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Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time. [ABSTRACT FROM AUTHOR]

In the initial production phase of an inclined seam opencast mine, due to initial investment and production capacity constraints, zoning or phasing is usually used, resulting in changes in the length of the working line during the transition of the mining area. To simplify the process, this article analyses the changes in the length of the working line during the period of slow gang steering in inclined seam opencast mines. In order to simplify the process, this article analyses the changes in the length of the working line in each period. The relationship between the length of the working line and the realm stripping ratio in the overlapping area of the two mining areas is discussed, and the variation in the realm stripping ratio and the economically reasonable stripping ratio under different working line lengths is illustrated. The advantages and disadvantages of the different optimization options are described. The optimization method was applied to the Zhundong opencast coal mine to calculate the working line lengths for the two stages. The optimized stripping ratio for the second-stage south gang location is equal to the economically reasonable stripping ratio of 3.50 m3/t, which reduces coal compression and improves economic efficiency, and is a guideline for the study of the working line lengths in each stage of the open-pit mine slow gang turning. [ABSTRACT FROM AUTHOR]

The term "smart materials" can be used to refer a wide variety of different kinds of substances. One capacity that is shared by all of them is the capability to radically alter one or more aspects while operating inside carefully controlled conditions. The age of intelligent materials is arrived, and we are living in it right now. An older definition of smart material described it as a substance that responds swiftly to the environment in which it is located. It has been argued that the word "smart material" now refers to any substance that is capable of detecting, transmitting, or processing a stimulus in order to produce a beneficial effect. This idea has been put forward as a possible expansion of the original meaning of the term. The purpose of this research is to define and categorize different types of intelligent materials. Uses of smart materials are being contemplated in a wide variety of domains, spanning from the realm of technology to that of the modern environment. [ABSTRACT FROM AUTHOR]

The fractional-stochastic Drinfel'd–Sokolov–Wilson equations (FSDSWEs) perturbed by the multiplicative Wiener process are studied. The mapping method is used to obtain rational, hyperbolic, and elliptic stochastic solutions for FSDSWEs. Due to the importance of FSDSWEs in describing the propagation of shallow water waves, the derived solutions are significantly more useful and effective in understanding various important challenging physical phenomena. In addition, we use the MATLAB Package to generate 3D graphs for specific FSDSWE solutions in order to discuss the impact of fractional order and the Wiener process on the solutions of FSDSWEs. [ABSTRACT FROM AUTHOR]

In order to improve the processing effect of English online teaching resources, this paper improves the efficiency of resource processing by building an intelligent cloud system. Aiming at the problem of insufficient acquisition of traditional teaching resources, this paper adopts the intelligent data acquisition method and proposes an English resource mining method based on crawler technology. Moreover, in view of the difficulty of English semantic recognition, this paper proposes a method for calculating weights of feature items weighted by mixed factors on the basis of traditional algorithms and constructs a system model based on the characteristics of online English teaching. In addition, in order to explore the system effect, the model proposed in this paper is verified by clustering experiments. Through the experimental research, it can be seen that the English online teaching resource processing system has a good effect in the processing of English teaching resources. [ABSTRACT FROM AUTHOR]

The order system plays the role of the central nervous system in the integrated logistics operation. Theoretically speaking, the whole operation process of logistics service is to complete the order, the order information runs through all the links of the whole logistics, and the order processing efficiency affects the logistics process. This paper studies the high-concurrency and high-performance applications of the microservice order system based on big data, which prove that on the basis of big data technology, the performance of the microservice order system will be greatly improved. To this end, the analysis provides an overview of big data technologies and related content of microservices. It describes their application scenarios and then combines the development of the order system with a comprehensive analysis to prove its feasibility. At the same time, we compared multiple algorithms applied in microservices and found the most suitable algorithm for the order system and applied it, so that the order system can achieve high concurrency and high performance. The big data-based microservices control system proved to be useful for reasonable resource planning and for improving system performance in experiments. The final experiment found that in the microservice order system using big data, its transaction average response time was 0.01 s, and the success rate was 100%. [ABSTRACT FROM AUTHOR]

