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IoT-based smart agriculture plays a significant role in building a high-yield, sustainable, and intelligent modern agriculture. However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To overcome the challenges, we introduced solar harvesting and multiaccess edge computing (MEC) to investigate sustainable monitoring of smart agriculture in solar-powered MEC-enabled WSNs. Considering the cyclical and day-night fluctuations of solar energy, we formulate a joint optimization problem for resource scheduling and computation offloading strategy to maximize the minimum weighted computation capacity across the time slots under solar energy constraints. To solve the mixed-integer nonlinear program (MINLP), we propose a multiply-iterated decoupling optimization algorithm by jointly optimizing a computation offloading strategy, energy provision of the solar-powered hybrid access point (HAP), and local CPU frequency as well as time scheduling. Simulation results show that the proposed algorithm can efficiently use solar energy to balance network calculations, improve network energy efficiency, and realize unmanned and sustainable agricultural WSN.

Guild is the most important long-term virtual community and emotional bond in massively multiplayer online role-playing games (MMORPGs). It matters a lot to the player retention and game ecology how the guilds are going, e.g., healthy or not. The main challenge now is to characterize and predict the guild health in a quantitative, dynamic, and multi-dimensional manner based on complicated multi-media data streams. To this end, we propose a novel framework, namely Stability-Aware Multi-task Learning Approach(SAMLA) to address these challenges. Specifically, different media-specific modules are designed to extract information from multiple media types of tabular data, time seriescharacteristics, and heterogeneous graphs. To capture the dynamics of guild health, we introduce a representation encoder to provide a time series view of multi-media data that is used for task prediction. Inspiredby well-received theories on organization management, we delicately define five specific and quantitative dimensions of guild health and make parallel predictions based on a multi-task approach. Besides, we devise a novel auxiliary task, i.e.,the guild stability, to boost the performance of the guild health prediction task. Extensive experiments on a real-world large-scale MMORPG dataset verify that our proposed method outperforms the state-of-the-art methods in the task of organizational health characterization and prediction. Moreover, our work has been practically deployed in online MMORPG, and case studies clearly illustrate the significant value.

Market popularity prediction has always been a hot research topic, such as sales prediction and crowdfunding prediction. Most of these studies put the perspective on isolated markets, relying on the knowledge of certain market to maximize the prediction performance. However, these market-specific approaches are restricted by the knowledge limitation of isolated markets and incapable of the complicated and potential relations among different markets, especially some with strong dependence such as the financing market and sales market. Fortunately, we discover potentially symbiotic relations between the financing market and the sales market, which provides us with an opportunity to co-promote the popularity predictions of both markets. Thus, for bridgly learning the knowledge interactions between financing market and sales market, we propose a cross-market approach, namely CATN: Cooperative-competitive Attention Transfer Network, which could effectively transfer knowledge of financing capability from the crowdfunding market and sales prospect from the E-commerce market. Specifically, for capturing the complicated relations especially the cooperation or complement of items and enhancing the knowledge transfer between the two heterogeneous markets, we design a novel Cooperative Attention; meanwhile, for finely computing the relations of items especially the competition in specific same market, we further design Competitive Attentions for the two markets respectively. Besides, we also distinguish aligned features and unique features to adapt the cross-market predictions. With the real-world datasets collected from Indiegogo and Amazon, we construct extensive experiments on three types of datasets from the two markets and the results demonstrate the effectiveness and generalization of our CATN model.

Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity., Comment: Accepted by WWW'23

As an independent social and economic entity, game servers plays a dominant role in building a stable, living, and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) for maintaining the game ecology at the macro level. The services provided by this system include server health diagnosis, server merge assessment, and combination strategy recommendation. Specifically, we design an effective time series prediction algorithm to diagnose the health status of one server (e.g., user activity, online time, daily revenue) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system into practice for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.

