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A self-degradable rubber plug (SDRP) was developed to address issues in existing crosslinked polymer temporary plugging technology, such as poor self-degradation properties. The synthesis formula was optimized using response surface analysis, resulting in an optimized composition of the SDRP: 13 wt% monomer, 0.02 wt% initiator, 0.7 wt% crosslinker, and 1.8 wt% degradation catalyst. Under the condition of 70–120 °C, the SDRP was transformed from a liquid to a solid gel in 30–110 min; the degradation time was 3–10 days, and the viscosity of the completely degraded solution was lower than 20 mPa·s. At an injection volume of 1 PV SDPR, a breakthrough pressure of 8.34 MPa was achieved. The hydrolysis of the unstable crosslinker was found to have caused the breakage of the SDRP. Over time, the functional groups within the unstable crosslinker underwent hydrolysis due to the combined effects of temperature and the degradation catalyst. This process led to the disruption of crosslinking points, resulting in a gradual deterioration of the network structure. As a consequence, some immobile water was converted into free water. The mobility of water molecules increased until the plug was completely degraded into a viscous liquid. This study enriches the temporary plugging gel system.

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system’s transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system’s transient-state model can reduce the errors between day-ahead scheduling and rolling correction.

In order to effectively protect and verify the copyright information of multimedia digital works, this paper proposes a zero-watermarking algorithm based on carrier image feature point descriptors. The constructed feature matrix of this algorithm consists of two parts: the feature descriptor vector calculated from scale-invariant feature reconstruction transform (SIFRT) and the multi-radius local binary pattern (MrLBP) descriptor vector. The algorithm performs a standardization, feature decomposition, and redundancy reduction on the traditional keypoint descriptor matrix, combines it with the texture feature matrix, and achieves the dimensional matching of copyright information. The advantage of this algorithm lies in its non-modification of the original data. Compared to computing global features, the local features computed from a subset of key points reduce the amount of attack interference introduced during copyright verification, thereby reducing the number of erroneous pixel values that are introduced. The algorithm introduces a timestamp mechanism when uploading the generated zero-watermarking image to a third-party copyright center, preventing subsequent tampering. Experimental data analysis demonstrates that the algorithm exhibits good discriminability, security, and robustness.

As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduce a transformer-based multi-task framework that concurrently leverages both labeled and unlabeled volumes by encoding shared representation patterns. We first integrate transformers into YOLOv5 to enhance segmentation capabilities and adopt a multi-task approach spanning shadow region detection and boundary localization. Subsequently, we leverage the mean teacher model to simultaneously learn from labeled and unlabeled inputs alongside orthogonal view representations, enabling our approach to harness all available annotations. Our network can improve the learning ability and attain superior performance. Extensive experiments demonstrate that the transformer-powered architecture encodes robust inter-sample relationships, unlocking substantial performance gains by capturing shared information between labeled and unlabeled data. By treating both data types concurrently and encoding their shared patterns, our framework addresses the limitations of existing semi-supervised approaches, leading to improved segmentation accuracy and robustness.

As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

Automatic image segmentation is vital for the computer-aided determination of treatment directions, particularly in terms of labelling lesions or infected areas. However, the manual labelling of disease regions is inconsistent and a time-consuming assignment. Meanwhile, radiologists’ comments are exceedingly subjective, regularly impacted by personal clinical encounters. To address these issues, we proposed a transformer learning strategy to automatically recognize infected areas in medical images. We firstly utilize a parallel partial decoder to aggregate high-level features and then generate a global feature map. Explicit edge attention and implicit reverse attention are applied to demonstrate boundaries and enhance their expression. Additionally, to alleviate the need for extensive labeled data, we propose a segmentation network combining propagation and transformer architectures that requires only a small amount of labeled data while leveraging fundamentally unlabeled images. The attention mechanisms are integrated within convolutional networks, keeping their global structures intact. Standalone transformers connected straightforwardly and receiving image patches can also achieve impressive segmentation performance. Our network enhanced the learning ability and attained a higher quality execution. We conducted a variety of ablation studies to demonstrate the adequacy of each modelling component. Experiments conducted across various medical imaging modalities illustrate that our model beats the most popular segmentation models. The comprehensive results also show that our transformer architecture surpasses established frameworks in accuracy while better preserving the natural variations in anatomy. Both quantitatively and qualitatively, our model achieves a higher overlap with ground truth segmentations and improved boundary adhesion.

