Search

Predicting molecular properties is crucial in drug synthesis and screening, but traditional molecular dynamics methods are time-consuming and costly. Recently, deep learning methods, particularly Graph Neural Networks (GNNs), have significantly improved efficiency by capturing molecular structures’ invariance under translation, rotation, and permutation. However, current GNN methods require complex data processing, increasing algorithmic complexity. This high complexity leads to several challenges, including increased computation time, higher computational resource demands, increased memory consumption. This paper introduces InvarNet, a GNN-based model trained with a composite loss function that bypasses intricate data processing while maintaining molecular property invariance. By pre-storing atomic feature attributes, InvarNet avoids repeated feature extraction during forward propagation. Experiments on three public datasets (Electronic Materials, QM9, and MD17) demonstrate that InvarNet achieves superior prediction accuracy, excellent stability, and convergence speed. It reaches state-of-the-art performance on the Electronic Materials dataset and outperforms existing models on the R2 and alpha properties of the QM9 dataset. On the MD17 dataset, InvarNet excels in energy prediction of benzene without atomic force. Additionally, InvarNet accelerates training time per epoch by 2.24 times compared to SphereNet on the QM9 dataset, simplifying data processing while maintaining acceptable accuracy.

BackgroundThe characteristics of the tumor immunosuppressive microenvironment represent a major challenge that limits the efficacy of immunotherapy. Our previous results suggested that cryo-thermal therapy, a tumor ablation system developed in our laboratory, promotes macrophage M1-type polarization and the complete maturation of DCs to remodel the immunosuppressive environment. However, the cells that respond promptly to CTT have not yet been identified. CTT can cause extensive cell death and the release of danger-associated molecular patterns and antigens. Neutrophils are the first white blood cells recruited to sites of damage and acute inflammation. Therefore, we hypothesized that neutrophils are the initial cells that respond to CTT and are involved in the subsequent establishment of antitumor immunity.MethodsIn this study, we examined the kinetics of neutrophil recruitment after CTT via flow cytometry and immunofluorescence staining and explored the effect of neutrophils on the establishment of systemic antitumor immunity by in vivo neutrophil depletion and in vitro co-culture assays.ResultsWe found that CTT led to a rapid and strong proinflammatory neutrophil response, which was essential for the long-term survival of mice. CTT-induced neutrophils promoted the activation of monocytes via reactive oxygen species and further upregulated the expression of IFN-γ and cytotoxic molecules in T and NK cells. Adoptive neutrophil transfer further enhanced the antitumor efficacy of CTT in tumor models of spontaneous and experimental metastasis.ConclusionThese results reveal the important role of neutrophil‒monocyte interactions in the development of anti-tumor immunity and highlight that CTT could be used as an immunotherapy for targeting neutrophils and monocytes to enhance antitumor immunity.

Objective: To evaluate the independent, mediating, interactive, and associated effects of C-reactive protein (CRP) and Life's Essential 8 (LE8) on all-cause and cardiovascular mortality. Methods: Utilizing data from 10,043 participants in the NHANES 2005–2010, we employed Cox proportional hazards regression models and causal mediation analysis to investigate the joint and interactive effects of Life's Essential 8 (LE8) and C-reactive protein (CRP) on mortality risk. Results: During an average follow-up of 137.10 months, there were 1591 all-cause deaths and 485 cardiovascular deaths. Weighted linear regression showed that for patients with low cardiovascular health (CVH), the adjusted β was −0.22 (95 % CI: −0.27 to −0.17) for moderate cardiovascular health (CVH) and -0.36 (95 % CI: −0.43 to −0.30) for high cardiovascular health (CVH). Mediation analysis revealed that C-reactive protein (CRP) mediated 10.43 % of all-cause mortality and 9.20 % of cardiovascular mortality for moderate cardiovascular health (CVH) compared to low cardiovascular health (CVH), and 9.95 % and 8.32 % respectively for high cardiovascular health (CVH) compared to low cardiovascular health (CVH). No significant multiplicative or additive interactions between Life's Essential 8 (LE8) and C-reactive protein (CRP) were found in all-cause mortality or cardiovascular mortality. Individuals with high cardiovascular health (CVH) and the first quartile of C-reactive protein (CRP) had HRs of 0.30 (95 % CI 0.18–0.48) for all-cause mortality and 0.31 (95 % CI 0.13–0.74) for cardiovascular mortality compared to those with low cardiovascular health (CVH) and the fourth quartile of C-reactive protein (CRP). Conclusion: These findings suggest that CRP could significantly influence the relationship between cardiovascular health (CVH) and mortality. Interventions that target both lifestyle factors and CRP levels may improve cardiovascular health and potentially lower mortality risks.