An extended method of semidiscrete high-resolution finite volume is used in this paper to obtain numerical solutions for a formulated nonlinear lumped kinetic model of liquid chromatographic process to examine the effect of chromatographic column overloading gradient elution considering core-shell particles. The model constitutes linear solvent strength (LSS), Henry's constant, coefficient of nonlinearity, and coefficient of axial dispersion. The effects of modulator concentration changes for the elution of single and two components are analyzed. The advantages of introducing gradient elution against isocratic elution in terms of core radius fraction are investigated intensively. Numerical temporal moments are generated from the solutions obtained for a more in-depth examination of the considered model. Moreover, multiple forms of a single- and two-component mixture are generated to analyze the influences of core radius fractions on gradient elution. For example, the obtained results are utilized to investigate the effects of the slope of gradient, concentration of modulator, solvent strength parameter, coefficient of nonlinearity, coefficient of mass transfer, and coefficient of axial dispersion on the profiles of concentration in order to improve the process performance using optimal core radius fraction parameter values. [ABSTRACT FROM AUTHOR]

The rapid development of e-commerce has impacted the intelligence level of warehouses. The robotic mobile fulfillment system (RMFS) demonstrated that mobile-pod warehouse systems could process a significant number of order-picking operations within the same amount of time as other systems, saving money and improving operational efficiency. The order-picking mode in such a system is "parts-to-picker," which is becoming both a prevailing operation mode in industrial warehouses and an encouraging research field during these years. One of the most significant fundamental factors in RMFS is the operational efficiency that is affected by storage assignment and order batching. This paper considers the joint impact of the storage assignment policies and order batching policies on order picking process. Our goal is to minimize the moving times of robots, which reflects the order-picking cost or efficiency. We propose using order similarity and item similarity to batch orders and assign item storage locations, respectively. Both the order batching and item grouping are tackled by a clustering model which is an integer linear program. We also develop a policy evaluation model to measure the order picking cost. We conduct numerical tests on six order batching and storage assignment policy combinations. A comparative analysis and an ANOVA analysis are then performed on the test results to compare the performances of these policy combinations. We find that the Weighted Support Count-based storage allocation combining with the correlation-based order batching achieves the best order-picking performance. Also, the more accurate information about the items and orders we can get from the historical data, the more order-picking workload we can save by exploiting the similarity features. [ABSTRACT FROM AUTHOR]

In the order picking process of the warehouse center, considering the rapid increase in the volume of orders arriving at the picking center at the time of the promotional festival, a hybrid operation mode with multiple picking tables is used to meet the picking requirements of the huge number of real-time orders. Therefore, in this paper, a hybrid picking mode is proposed, taking into account both the idle degree of picking stations and their order item centers of gravity, and a new reinforcement learning algorithm embedding mechanism (PRL) with placeholder control is designed to solve the problem of a huge number of real-time item orders arriving at the picking center system on promotional holidays and in inconsistent quantities, and numerical simulations are performed for this algorithm. The experimental results show that the PRL algorithm in hybrid picking mode can handle a huge number of orders simultaneously and improve picking efficiency effectively. [ABSTRACT FROM AUTHOR]

The fatigue design of bus structures is directly dependent on the loads that the vehicle will support throughout its lifespan. The determination of such operational loads, obtained in form of accelerations through vehicle sensing, is usually done with proprietary and expensive acquisition systems. In this paper, a low-cost acquisition system is presented and studied, which is designed, calibrated, and oriented for this purpose, based on an Arduino UNO and a low-cost accelerometer. The acquisitions are adapted to obtain the operational loads of several urban bus lines in the city of Madrid. The obtained data is later processed in order to characterize the acceleration cycles in these structures, which can be used as an input for the appropriate structural design. As a result, it is proved that the low-cost acquisition system is adequate and provides a simple and cheap way to characterize the acceleration of these vehicles during ordinary service. [ABSTRACT FROM AUTHOR]