Objective To study the value of multitime point salivary pepsin testing (MTPSPT) for the diagnosis of laryngopharyngeal reflux (LPR). Study Design Prospective noncontrolled. Methods For patients who met the enrollment criteria, the reflux symptom index (RSI) and reflux finding score (RFS) were calculated and salivary pepsin testing was performed. The pepsin test was performed every hour from 7:00 a.m. to 6:00 p.m. by collecting fresh saliva samples. A single positive test result was needed for the diagnosis of LPR. The consistency in the diagnosis of LPR between the two methods was compared with the weighted Cohen's kappa statistic. Results A total of 204 patients were included. The kappa value between the two methods was 0.566 (p = .00). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MTPSPT were 76.43%, 85.94%, 92.24%, and 62.5%, respectively. We also compared a single pepsin measure at 7 a.m. with the screening results based on the RSI and RFS, and found a much lower kappa agreement value (0.223, p = .00). The sensitivity, specificity, PPV, NPV, and false‐negative rate of pepsin testing at 7 a.m. (fasting) were 37.86%, 92.18%, 91.38%, 40.41%, and 58.57%, respectively. Conclusion The use of the result of a single salivary pepsin test in the morning yields a relatively higher rate of missed diagnosis of LPR, and multitime point testing through a day increased the accuracy and sensitivity of detection of LPR twofold compared to a single morning fasting sample. Level of Evidence 3

Purpose To characterize the diagnostic performance of [18F]FDG dose de-escalation with shortened acquisition times using total-body PET/CT in pediatric tumor imaging in terms of the subjective image quality and quantification of tracer uptake. Methods In this single-center prospective study, 31 pediatric oncology patients under 14 years old were enrolled and underwent total-body PET/CT using the uEXPLORER PET/CT scanner. All patients were randomly assigned to one of four [18F]FDG dose groups: full-dose (3.7 MBq/kg), 1/2-dose (1.9 MBq/kg), 1/3-dose (1.2 MBq/kg), and 1/4-dose (0.9 MBq/kg). Images with a shortened acquisition time frame (20 min, 12 min, 10 min, 8 min, 6 min, 5 min, 4 min, 3 min, 2 min, 1.5 min, 1 min, and 0.5 min) were reconstructed for each study, with the 20-min images as controls for each dose group. Semiquantitative uptake metrics were assessed using region-of-interest (ROI) analysis of healthy liver and suspected lesions. The subjective analysis was performed using 5-point Likert scales. Suspected major lesions and microlesions were recorded, while 3-point Likert scales were used for diagnostic confidence. Results With shortened acquisition times, the liver maximum standard uptake value (SUVmax) and standard deviation (SD) increased in each dose group. The signal-to-noise ratio (SNR) was significantly reduced with shortened acquisition time, while the lesion SUVmax and tumor-to-background ratio (TBR) showed no significant deviation. A decent subjective image quality score could be achieved in the full-, 1/2-, 1/3-, and 1/4-dose groups with at least 2-min, 4-min, 6-min, and 8-min acquisitions, respectively, where great overall image quality and brain delineation (scored 5.0) and superior organ boundaries and image noise (scored over 4.0) could be achieved, and all suspicious lesions found in 20-min images were detectable with high diagnostic confidence. Conclusions The regimen of full-dose [18F] FDG with a 2-min scan, 1/2-dose with a 4-min scan, 1/3-dose with a 6-min scan, and 1/4-dose with an 8-min scan using total-body PET/CT can provide great image qualities, can maintain a desired diagnostic performance and is feasible for pediatric oncological clinical applications. Trial registration: ChiCTR2000036334. Registered 22 August 2020.

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Match outcome prediction in group comparison setting is a challenging but important task. Existing works mainly focus on learning individual effects or mining limited interactions between teammates, which is not sufficient for capturing complex interactions between teammates as well as between opponents. Besides, the importance of interacting with different characters is still largely underexplored. To this end, we propose a novel Neural Attentional Cooperation-competition model (NeuralAC), which incorporates weighted-cooperation effects (i.e., intra-team interactions) and weighted-competition effects (i.e., inter-team interactions) for predicting match outcomes. Specifically, we first project individuals to latent vectors and learn complex interactions through deep neural networks. Then, we design two novel attention-based mechanisms to capture the importance of intra-team and inter-team interactions, which enhance NeuralAC with both accuracy and interpretability. Furthermore, we demonstrate NeuralAC can generalize several previous works. To evaluate the performances of NeuralAC, we conduct extensive experiments on four E-sports datasets. The experimental results clearly verify the effectiveness of NeuralAC compared with several state-of-the-art methods.

