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The pronounced increase in the number of young people struggling with mental health and wellbeing continues to puzzle researchers, politicians and practitioners. Discussions around this seem to fluctuate between individualizing understandings of the problem matter and more structural, institutional and cultural ones, calling for research exploring the role of the latter. In this paper, we present analyses of how broader societal frameworks linked to acceleration, performance and psychologization affect the wellbeing of young people. Based on a longitudinal qualitative study in Denmark with 37 young men and women between the ages of 16 and 24 struggling with wellbeing, this paper unfolds structural, cultural and institutional influences on mental health issues experienced by young people. The analyses are produced using a social-analytical lens of ‘diagnosis of the times’ to explore how young people’s subjective everyday life experiences of wellbeing and wellbeing struggles must be understood as interlinked with broader societal frameworks and expectations. [ABSTRACT FROM AUTHOR]

Compaction is a critical step in the construction of an asphalt mixture. To effectively compact an asphalt mixture, the locking point, which identifies the effective compaction, was introduced based on the change in volumes of the asphalt mixture during gyratory compaction. However, the existing definition of the locking point is solely dependent on the volumetric properties of compacted mixtures and may not be necessarily associated with the skeleton of the mixture. In this paper, the locking point for a compacted asphalt mixture was defined and determined using dynamic responses measured by a particle sensor. Gyratory compaction and field compaction tests were carried out with particle sensors embedded to analyze the evolution of the aggregates' dynamic responses. Then, a novel dynamic response rate of the change index Rs was proposed to represent the evolution process of the asphalt mixture compaction. The Superpave gyratory compactor (SGC) results show that the acceleration and rotation angle of the particle sensors varied during compaction and did not converge in the end. The SGC compaction process can be divided into three stages: the initial compaction stage, transition stage, and plateau stage based on the rate of stress (Rs). Meanwhile, the inflection point between the transition stage and plateau stage in the compaction curve was defined as the locking point. It was found that the locking point determined by the sensors in the middle of the compacted specimens was later than that determined by the gyratory compaction. The contact interlocking initiated at the bottom of a compacted specimen and moved upward. The field compaction test results of the particle sensors showed that the contact stress also had a significant trend of convergence after being compacted by a pneumatic-tired roller, which indicates that the test stress index based on the particle sensors could be used for the evaluation of compaction quality. [ABSTRACT FROM AUTHOR]

AbstractThe quasidiffusion (QD) method is an established and efficient iterative technique for solving particle transport problems. Each QD iteration consists of a high-order SN sweep, followed by a low-order QD calculation. QD has two defining characteristics: (1) its iterations converge rapidly for any spatial grid and (2) the converged scalar fluxes from the high-order SN sweep and the low-order QD calculation differ, by spatial truncation errors, from each other and from the scalar flux solution of the SN equations. In this paper, we show that by including a transport consistency factor in the low-order equation, the converged high-order and low-order scalar fluxes become equal to each other and to the converged SN scalar flux. However, the inclusion of the transport consistency factor has a negative impact on the convergence rate. We present numerical results that demonstrate the effect of the transport consistency factor on stability. [ABSTRACT FROM AUTHOR]

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is 5 × 10−6 in the case of no noise addition; the optimal training MSE is 1.3 × 10−5 in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage. [ABSTRACT FROM AUTHOR]

In this paper, the mechanical and durability properties of aerated blocks are discussed. Aerated blocks are prepared using different cementitious materials with an alkaline solution in the absence of aluminium powder. The proportions of raw materials are chosen after performing two mix trials P1 and P2, keeping the density of the blocks constant at 800 kg/m3. Each mix is further tested under six curing mechanisms, namely, normal water curing, humidity chamber curing (at 50°C and 70% relative humidity) for 6 h and 10 h, curing by oven drying at 180°C for 6 h and at 80°C for 24 h and using an accelerated curing tank (ACT) at 65°C ± 5°C for 10 ± 2 h; this is termed trial 1. In trial 2, the best curing mechanism is taken and construction and demolition wastes (CDW) are substituted at different proportions in place of fly ash in both the mix proportions. In addition, trial 3 investigates the best mix proportions by substituting glass powder at 50% and 100% with fly ash and CDW, respectively. It is observed from experiments that aerated blocks manufactured with 50% CDW and heat curing by ACT showed high mechanical and durability properties after 7 days of hardening. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demand substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on deep neural networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the acceleration of a fast boat using a simulation model. Mathematical equations of ship motion dynamics with two types of propeller capabilities are developed using MATLAB and Simulink as simulation tools. The equations are extended to include the acting thrust, resistance, propeller's performance curves, and the PID governor curve for the acceleration manoeuvre. The application models the dynamic differential equations representing the vessel dynamics in one degree of freedom. MATLAB code was used to develop the ship acceleration as a multibody system. Modules of hydrodynamic forces, resistance, moments, and propeller performances were implemented to simulate the ship manoeuvring process. A comparison of the results for the boat's propulsion performance with two different propellers and the characteristics of the PID governor, which controls the fuel dose in the gas turbines, was carried out. We present a summary including a comparative analysis of the results for the boat dynamics with and without the PID governor. The results obtained here confirm significant discrepancies between the results of numerical simulations with and without the PID governor. [ABSTRACT FROM AUTHOR]