BackgroundThe current lower limb robotic exoskeleton training (LRET) for treating and managing stroke patients remains a huge challenge. Comprehensive ICF analysis and informative treatment options are needed. This review aims to analyze LRET’ s efficacy for stroke patients, based on ICF, and explore the impact of intervention intensities, devices, and stroke phases.MethodsWe searched Web of Science, PubMed, and The Cochrane Library for RCTs on LRET for stroke patients. Two authors reviewed studies, extracted data, and assessed quality and bias. Standardized protocols were used. PEDro and ROB2 were employed for quality assessment. All analyses were done with RevMan 5.4.ResultsThirty-four randomized controlled trials (1,166 participants) were included. For function, LRET significantly improved motor control (MD = 1.15, 95%CI = 0.29–2.01, p = 0.009, FMA-LE), and gait parameters (MD = 0.09, 95%CI = 0.03–0.16, p = 0.004, Instrumented Gait Velocity; MD = 0.06, 95%CI = 0.02–0.09, p = 0.002, Step length; MD = 4.48, 95%CI = 0.32–8.65, p = 0.04, Cadence) compared with conventional rehabilitation. For activity, LRET significantly improved walking independence (MD = 0.25, 95%CI = 0.02–0.48, p = 0.03, FAC), Gait Velocity (MD = 0.07, 95%CI = 0.03–0.11, p = 0.001) and balance (MD = 2.34, 95%CI = 0.21–4.47, p = 0.03, BBS). For participation, social participation (MD = 0.12, 95%CI = 0.03–0.21, p = 0.01, EQ-5D) was superior to conventional rehabilitation. Based on subgroup analyses, LRET improved motor control (MD = 1.37, 95%CI = 0.47–2.27, p = 0.003, FMA-LE), gait parameters (MD = 0.08, 95%CI = 0.02–0.14, p = 0.006, Step length), Gait Velocity (MD = 0.11, 95%CI = 0.03–0.19, p = 0.005) and activities of daily living (MD = 2.77, 95%CI = 1.37–4.16, p = 0.0001, BI) for the subacute patients, while no significant improvement for the chronic patients. For exoskeleton devices, treadmill-based exoskeletons showed significant superiority for balance (MD = 4.81, 95%CI = 3.10–6.52, p

With the increasing complexity of neural network models, the huge communication overhead in federated learning (FL) has become a significant issue. To mitigate resource consumption, incorporating pruning algorithms into federated learning has emerged as a promising approach. However, existing pruning algorithms exhibit high sensitivity to network architectures and typically require multiple sessions of retraining to identify optimal structures. The direct application of such strategies to FL would inevitably introduce an additional communication cost. To this end, we propose a novel communication-efficient federated learning framework, DualPFL (Dual Sparse Pruning Federated Learning), designed to address these issues by implementing dynamic sparse pruning and adaptive model aggregation strategies. The experimental results demonstrate that, compared to similar works, our framework can improve convergence speed by more than two times under non-IID data, achieving up to 84% accuracy on the CIFAR-10 dataset, 95% mean average precision (mAP) on the COCO dataset using YOLOv8, and 96% accuracy on the TT100K traffic sign datasets. These findings indicate that DualPFL facilitates secure and efficient collaborative computing in smart city applications.