Designing an order picking system can be very complex, as several interrelated control variables are involved. We address the sizing of the storage capacity of the picking bay, the crew of pickers, and the AGV fleet, which are the most important variables from a tactical viewpoint in a parts-to-pickers system. Although order picking is a widely explored topic in the literature, no analytical model that can simultaneously deal with these variables is currently available. To bridge this gap, we introduce a queue model for Markovian processes, which enables us to jointly optimise the aforementioned control variables. A discrete-event simulation is then used to validate our model, and we then test our proposal with real data under different operative scenarios, with the aim of assessing the usefulness of the proposal in real settings. [ABSTRACT FROM AUTHOR]

Downward-looking linear array synthetic aperture radar (DLLA SAR) is a kind of three-dimensional (3-D) radar imaging system. To obtain the superresolution along the crosstrack direction of DLLA SAR, the sparse regularization models with single measurement vector (SMV) have been widely applied. However, the robustness of the sparse regularization models with SMV is unsatisfactory, especially in the low signal-to-noise rate (SNR) environment. To solve this problem, we proposed a novel imaging method for DLLA SAR based on the multiple measurement vector (MMV) model with L 2 , 1 -norm. At first, we exchange the processing order between the along-track (AT) domain and the crosstrack (CT) domain to keep the same sparse structure of the signal in the crosstrack domain so that we can establish the imaging problem as a sparse regularization model based on the MMV model. Moreover, the mixed L 2 , 1 -norm is introduced into the regularization term of the MMV model. Finally, the modified orthogonal matching pursuit (OMP) algorithm is designed for the MMV model with the L 2 , 1 -norm. The simulations verify that the proposed method has better performance in the lower SNR environment and requires lower computation compared with the conventional methods. [ABSTRACT FROM AUTHOR]

This paper studies joint algorithms of order picking and distribution in new retail enterprises. The problem will consider many factors, such as the type of goods, picking time, batch capacity of distribution, distribution time, and distribution cost. First of all, the research problems are summarized as mathematical programming problems. Then, a genetic algorithm and comparison algorithms are proposed. Finally, the rationality of the model and the effectiveness of the algorithms are verified by computational experiments, and management enlightenments are revealed. [ABSTRACT FROM AUTHOR]

The Dongsheng gas field is characterized by low porosity and low permeability. Its principal producing reservoir is the H-1 zone, belonging to the Lower Shihezi Formation. Sand is the main lithology in the H-1 zone, while mud interlayers are also well developed in a vertical direction. As a result, the reservoir is a sand-mud interbed, which brings difficulty to fracture height extension. In order to understand the process of fracture height growth in a sand-mud interbed reservoir and obtain the responses of injection pressure and fracture width during a hydraulic fracturing, a hydromechanical-coupled model is established. Mud interlayers are fully considered and a cohesive zone model is adopted to deal with fracture propagation. Numerical results show that the fracture extends quickly to the sand-mud interface after initiation and breaks through rather than propagating along the interfaces. Pressure and width both increase continuously when fracture propagates in the mud interlayer. High-viscosity and high-injection rates are helpful for the fracture to break through the mud interlayer, especially at an early period. When the mud interlayers are asymmetric, pressure and width fluctuate several times once fracture propagates inside and breaks through the mud interlayer. Perforations close to the thinner mud interlayer can increase the fracture width and reduce fracturing risks. [ABSTRACT FROM AUTHOR]

Robotic mobile fulfillment system (RMFS) is a new type of parts-to-picker order picking system, where robots carry inventory pods to stationary pickers. Because of the difference in working mode, traditional storage assignment methods are not suitable for this new kind of picking system. This paper studies the storage assignment optimization of RMFS, which is divided into products assignment stage and pods assignment stage. In the products assignment stage, a mathematical model maximizing the total correlation of products in the same pods is established to reduce the times of pod visits, and a scattered storage policy is adopted to reduce system congestion. A heuristic algorithm is designed to solve the model. In the pods assignment stage, a model is established minimizing the total picking distance of the mobile robots considering the turnover rate and the correlation of pods as well as the workload balance among picking corridors. A two-stage hybrid algorithm combining greedy algorithm and improved simulated annealing is designed to solve the model. Finally, a simulation experiment is carried out based on the historical order data of an e-commerce company. Results show that the storage assignment method proposed in the paper significantly improves the efficiency of order picking. [ABSTRACT FROM AUTHOR]

Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order picking system and has become the development trend of e-commerce logistics distribution centers. There are usually a large number of tasks need to be allocated to many robots and the picking time for e-commerce orders is usually very tight, which puts forward higher requirements for the efficiency of multirobot task allocation (MRTA) in e-commerce RMFS. Current researches on MRTA in RMFS seldom consider task correlation and the balance among picking stations. In this paper, a task time cost model considering task correlation is built according to the characteristics of the picking process. Then, a multirobot task allocation model minimizing the overall picking time is established considering both the picking time balance of picking stations and the load balance of robots. Finally, a four-stage balanced heuristic auction algorithm is designed to solve the task allocation model and the tasks with execution sequence for each robot are obtained. By comparing with the traditional task time cost model and the algorithm without considering the balance among picking stations, it is found that the proposed model and algorithm can significantly shorten the overall picking time. [ABSTRACT FROM AUTHOR]

With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%. [ABSTRACT FROM AUTHOR]

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods' performances, their robustness, and their fast fault detection during distinct operating conditions. [ABSTRACT FROM AUTHOR]

To explore the relationship between the cutting vibration and the cutting load of a single pick, this paper studied a new method for a single pick cutting rock load identification. This paper improved the low accuracy problem of the regularization method in the inverse process of frequency response function in the traditional load identification method by introducing a filter operator. By combining the inverse pseudoexcitation method and the improved regularization method, the identification of the load dependent on the vibration signal was realized. A single pick cutting rock test equipment was built, which could simulate the actual working conditions of pick cutting rock in the underground or tunnel. By changing cutting speed, cutting angle, cutting spacing, and cutting depth of the single pick, the change trends of real cutting load and identification load were obtained. The load identification method proposed in this paper was consistent with the change trend of the real load under the single pick cutting state. Therefore, the method had good recognition accuracy and the maximum load recognition error was 17.35%. Compared with other traditional load identification methods, the identification error was reduced by a maximum of 1.98%. This method can identify the cutting load of single pick and modify the morbidity problem of frequency response function matrix. The method has a better recognition effect on the cutting load of the pick than the traditional recognition methods. The research could benefit the design of the cutting system and the arrangement of the pick on the coal mine or tunneling machinery. [ABSTRACT FROM AUTHOR]

In transfer learning, two major activities, i.e., pretraining and fine-tuning, are carried out to perform downstream tasks. The advent of transformer architecture and bidirectional language models, e.g., bidirectional encoder representation from transformer (BERT), enables the functionality of transfer learning. Besides, BERT bridges the limitations of unidirectional language models by removing the dependency on the recurrent neural network (RNN). BERT also supports the attention mechanism to read input from any side and understand sentence context better. It is analyzed that the performance of downstream tasks in transfer learning depends upon the various factors such as dataset size, step size, and the number of selected parameters. In state-of-the-art, various research studies produced efficient results by contributing to the pretraining phase. However, a comprehensive investigation and analysis of these research studies is not available yet. Therefore, in this article, a systematic literature review (SLR) is presented investigating thirty-one (31) influential research studies published during 2018–2020. Following contributions are made in this paper: (1) thirty-one (31) models inspired by BERT are extracted. (2) Every model in this paper is compared with RoBERTa (replicated BERT model) having large dataset and batch size but with a small step size. It is concluded that seven (7) out of thirty-one (31) models in this SLR outperforms RoBERTa in which three were trained on a larger dataset while the other four models are trained on a smaller dataset. Besides, among these seven models, six models shared both feedforward network (FFN) and attention across the layers. Rest of the twenty-four (24) models are also studied in this SLR with different parameter settings. Furthermore, it has been concluded that a pretrained model with a large dataset, hidden layers, attention heads, and small step size with parameter sharing produces better results. This SLR will help researchers to pick a suitable model based on their requirements. [ABSTRACT FROM AUTHOR]