There is a need to improve the validity of the driving test as a measure of an individual’s ability to drive safely. This paper explores the use of algorithms to analyze acceleration and GPS data from a smartphone and a GoPro to distinguish between different driving styles, as performed by experienced examiners portraying stereotypical driving test candidates. Measures from nine driving tests were analyzed, including (harsh) accelerations, jerk, mean speed, and speeding. Results showed that the type of car, instructed driving style, and driving route impacted the dependent measures. It is concluded that GPS and accelerometer data can effectively distinguish between cautious, normal, and aggressive driving. However, it is important to consider additional sensors, such as cameras, to allow for more context-aware assessments of driving behavior. Furthermore, we demonstrate methods to quantify variations in road conditions and provide suggestions for presenting the data to driving examiners. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of dynamic multi- objective optimization problems (DMOPs), by demonstrating new approaches to change detection and change prediction in an evolutionary algorithm framework. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. First, we propose a new change detection method which achieves greater sensitivity by considering changes in both the PS and the PF, unlike most previous approaches. Second, when changes occur, a second-order (acceleration-based) prediction strategy is proposed to predictively reinitialize the population close to the new set of optima. We compare the performance of the proposed algorithm against two other state-of-the-art algorithms from the literature, using ten different dynamic benchmark problems. Experimental results show that the proposed change detection strategy in this paper can not only consider the effect of the optimal individuals but also can consider the effect of their corresponding objective values. Compared with the other two methods, the DMOPs achieved both the ability of precisely predicting the direction of changes and the ability of predicting the future trend of change direction. So, the DMOPs can also converge to the true PF in much less iterations compared with other methods. After multiple experiments, the proposed method outperforms the other algorithms on most of the test problems. [ABSTRACT FROM AUTHOR]

The structural collapse of a street lighting pole represents an aspect that is often underestimated and unpredictable, but of relevant importance for the safety of people and things. These events are complex to evaluate since several sources of damage are involved. In addition, traditional inspection methods are ineffective, do not correctly quantify the residual life of poles, and are inefficient, requiring enormous costs associated with the vastness of elements to be investigated. An advantageous alternative is to adopt a distributed type of Structural Health Monitoring (SHM) technique based on the Internet of Things (IoT). This paper proposes the design of a low-cost system, which is also easy to integrate in current infrastructures, for monitoring the structural behavior of street lighting poles in Smart Cities. At the same time, this device collects previous structural information and offers some secondary functionalities related to its application, such as meteorological information. Furthermore, this paper intends to lay the foundations for the development of a method that is able to avoid the collapse of the poles. Specifically, the implementation phase is described in the aspects concerning low-cost devices and sensors for data acquisition and transmission and the strategies of information technologies (ITs), such as Cloud/Edge approaches, for storing, processing and presenting the achieved measurements. Finally, an experimental evaluation of the metrological performance of the sensing features of this system is reported. The main results highlight that the employment of low-cost equipment and open-source software has a double implication. On one hand, they entail advantages such as limited costs and flexibility to accommodate the specific necessities of the interested user. On the other hand, the used sensors require an indispensable metrological evaluation of their performance due to encountered issues relating to calibration, reliability and uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes a new control scheme that is composed by the sliding mode control(SMC) with three-dimensional ceiling control to reduce the multi-dimensional vibration of the vehicle's seat. The seat suspension system is made of a kind of 3-RPS parallel mechanism, in which each limb is mainly composed by the spherical pair, spring, piston rod, damping fluid and rotary pair. Based on the single-open-chain theory, Position and Orientation Characteristics (POC) set of a 3-RPS seat mechanism was analyzed to verify its movement law. Three-dimensional seat system mathematical model was established, and the SMC and ceiling control were combined to obtain the required force. The ceiling control belongs to an ideal control method, it could provide the reference for the SMC. The comparisons between the SMC and passive seat suspension system were done when the vehicles were walking or working on E, F and G-roads. The results show that the transient amplitudes of seat acceleration outputs were reduced with the proposed control scheme. The research in this work can be extended and applied for other vehicles and improve their performance. [ABSTRACT FROM AUTHOR]

Switches are an essential, safety-critical part of the railway infrastructure. Compared to open tracks, their complex geometry leads to increased dynamic loading on the track superstructure from passing trains, resulting in high maintenance costs. To increase efficiency, condition monitoring methods specific to railway switches are required. A common approach to track superstructure monitoring is to measure the acceleration caused by vehicle track interaction. Local interruptions in the wheel–rail contact, caused for example by local defects or track discontinuities, appear in the data as transient impact events. In this paper, such transient events are investigated in an experimental setup of a railway switch with track-side acceleration sensors, using frequency and waveform analysis. The aim is to understand if and how the origins of these impact events can be distinguished in the data of this experiment, and what the implications for condition monitoring of local track discontinuities and defects with wayside acceleration sensors are in practice. For the same experimental configuration, individual impact events are shown to be reproducible in waveform and frequency content. Nevertheless, with this track-side sensor setup, the different types of track discontinuities and defects (squats, joints, crossing) could not be clearly distinguished using characteristic frequencies or waveforms. Other factors, such as the location of impact event origin relative to the sensor, are shown to have a much stronger influence. The experimental data suggest that filtering the data to narrow frequency bands around certain natural track frequencies could be beneficial for impact event detection in practice, but differentiating between individual impact event origins requires broadband signals. A multi-sensor setup with time-synchronized acceleration sensors distributed over the switch is recommended. [ABSTRACT FROM AUTHOR]