IntroductionRecently, more and more research illustrated the importance of inducing CD4+ T helper type (Th)-1 dominant immunity for the success of tumor immunotherapy. Our prior studies revealed the crucial role of CD4+ Th1 cells in orchestrating systemic and durable antitumor immunity, which contributes to the satisfactory outcomes of the novel cryo-thermal therapy in the B16F10 tumor model. However, the mechanism for maintaining the cryo-thermal therapy-mediated durable CD4+ Th1-dominant response remains uncovered. Additionally, cryo-thermal-induced early-stage CD4+ Th1-dominant T cell response showed a correlation with the favorable prognosis in patients with colorectal cancer liver metastasis (CRCLM). We hypothesized that CD4+ Th1-dominant differentiation induced during the early stage post cryo-thermal therapy would affect the balance of CD4+ subsets at the late phase.MethodsTo understand the role of interferon (IFN)-γ, the major effector of Th1 subsets, in maintaining long-term CD4+ Th1-prone polarization, B16F10 melanoma model was established in this study and a monoclonal antibody was used at the early stage post cryo-thermal therapy for interferon (IFN)-γ signaling blockade, and the influence on the phenotypic and functional change of immune cells was evaluated.ResultsIFNγ at the early stage after cryo-thermal therapy maintained long-lasting CD4+ Th1-prone immunity by directly controlling Th17, Tfh, and Tregs polarization, leading to the hyperactivation of Myeloid-derived suppressor cells (MDSCs) represented by abundant interleukin (IL)-1β generation, and thereby further amplifying Th1 response.DiscussionOur finding emphasized the key role of early-phase IFNγ abundance post cryo-thermal therapy, which could be a biomarker for better prognosis after cryo-thermal therapy.

Quantum computers have provided a promising tool for tackling NP hard problems. However, most of the existing work on quantum annealers assumes exclusive access to all resources available in a quantum annealer. This is not resource efficient if a task consumes only a small part of an annealer and leaves the rest wasted. We ask if we can run multiple tasks in parallel or concurrently on an annealer, just like the multitasking capability of a classical general-purpose processor. By far, multitasking is not natively supported by any of the existing annealers. In this paper, we explore Multitasking in Quantum Annealer (QAMT) by identifying the parallelism in a quantum annealer from the aspect of space and time. Based on commercialised quantum annealers from D-Wave, we propose a realisation scheme for QAMT, which packs multiple tasks into a quantum machine instruction (QMI) and uses predefined sampling time to emulate task preemption. We enumerate a few scheduling algorithms that match well with QAMT and discuss the challenges in QAMT. To demonstrate the potential of QAMT, we simulate a quantum annealing system, implement a demo QAMT scheduling algorithm, and evaluate the algorithm. Experimental results suggest that there is great potential in multitasking in quantum annealing.

The SKA (Square Kilometre Array) radio telescope will become the most sensitive telescope by correlating a large number of antenna nodes to form a giant antenna array. The data generated from such a large number of antenna nodes will pose a huge storage problem and require real-time data processing to make the best use of data, and the SKA Scientific Data Processing becomes the bottleneck of the whole processing flow. However, the existing high-performance CPU- and GPU (Graphics Processing Unit)-based solutions cannot satisfy the performance requirements and power budget requirements well [1]. Due to the consideration of the high energy efficiency of hardware accelerators and the flexibility and cost of prototype design, in this paper, we explore the FPGA(Field Programmable Gate Array)-based prototype of one of the most computationally demanding procedures in SKA scientific data processing: degridding. Through the analysis of algorithm behavior and bottlenecks, we design and optimize the memory architecture and computing logic of an FPGA-based prototype. Besides, with the consideration of the relations between the required data of processing multiple spectral channels, we reuse the shared data in processing neighboring spectral channels, and the performance further improves. The functionality and performance of our design have been verified on the target FPGA board, and the software-based benchmarks were also measured on comparable CPU and GPU platforms, indicating that the FPGA-based prototype achieves 2.74 times and 2.03 times speedup, 7.64 times and 7.42 times energy efficiency than the MPI(Message Passing Interface)-based CPU benchmark and the CUDA (Compute Unified Device Architecture)-based GPU benchmark, respectively.