In this research, we study an extended version of the joint order batching and scheduling optimization for manual vegetable order picking and packing lines with consideration of workers' fatiguing effect. This problem is faced by many B2C fresh produce grocers in China on a daily basis which could severely decrease overall workflow efficiency in distribution center and customer satisfaction. In this order batching and sequencing problem, the setup time for processing each batch is volume-dependent and similarity dependent, as less ergonomic motion is needed in replenishing and picking similar orders. In addition, each worker's fatiguing effect, usually caused by late shift and repetitive operation, which affects order processing times, is assumed to follow a general form of logistic growth with respect to the start time of order processing. We develop a heuristic approach to solve the resultant NP-hard problem for minimization of the total completion time. For order batching, a revised similarity index takes into account not only the number of common items in any two orders but also the proportion of these items based on the vegetable order feature. To sequence batches, the genetic algorithm is adapted and improved with proposed several efficient initialization and precedence rules. Within each batch, a revised nondecreasing item quantity algorithm is used. The performance of the proposed heuristic solution approach is evaluated using numerical instances generated from practical warehouse operations of our partnering B2C grocer. The efficiency of the proposed heuristic approach is demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents the application of an Artificial Intelligence-based method in analyzing the effects of environmental conditions, chloride contamination in concrete, and surface corrosion of rebars on the amplitude of Ground Penetrating Radar (GPR) signals. Six reinforced concrete slabs with different chloride contamination mixtures were fabricated and tested. GPR data were collected under various temperature and ambient humidity combinations. A total of 288 rebar picks were used for training, validation, and testing the proposed Artificial Neural Network (ANN) model. Multiple ANN model configurations with a variation in learning algorithms and the number of nodes in the hidden layer were explored to obtain the optimal model for the nondestructive data. It is shown that the "trainlm" learning algorithm produced the high accuracy prediction of the reflection amplitude of GPR signals. The sensitivity analysis was also conducted with the ANN model to investigate the effects of the input on the output parameters. Results from the sensitivity analysis revealed that the GPR reflection amplitudes were more sensitive to the changes of temperature parameter (TEM) and chloride contamination level (CCL), while they were less sensitive to the variation of ambient relative humidity (ARH) and rust condition on the rebar surface (CSR). [ABSTRACT FROM AUTHOR]

Considering time-varying demand of online retail industry, the traditional static storage location assignment is converted into a multistage storage location assignment process based on the idea of gradual and small-step-forward optimization, which can respond to rapid changes in demand by adjusting the storage location of SKUs in the warehouse in real time and dynamically. First, the study formulates the framework dynamic storage location assignment. Then, the adjustment gain model of dynamic storage location assignment is built, and a genetic algorithm is designed to find the final adjustment solution. Finally, the computer program is developed to simulate the whole process. Simulation and data analysis results show that dynamic storage location assignment can effectively improve picking efficiency when the average order size is small and large demand correlation strength. Dynamic storage location assignment simplifies the warehouse operation process by combining the picking operation and storage location assignment into one without changing the picker's current walking route, which can offer some theoretical guidance for online retail enterprises implementing dynamic storage location assignment. [ABSTRACT FROM AUTHOR]

This paper is in view of the current algorithm to solve Multiple-Devices-Process integrated scheduling problems without considering parallel process between the parallel processing. Meanwhile, the influence of the first processing procedure on the second processing procedure is ignored, resulting in poor tightness between serial processes and poor parallelism between parallel processes, ultimately affecting the product scheduling results. We proposed Multiple-Devices-Process integrated scheduling algorithm with time-selective for process sequence. The proposed Multiple-Devices-Process sequencing strategy determines the process of scheduling order and improves the tightness between serial process. This paper presents a method to determine the quasischeduling time point of the multiequipment process, the time-selective strategy of Multiple-Devices-Process, and the time-selective adjustment strategy of Multiple-Devices-Process so that the first and the second processing processes cooperate with each other, and the purpose of improving the tightness of the serial process and the parallelism of the parallel processes is achieved, so as to shorten the product processing time. [ABSTRACT FROM AUTHOR]