The new Broadway Bridge over the Arkansas River is located in downtown Little Rock, AR, USA. The Arkansas Department of Transportation replaced the existing historical structure, which was costly to maintain and considered structurally deficient. The new iconic structure consists of two basket-handled 134 m tied-arch bridges, which are designed to accommodate 24 000 vehicles daily, and includes a shared-used path. The US$98 million bridge was completed in 2017. Located along the existing alignment in a downtown metropolitan area, impacts to traffic were a prime concern. The bridge was closed to traffic for 6 months to allow for construction and to float the new arches into place. Throughout design, fracture-critical members were given careful consideration. The tie girder was designed with a bolted connection to prevent a fracture in one plate from propagating throughout the cross-section. It was then analysed as a three-sided section at the extreme event limit state. This internal redundancy minimised the potential risk of a catastrophic structural failure. This paper discusses the design considerations for the fracture-critical members, the construction technique and the expected movement of the main-span tied-arch superstructures. [ABSTRACT FROM AUTHOR]

Comfort is an important consideration in passenger vehicles provided by a suspension system. The suspension system must be associated with the vehicle body to improve comfort for the passenger. The active suspension system (ASS) has been introduced to perform this task. Moreover, the sliding mode controller (SMC) is well known for its continuous control signals. This paper proposes the Bayesian-based proportional resonant sliding mode controller (B-PRSMC) and two degrees of the freedom-proportional resonant controller (2d-f-PR) to stabilize passenger and ride comfort. The B-PRSMC is used for examining system states under varying road disturbances based on the Bayesian theorem. After examining the system state, the system performance is controlled by the 2d-f-PR controller. The proposed method is performed on Matlab, and the results are taken regarding body acceleration, body travel and suspension deflection. The mastery of proposed method is estimated under different road profiles. The comparative analysis demonstrates that the suggested controller enhances ride and passenger comfort in varied road profiles due to the combination of B-PRSMC and 2d-f-PR controllers. [ABSTRACT FROM AUTHOR]

In this paper a continuation of the previous research has been presented. A simulation of a vehicle's acceleration in various road conditions but without a driver's control has been taken into account. The main aim of this paper was to answer the question whether the random road conditions could affect the vehicle while accelerating while a driver cannot control the direction of its motion. The three various maximum amplitudes of the irregularities for both the dry and the icy road surface were used. Almost different road profiles were also adopted for the analyzed maneuver, which defines a specific coefficient. Apart from no control of the vehicle the initial speed was set to 5 km/h and after 1 s the vehicle started to accelerate. The time of reaching the full throttle (100%) was only 0.25 s which provided a rapid acceleration. The simulation time was 10 s. [ABSTRACT FROM AUTHOR]

Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative model for heavy-haul train–track–bridge interaction, utilizing a coupling matrix representation based on the virtual work principle. This model establishes the relationship between the wheel–rail contact surface and the bridge–rail interface concerning internal forces and geometric constraints. In this coupled system's motion equation, the degrees of freedom (DOFs) of the wheelsets in a heavy-haul train lacking primary suspension are interdependent. Additionally, the vertical and nodding DOFs of the bogie frame are linked with the rail element. A practical application, a Yellow River Bridge with a heavy-haul railway line, is used to examine the accuracy of the proposed model with regard to discrepancy between the simulated and measured displacement ranging from 1% to 11%. A comprehensive parametric analysis is conducted, exploring the impacts of track irregularities of varying wavelengths, axle load lifting, and the degradation of bridge stiffness and damping on the dynamic responses of the coupled system. The results reveal that the bridge's dynamic responses are particularly sensitive to track irregularities within the wavelength range of 1 to 20 m, especially those within 1 to 10 m. The vertical displacement of the bridge demonstrates a nearly linear increase with heavier axle loads of the heavy-haul trains and the reduction in bridge stiffness. However, there is no significant rise in vertical acceleration under these conditions. [ABSTRACT FROM AUTHOR]

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of hardening accelerator and steel fibre on concrete adhesion strength for the repair of rigid highway and airfield pavements. The concretes were mixed based on CEM II/A-S 42.5 and included MasterGlenium SKY 608 superplasticiser. Experiments with two variable concrete composition factors were carried out. The amount of steel fibre varied from 0 to 100 kg/m3 and the amount of SikaRapid hardening accelerator varied from 0 to 9.6 kg/m3. It was found that modified repair concretes have a sufficiently high adhesion strength to 'old' concrete, from 2.30 MPa when tested by the pull-off method and from 2.05 MPa when tested by the flexural strength test method. Fibre-reinforcement increases the adhesion strength of repaired concrete by 7–15% due to the reduction of shrinkage during hardening. Treating the contact surface of 'old' concrete with a primer additionally increases adhesion strength by 6–10%. The maximum adhesion strength of fibre-reinforced concrete to the base reaches 3 MPa. Due to the high early and design strength, modified steel fibre-reinforced concrete provides the possibility of quick resumption of traffic while ensuring the integrity of the road structure due to the joint work of the repair material with the old concrete repair area. [ABSTRACT FROM AUTHOR]