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. CPSS extend cyber-physical systems (CPS) to include the social domain, which introduces a challenge of massive data processing. As a typically giant CPS, the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the world's largest filled-aperture radio telescope, generates massive volume of data which poses a huge storage problem that CPSS face likewise and requires real-time data compressing to reduce data storage and movement overhead. The recently introduced Bitshuffle preprocessing algorithm is a novel approach towards exploiting spatial redundancy incorporation to improve the compression ratio with a specific compressor. However, the existing high-performance CPU-based solutions cannot satisfy the performance requirement and power budget requirement simultaneously. In the paper, we propose the implementation of this algorithm on Field Programmable Gate Array (FPGA) and present an unique data transformation strategy to turn raw FAST data in classic FITS format into another format to support huge file sizes, i.e. Hierarchical Data Format (HDF5). Evaluation results show that our implementation can achieve 3.2Gbyte/s throughput which can be equipped with LZ4 compressor to be high performance compressor. This makes Bitshuffle on FPGAs a candidate for meeting the computational and energy efficiency constraints of radio telescopes and provide reference for CPSS facing the same situation.

Change detection (CD) is a crucial task in remote sensing (RS) to distinguish surface changes from bitemporal images. Recently, deep learning (DL) based methods have achieved remarkable success for CD. However, the existing methods lack robustness to various kinds of changes in RS images, which suffered from problems of feature misalignment and inefficient supervision. In this paper, a deeply supervised attentive high-resolution network (DSAHRNet) is proposed for remote sensing image change detection. First, we design a spatial-channel attention module to decode change information from bitemporal features. The attention module is able to model spatial-wise and channel-wise contexts. Second, to reduce feature misalignment, the extracted features are refined by stacked convolutional blocks in parallel. Finally, a novel deeply supervised module is introduced to generate more discriminative features. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed DSAHRNet outperforms other state-of-the-art methods, and achieves a great trade-off between performance and complexity.

Generally, facial expressions could be classified into two categories: static facial expressions and micro-expressions. There are many promising applications of facial expression recognition, such as pain detection, lie detection, and babysitting. Traditional convolutional neural network (CNN)-based methods suffer from two critical problems when they are adopted to recognize micro-expressions. First, they are usually dependent on very deep architectures that overfit on small datasets. However, reliable expressions are relatively difficult to collect and relevant datasets are usually relatively small. Second, for micro-expressions, these methods usually neglect the temporal redundancy of micro-expressions which could be utilized to reduce the temporal complexity. In this paper, we propose a shallow CNN (SHCNN) architecture with only three layers to classify static expressions and micro-expressions simultaneously without big training datasets. To better explain the functionality of our SHCNN architecture, we improve the saliency maps by introducing a shrinkage factor after studying the vanishing gradient problem of existing saliency maps. Experiments are conducted on five open datasets: FER2013, FERPlus, CASME, CASME II, and SAMM. To the best of our knowledge, by comparing with other methods offering source code (or pseudo code), we believe that our method would be the best on FERPlus, CASME, and CASME II and competitive on FER2013 and SAMM.