We studied the layer structure of bubbles just below water/air and water/EPE (Expand aple poly ephylene) interfaces using high-speed photography. The layer structure was generated by floating spherical clusters, the source bubbles of which were identified to come from the attachment of bubble nuclei at the interface, the floating of bubbles in the bulk liquid, or bubbles generated on the surface of the ultrasonic transducer. The boundary shape affected the layer structure, which assumed a similar profile below the water/EPE interface. We developed a simplified model composed of a bubble column and bubble chain to describe interface impacts and the interaction of bubbles in a typical branching structure. We found that the resonant frequency of the bubbles is smaller than that of an isolated single bubble. Moreover, the primary acoustic field plays an important role in the generation of the structure. A higher acoustic frequency and pressure were found to shorten the distance between the structure and the interface. A hat-like layer structure of bubbles was more likely to exist in the low-frequency (28 and 40 kHz) intense inertial cavitation field, in which bubbles oscillate violently. By contrast, structures composed of discrete spherical clusters were more likely to form in the relatively weak cavitation field at 80 kHz, in which stable and inertial cavitation coexisted. The theoretical predictions were in good agreement with the experimental observations.

This study aims to explore the correlation between the activity trajectory of Crested Ibis and meteorological changes. The trajectory data of Crested Ibis were obtained using the HQBG3621L backpack-style tracker, and the spatiotemporal characteristics of the trajectory data were analyzed to obtain information on the activity range and habitat of Crested Ibis. The Scheirer–Ray–Hare test and Kruskal–Wallis test were used to investigate the impact of meteorological factors on the activity of Crested Ibis. The study found that meteorological factors have a certain influence on the habitat selection and activity patterns of Crested Ibis. Through this research, a better understanding of the interaction between Crested Ibis and the meteorological environment can be achieved, providing a scientific basis for the conservation and ecological management of Crested Ibis.

Aiming at the problem of insufficient accuracy caused by the insufficient mining of spatiotemporal features in the process of unsafe behavior and danger identification of construction personnel, the traditional two-stream convolution model is improved, and a two-stream convolution dangerous behavior recognition model based on Faster R-CNN-LSTM is proposed. In this model, the Faster R-CNN network is connected in parallel with the LSTM network. The Faster R-CNN network is used as the spatial flow, and the human spatial motion posture is divided into static and dynamic features to extract the anchor point features, respectively. The fusion of the two is used as the output of the spatial flow. An improved sliding long-term and short-term memory network is used in the time flow to increase the extraction ability of the time series features of the construction personnel. Finally, the two branches are fused in time and space to classify and identify whether the construction personnel wear safety helmets. The results show that the MAP of the improved Faster R-CNN-LSTM network framework is increased by 15%. The original CNN-LSTM network framework detected four targets, but there was one misdetection, with an accuracy of 91.48%. The improved frame detection accuracy reaches 99.99%, and there is no error detection. The proposed method is superior to the pre-improvement and other methods that can effectively identify the unsafe behavior of construction workers on construction sites and also has a good distinction effect on fuzzy actions.

Water is one of the most important compounds and plays an important role in biological process, chemical synthesis and material science. Thus, studies about water and related particles water radical cations are always the top research fields. In this work, water dimer radical cation (H2O)2+• was prepared using home made low energy corona discharge device at ambient condition, and its structure was further studied using collision induced dissociation (CID) experiment and isotope labelling method. The dissociation results showed that (H2O)2+• (m/z 36) will lose OH• and H2O to give ions of m/z 19 (H2OH+) and m/z 18 (H2O+•), competitively, revealing that (H2O)2+•can exist in two different structures, including ［H2OH+—•OH］ (hydrogen atom bound structure (A)) and ［H2O∴OH2］+ (oxygen oxygen atom bound structure (B)). Structure A is asymmetry and structure B is symmetry. Furthermore, isotopic labeled experiments were conducted using deuterated water (D2O) and 18O-enriched water (H218O). The dissociation of (D2O)2+•(m/z 40) gave the corresponding protonated water D2OD+ (m/z 22) and water radical cation (D2O)+• (m/z 20). Dissociation of (H2O•D2O)+•(m/z 38) produced the protonated water H2OD+ (m/z 20)/D2OH+ (m/z 21), and water radical cation H2O+• (m/z 18), HOD+• (m/z 19), D2O (m/z 20)+•. In 18O-enriched water (H218O) labelled isotopic experiments, (H2O•H218O)+•(m/z 38) also was dissociated into its corresponding protonated water H2OH+(m/z 19)/H218OH+(m/z 21) and water radical cation H2O+• (m/z 18) and H218O+•(m/z18). The CID and the isotope labelling results indicated that water dimer radical cation (D2O)2+•, (H2O•D2O)+• and (H2O•H218O)+•also exist A and B two structured forms. In conclusion, two structures of water dimer radical cation (H2O)2+•will further reveal that mass spectrometry may help to elucidate chemical mechanism related with (H2O)2+•in the biological processes and chemical reactions.

