a reinforcement learning framework based on policy gradient is used to learn the parameters of the DAFB. Further, on the feature storage based on the matrix method, we compress the feature storage space of the candidate POIs. Finally, the proposed method is compared with seven benchmark methods using two real-world datasets. Experimental results show that the proposed method is adaptive and effective.

A facile asymmetric synthesis of (s)-14-methyl-1-octadecene, the sex pheromone of the peach leafminer moth.

An asymmetric synthesis of 14-methyl-1-octadecene, the sex pheromone of the peach leafminer moth has been achieved. The target molecule was synthesized in six linear steps and in 30.3% overall yield from commercially available hexanoyl chloride, (S)-4-benzyloxazolidin-2-one and 1,9-nonanediol. The hexanoyl chloride was connected with (S)-4-benzyloxazolidin-2-one, and with the induction of the chiral oxazolidinone auxiliary, after chiral methylation, LAH reduction and then tosylation gave the chiral key intermediate 5 in high stereoselectivity. 1,9-Nonanediol, was selectively brominated, THP protected and subjected to Li2CuCl4-mediated C-C coupling to afford a C12 intermediate. The target molecule, (S)-14-methyl-1-octadecene, was obtained after the two parts were subjected to a second Li2CuCl4-mediated C-C coupling. Our synthetic approach represents the first time a substrate-control asymmetric synthesis of (S)-14-methyl-1-octadecene has been reported.

Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures.

Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Hierarchical Active Learning With Qualitative Feedback on Regions.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data regions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be a qualitative assessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise a hierarchical active learning process that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.

Micro-Supervised Disturbance Learning: A Perspective of Representation Probability Distribution.

The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore more expressive representation learning methods which depends on as few labels as possible. To address above issues, the small-perturbation ideology is firstly introduced on the representation learning model based on the representation probability distribution. The positive small-perturbation information (SPI) which only depend on two labels of each cluster is used to stimulate the representation probability distribution and then two variant models are proposed to fine-tune the expected representation distribution of Restricted Boltzmann Machine (RBM), namely, Micro-supervised Disturbance Gaussian-binary RBM (Micro-DGRBM) and Micro-supervised Disturbance RBM (Micro-DRBM) models. The Kullback-Leibler (KL) divergence of SPI is minimized in the same cluster to promote the representation probability distributions to become more similar in Contrastive Divergence (CD) learning. In contrast, the KL divergence of SPI is maximized in the different clusters to enforce the representation probability distributions to become more dissimilar in CD learning. To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has no external stimulation. Experimental results demonstrate that the proposed deep Micro-DL architecture shows better performance in comparison to the baseline method, the most related shallow models and deep frameworks for clustering.

Biochar aerogel enhanced remediation performances for heavy oil-contaminated soil through biostimulation strategy.

Petroleum leakages can seriously damage the soil environment and cause a persistent harm to human health, due to the release of heavy oil pollutants with a high viscosity and high molecular weight. In this paper, biochar aerogel materials were successfully prepared under 600, 700 and 800 â„ƒ (accordingly labeled as 600-aerogel, 700-aerogel and 800-aerogel) with green, sustainable and abundant sisal leaves as raw materials for the remediation of heavy oil-contaminated soil. The remediation performances of biochar aerogel supplement for heavy oil-contaminated soil were investigated, while microbial abundance and community structure were characterized. The degradation efficiency of 600-aerogel, 700-aerogel and 800-aerogel treatments was accordingly 80.69%, 86.04% and 86.62% after 60 days. Apart from adsorption behavior, biostimulation strengthened the degradation efficiency, according to findings from first-order degradation kinetics. Biochar aerogel supplement basically increased genera microbial abundance for Sinomonas, Streptomyces, Sphingomonas and Massilia with petroleum degradation abilities through microorganisms' biostimulation. Sinomonas as the dominant genus with the highest abundance probably contributed much higher capacities to heavy oil degradation. This study can provide an inspiring reference for the development of green carbon-based materials to be applied in heavy oil-contaminated soils through biostimulation mechanisms.

Multi-Level Graph Neural Network With Sparsity Pooling for Recognizing Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disease of the brain associated with motor symptoms. With the maturation of machine learning (ML), especially deep learning, ML has been used to assist in the diagnosis of PD. In this paper, we explore graph neural networks (GNNs) to implement PD prediction using MRI data. However, most existing GNN models suffer from the efficiency of graph construction on MRI data and the problem of overfitting on small data. This paper proposes a novel multi-layer GNN model that incorporates a fast graph construction method and a sparsity-based pooling layer with an attention mechanism. In addition, graph structure sparsity is plugged into the graph pooling layer as prior knowledge to mitigate overfitting in model training. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model and its superiority over baseline methods.

Metabolomic and transcriptomic analyses of rice plant interaction with invasive weed Leptochloa chinensis.