The software-defined networks-enable mobile edge computing (SDN-enable MEC) architecture, which integrates SDN and MEC technologies, realizes the flexibility and dynamic management of the underlying network resources by the MEC, reduces the distance between the access terminal and computing resources and network resources, and increases the terminal's access to resources. However, the static distribution relationship between MEC servers (MECSs) and controllers in the multi-controller architecture may result in unbalanced load distribution among the controllers, which would degrade network performance. In this paper, a multi-objective optimization MECS redistribution algorithm (MOSRA) is proposed to decrease the response time and overhead. A controller response time model and link transmit overhead model are introduced as basis of an evolutionary algorithm which is proposed to optimize MECS redistribution. The proposed algorithm aims to select an available sub-optimizes individual by using a strategy based coordination transformation from Pareto Front. That is, when the master controller of the MECS is redistributed, both of the network overhead of the MECS to the controller and the response time of the controller to the MECS processing request are optimized. Finally, the simulation results demonstrate that the MOSRA can solve the redistribution problem in different network load levels and different network sizes within the effective time, and has a lower control plane response time, while making the edge network plane transmission overhead lower, compared with other algorithms .

In this paper, a novel Co3O4 micro-bundles structure (Co3O4 MBs) was obtained at 120 °C after a hydrothermal reaction for 24 h and followed by an annealing treatment at 300 °C in air. The unique Co3O4 MBs are constructed by many adjacent flakes with 0.4 μm in thickness, and exhibit a large surface area of 81.2 m2 g−1 and a mean pore diameter of 6.14 nm, which may facilitate a sufficient contact with electrolyte and then shorten the diffusion pathway of ions. A remarkable electrochemical behavior including specific capacity of 282.3 C g−1 at 1 A g−1 and 205.9 C g−1 at 10 A g−1, and an excellent cycling performance with 74.6% capacity retention after 4000 charge-discharge process at 5 A g−1 are achieved when the test of Co3O4 MBs-modified electrode is performed using three-electrode configuration. Additionally, a hybrid supercapacitor (HSC) was fabricated with the obtained Co3O4 MBs as positive electrode and commercial activated carbon (AC) as negative electrode. The HSC exhibits a specific capacity of 144.1 C g−1 at 1 A g−1 and 126.4% capacity retention after 5000 cycles at 5 A g−1. An energy density of 38.5 W h kg−1 can be obtained at a power density of 962.0 W kg−1, and 29.5 W h kg−1 is still retained at 8532.5 W kg−1. The simple synthetic strategy can be applicable to the synthesis of other transition metal oxides with superior electrochemical performance.

In this work, uniform MnCo2O4.5 nanowires (NWs) on stainless steel foil (SSF) were prepared through a facile, cost-efficient, and eco-friendly hydrothermal method at 120 °C with a post-calcination process in air. The microstructure of MnCo2O4.5 samples could be tuned at different hydrothermal temperatures and quasi-cubes (QCs) were obtained in high yield at 150 °C. The MnCo2O4.5 NW powder peeled off from the SSF delivered an outstanding capacity of 248.62 C g−1 at 1 A g−1 with a capacity preservation of 179.43 C g−1 at 8 A g−1, while the QCs exhibited 177.19 and 111.73 C g−1, respectively. To assess the possibility of its actual applications, a hybrid supercapacitor (HSC) device has been assembled by utilizing these MnCo2O4.5 NWs (QCs) and activated carbon (AC) as the cathode and anode, respectively. The MnCo2O4.5 NWs//AC HSC delivered a maximum capacity up to 116.95 C g−1 and extraordinary cycling durability with only 3.56% capacity loss over 5000 cycles. Besides, the MnCo2O4.5 NWs//AC HSC achieved a maximum energy density of 25.41 W h kg−1 at a power density of 782.08 W kg−1, and for the QC-based HSC, it showed a lower energy density of 20.54 W h kg−1 at 843.34 W kg−1. These remarkable electrochemical properties demonstrate that the porous MnCo2O4.5 NWs and QCs may serve as promising cathodes for advanced hybrid supercapacitors with superior performance, and the present synthetic methodology may be applied to the preparation of other cobalt-based binary metal oxides with excellent electrochemical properties.

In this work, MgCo

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones., To appear in SIGIR 2022

Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.

Bone morphogenetic protein 7 (BMP7), a member of the transforming growth factor-β (TGF-β) family, plays pivotal roles in energy expenditure. However, whether and how BMP7 regulates hepatic insulin sensitivity is still poorly understood. Here, we show that hepatic BMP7 expression is reduced in high-fat diet (HFD)-induced diabetic mice and palmitate (PA)-induced insulin-resistant HepG2 and AML12 cells. BMP7 improves insulin signaling pathway in insulin resistant hepatocytes. On the contrary, knockdown of BMP7 further impairs insulin signal transduction in PA-treated cells. Increased expression of BMP7 by adenovirus expressing BMP7 improves hyperglycemia, insulin sensitivity and insulin signal transduction. Furthermore, BMP7 inhibits mitogen-activated protein kinases (MAPKs) in both the liver of obese mice and PA-treated cells. In addition, inhibition of MAPKs recapitulates the effects of BMP7 on insulin signal transduction in cultured hepatocytes treated with PA. Activation of p38 MAPK abolishes the BMP7-mediated upregulation of insulin signal transduction both in vitro and in vivo. Together, our results show that hepatic BMP7 has a novel function in regulating insulin sensitivity through inhibition of MAPKs, thus providing new insights into treating insulin resistance-related disorders such as type 2 diabetes.