Summary: Persulfate-based nonradical oxidation processes are appealing in water treatment for the efficient and selective degradation of trace contaminants in complex water matrices. However, there is still lacking of systematic understanding of the relationship between multiple nonradical pathways and the active sites of catalyst. Herein, a single-atom Cu catalyst with saturated Cu-N4 sites on a carbon substrate (SA-Cu-NC) was constructed to activate peroxymonosulfate (PMS), which exhibited high catalytic performance and selectivity for pollutant degradation in different water conditions. Combined with the results of density functional theory (DFT) calculations, the electron-rich area around Cu site and the electron-poor area around C site in the saturated Cu-N4 configuration could efficiently adsorb and activate PMS, which promoted pollutant degradation through the oxidation of singlet oxygen (1O2) and electron transfer process, respectively. This study advances the understanding of the saturated coordination structure of metals and the superiority of multiple nonradical pathways in wastewater treatment.

Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.

The evolution of acoustic Lichtenberg figure (ALF) in ultrasound fields is studied using high-speed photography. It is observed that bubbles travel along the branch to the aggregation region of an ALF, promoting the possibility of large bubble or small cluster formation. Large bubbles move away from the aggregation region while surrounding bubbles are attracted into this structure, and a bubble transportation cycle arises in the cavitation field. A simplified model consisting of a spherical cluster and a chain of bubbles is developed to explain this phenomenon. The interaction of the two units is analyzed using a modified expression for the secondary Bjerknes force in this system. The model reveals that clusters can attract bubbles on the chain within a distance of 2 mm, leading to a bubble transportation process from the chain to the bubble cluster. Many factors can affect this process, including the acoustic pressure, frequency, bubble density, and separation distance. The larger the bubble in the cluster, the broader the attraction region. Therefore, the presence of large bubbles might enhance the process in this system. Local disturbances in bubble density could destroy the ALF structure. The predictions of the model are in good agreement with the experimental phenomena.

According to classical nucleation theory, a gas nucleus can grow into a cavitation bubble when the ambient pressure is negative. Here, the growth process of a gas nucleus in a micro-cavity was simplified to two “events”, and the full confinement effect of the surrounding medium of the cavity was considered by including the bulk modulus in the equation of state. The Rayleigh–Plesset-like equation of the cavitation bubble in the cavity was derived to model the radial oscillation and translational motion of the cavitation bubble in the local acoustic field. The numerical results show that the nucleation time of the cavitation bubble is sensitive to the initial position of the gas nucleus. The cavity size affects the duration of the radial oscillation of the cavitation bubble, where the duration is shorter for smaller cavities. The equilibrium radius of a cavitation bubble grown from a gas nucleus increases with increasing size of the cavity. There are two possible types of translational motion: reciprocal motion around the center of the cavity and motion toward the cavity wall. The growth process of gas nuclei into cavitation bubbles is also dependent on the compressibility of the surrounding medium and the magnitude of the negative pressure. Therefore, gas nuclei in a liquid cavity can be excited by acoustic waves to form cavitation bubbles, and the translational motion of the cavitation bubbles can be easily observed owing to the confining influence of the medium outside the cavity.