The accelerometers and the angular velocity sensor, which are part of the meteorological complex of the ExoMars-2022 landing platform (LP), are designed to measure the acceleration during the deceleration of the lander in the Martian atmosphere. Based on the data, the main parameters of the Martian atmosphere are calculated: density, pressure, and temperature. After landing, the sensors are used to determine the acceleration on the surface and vibration effects on the lander of various nature. The sensors are activated prior to entry into the atmosphere and operate during the entire descent until landing. After landing, a long-term monitoring is carried out to identify vibration effects from the atmosphere and the surface. In the paper we consider the scientific problems solved by the sensors, describe the measurement program and consider in detail the design of the sensors and their characteristics. [ABSTRACT FROM AUTHOR]

The safety of road users is one of the priority issues taken into account in both the operation and design of vehicles. The presented work is part of a study that aims to develop a method for parametric assessment of driver behavior. The driving style of a driver depends on their skills and psychophysical characteristics, the type and performance of the vehicle used by the driver, and the type of road. This method involves the continuous measurement of the longitudinal and lateral acceleration values of a vehicle body. The paper analyzes how the type of road influences the structure of the maneuvers undertaken by the driver. The paper formulates criteria for distinguishing basic maneuvers (acceleration, braking, and turning). The structure of maneuvers was analyzed for two parameters: the extreme value of acceleration occurring during the execution of a given maneuver and the frequency of maneuvers during the passage of a given route. The analysis presented in this paper confirms that the type of road has a significant influence on the structure of the maneuvers undertaken by the driver. [ABSTRACT FROM AUTHOR]

In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE updates one single search sequence by solving a single projection subproblem in each iteration. We show that OE can achieve the optimal rate of convergence for solving a variety of VI problems in a much simpler way than existing approaches. We then introduce the stochastic operator extrapolation (SOE) method and establish its optimal convergence behavior for solving various stochastic VI problems. In particular, SOE achieves the optimal complexity for solving a fundamental problem, i.e., stochastic smooth and strongly monotone VI, for the first time in the literature. We also present a stochastic block operator extrapolation method to further reduce the iteration cost for the OE method applied to large-scale deterministic VIs with a certain block structure. Numerical experiments have been conducted to demonstrate the potential advantages of the proposed algorithms. In fact, all these algorithms are applied to solve generalized monotone variational inequality problems whose operator is not necessarily monotone. We will also discuss optimal OE-based policy evaluation methods for reinforcement learning in a companion paper. [ABSTRACT FROM AUTHOR]

Wireless smart sensors (WSS) process field data and inform inspectors about the infrastructure health and safety. In bridge engineering, inspectors need reliable data about changes in displacements under loads to make correct decisions about repairs and replacements. Access to displacement information in the field and in real-time remains a challenge as inspectors do not see the data in real time. Displacement data from WSS in the field undergoes additional processing and is seen at a different location. If inspectors were able to see structural displacements in real-time at the locations of interest, they could conduct additional observations, creating a new, information-based, decision-making reality in the field. This paper develops a new, human-centered interface that provides inspectors with real-time access to actionable structural data during inspection and monitoring enhanced by augmented reality (AR). It summarizes and evaluates the development and validation of the new human-infrastructure interface in laboratory experiments. The experiments demonstrate that the interface that processes all calculations in the AR device accurately estimates dynamic displacements in comparison with the laser. Using this new AR interface tool, inspectors can observe and compare displacement data, share it across space and time, visualize displacements in time history, and understand structural deflection more accurately through a displacement time history visualization. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the late-time accelerated universe evolution in a flat, homogeneous and isotropic model in the context of f (Q , T) gravity, where Q and T are non-metricity scalar and trace of energy–momentum tensor, respectively. The scale factor, by construction, yields ekpyrotic contraction era followed by a non-singular bounce. The expanding era of the universe yields late-time dark energy era preceded by matter-dominating decelerating era. The model unifies an ekpyrotic, non-singular bounce with the present dark energy-dominated epoch. The model parameters in the functional form of f (Q , T) gravity affect the dynamical evolution of the equation of state (EoS) parameter. The theoretical value of EoS parameter is found to be ω 0 = − 1. 0 1 3 6 , − 1. 0 2 7 3 for n = 0 , 1 , respectively, and it lies in range of the estimated value of EoS parameter from the Planck+SNe+BAO observational data. Different aspects of this bouncing model including behavior of geometrical and physical quantities along with energy conditions have been discussed in detail. [ABSTRACT FROM AUTHOR]

This paper presents an innovative experimental setup that employs the principles of audio technology to subject adherent cells to rhythmic vertical vibrations. We employ a novel approach that combines three-axis acceleration measurements and particle tracking velocimetry to evaluate the setup's performance. This allows us to estimate crucial parameters such as root mean square acceleration, fluid flow patterns, and shear stress generated within the cell culture wells when subjected to various vibration types. The experimental conditions consisted of four vibrational modes: No Vibration, Continuous Vibration, Regular Pulse, and Variable Pulse. To evaluate the effects on cells, we utilized fluorescence microscopy and a customized feature extraction algorithm to analyze the F-actin filament structures. Our findings indicate a consistent trend across all vibrated cell cultures, revealing a reduction in size and altered orientation (2D angle) of the filaments. Furthermore, we observed cell accumulations in the G1 cell cycle phase in cells treated with Continuous Vibration and Regular Pulse. Our results demonstrate a negative correlation between the magnitude of mechanical stimuli and the size of F-actin filaments, as well as a positive correlation with the accumulations of cells in the G1 phase of the cell cycle. By unraveling these analyses, this study paves the way for future investigations and provides a compelling framework for comprehending the intricate cellular responses to rhythmic mechanical stimulation. [ABSTRACT FROM AUTHOR]