Vulvovaginal candidiasis (VVC) is one of the most frequent diseases induced by Candida albicans (C. albicans) during pregnancy, which results in enormous pain to women and their partners in daily life. Perillaldehyde (PAE), a natural monoterpenoid, has significant anti-microbial, anti-inflammatory and anti-oxidation effects. Reactive oxygen species (ROS) are key factors for the host to resist the invasion of fungi. However, excess ROS can cause additional damage independent of the pathogen itself, and the mechanism of ROS in VVC has not been investigated. In this murine study, we revealed that C. albicans infection increased the expression of NADPH oxidase 2 (NOX2) and the content of malonaldehyde (MDA). C. albicans inhibited the activity of antioxidant enzymes in the vagina, including superoxide dismutase (SOD), Catalase (CAT), glutathione peroxidase (GSH-PX) and heme oxygenase (HO-1), which were returned to normal levels after treatment with PAE. Furthermore, PAE inhibited the activities of Keap1 and promoted Nrf2 transfer from cytoplasm to nucleus, which were mediated by excessive accumulation of ROS in the VVC mice. In this study, we also indicated that PAE inhibited the apoptosis of vagina cells via Caspase 9- Caspase 7-PARP pathway and prevented the release of IL-1ꞵ in VVC mice. In summary, this study revealed that the treatment of VVC in mice with PAE might be mediated by inhibition of ROS, and established the therapeutic potential of PAE as an antifungal agent for the treatment of VVC.

As the fundamental technology of autonomous vehicles and high-speed tracking, high-speed vision always suffers from the bottlenecks of on-chip bandwidth and storage due to the resource constraints. To improve the resource efficiency, we propose a hardware-efficient image compression circuit based on the vector quantization for a high-speed image sensor. In this circuit, a self-organizing map is implemented for the on-chip learning of codebook to flexibly satisfy the requirements of different applications. To reduce the hardware resources, we present a reconfigurable complete-binary-adder-tree, where the arithmetic units are reused completely. In addition, a mechanism of partial vector-component storage is adapted to make the compression ratio adjustable. Finally, a parallel-elementary-stream design ensures a high processing speed. The proposed circuit has been implemented on the field-programmable gate araray and also applied in a high-speed object tracking system. The experimental results indicate that it achieves an encoding speed of 722 frames/s with 128 weight vectors when working at 79.8 MHz, and the worst tracking error caused by the proposed circuit is merely 9 pixels. These results evince that our proposed circuit can be completely integrated with a high-speed image sensor and used in high-speed vision systems.

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

A three-dimensional (3D) image sensor based on Single-Photon Avalanche Diode (SPAD) requires a time-to-digital converter (TDC) with a wide dynamic range and fine resolution for precise depth calculation. In this paper, we propose a novel high-performance TDC for a SPAD image sensor. In our design, we first present a pulse-width self-restricted (PWSR) delay element that is capable of providing a steady delay to improve the time precision. Meanwhile, we employ the proposed PWSR delay element to construct a pair of 16-stages vernier delay-rings to effectively enlarge the dynamic range. Moreover, we propose a compact and fast arbiter using a fully symmetric topology to enhance the robustness of the TDC. To validate the performance of the proposed TDC, a prototype 13-bit TDC has been fabricated in the standard 0.18-µm complementary metal–oxide–semiconductor (CMOS) process. The core area is about 200 µm × 180 µm and the total power consumption is nearly 1.6 mW. The proposed TDC achieves a dynamic range of 92.1 ns and a time precision of 11.25 ps. The measured worst integral nonlinearity (INL) and differential nonlinearity (DNL) are respectively 0.65 least-significant-bit (LSB) and 0.38 LSB, and both of them are less than 1 LSB. The experimental results indicate that the proposed TDC is suitable for SPAD-based 3D imaging applications.

Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.

In the background of high-power propulsion application on ships and existing uncertain electromagnetic and vibration features of multiphase fractional-slot concentrated-winding permanent magnet (PM) motor, the electromagnetic design and analysis methods of six-phase fractional-slot concentrated-winding PM motor are presented. Four different pole and slot combination schemes are sifted out, and air-gap magnetic field and electromagnetic force of these four motors are studied in contrast. On the basis of the above, a 20-kW prototype of six-phase fractional-slot concentrated-winding PM motor with 48 slots and 44 poles is designed and manufactured, and its electromagnetic and vibration performances are tested through experiments. The test results are in good agreement with calculations, which could guide the development of high-power ship propulsion.