The paper proposes an algorithm based on the Multi-State Constraint Kalman Filter (MSCKF) algorithm to construct the map for robots special in the poor GPS signal environment. We can calculate the position of the robots with the data collected by inertial measurement unit and the features extracted by the camera with MSCKF algorithm in a tight couple way. The paper focuses on the way of optimizing the position because we adopt it to compute Kalman gain for updating the state of robots. In order to reduce the processing time, we design a novel fast Gauss–Newton MSCKF algorithm to complete the nonlinear optimization. Compared with the performance of conventional MSCKF algorithm, the novel fast-location algorithm can reduce the processing time with the kitti datasets. [ABSTRACT FROM AUTHOR]

Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing. [ABSTRACT FROM AUTHOR]

Deciding whether certain factors should be considered drivers of innovation in construction firms is crucial in terms of improving their performance and survival in an environment that is changing by leaps and bounds. Throughout the years, construction companies have been considered to be traditional and without the tendency to innovate. However, several studies have confirmed that this perception of the sector is evolving and that successful instruments from other industries are gradually being adapted for the benefit of the industry. The objective of this paper is therefore to investigate the potential factors affecting the performance of these organizations. Eighteen factors related to the individual, group, and organizational levels were identified through a review of the literature and an instrument developed that was validated by experienced professionals. A questionnaire was sent to 103 people working in the sector at the national level to obtain their views. The results of the classification analysis indicate that "technology and equipment" and "software acquisition" are considered the two most significant factors. In addition, these 18 factors can be classified into 7 groups: (i) internal drivers of innovation; (ii) innovation within the organization; (iii) technological innovation; (iv) technological links with the environment; (v) external drivers of innovation; (vi) innovation in processes; (vii) a culture of innovation in the company. Innovation in processes has the highest level of impact. This research deepens the current understanding of the factors at different organizational levels that must be highlighted in the implementation of an R&D system in order for companies to improve their performance and survival in future processes. [ABSTRACT FROM AUTHOR]

In order to monitor the process variance, this paper proposes a combined upper-sided synthetic S2 chart for monitoring the process standard deviation of a normally distributed process. This combined upper-sided synthetic S2 chart comprises a synthetic chart and an upper-sided S2 chart. The design and performance of the proposed chart are presented, and the steady-state average run length comparisons show that the combined upper-sided synthetic S2 chart outperforms the standard synthetic S2 chart as well as several run rules S2 charts, especially for larger shifts in the process variance. [ABSTRACT FROM AUTHOR]

In this article, we investigate spectrum estimation of law order moving average (MA) process. The main tool is the lag window which is one of the important components of the consistent form to estimate spectral density function (SDF). We show, based on a computer simulation, that the Blackman window is the best lag window to estimate the SDF of MA 1 and MA 2 at the most values of parameters β i and series sizes n , except for a special case when β = − 1 and n ≥ 40 in MA 1 . In addition, the Hanning–Poisson window appears as the best to estimate the SDF of MA 2 when β 1 = β 2 = − 0.5 and n ≥ 40. [ABSTRACT FROM AUTHOR]

Injection molding is widely used in industries to produce polymeric products. At present, compound NURBS surfaces are commonly used to represent freeform surfaces in mold models. This work uses T-spline surface with extraordinary control points instead of NURBS to represent freeform surfaces in mold models. Compared with NURBS, T-splines' higher-order continuity facilitates the mold quality control especially at the patch boundary. And, its patch layout information can be utilized for tool path planning. We propose an algorithm to determine the patch processing order and generate nonretraction tool path for T-spline surface models. The tool paths are generated patch by patch using isoparametric strategy. Actual machining and injection molding experiments have been conducted. The result shows the feasibility of the proposed method, and the final product is in good quality. [ABSTRACT FROM AUTHOR]