This paper builds on an acceleration mechanisms framework from transition studies and analyzes the role of green events for sustainable urban transformations. We study eight regional garden shows in German small and medium-sized cities, using interview and document data as well as on-site visits for an in-depth qualitative analysis. Our findings reveal that garden shows contribute to urban sustainability through implementing green projects and supporting small initiatives. We find that instrumentalizing and upscaling are crucial mechanisms for accelerating urban transitions, while replicating, partnering and embedding play a supporting role. The importance of these mechanisms, however, varies over time. [ABSTRACT FROM AUTHOR]

The Kaczmarz method is an iterative numerical method for solving large and sparse rectangular systems of linear equations. Gearhart, Koshy and Tam have developed an acceleration technique for the Kaczmarz method that minimizes the distance to the desired solution in the direction of a full Kaczmarz step. The present paper generalizes this technique to an acceleration scheme that minimizes the Euclidean norm error over an affine subspace spanned by a number of previous iterates and one additional cycle of the Kaczmarz method. The key challenge is to find a formulation in which all parameters of the least-squares problem defining the unique minimizer are known, and to solve this problem efficiently. When only a single Kaczmarz cycle is considered, the proposed affine search is more effective than the Gearhart-Koshy/Tam line-search, which in turn is more effective than the underlying Kaczmarz method. A numerical experiment from the context of computerized tomography suggests that the proposed affine search has the potential to outperform the the Gearhart-Koshy/Tam line-search and the underlying Kaczmarz method in terms of the computational cost that is needed to achieve a given error tolerance. [ABSTRACT FROM AUTHOR]

Time plays an integral role in understanding how the social is possible. However, most discussions of sociological classical thinkers—such as Georg Simmel—remain starkly underexplored in terms of their theories' temporal presuppositions. While most responses to Simmel's work credit him for his major contributions to the sociology of space, in this paper I aim to systematically reconstruct his explicit and implicit temporal assumptions and explore how these inform some of his social-theoretical writings and his sharp temporal diagnosis of modernity. For this purpose, I re-evaluate some of his central works using a theory distinction between social-theory, which aims to answer what constitutes the social, versus theory-of-society, where the key focus is what form or forms human societies have taken so far, and especially what form modern society takes. By offering a new reading of Simmel's philosophical and sociological writings, I formulate a comprehensive social theory of time, in which time is both located within individual consciousness and reciprocally mediated by a culturally fixed and supra-individual timeframe, thus highlighting the temporal tensions between individual flexibility and social standardization and coordination. Simmel's diagnosis of modernity reveals a conceptualization of time in spatialized terms, a monetization of time, and an acceleration of life. [ABSTRACT FROM AUTHOR]

In recent years, Krylov subspace methods based on the tensor format have demonstrated their superiority over classical ones for handling various kinds of tensor equations. In this paper, according to the efficient computational cost of the classical Simpler GMRES method (SGMRES), we adopt the tensor format of this method, which is called SGMRES − BTF, for solving the Sylvester tensor equations. This paper is divided into two parts describing the tensor format of two types of acceleration approaches to tackle some serious problems of the restarted methods GMRES − BTF and SGMRES − BTF, such as the "stalling" and "alternating phenomenon". In the first part of this two-part work, the truncated Loose Simpler GMRES based on the tensor format (LSGMRES − BTF) is introduced, which is an acceleration approach that aims to construct an augmented tensor Krylov subspace by means of approximation error tensors. Moreover, a detailed study is carried out on the connection between the values of sequential and skip angles and the convergence behavior of both SGMRES − BTF and truncated LSGMRES − BTF. In the second part of this paper, an acceleration approach based on the idea of inner-outer iteration in the truncated version of the generalized conjugate residual with inner orthogonalization (GCRO) method is developed. In this method, SGMRES − BTF is applied in the inner iteration, and the generalized conjugate residual based on the tensor format (GCR − BTF) method is used in the outer iteration. Numerical experiments show that SGMRES − BTF achieves an appropriate performance compared with GMRES − BTF. In addition, the numerical results reveal the high potential of the presented accelerating strategies to deal with Sylvester tensor equations. [ABSTRACT FROM AUTHOR]

This paper demonstrated the design and implementation of a unique algorithm used to generate interim coordinates for focus transitions in time-lapse photography. Through a prototype design methodology, the unique algorithm was developed to function within the limitations imposed by a low-cost 8-bit microcontroller for the development of a cost-effective focus and motion control system for deployment in photographic higher education. Through conceptualization and virtualization in MathLab and comparison with values generated in simulated real-world use-case, the proposed algorithm was tested for function and efficacy in generating interim coordinates that included focus coordinate estimation to facilitate smooth non-linear motion with focus transitions during image sequence capture. The 8-bit Arduino Mega 2560 proved capable of completing calculations required by the developed algorithm and generated coordinate data sets identical to sets generated in simulation. The generated coordinated illustrated effective user-defined control over motion and the smoothness of focus transitions. The developed algorithm is thus capable of facilitating the required movement and focus transition coordinate calculation and estimation required while deployed on a low-cost 8-bit microcontroller and is well suited for deployments in time-lapse capture applications. [ABSTRACT FROM AUTHOR]