The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array.

Blockchain naturally fits multiple industry sectors due its characteristics of decentralization, enhanced security, tamper-proof, improved traceability and transparency. However, there is a significant concern of blockchain’s performance, since blockchain trades off its performance for a completely distributed feature, which enhances its security. In this paper, we investigate the state-of-the-art progress of blockchain, mainly from a performance and security perspective. We extracted 42 primary papers from major scientific databases and 34 online technical articles. The objective is to understand the current research trends, challenges and future directions. We briefly introduce the key technologies of blockchain including distributed ledger, cryptography, consensus, smart contracts and benchmarks. We next summarize the performance and security concerns raised in the investigation. We discuss the architectural choices, performance metrics, database management enhancements, and hybrid blockchains, and try to identify the effort that the state-of-the-art has made to balance between the performance and security. We also make experiments on Ethereum and survey other popular blockchain platforms on the scalability feature of blockchain. We later discuss the potential applications and present the lessons learned. Finally, we attempt to identify the open issues and possible research directions.

We, the authors, wish to make the following corrections to our published paper [...]

This paper presents a compact vector quantizer based on the self-organizing map (SOM), which can fulfill the data compression task for high-speed image sequence. In this vector quantizer, we solve the most severe computational demands in the codebook learning mode and the image encoding mode by a reconfigurable complete-binary-adder-tree (RCBAT), where the arithmetic units are thoroughly reused. In this way, the hardware efficiency of our proposed vector quantizer is greatly improved. In addition, by distributing the codebook into the multi-parallel processing sub-blocks, our design obtains a high compression speed successfully. Furthermore, a mechanism of partial vector-component storage (PVCS) is adopted to make the compression ratio adjustable. Finally, the proposed vector quantizer has been implemented on the field programmable gate array (FPGA). The experimental results indicate that it respectively achieves a compression speed of 500 frames/s and a million connections per second (MCPS) of 28,494 (compression ratio is 64) when working at 79.8 MHz. Besides, compared with the previous scheme, our proposed quantizer achieves a reduction of 8% in hardware usage and an increase of 33% in compression speed. This means the proposed quantizer is hardware-efficient and can be used for high-speed image compression.

Accelerated growth of urban population in the world put incremental stresses on metropolitan cities. Smart city centric strategies are expected to comprise solutions to sustainable environment and urban life. Acting as an indispensable role in smart city, IoT (Internet of Things) connects the executive ability of the physical world and the intelligence of the computational world, aiming to enlarge the capabilities of things in real city and strengthen the practicality of functions in cyber world. One of the important application areas of IoT in cities is food industry. Municipality governors are withstanding all kinds of food safety issues and enduring the hardest time ever due to the lack of sufficient guidance and supervision. IoT systems help to monitor, analyze, and manage the real food industry in cities. In this paper, a smart sensor data collection strategy for IoT is proposed, which would improve the efficiency and accuracy of provenance with the minimized size of data set at the same time. We then present algorithms of tracing contamination source and back tracking potential infected food in the markets. Our strategy and algorithms are evaluated with a comprehensive evaluation case of this IoT system, which shows that this system performs well even with big data as well.

Robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The controls are based on structural knowledge of the dynamics of the robot, and the actuator dynamics is also taken into account. The proposed control is robust not only to structured uncertainty such as mass variation but also to unstructured one such as disturbances. The system stability and the boundness of tracking errors are proved using Lyapunov stability theory. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance. Simulation results validate that not only the states of the system asymptotically converge to the desired trajectory, but also the constraint force asymptotically converges to the desired force.