We present a low-intensity noise single-frequency Yb-doped all-fiber amplifier oriented to space-borne gravitational wave detection. Relative intensity noise (RIN) below −70 dBc/Hz @ 1 mHz~1 Hz was achieved by virtue of feedback-loop-based intensity noise suppression. Based on systematic noise analysis and experimental investigation, we found that the pump noise and temperature-dependent noise of the fiber splitter and the photodetector contributed mainly to the RIN of the fiber amplifier. Therefore, we carefully designed a feedback-loop-based Yb-doped all-fiber amplifier, and finely stabilized the temperature of the pump diode, fiber splitters, and photodetectors. Consequently, the RIN can be suppressed down to −72.5 dBc/Hz around 1 mHz. This low-intensity all-fiber Yb-doped amplifier can be used for space-borne gravitational-wave detection.

It’s of paramount importance to develop renewable nanocarbon materials to replace conventional precious metal catalysts in alkane dehydrogenation reactions. Graphene-based materials with high surface area have great potential for light alkane dehydrogenation. However, the powder-like state of the graphene-based materials seriously limits their potential industrial applications. In the present work, a new synthetic route is designed to fabricate nitrogen-doped graphene-based monolith catalysts for oxidative dehydrogenation of propane. The synthetic strategy combines the hydrothermal-aerogel and the post thermo-treatment procedures with urea and graphene as precursors. The structural characterization and kinetic analysis show that the monolithic catalyst well maintains the structural advantages of graphene with relatively high surface area and excellent thermal stability. The homogeneous distributed nitrogen species can effectively improve the yield of propylene (5.3% vs. 1.9%) and lower the activation energy (62.6 kJ mol−1 vs. 80.1 kJ mol−1) in oxidative dehydrogenation of propane reaction comparing with un-doped graphene monolith. An optimized doping amount at 1:1 weight content of the graphene to urea precursors could exhibit the best catalytic performance. The present work paves the way for developing novel and efficient nitrogen-doped graphene monolithic catalysts for oxidative dehydrogenation reactions of propane.

The shield tunnel end reinforcement is important in the water-rich sandy strata. In this paper, a trapezoidal freezing soil mass was proposed to reinforce the shield tunnel end, and the reinforcement effects were evaluated based on the Wuhu River-Crossing Tunnel. Firstly, the influences of the freezing soil mass geometric dimensions on the stability of the tunnel portal were analyzed. Then, displacements of the tunnel portal with different trapezoidal freezing soil masses were simulated. Finally, the trapezoidal freezing soil mass was applied in the Wuhu River-Crossing Tunnel. The results show that the portal’s stability is improved significantly with increased freezing soil mass longitudinal length if the length is less than the tunnel diameter. The lower side of the trapezoidal freezing soil mass has a greater influence on the tunnel portal stability than the upper side. The trapezoidal freezing soil mass is more effective than the rectangular freezing soil mass for the Wuhu River-Crossing Tunnel end reinforcement. The trapezoidal freezing soil mass is a good choice for the shield tunnel end soil reinforcement in water-rich sandy strata. This study provides a freezing reinforcement method for the large-diameter shield tunnel end, which provides guidelines for similar engineering.

Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively.

Knowledge tracing, which is used to predict students’ performance based on their previous practices, has attracted many researchers’ attention. Especially in this rising period of intelligent education, many knowledge tracing methods have been developed. However, most of the existing knowledge tracing methods focus on the personality of practices and knowledge concepts but ignore the contexts related to the studying process. In this paper, we propose a context-based knowledge tracing model, which combines students’ historical performance and their studying contexts during knowledge mastery. To be specific, we first define five studying contexts for performance prediction. The basic context is the current knowledge state of a student, which is described by their practice sequences. Then, a QR-matrix is defined to represent the relationship among questions, knowledge concepts, and responses, which describes the contexts of questions and knowledge. Furthermore, an improved LSTM model is proposed to capture the context of students’ memory and forgetness, and a multi-head attention mechanism is designed to capture the context of students’ behaviors. Finally, based on the captured contexts, the prediction model ContextKT is established. Our prediction model is evaluated on two real educational datasets. The experimental results show our model is effective and efficient in student performance prediction, and it outperforms the other existing methods.