Accelerated regularization algorithms for ill-posed problems have received much attention from researchers of inverse problems since the 1980s. The current optimal theoretical results indicate that some regularization algorithms, e.g. the ν -method and the Nesterov method, are such that under conventional source conditions the optimal convergence rates can be obtained with approximately the square root of the iterations of those needed for the benchmark (i.e. the Landweber iteration). In this paper, we propose a new class of regularization algorithms with parameter n, called the Acceleration Regularization of order n (AR n ). Theoretically, we prove that, for an arbitrary number n > - 1 , AR n can attach the optimal convergence rates with approximately the n + 1 root of the iterations needed for the benchmark method. Moreover, unlike the existing accelerated regularization algorithms, AR n s have no saturation restriction. Some symplectic iterative regularizing algorithms are developed for the numerical realization of AR n . Finally, numerical experiments with integral equations and inverse problems in partial differential equations demonstrate that, at least for n ≤ 2 , the numerical behavior of AR n matches our theoretical findings, also breaking the practical acceleration capability of all existing regularization algorithms. [ABSTRACT FROM AUTHOR]

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00 × and 15.24 × , respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90 × and 3.38 × , respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper has three parts. In first step we use modified scalar tensor vector Brans Dicke gravity (Ghaffarnejad in Gen Relativ Gravit 40:2229, 2008) to obtain metric solution of a spherically symmetric static curved space time in two kind isotropic and anisotropic coordinates system in which stress tensor behaves as anisotropic spherically symmetric perfect fluid. Metric solution is similar to a wormhole line element with a power law mass function in the anisotropic coordinates system. Exponent in the mass function is ratio of directional barotropic indexes. Second part of the paper is dedicated to review Fresnel–Kirchhoff diffraction theory for massless scalar field instead of the vector electromagnetic waves. In third part of the work we consider the obtained wormhole metric solution to be gravitational lens and we study weak gravitational lensing of moving light which is originated from a point star. Production of stationary images is investigated via both interference of waves and geometric optics approaches. At last we check correspondence of produced images between two approaches and give some outlooks. [ABSTRACT FROM AUTHOR]

This paper reviews the work done on science and technology of aircraft seat ejection with history, present, and futuristic interest. It is topic of interest that is receiving significant attention in the recent years due to importance of the pilot life saving during war, mishaps, and emergencies. In the early days, the ejection seats used the concept of compressed air and simple mechanical springs. The escape of the aircrew from high-speed aircraft is a serious drawback during World War (WW) II. The jumping out of aeronauts from high-speed aircraft is extremely difficult because of high blast wind pressure, turbulence around the exposed cockpit, and accelerations during the aircraft maneuvers. All these systems are dangerous and damage to the vertebra column of an aviator. As the speed of the aircraft is low, the pilot has to jump out with his parachute. However, as the speed of the aircraft increases, jumping out from the disabled aircraft with the parachute is merely a matter of luck and does not guarantee the aeronaut’s survival. Therefore, it is highly essential to use an ejection seat when damage is caused due to either the fighter or trainer plane. The ejection of the pilot is of utmost importance to the services. Hence, the lives of the pilots are crucial than saving military planes. Safeguarding the pilots from disabled aircraft plays a significant role in services such as Indian Air Force and Indian Navy since their introduction. Different policies and regulations are enforced to ensure the survival and safety of pilots. These are considered an essential area in the safety programs. Security and safety are always threat to the pilots. The novelty of this research paper is to bring about the historical developmental concepts to advanced concepts through science and technologyelated aircraft seat ejection. This paper is based on oration presented by the author on the occasion of National Science Day in Feb 2021. Therefore, an attempt is also made to highlight the important contributions made by various scientists, researchers in the frontier areas related to science and technology of ejection seat for military fighter aircraft. [ABSTRACT FROM AUTHOR]

The article presents abstracts of studies presented at the 38th annual meeting of the Association of Applied Psychophysiology and Biofeedback. These include "Enhancing the Diagnostic Accuracy of ADHD," "LORETA Neurofeedback: A Cortical--Subcortical Comparison," and "ERP Study of Medial Frontal Deficits of Error Processing in PTSD."

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for discussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines. [ABSTRACT FROM AUTHOR]

This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives. [ABSTRACT FROM AUTHOR]

Functional constrained optimization is becoming more and more important in machine learning and operations research. Such problems have potential applications in risk-averse machine learning, semisupervised learning and robust optimization among others. In this paper, we first present a novel Constraint Extrapolation (ConEx) method for solving convex functional constrained problems, which utilizes linear approximations of the constraint functions to define the extrapolation (or acceleration) step. We show that this method is a unified algorithm that achieves the best-known rate of convergence for solving different functional constrained convex composite problems, including convex or strongly convex, and smooth or nonsmooth problems with stochastic objective and/or stochastic constraints. Many of these rates of convergence were in fact obtained for the first time in the literature. In addition, ConEx is a single-loop algorithm that does not involve any penalty subproblems. Contrary to existing primal-dual methods, it does not require the projection of Lagrangian multipliers into a (possibly unknown) bounded set. Second, for nonconvex functional constrained problems, we introduce a new proximal point method which transforms the initial nonconvex problem into a sequence of convex problems by adding quadratic terms to both the objective and constraints. Under certain MFCQ-type assumption, we establish the convergence and rate of convergence of this method to KKT points when the convex subproblems are solved exactly or inexactly. For large-scale and stochastic problems, we present a more practical proximal point method in which the approximate solutions of the subproblems are computed by the aforementioned ConEx method. Under a strong feasibility assumption, we establish the total iteration complexity of ConEx required by this inexact proximal point method for a variety of problem settings, including nonconvex smooth or nonsmooth problems with stochastic objective and/or stochastic constraints. To the best of our knowledge, most of these convergence and complexity results of the proximal point method for nonconvex problems also seem to be new in the literature. [ABSTRACT FROM AUTHOR]