Introduction: Leptochloa chinensis is an annual weed in paddy fields, which can engage in competition with rice, leading to a severe yield reduction. However, theunderlying mechanism governing this interaction remain unknown. Methods: In this study, we investigated the mutual inhibition between rice and the weed undermono-culture and co-culture conditions. We found that the root exudates of both species played essential roles in mediating the mutual inhibition. Further metabolomic analysis identified a significant number of differential metabolites. These metabolites were predominantly enriched in the phenylpropanoid and flavonoid biosynthesis pathways in weed and rice. Transcriptomic analysis revealed that the differentially expressed genes responding to the interaction were also enriched in these pathways. Results: Phenylpropanoid and flavonoid biosynthesis pathways are associated with allelopathy, indicating their pivotal role in the response of rice-weed mutual inhibition. Discussion: Our findings shed light on the conserved molecular responses of rice and L. chinensis during theirinteraction, provide evidence to dissect the mechanisms underlying the allelopathic interaction and offer potential strategies for weed management in rice paddies.

Stable and efficient perovskite solar cells via hydrogen bonding and coordination.

The instability of perovskite solar cells (PSCs) is primarily caused by the unavoidable ion migration in the perovskite layer. Ion migration and accumulation influence the properties of perovskite and functional layers, resulting in severely degraded device performance. Herein, we introduced an n-type, low optical gap-conjugated organic molecule (i.e., COTIC-4F or COTIC-4Cl) to serve as the perovskite photoactive layer in a perovskite precursor solution for broadening the near-infrared spectral response and enhancing the efficiency of PSCs. Various characterization studies have determined that COTIC-4F forms hydrogen bonds with perovskites, thereby remarkably enhancing the anchoring ability of MA+, suppressing ion migration, and reducing photocurrent hysteresis. Meanwhile, the carbonyl (CO) group of COTIC-4F and COTIC-4Cl can donate a lone electron pair to passivate the Pb trap, avoiding possible carrier recombination. The COTIC-4F- and COTIC-4Cl-treated perovskite films exhibit an optical response in the near-infrared region and an excellent morphology. Through ultraviolet photoelectron spectroscopy, it has been determined that COTIC-4F can facilitate more charge transfer than COTIC-4Cl, which results in a larger photocurrent from the PSCs. The PSCs of the COTIC-4F-treated perovskite films demonstrate a maximum power conversion efficiency of 21.72%. They exhibit a high fill factor of 82.02% and possess long-term stability under an air atmosphere.

An improved synthesis of 1,2-diarylethanols under conventional heating and ultrasound irradiation.

A simple and efficient synthesis of 1,2-diarylethanols has been developed. The procedure involved the reaction between a variety of toluene derivatives and aryl aldehydes under conventional heating and ultrasound irradiation. This procedure possesses several advantages such as operational simplicity, high yield, safety and environment benignancy. Ultrasound was proved to be very helpful to the reaction, markedly improving the yield and the reaction rate.

Physiological and transcriptomic responses of Chlorella sorokiniana to ciprofloxacin reveal molecular mechanisms for antibiotic removal.

Microalgae-based technology is an effective and environmentally friendly method for antibiotics-contaminated wastewater treatment. To assess the tolerance and removal ability of Chlorella sorokiniana to ciprofloxacin (CIP), this study comprehensively revealed the responses of C. sorokiniana to CIP exposure and its degradation processes through physiological and transcriptomic analyses. Although the photosynthetic system was inhibited, the growth of C. sorokiniana was not negatively affected by CIP. Dissolved organic matter was analyzed and indicated that humic-like substances were released to alleviate the stress of CIP. In addition, the maximum removal of CIP was 83.3% under 20 mg L-1 CIP exposure. HPLC-MS/MS and RNA-Seq analyses suggested that CIP could be bioaccumulated and biodegraded by C. sorokiniana through the reactions of hydroxylation, demethylation, ring cleavage, oxidation, dehydrogenation, and decarboxylation with the help of intracellular oxidoreductases, especially cytochrome P450. Collectively, this research shows that C. sorokiniana have a great potential for removing CIP from wastewater.

Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data.

Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data. In this article, we introduce fuzzy rough sets (FRSs) to deal with the problem of outlier detection in mixed attribute data. Since the outlier detection model of the classical rough set is only applicable to the categorical attribute data, we use FRS to generalize the outlier detection model and construct a generalized outlier detection model based on fuzzy rough granules. First, the granule outlier degree (GOD) is defined to characterize the outlier degree of fuzzy rough granules by employing the fuzzy approximation accuracy. Then, the outlier factor based on fuzzy rough granules is constructed by integrating the GOD and the corresponding weights to characterize the outlier degree of objects. Furthermore, the corresponding fuzzy rough granules-based outlier detection (FRGOD) algorithm is designed. The effectiveness of the FRGOD algorithm is evaluated through experiments on 16 real-world datasets. The experimental results show that the algorithm is more flexible for detecting outliers and is suitable for numerical, categorical, and mixed attribute data.