In order to explore the process of acid- and CO2-induced degradation of wellbore cement and the development of pre-existing leakage channels in wellbore cement under sulfate-rich geological CO2 storage conditions, wellbore cement samples were immersed in SO42--bearing brine solution for 7 days, and the samples after reacting with the low and circumneutral pH solutions were scanned by a micro-CT scanner. HCl+Na2SO4 solution was used to simulate the low-pH condition in deep formation waters and the possible existence of high sulfate ion content in deep formation waters. The acidification and carbonation results were compared, and the results given different pH values and different curing conditions were compared as well. The results show that the degradation of cement was related to the pH value of the reaction solution. There was a significant dissolution in the exterior of the cement sample after exposure to the low-pH solution, but the dissolution surrounding a penetrating borehole at the center of the sample (mimicking a leakage pathway within the wellbore cement in geological CO2 storage environment) was limited. Comparison between acidification and carbonation results in this study shows formation of a thick carbonate layer due to cement carbonation, and this layer was not observed in the acidification result. As for different curing conditions of cement samples, no significant difference in cement alteration depth was observed for the acidification case. For the carbonation case, precipitations in the borehole occurred in the cement sample cured at ambient pressure, while the cement sample cured at high pressure did not produce any precipitation in the borehole. This study provides valuable information on how low pH-induced corrosion and HCO3--induced cement carbonation contribute to structure evolution of wellbore cement in SO42--bearing brine under geological CO2 storage environment. [ABSTRACT FROM AUTHOR]

A massive amount of spatial-temporal records generated by sensors across the city help describe our day-to-day activities. Since the lifestyle represented by moving data varies from one individual to another, data analysts could facilitate the suspect-detection task by analyzing and classifying related trajectories of a given target. However, there are still some challenges that need to be overcome in real-life cases; for instance, the positive instances are limited, the trajectories are too diverse, and the transit behavior features are both too broader and costly to define. Moreover, people living in different areas of the city may have different life habits which can result in incorrect conclusions due to data-sensitive factors. In this paper, we describe the particular characteristics of movement behaviors regarding trajectory features. We also propose two models to improve the identification performance, namely, the trajectory pattern model (TPM) and neural network-based model. The trajectory pattern model (TPM) offers a novel view to describe users' movement behaviors and generates more effective and universal features other than location and timestamp dimensions. The end-to-end neural network-based model aims to avoid picking human features. Statistical analysis and insightful explanations are provided to help understand the behavior of a given target. The effectiveness of our proposed solutions compared to peer solutions is demonstrated and proved via extensive evaluation. [ABSTRACT FROM AUTHOR]

We present an algorithm for P-phase arrival time automatic determination and optimization for microseismic events of a coal mine in steps. For P-phase detection, we apply four modules: single denoising (band-filter), fast searching (STA/LTA and peak value), accurately picking (AIC), and optimization selection (time difference of arrival time and envelope circle judgment). This method is applied for a precise determination of P-phase arrival time; in addition, an automatic quality estimate is obtained to optimize and select the effective stations and used for source location. Through the analysis and validation of seismic data in a mine located in Hebei province, the result shows that this method can pick up the first arrival time of MS signal fast and accurately and automatically judge and remove the error station. Comparing automatic picks with the manual method in terms of speed and accuracy, computation cost (0.828 s) is far less than manual method (15 s per set), precision of manual and automatic represented by picking error is up to 71.7% (between −5 ms and 5 ms, 190 sets), 83.4% (between −10 ms and 10 ms, 221 sets), and 90.57% (between −20 ms and 20 ms, 240 sets). This study shows that the method presented in this paper is feasible to precise automatic P-phase determination and is a basis on automatic and precision location. [ABSTRACT FROM AUTHOR]

A problem-specific routing algorithm integrating ant colony optimization (ACO) and integer-coded genetic algorithm (GA) is developed to address the newly observed limitations imposed by ultranarrow aisles and access restriction, which exist in the largest e-commerce enterprise with self-run logistics in China. Those limitations prohibit pickers from walking through the whole aisle, and the access restriction even allows them to access the pick aisles only from specific entrances. The ant colony optimization is mainly responsible for generating the initial chromosomes for the genetic algorithm, which then searches the near-optimal solutions of picker-routing with our novel chromosome design by recording the detailed information of access modes and subaisles. To demonstrate the merits of the proposed algorithm, a comprehensive simulation for comparison is conducted with 12 warehouse layouts with real order information. The simulation results show that the proposed hybrid algorithm is superior to dedicated heuristics in terms of solution quality. The impacts of the parameters with respect to warehouse layout on the picking efficiency are analyzed as well. Setting more connect aisles and cross aisles is suggested to effectively optimize the picking-service efficiency in the presence of access limitations. [ABSTRACT FROM AUTHOR]