Summary: High energy consumption is impedimental for eliminating refractory organic pollutants in water by applying advanced oxidation processes (AOPs). Herein, we develop a novel process for destructing these organics in chemical conjuncted Fe0-FeyCz/Fex, graphited ZIF-8, and rGO air-saturated aqueous suspension without additional energy. In this process, a strong Fe-π interaction occurs on the composite surface, causing the surface potential energy ∼310.97 to 663.96 kJ/mol. The electrons for the adsorbed group of pollutants are found to delocalize to around the iron species and could be trapped by O2 in aqueous suspension, producing ⋅OH, H, and adsorbed organic cation radicals, which are hydrolyzed or hydrogenated to intermediate. The target pollutants undergo surface cleavage and convert H2O to ⋅OH, consuming chemical adsorption energy (∼2.852–9.793 kJ/mol), much lower than that of AOPs. Our findings provide a novel technology for water purification and bring new insights into pollutant oxidation chemistry.

Crude oil spills seriously harm the ocean environment and endanger the health of various animals and plants. In the present study, a totally biodegradable polymer, poly(L-lactic acid) (PLLA), was employed to fabricate highly porous oil absorbent nanofibrous materials by using a combination of electrospinning technique and subsequent acetone treatment. We systematically investigated how the electrospinning parameters affected formation of the porous structure of PLLA nanofibers and demonstrated that PLLA nanofibers with decreased and uniform diameter and improved porosity could be rapidly prepared by adjusting solution parameters and spinning parameters. We also demonstrated that the acetone treatment could obviously enhance the pore diameter and specific surface area of as-optimized electrospun PLLA nanofibers. The acetone treatment could also improve the hydrophobic property of as-treated PLLA nanofiber membranes. All these led to a significant increase in oil absorption performance. Through our research, it was found that the oil absorption of PLLA nanofiber membrane increased by more than double after being treated with acetone and the oil retention rate was also improved slightly.

Foamed concrete consists of cement matrix and air-foam and has more complicated structure characteristics than normal concrete. However, current research on the elastic modulus of foamed concrete is still limited to empirical equations. In this work, a new theoretical prediction model was proposed for calculating the elastic modulus of foamed concrete. The elastic modulus calculation model for the spherical shell element of foamed concrete is constructed based on the Walsh formula and the assumption of spherical pores. Moreover, the theoretical prediction model is established by introducing the two-layer embedded model for the elastic modulus of foamed concrete. Then, the compressive test is employed to verify the accuracy of the model. The results show that the elastic modulus of foamed concrete decreases with the increase in porosity and matrix Poisson’s ratio, and increases with the increase in the matrix elastic modulus. The research results can improve the mechanical theories of foamed concrete materials and have good engineering application values.

Monolithic GaN High Electron Mobility Transistor (HEMT)-integrated circuits are a promising application of wide band-gap materials. To date, most GaN-based devices behave as NMOS-like transistors. As only NMOS GaN HEMT is currently commercially available, its control circuit requires special design if monolithic integration is desired. This article analyzes the schematics of a GaN-based comparator, and three comparator structures are compared through ADS simulation. The optimal structure with the bootstrapped technique is fabricated based on AlGaN/GaN Metal–Insulator–Semiconductor (MIS) HEMT with the recessed gate method. The comparator has excellent static characteristics when the reference voltage increases from 3 V to 8 V. Dynamic waveforms from 10 kHz to 1 MHz are also obtained. High-temperature tests from 25 °C to 250 °C are applied upon both DC and AC characteristics. The mechanisms of instability issues are explained under dynamic working condition. The results prove that the comparator can be used in the state-of-art mixed-signal circuits, demonstrating the potential for the monolithic all-GaN integrated circuits.

Transition-metal dichalcogenides (TMDs) materials have attracted much attention for hydrogen evolution reaction (HER) as a new catalyst, but they still have challenges in poor stability and high reaction over-potential. In this study, ultra-thin SnS2 nanocatalysts were synthesized by simple hydrothermal method, and low load of Pt was added to form stable SnS2-Pt-3 (the content of platinum is 0.5 wt %). The synergistic effect between ultra-thin SnS2 rich in active sites and individual dispersed Pt nanoclusters can significantly reduce the reaction barrier and further accelerate HER reaction kinetics. Hence, SnS2-Pt-3 exhibits a low overpotential of 210 mV at the current density of 10 mA cm−2. It is worth noting that SnS2-Pt-3 has a small Tafel slope (126 mV dec−1) in 0.5 M H2SO4, as well as stability. This work provides a new option for the application of TMDs materials in efficient hydrogen evolution reaction. Moreover, this method can be easily extended to other catalysts with desired two-dimensional materials.

Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity.

In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital “make-to-order” (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system.