The study of machine parts movements required by inertial forces is actually a dynamic problem. The machine parts in motion are under dynamic stress due to the inertial forces that appear and overlap the directly applied forces and also connection forces. In the paper, the movements that are studied from a dynamic point of view are those that involve acceleration. [ABSTRACT FROM AUTHOR]

With the increasing of the data size and the development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and also makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates at all iterations. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both strongly convex and non-strongly convex problems, while existing asynchronous algorithms (e.g., KroMagnon and ASAGA) only have convergence guarantees for strongly convex problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical experimental results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that the success of deep learning is built upon large-scale training data and tremendous computing power. However, the data and computing power are not always available for many real-world applications. In this paper, we address the machine learning problem where it lacks training data and limits computing power. Specifically, we investigate domain adaptation which is able to transfer knowledge from one labeled source domain to an unlabeled target domain, so that we do not need much training data from the target domain. At the same time, we consider the situation that the running environment is confined, e.g., in edge computing the end device has very limited running resources. Technically, we present the Faster Domain Adaptation (FDA) protocol and further report two paradigms of FDA: early stopping and amid skipping. The former accelerates domain adaptation by multiple early exit points. The latter speeds up the adaptation by wisely skip several amid neural network blocks. Extensive experiments on standard benchmarks verify that our method is able to achieve the comparable and even better accuracy but employ much less computing resources. To the best of our knowledge, there are very few works which investigated accelerating knowledge adaptation in the community. This work is expected to inspire the topic for more discussion. [ABSTRACT FROM AUTHOR]

In this paper, we explore the homogeneous and isotropic flat Friedmann–Robertson–Walker (FRW) model in Chameleon cosmology. By considering a non-minimal coupling between the scalar field and matter, we present a non-singular bouncing cosmological scenario of the universe. The universe initially exhibits the ekpyrotic phase during the contracting era, undergoes a non-singular bounce, and then in expanding era, it smoothly transits to the decelerating era having matter and radiation-dominated phases. Further, this decelerating era is smoothly connected to the late-time dark energy-dominated era of the present epoch. We use numerical solution techniques to solve non-minimally coupled gravity equations for understanding the evolution of scalar field along with other quantities like effective potential in the model. The model thus unifies an ekpyrotic, non-singular, asymmetric bounce with the dark energy era of the present epoch. We study the evolution of bouncing model and confront the model with observational results on the equation of state parameter by constraining the model parameters. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for the parallel in time (PiT) numerical simulation of time dependent partial/ordinary differential equations. We propose a reliable alternative to the well know parareal in time algorithm, by formulating the parallel in time problem algebraically and solve it using an adapted Bi-Conjugate gradient stabilized method. The proposed Parallel in time Stable Bi-Conjugate algorithm (PiTSBiCG for short) has a great potential to stabilizing the parallel resolution for a variety of problems. In this work, we describe the mathematical approach to the new algorithm and provide numerical evidence that shows its superiority to the standard parareal method. [ABSTRACT FROM AUTHOR]

Sequential residual methods try to solve nonlinear systems of equations F(x) = 0 by iteratively updating the current approximate solution along a residual-related direction. Therefore, memory requirements are minimal and, consequently, these methods are attractive for solving largescale nonlinear systems. However, the convergence of these algorithms may be slow in critical cases; therefore, acceleration procedures are welcome. In this paper, we suggest employing a variation of the sequential secant method in order to accelerate sequential residual methods. The performance of the resulting algorithm is illustrated by applying it to the solution of very large problems coming from the discretization of partial differential equations. [ABSTRACT FROM AUTHOR]