Congenital deafness is often compensated by early sign language use leading to typical language development with corresponding neural underpinnings. However, deaf individuals are frequently reported to have poorer numerical abilities than hearing individuals and it is not known whether the underlying neuronal networks differ between groups. In the present study, adult deaf signers and hearing nonsigners performed a digit and letter order tasks, during functional magnetic resonance imaging. We found the neuronal networks recruited in the two tasks to be generally similar across groups, with significant activation in the dorsal visual stream for the letter order task, suggesting letter identification and position encoding. For the digit order task, no significant activation was found for either of the two groups. Region of interest analyses on parietal numerical processing regions revealed different patterns of activation across groups. Importantly, deaf signers showed significant activation in the right horizontal portion of the intraparietal sulcus for the digit order task, suggesting engagement of magnitude manipulation during numerical order processing in this group. [ABSTRACT FROM AUTHOR]

This paper addresses the order picking problem in a material handling system consisting of multiple carousels and one picker. Carousels are rotatable closed-loop storage systems for small items, where items are stored in bins along the loop. An order at carousels consists of n different items stored there. The objective is to find an optimal picking sequence to minimizing the total order picking time. After proving the problem to be strongly NP-hard and deriving two characteristics, we develop a dynamic programming algorithm (DPA) for a special case (two-carousel storage system) and an improved nearest items heuristics (INIH) for the general problem. Experimental results verify that the solutions are quickly and steadily achieved and show their better performance. [ABSTRACT FROM AUTHOR]

This letter considers carrier recovery for offset quadrature phase shift keying (OQPSK) and minimum shift keying-type (MSKtype) modulations based on phase-lock loop (PLL). The concern of the letter is the optimization of the loop filter of the PLL. The optimization is worked out in the light of Wiener's theory taking into account the phase noise affecting the incoming carrier, the additive white Gaussian noise that is present on the channel, and the self-noise produced by the phase detector. Delay in the loop, which may affect the numerical implementation of the PLL, is also considered. Closed-form expressions for the loop filter and for the mean-square error are given for the case where the phase noise is characterized as a first-order process. [ABSTRACT FROM AUTHOR]

Medical conditions involving hair are typically managed by a dermatologist. However, a significant psychosocial association has been found both in the development of hair conditions through primary pathologic involvement and as secondary comorbidity. Trichopsychodermatology is an emerging field aiming to better understand the psychiatric and psychosocial impacts of disorders involving hair. Trichotillomania or hair‐pulling disorder, alopecia areata, telegenic effluvium, anagen effluvium, and cicatricial alopecia represent common conditions within trichopsychodermatology. A lack in awareness, knowledge, and training of the field exists, which can act as a barrier for improving patient outcomes. This review provides a holistic understanding of specific psychotrichologic conditions that healthcare providers may encounter in order to facilitate the process of developing high‐quality future therapeutic interventions. [ABSTRACT FROM AUTHOR]

Summary: Peak picking is used to determine locations of salient peaks in a graphical representation of a physical quantity. It is often used to extract possible natural frequencies from the frequency domain representation of structural responses. One of the challenges in peak picking is to establish a method to automatically distinguish peaks from data containing noise peaks. As selecting peaks intrinsically depends on human perception, algorithms for automated peak picking have exhibited only partial success to date. This paper presents an automated peak picking method that utilizes a region‐based convolutional neural network with possible object locations to accurately identify the peaks. A tailored deep learning architecture is proposed, which is subsequently trained using only the peaks numerically generated by computer software. Laboratory and field tests are conducted using acceleration responses obtained from three test structures of beam, truss, and stay cable to verify the identification performance of the proposed peak detector. The proposed peak detector is shown to successfully identify most of the salient peaks with high accuracy. [ABSTRACT FROM AUTHOR]