FPGA-Based High-Throughput CNN Hardware Accelerator With High Computing Resource Utilization Ratio.

The field-programmable gate array (FPGA)-based CNN hardware accelerator adopting single-computing-engine (CE) architecture or multi-CE architecture has attracted great attention in recent years. The actual throughput of the accelerator is also getting higher and higher but is still far below the theoretical throughput due to the inefficient computing resource mapping mechanism and data supply problem, and so on. To solve these problems, a novel composite hardware CNN accelerator architecture is proposed in this article. To perform the convolution layer (CL) efficiently, a novel multiCE architecture based on a row-level pipelined streaming strategy is proposed. For each CE, an optimized mapping mechanism is proposed to improve its computing resource utilization ratio and an efficient data system with continuous data supply is designed to avoid the idle state of the CE. Besides, to relieve the off-chip bandwidth stress, a weight data allocation strategy is proposed. To perform the fully connected layer (FCL), a single-CE architecture based on a batch-based computing method is proposed. Based on these design methods and strategies, visual geometry group network-16 (VGG-16) and ResNet-101 are both implemented on the XC7VX980T FPGA platform. The VGG-16 accelerator consumed 3395 multipliers and got the throughput of 1 TOPS at 150 MHz, that is, about 98.15% of the theoretical throughput ( 2 ×3395 ×150 MOPS). Similarly, the ResNet-101 accelerator achieved 600 GOPS at 100 MHz, about 96.12% of the theoretical throughput ( 2 ×3121 ×100 MOPS).

Heterogeneous Feature Selection Based on Neighborhood Combination Entropy.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

Evaluation of key microbial community succession and enzyme activities of nitrogen transformation in pig manure composting process through multi angle analysis.

This experiment aimed to investigate changes in enzyme activity, microbial succession, and nitrogen conversion caused by different initial carbon-to-nitrogen ratios of 25:1, 35:1 and 20:1 (namely CK, T1 and T2) during pig manure composting. The results showed that the lower carbon-to-nitrogen ratio (T2) after composting retained 19.64 g/kg of TN which was more than 16.74 and 17.32 g/kg in treatments of CK and T1, respectively, but excessive conversion of ammonium nitrogen to ammonia gas resulted in nitrogen loss. Additional straw in T1 could play the role as a bulking agent. After composting, TN in T1 retained the most, and TN contents were 63.51 %, 67.34 % and 56.24 % in CK, T1 and T2, respectively. Network analysis indicated that many types of microorganisms functioned as a whole community at various stages of nitrogen cycle. This study suggests that microbial community structure modification might be a good strategy to reduce ammonium nitrogen loss.

A Robust Breast ultrasound segmentation method under noisy annotations.

BACKGROUND AND OBJECTIVE: A large-scale training data and accurate annotations are fundamental for current segmentation networks. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout. Further complications arise as the contrast between the region of interest and background is often low. Without double-check from professionals, it is hard to guarantee that there is no noisy annotation in segmentation datasets. However, among the deep learning methods applied to ultrasound segmentation so far, no one can solve this problem. METHOD: Given a dataset with poorly labeled masks, including a certain amount of noises, we propose an end-to-end noisy annotation tolerance network (NAT-Net). NAT-Net can detect noise by the proposed noise index (NI) and dynamically correct noisy annotations in the training stage. Simultaneously, noise index is used to correct the noise along with the output of the learning model. This method does not need any auxiliary clean datasets or prior knowledge of noise distributions, so it is more general, robust and easier to apply than the existing methods. RESULTS: NAT-Net outperforms previous state-of-the-art methods on synthesized data with different noise ratio. For real-world dataset with more complex noise types, the IoU of NAT-Net is higher than that of state-of-art approaches by nearly 6%. Experimental results show that our method can also achieve good results compared with the existing methods for clean dataset. CONCLUSION: The NAT-Net reduces manual interaction of data annotation, reduces dependence on medical personnel. After tumor segmentation, disease diagnosis efficiency is improved, which provides an auxiliary strategies for subsequent medical diagnosis systems based on ultrasound.

CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression.

Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer's Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is referred to as multi-view learning, which has received certain attention in recent years. In this paper, we propose a new multi-view clustering model called Consensus Multi-view Clustering (CMC) based on nonnegative matrix factorization for predicting the multiple stages of AD progression. The proposed CMC performs multi-view learning idea to fully capture data features with limited medical images, approaches similarity relations between different entities, addresses the shortcoming from multi-view fusion that requires manual setting parameters, and further acquires a consensus representation containing shared features and complementary knowledge of multiple view data. It not only can improve the predication performance of AD, but also can screen and classify the symptoms of different AD's phases. Experimental results using data with twelve views constructed by brain Magnetic Resonance Imaging (MRI) database from Alzheimer's Disease Neuroimaging Initiative expound and prove the effectiveness of the proposed model.