As one of the most commonly used energy-saving methods in buildings, thermal insulation layer is widely used in building façade and it also endangers the human safety in buildings due to its combustibility. U-shaped facade is a commonly used structure in high-rise buildings since it could improve both the light and ventilation conditions indoors. This paper investigated the flame acceleration over the thermal insulation in u-shaped building facade fires. It was found that the flame spread rate over u-shaped facade had an exponential growth with time as the direction of preheating zone location is the same direction of the heat transfer from the pyrolysis zone. The air entrainment and the upward induced airflow played an important role in the acceleration. As the side wall length increased or the back wall length decreased, the flame spread rate accelerated more rapidly. Furthermore, a mathematical prediction model of the unsteady flame spread rate over u-shaped structure façade was established and validated through experiments. This study provides technical guidance for the fire safety design of building facade. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for detecting bridge damage that uses a partially instrumented vehicle fleet, each vehicle being instrumented with just one accelerometer. Importantly, no prior knowledge of the bridge or individual vehicle properties is required. Firstly, a model simplification concept is proposed to calculate the displacement under the vehicle wheel from single acceleration measurements on a half-car model. Then, optimisation is used to find individual vehicle properties using simulated noisy measurements from a fleet of vehicles. Finally, accelerations from the fleet are used to find the 'apparent profile difference', which contains bridge deflection data. The difference is found to be independent of surface profile but dependent on vehicle axle weight differences and a moving reference influence function (MRIF). When the axle spacings and the relative axle weights are reasonably consistent in a subset of the fleet, the MRIF can be simplified into a compound MRIF. Even when the MRIF and individual axle weights are not available on-site, the shape of the compound MRIF can be determined through an iterative process and used to monitor bridge health condition. Bearing damage is represented in this paper as an increase of rotational resistance of the bearings. The numerical results show that the damage severity and location can be identified. • A new drive-by fleet monitoring concept is introduced using a partially instrumented vehicle fleet without knowing individual vehicle properties. • A new concept, vehicle model simplification, is introduced to enable monitoring bridges using partially instrumented vehicles. • Vehicle wheel displacement is successfully obtained from vehicle accelerations and used in structural health monitoring. • The relation between bridge deflection under the wheel and vehicle load is modelled as a moving reference influence function. This influence function is simplified to monitor bridges. [ABSTRACT FROM AUTHOR]

In view of the influence of vibration factors on the vibration characteristics of potato harvester, to study the effect of vibration parameters on the dynamic characteristics of the system and harvested potato quality, in this paper an indoor conveyor separation device for potato harvester with adjustable two-way synchronous conveying vibration parameters was designed. The device, with double-side chain drive to improve conveying efficiency and replaceable eccentric parts to change vibration parameters, allows the system to harvest potatoes under optimal working conditions. A simulation analysis of a dynamics model of the system, which was first established, was carried out. A potato soil separation test was conducted using the vibration testing system and acceleration sensor, and its data was compared with the simulation data to verify the correctness of the model. The results showed that the eccentricity, rotation speed, and inclination had an impact on the potato-soil separation rate and the vibration of the system. The analysis showed that the system had a higher potato-soil separation rate and slighter vibration under optimal working conditions, that is, eccentricity of 0.023 m, rotation speed of 130 r/min, and inclination of 26 ∘. [ABSTRACT FROM AUTHOR]

This paper explores the non-convex composition optimization consisting of inner and outer finite-sum functions with a large number of component functions. This problem arises in important applications such as nonlinear embedding and reinforcement learning. Although existing approaches such as stochastic gradient descent (SGD) and stochastic variance reduced gradient (SVRG) descent can be applied to solve this problem, their query complexities tend to be high, especially when the number of inner component functions is large. Therefore, to significantly improve the query complexity of current approaches, we have devised the stochastic composition via variance reduction (SCVR). What's more, we analyze the query complexity under different numbers of inner function and outer function. Based on different kinds of estimation of inner component function, we also present the SCVRII algorithm, though the order of query complexities are the same with SCVR. Additionally, we propose an extension to handle the mini-batch cases, which improve the query complexity under the optimal mini-batch size. The experimental results validate our proposed algorithms and theoretical analyses. [ABSTRACT FROM AUTHOR]

Circular motion changes the perception of up and down. For a skater, the need to lean in towards the centre of an arc constitutes an embodiment of centripetal acceleration. This paper presents a discussion between a physicist and a high-school science teacher without previous physics studies at university level. The discussions started as part of a course assignment and focused on a photo of skaters practicing a ‘wing’, which is a move in synchronised skating. The skating experiences are integrated with an understanding of the relations between radius, acceleration and the angles the total force from the ice deviates from the vertical. [ABSTRACT FROM AUTHOR]

We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the feature maps of the pre-trained models and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, while the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. In this paper, we propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power. To this end, we first introduce additional discrimination-aware losses into the network to increase the discriminative power of the intermediate layers. Next, we select the most discriminative channels for each layer by considering the discrimination-aware loss and the reconstruction error, simultaneously. We then formulate channel pruning as a sparsity-inducing optimization problem with a convex objective and propose a greedy algorithm to solve the resultant problem. Note that a channel (3D tensor) often consists of a set of kernels (each with a 2D matrix). Besides the redundancy in channels, some kernels in a channel may also be redundant and fail to contribute to the discriminative power of the network, resulting in kernel level redundancy. To solve this issue, we propose a discrimination-aware kernel pruning (DKP) method to further compress deep networks by removing redundant kernels. To avoid manually determining the pruning rate for each layer, we propose two adaptive stopping conditions to automatically determine the number of selected channels/kernels. The proposed adaptive stopping conditions tend to yield more efficient models with better performance in practice. Extensive experiments on both image classification and face recognition demonstrate the effectiveness of our methods. For example, on ILSVRC-12, the resultant ResNet-50 model with 30 percent reduction of channels even outperforms the baseline model by 0.36 percent in terms of Top-1 accuracy. We also deploy the pruned models on a smartphone (equipped with a Qualcomm Snapdragon 845 processor). The pruned MobileNetV1 and MobileNetV2 achieve 1.93× and 1.42× inference acceleration on the mobile device, respectively, with negligible performance degradation. The source code and the pre-trained models are available at https://github.com/SCUT-AILab/DCP. [ABSTRACT FROM AUTHOR]