In this work, uniform MnCo

OBJECTIVES: To investigate the impact of total variation regularized expectation maximization (TVREM) reconstruction on the image quality of (68)Ga-PSMA-11 PET/CT using phantom and patient data. METHODS: Images of a phantom with small hot sphere inserts and 20 prostate cancer patients were acquired with a digital PET/CT using list-mode and reconstructed with ordered subset expectation maximization (OSEM) and TVREM with seven penalisation factors between 0.01 and 0.42 for 2 and 3 minutes-per-bed (m/b) acquisition. The contrast recovery (CR) and background variability (BV) of the phantom, image noise of the liver, and SUV(max) of the lesions were measured. Qualitative image quality was scored by two radiologists using a 5-point scale (1-poor, 5-excellent). RESULTS: The performance of CR, BV, and image noise, and the gain of SUV(max) was higher for TVREM 2 m/b groups with the penalization of 0.07 to 0.28 compared to OSEM 3 m/b group (all p < 0.05). The image noise of OSEM 3 m/b group was equivalent to TVREM 2 and 3 m/b groups with a penalization of 0.14 and 0.07, while lesions’ SUV(max) increased 15 and 20%. The highest qualitative score was attained at the penalization of 0.21 (3.30 ± 0.66) for TVREM 2 m/b groups and the penalization 0.14 (3.80 ± 0.41) for 3 m/b group that equal to or greater than OSEM 3 m/b group (2.90 ± 0.45, p = 0.2 and p < 0.001). CONCLUSIONS: TVREM improves lesion contrast and reduces image noise, which allows shorter acquisition with preserved image quality for PSMA PET/CT. ADVANCES IN KNOWLEDGE: TVREM reconstruction with optimized penalization factors can generate higher quality PSMA-PET images for prostate cancer diagnosis.

Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods need well-predefined IVs, while it is an art rather than science to find valid IVs in many real-world scenes. Moreover, the predefined hand-made IVs could be weak or erroneous by violating the conditions of valid IVs. These thorny facts hinder the application of the IV-based counterfactual prediction methods. In this paper, we propose a novel Automatic Instrumental Variable decomposition (AutoIV) algorithm to automatically generate representations serving the role of IVs from observed variables (IV candidates). Specifically, we let the learned IV representations satisfy the relevance condition with the treatment and exclusion condition with the outcome via mutual information maximization and minimization constraints, respectively. We also learn confounder representations by encouraging them to be relevant to both the treatment and the outcome. The IV and confounder representations compete for the information with their constraints in an adversarial game, which allows us to get valid IV representations for IV-based counterfactual prediction. Extensive experiments demonstrate that our method generates valid IV representations for accurate IV-based counterfactual prediction., Comment: Accepted by ACM Transactions on Knowledge Discovery from Data (TKDD) 2022

In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the \emph{virtual mall} in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60\% improvement on conversion rate of bundles, and a relative improvement of more than 15\% on gross merchandise volume (GMV)., Comment: 8 pages, 10 figures, accepted paper on CIKM 2020

In this work, porous MgCo

For energy-limited wireless sensor networks (WSNs), we propose a multiparameter fusion decision routing (MPFDR) algorithm in this study. This algorithm gives a comprehensive account of the residual energy and forward distance, single-hop transmission ratio, cache queue, and energy equilibrium degree. It calculates the routing evaluation parameters of the forward neighbors, realizing a multidirectional reflection of the network status. Simultaneously, combined with the defined routing selection strategy based on the parameter contribution degree and fuzzy contribution degree, the fusion contribution degree of each forward neighbor is obtained. Then, the node with the most considerable fusion contribution degree is selected as the next hop. Finally, the performance of the MPFDR algorithm is simulated and compared with other algorithms. Simulation results indicate that our algorithm has good congestion control ability in energy-limited wireless sensor networks and can significantly reduce the packet loss rate and average hops.